What is machine learning and how does Netflix use it for its recommendation engine?

What is machine learning and how does Netflix use it for its recommendation engine?

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What is machine learning and how does Netflix use it for its recommendation engine?

What is an online recommendation engine?

Think about examples of machine learning you may have encountered in the past such as a website like Netflix  that recommends what video you may be interested in watching next?
Are the recommendations ever wrong or unfair? We will  give an example and explain how this could be addressed.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

Machine learning is a field of artificial intelligence that Netflix uses to create its recommendation algorithm. The goal of machine learning is to teach computers to learn from data and make predictions based on that data. To do this, Netflix employs Machine Learning Engineers, Data Scientists, and software developers to design and build algorithms that can automatically improve over time. The Netflix recommendations engine is just one example of how machine learning can be used to improve the user experience. By understanding what users watch and why, the recommendations engine can provide tailored suggestions that help users find new shows and movies to enjoy. Machine learning is also used for other Netflix features, such as predicting which shows a user might be interested in watching next, or detecting inappropriate content. In a world where data is becoming increasingly important, machine learning will continue to play a vital role in helping Netflix deliver a great experience to its users.

What is machine learning and how does Netflix use it for its recommendation engine?
What is machine learning and how does Netflix use it for its recommendation engine?

Netflix’s recommendation engine is one of the company’s most valuable assets. By using machine learning, Netflix is able to constantly improve its recommendations for each individual user.

Machine learning engineers, data scientists, and developers work together to build and improve the recommendation engine.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
  • They start by collecting data on what users watch and how they interact with the Netflix interface.
  • This data is then used to train machine learning models.
  • The models are constantly being tweaked and improved by the team of engineers.
  • The goal is to make sure that each user sees recommendations that are highly relevant to their interests.

Thanks to the work of the team, Netflix’s recommendation engine is constantly getting better at understanding each individual user.

How Does It Work?

In short, Netflix’s recommendation algorithm looks at what you’ve watched in the past and then makes recommendations based on that data. But of course, it’s a bit more complicated than that. The algorithm also looks at data from other users with similar watching habits to yours. This allows Netflix to give you more tailored recommendations.

For example, say you’re a big fan of Friends (who isn’t?). The algorithm knows that a lot of Friends fans also like shows like Cheers, Seinfeld, and The Office. So, if you’re ever feeling nostalgic and in the mood for a sitcom marathon, Netflix will be there to help you out.

But That’s Not All…

Not only does the algorithm take into account what you’ve watched in the past, but it also looks at what you’re currently watching. For example, let’s say you’re halfway through Season 2 of Breaking Bad and you decide to take a break for a few days. When you come back and finish Season 2, the algorithm knows that you’re now interested in similar shows like Dexter and The Wire. And voila! Those shows will now be recommended to you.

Of course, the algorithm isn’t perfect. There are always going to be times when it recommends a show or movie that just doesn’t interest you. But hey, that’s why they have the “thumbs up/thumbs down” feature. Just give those shows the old thumbs down and never think about them again! Problem solved.

Another angle :

When it comes to TV and movie recommendations, there are two main types of data that are being collected and analyzed:


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1) demographic data

2) viewing data.

Demographic data is information like your age, gender, location, etc. This data is generally used to group people with similar interests together so that they can be served more targeted recommendations. For example, if you’re a 25-year-old female living in Los Angeles, you might be grouped together with other 25-year-old females living in Los Angeles who have similar viewing habits as you.

Viewing data is exactly what it sounds like—it’s information on what TV shows and movies you’ve watched in the past. This data is used to identify patterns in your viewing habits so that the algorithm can make better recommendations on what you might want to watch next. For example, if you’ve watched a lot of romantic comedies in the past, the algorithm might recommend other romantic comedies that you might like based on those patterns.

Are the Recommendations Ever Wrong or Unfair?
Yes and no. The fact of the matter is that no algorithm is perfect—there will always be some error involved. However, these errors are usually minor and don’t have a major impact on our lives. In fact, we often don’t even notice them!

The bigger issue with machine learning isn’t inaccuracy; it’s bias. Because algorithms are designed by humans, they often contain human biases that can seep into the recommendations they make. For example, a recent study found that Amazon’s algorithms were biased against women authors because the majority of book purchases on the site were made by men. As a result, Amazon’s algorithms were more likely to recommend books written by men over books written by women—regardless of quality or popularity.

These sorts of biases can have major impacts on our lives because they can dictate what we see and don’t see online. If we’re only seeing content that reflects our own biases back at us, we’re not getting a well-rounded view of the world—and that can have serious implications for both our personal lives and society as a whole.

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One of the benefits of machine learning is that it can help us make better decisions. For example, if you’re trying to decide what movie to watch on Netflix, the site will use your past viewing history to recommend movies that you might like. This is possible because machine learning algorithms are able to identify patterns in data.

Another benefit of machine learning is that it can help us automate tasks. For example, if you’re a cashier and have to scan the barcodes of the items someone is buying, a machine learning algorithm can be used to automatically scan the barcodes and calculate the total cost of the purchase. This can save time and increase efficiency.

The Consequences of Machine Learning

While machine learning can be beneficial, there are also some potential consequences that should be considered. One consequence is that machine learning algorithms can perpetuate bias. For example, if you’re using a machine learning algorithm to recommend movies to people on Netflix, the algorithm might only recommend movies that are similar to ones that people have already watched. This could lead to people only watching movies that confirm their existing beliefs instead of challenged them.

Another consequence of machine learning is that it can be difficult to understand how the algorithms work. This is because the algorithms are usually created by trained experts and then fine-tuned through trial and error. As a result, regular people often don’t know how or why certain decisions are being made by machines. This lack of transparency can lead to mistrust and frustration.

What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

What are some good datasets for Data Science and Machine Learning?

This scene in the Black Panther trailer, is it T’Challa’s funeral?

r/marvelstudios - This scene in the Black Panther trailer, is it T’Challa’s funeral?

Recommended New Netflix  Movies 2022

  • SEX EDUCATION FINALE IS DISAPPOINTING.
    by /u/Flimsy_Appearance_16 (Netflix) on September 25, 2023 at 9:26 am

    I had my hopes up for the last season of 'Sex Education,' and after finishing watching it last night, I have to say it's disappointing, to say the least. After watching the series, this quote comes to mind: 'Write characters to tell a story, not to prove a point.' Season 4 was all about the latter part. I don't understand why, if it's the show's final season, they introduced new characters with half-baked storylines that the audience doesn't even resonate with. They should have focused on the main characters (Otis, Maeve, Eric, Ruby, Amie, Adam) to give them a fitting ending instead of adding unnecessary characters just to prove a point or showcase LGBTQ characters. What I liked about the finale: - Adam and his father's arc: I really enjoyed watching how these two characters were developed from the start of the season to the end. It showed a father-son relationship with great depth and amazing acting. - More screen time for Ruby: I'm glad they gave her more screen time, but again, it didn't reveal the character's future or whether she moved on from Otis or not. Anyway, overall, the 'Sex Education' series would be a 6/10 for me. submitted by /u/Flimsy_Appearance_16 [link] [comments]

  • Lost Ollie is messed up!
    by /u/MrHorns7 (Netflix) on September 25, 2023 at 8:49 am

    I’m trying to watch Lost Ollie on my iPhone 15’s HDR OLED display and the video is very low quality and sometimes, the audio didn’t match the video at all. submitted by /u/MrHorns7 [link] [comments]

  • When will Season 17 of ‘Heartland’ be on Netflix?
    by /u/Yummie23 (Netflix) on September 25, 2023 at 8:28 am

    submitted by /u/Yummie23 [link] [comments]

  • Jamie Lee Curtis Sets Eyes On ‘One Piece’ Role In Second Season Of Netflix Adaptation
    by /u/Logical_Garage_7157 (Netflix) on September 25, 2023 at 7:43 am

    submitted by /u/Logical_Garage_7157 [link] [comments]

  • Trying to find an old travelling show I watched many years ago
    by /u/Its_Teo_Mate (Netflix) on September 25, 2023 at 7:15 am

    Would appreciate if someone could tell me the name of the show if they know it Sorry if this is a bit vague, but it's a show about 2 guys travelling the world together, going to different countries and many different adventures. I can't remember if they were friends or brothers. Show had at least 3 seasons iirc. Came out in the early-mid 2010's. That's about all I remember submitted by /u/Its_Teo_Mate [link] [comments]

  • Netflix shares first trailer for David Beckham docu-series
    by /u/Logical_Garage_7157 (Netflix) on September 25, 2023 at 4:39 am

    submitted by /u/Logical_Garage_7157 [link] [comments]

  • Finding Myself Through Big Mouth
    by Alex Jordan (They/Them) (Netflix on Medium) on September 25, 2023 at 3:43 am

    The journey of self-discovery is a winding path, and for me, it took an unexpected turn through the world of animation with the Netflix…Continue reading on Medium »

  • How Netflix’s Series Sex Education Explores Intersectionality through the Character of Eric Effiong
    by Esthetic Em (they/she) (Netflix on Medium) on September 25, 2023 at 2:30 am

    I will be the first to say that I consume much media. Most of my free time is spent with media, whether it be my favorite TV shows, movies…Continue reading on Medium »

  • Girls incarcerated
    by /u/Fun-Ad-2211 (Netflix) on September 25, 2023 at 1:25 am

    So I recently started rewatching this show and got curious about where some of my favs were now. And I’m absolutely heartbroken to find out Aubrey has passed and Lakyn is currently locked up :(((( submitted by /u/Fun-Ad-2211 [link] [comments]

  • Who's the best character actor?
    by /u/MusingsOnLife (Movie News and Discussion) on September 25, 2023 at 1:05 am

    A character actor is one of the side characters in the story, as opposed to the main protagonist or antagonist. Usually, such actors don't ever play lead roles, so I want to exclude someone like Gary Oldman and Daniel Day-Lewis and Meryl Streep who have played lead roles. Pick someone less well-known. For me, it's Michael Stuhlbarg. He's fairly prolific so I have not seen a lot of his movies, but here are the ones that come to mind and a few I found searching YouTube. Arrival He plays Agent Halpern who is the guy that threatens to shoot the Amy Adams character when she is making a call to the Chinese general. Steve Jobs He plays Andy Hertzfeld, the guy who has been paying for Steve Job's daughter's tuition. I think he's also the guy in the first part of the movie that's being told that the Mac needs to talk to the audience as Jobs is pressing him to get it done before the big premiere. Call Me By Your Name He plays Elio's father who has the big speech at the end. His character is sort of a Gilligan's Island professor in a sense it's not clear what he specializes in, but he has this exuberant attitude as he quizzes Oliver about the etymology of a certain word. A Serious Man He's the lead in this, and he also plays a professor, but unlike the one in Call Me By Your Name, he's more neurotic. It's impressive to come up with two different characterizations of a professor. Men in Black 3 https://www.youtube.com/watch?v=PSxuL_54sMk Lincoln https://www.youtube.com/watch?v=1qjtugr2618 submitted by /u/MusingsOnLife [link] [comments]

  • Into the night - series
    by /u/Ibn-Abih (Netflix) on September 25, 2023 at 12:59 am

    Few years ago Netflix released two seasons of “into the night” plus a spin-off. It was one of the best foreign series but ended with an unresolved cliffhanger. Latest I understood Netflix has no intention of renewing this series. Anyone has any additional insight into its fate and how to learn more about the ending (perhaps if serialized into a novel)? submitted by /u/Ibn-Abih [link] [comments]

  • What performance by an actor/actress, that was short, left a strong impact/sold you?
    by /u/KrimsonKabuto (Movie News and Discussion) on September 25, 2023 at 12:39 am

    I just watched Asteroid City last night, and it was amazing how even if Margot Robbie was on-screen for less than 5 minutes, she made a lasting impression on me. That brief performance sold me more on her than Barbie did imo. I think Wes Anderson really has a way with utilizing actors for only a short period of time but can create a lasting impact. Hong Chau was also in the movie for less than 5 minutes but her performance was such a bittersweet highlight. I'd love to hear your thoughts submitted by /u/KrimsonKabuto [link] [comments]

  • What movie soundtrack has moved you?
    by /u/hollyandberry (Movie News and Discussion) on September 24, 2023 at 10:59 pm

    What movie soundtrack has moved you (one that elicited sadness, empathy, joy, shock, or something else) while watching the film? Two that come to mind for me are the soundtracks to ‘Once Upon a Time in America’ by Ennio Morricone, and ‘Wuthering Heights’, the 1992 version with Ralph Fiennes and Juliette Binoche, with a soundtrack by Ryuichi Sakamoto. To add some flavor, no musicals (that's too easy), and second, name the composer or writer of the soundtrack. If it's a compilation, then just say compilation. Such as the compilation for 'Almost Famous'. submitted by /u/hollyandberry [link] [comments]

  • Seeking Top-Quality Netflix Original Movies
    by /u/VerbalReality (Netflix) on September 24, 2023 at 10:37 pm

    Hey folks! Looking for Netflix Original movies that truly stand out in terms of storyline, character development, and cinematography. I've sifted through many, but I'm sure I've missed some gems. Which ones would you say are a must-watch? Appreciate your insights! ​ ​ ​ submitted by /u/VerbalReality [link] [comments]

  • This Stephen King interview that starts on page 26 of issue #113 from Fangoria, June 1992 is really interesting, especially about losing control over his catalog and short stories that became film rights and movies or sequels.
    by /u/unclefishbits (Movie News and Discussion) on September 24, 2023 at 10:30 pm

    submitted by /u/unclefishbits [link] [comments]

  • Did you know the majority of artists and craftsmen making the high-end props you see in sci-fi and super hero films aren't given credit?
    by /u/HVDprops (Movie News and Discussion) on September 24, 2023 at 10:09 pm

    It's just such an unfair shame. I've been working in props for over a decade now. some of my co-workers have work histories dating back to Predator, Terminator, and the Goonies. None of us have, or do, receive credit. And that extends to a multitude of shops around the world. Union-due paying shops. Talking to a lot of old-timers it seems props as a department had a bad-rap since the golden days. Known for corralling the drugs and alcohol, often referred to as 'Chair Jockies' because "all they did was set up the chairs for talent to sit in". But these days we're talking shops filled with hundreds of some of the best fabrication talent in the world working for movies and shows reaping billions of dollars and there's not even a lonely credit at the end of the flick! Often not even a credit to the shop they work for. When I was in VFX the joke was you'd get credit for walking through a production meeting. I got credit for things as simple as hard-drive management. But now I get to go to work seeing things we made on billboards, posters, T-shirts, toys - and nothing! Hell, my dad sent me a picture of something my manager made in the 90s HANGING IN THE SMITHOSONIAN! I dare you to find his name... What would it take to even change something like that? submitted by /u/HVDprops [link] [comments]

  • First Mother
    by Apollo (Netflix on Medium) on September 24, 2023 at 9:52 pm

    NeoTerraContinue reading on Medium »

  • What movie was ahead of its time and failed to realize its potential?
    by /u/plymouthvan (Movie News and Discussion) on September 24, 2023 at 9:52 pm

    I’m going to say The One with Jet Li and Jason Statham. Pretty cool premise that would fit right in with today’s slate of movies. Really cool cast, but lost on an intensely dated late-90’s/2000’s soundtrack, cringy dialogue and really badly under developed visual effects. Could have been an awesome movie. submitted by /u/plymouthvan [link] [comments]

  • What does Jimmy put a ton of ketchup on in the "Dinner with Mom" scene from Goodfellas? It's always bugged me, considering the closing dialog about ketchup and noodles
    by /u/Unique_Task_420 (Movie News and Discussion) on September 24, 2023 at 9:21 pm

    submitted by /u/Unique_Task_420 [link] [comments]

  • Bring back The Last Man on Earth
    by /u/HorrorAd4995 (Netflix) on September 24, 2023 at 9:20 pm

    Why this show wasn’t allowed to complete the series with a final season is so beyond me. It still has a loyal fan base to this day. It was funny, well written and performed and we were left on a cliff hanger 🙁 submitted by /u/HorrorAd4995 [link] [comments]

World’s Top 10 Youtube channels in 2022

r/dataisbeautiful - [OC] World's Top 10 Youtube Channels of 2022

T-Series, Cocomelon, Set India, PewDiePie, MrBeast, Kids Diana Show, Like Nastya, WWE, Zee Music Company, Vlad and Niki

What are some ways to increase precision or recall in machine learning?

What are some ways to increase precision or recall in machine learning?

You can translate the content of this page by selecting a language in the select box.

What are some ways to increase precision or recall in machine learning?

What are some ways to Boost Precision and Recall in Machine Learning?


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Sensitivity vs Specificity?



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In machine learning, recall is the ability of the model to find all relevant instances in the data while precision is the ability of the model to correctly identify only the relevant instances. A high recall means that most relevant results are returned while a high precision means that most of the returned results are relevant. Ideally, you want a model with both high recall and high precision but often there is a trade-off between the two. In this blog post, we will explore some ways to increase recall or precision in machine learning.

What are some ways to increase precision or recall in machine learning?
What are some ways to increase precision or recall in machine learning?


There are two main ways to increase recall:

by increasing the number of false positives or by decreasing the number of false negatives. To increase the number of false positives, you can lower your threshold for what constitutes a positive prediction. For example, if you are trying to predict whether or not an email is spam, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in more false positives (emails that are not actually spam being classified as spam) but will also increase recall (more actual spam emails being classified as spam).

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

To decrease the number of false negatives,

you can increase your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in fewer false negatives (actual spam emails not being classified as spam) but will also decrease recall (fewer actual spam emails being classified as spam).

What are some ways to increase precision or recall in machine learning?

There are two main ways to increase precision:

by increasing the number of true positives or by decreasing the number of true negatives. To increase the number of true positives, you can raise your threshold for what constitutes a positive prediction. For example, using the spam email prediction example again, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in more true positives (emails that are actually spam being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).

To decrease the number of true negatives,

you can lower your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example once more, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in fewer true negatives (emails that are not actually spam not being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).

What are some ways to increase precision or recall in machine learning?

To summarize,

there are a few ways to increase precision or recall in machine learning. One way is to use a different evaluation metric. For example, if you are trying to maximize precision, you can use the F1 score, which is a combination of precision and recall. Another way to increase precision or recall is to adjust the threshold for classification. This can be done by changing the decision boundary or by using a different algorithm altogether.

What are some ways to increase precision or recall in machine learning?

Sensitivity vs Specificity

In machine learning, sensitivity and specificity are two measures of the performance of a model. Sensitivity is the proportion of true positives that are correctly predicted by the model, while specificity is the proportion of true negatives that are correctly predicted by the model.

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Google Colab For Machine Learning

State of the Google Colab for ML (October 2022)

Google introduced computing units, which you can purchase just like any other cloud computing unit you can from AWS or Azure etc. With Pro you get 100, and with Pro+ you get 500 computing units. GPU, TPU and option of High-RAM effects how much computing unit you use hourly. If you don’t have any computing units, you can’t use “Premium” tier gpus (A100, V100) and even P100 is non-viable.

Google Colab Pro+ comes with Premium tier GPU option, meanwhile in Pro if you have computing units you can randomly connect to P100 or T4. After you use all of your computing units, you can buy more or you can use T4 GPU for the half or most of the time (there can be a lot of times in the day that you can’t even use a T4 or any kinds of GPU). In free tier, offered gpus are most of the time K80 and P4, which performs similar to a 750ti (entry level gpu from 2014) with more VRAM.

For your consideration, T4 uses around 2, and A100 uses around 15 computing units hourly.
Based on the current knowledge, computing units costs for GPUs tend to fluctuate based on some unknown factor.

Considering those:

  1. For hobbyists and (under)graduate school duties, it will be better to use your own gpu if you have something with more than 4 gigs of VRAM and better than 750ti, or atleast purchase google pro to reach T4 even if you have no computing units remaining.
  2. For small research companies, and non-trivial research at universities, and probably for most of the people Colab now probably is not a good option.
  3. Colab Pro+ can be considered if you want Pro but you don’t sit in front of your computer, since it disconnects after 90 minutes of inactivity in your computer. But this can be overcomed with some scripts to some extend. So for most of the time Colab Pro+ is not a good option.

If you have anything more to say, please let me know so I can edit this post with them. Thanks!

Conclusion:


In machine learning, precision and recall trade off against each other; increasing one often decreases the other. There is no single silver bullet solution for increasing either precision or recall; it depends on your specific use case which one is more important and which methods will work best for boosting whichever metric you choose. In this blog post, we explored some methods for increasing either precision or recall; hopefully this gives you a starting point for improving your own models!

 

What are some ways we can use machine learning and artificial intelligence for algorithmic trading in the stock market?

Machine Learning and Data Science Breaking News 2022 – 2023

  • [R] LEAP Hand: Low-Cost (<2KUSD), Anthropomorphic, Multi-fingered Hand -- Easy to Build (link in comments)
    by /u/pathak22 (Machine Learning) on September 25, 2023 at 2:33 am

    submitted by /u/pathak22 [link] [comments]

  • Help with Qn 4 [Discussion]
    by /u/Proof-Mongoose3850 (Machine Learning) on September 25, 2023 at 2:06 am

    submitted by /u/Proof-Mongoose3850 [link] [comments]

  • [P] OpenGL-based inference engine
    by /u/mtnwrw (Machine Learning) on September 25, 2023 at 12:15 am

    I created an OpenGL/OpenGLES based inference framework a while back which is rather GPU-agnostic and might be a good option for distributing multi-platform ML solutions for platforms ranging from Android over desktop to WebGL(2). Quite recently I added support for LLMs to that (restricted to 4-bit quantized Llama models for now). The LLM-enabled fork can be found here (compileable sample code inside). Maybe someone finds this useful. Also looking for collaborators to extend the functionality. ​ submitted by /u/mtnwrw [link] [comments]

  • [P] I create a small pytorch utility to Import custom dataset
    by /u/charles_data_dev (Machine Learning) on September 24, 2023 at 11:46 pm

    Hi guys , TorchClassifierData is A small pytorch utility to Import, Split ,Normalize and Visualize custom dataset for classification tasks. wich is indispensable for real word problem . You can find a full notebook that use TorchClassifierData to train a classifier on this kaggle dataset here. The code source is avalaible on my github. Thank you. submitted by /u/charles_data_dev [link] [comments]

  • [D] Why do Diffusion models work so well while SG-MCMC does not?
    by /u/Dangerous-Flan-6581 (Machine Learning) on September 24, 2023 at 11:03 pm

    Diffusion models are basically Langevin sampling. What are the key differences and tricks that set them apart from Langevin dynamics? Why do they work so well while very similar sampling methods don't? submitted by /u/Dangerous-Flan-6581 [link] [comments]

  • [D] In ML, a PhD gives you a 10-year head start over weekend warriors
    by /u/btcmx (Machine Learning) on September 24, 2023 at 10:53 pm

    ​ https://preview.redd.it/cczbhu367aqb1.png?width=1600&format=png&auto=webp&s=f1761911d7ce3bbefaef43774b5d60f638886893 ML is often portrayed as a magical field where anyone with a laptop and Python skills can build amazing AI systems. The reality is less democratic: mastering ML requires gritty, systematic work best learned through formal training. You need rock solid foundations in math, programming, and core concepts—skills acquired through advanced education, which (almost always) is beyond self-taught hackers. Most think a PhD is unnecessary, but the reality is that advanced degrees provide the deepest training. Patience and persistence do matter, but a PhD gives you a 10-year head start over weekend warriors. The hype overlooks the work and education needed to excel. This article has a great blueprint of all the required skills to become a ML Engineer (in the CV field). submitted by /u/btcmx [link] [comments]

  • [D] Offer From Bug 4 VS Startup
    by /u/zorenum (Machine Learning) on September 24, 2023 at 9:56 pm

    So briefly about my current experience, I graduated 2 years ago with a bachelor in data science and I have 2-3 years of experience as a data scientist/ml engineer/software engineer. So I’ve got competing offers, one from the big 4 accounting firms as a software systems engineer - AI/ML (Big 4) and the other as a machine learning engineer. The startup salary is higher while big 4 is lower. Additionally the startup isn’t necessarily a unicorn it’s a relatively small startup with an interesting product but it doesn’t necessarily blow me away. The salary at the startup is 15 percent higher that that of the big 4 offer. For those wondering I did already negotiate the salary and they did increase it marginally. I am conflicted because I think that the big 4 jobs will have 1) more career growth 2) more potential future opportunities and 3) more networking potential Is this an accurate assessment? Which is the best job to take for maximum future potential? submitted by /u/zorenum [link] [comments]

  • [P] Hardware Resources for training SwinBert
    by /u/Big-Brain_69 (Machine Learning) on September 24, 2023 at 9:56 pm

    So I've been thinking of implementing SwinBert for a college project and have been wondering what all resources i would be needing for a computer. Any ideas? submitted by /u/Big-Brain_69 [link] [comments]

  • [D] Image-to-text web-scraping
    by /u/ReddSpark (Machine Learning) on September 24, 2023 at 8:35 pm

    I'm curious if anyone has tried pix2struct-large for web-scraping text from wesites. If so - how well did it perform? If not - is there something else that is considered better to use? submitted by /u/ReddSpark [link] [comments]

  • [D] Where will the demand for AI work be in future?
    by /u/QuintBa (Machine Learning) on September 24, 2023 at 7:36 pm

    Hypothesis: Big tech companies are investing vast amounts of money to develop general models on which others will build. They'll develop interfaces to make it easier for others to fine-tune on top of their models. So that there will be less and less of a need for ML engineers that know how to create a deep learning model in Pytorch, and more and more of a need for data engineers that simply plug into pre-trained models. An AI assistant will also be quicker at coding up a more bespoke AI model for a companies needs, guided by data engineers. What do people think? Is this a scenario that they think will play out? Where will the demand for AI skills be coming from in the future? submitted by /u/QuintBa [link] [comments]

  • [R] Researchers announce GPT4Tools: a method for teaching LLMs how to use tools for visual tasks
    by /u/Successful-Western27 (Machine Learning) on September 24, 2023 at 7:11 pm

    LLMs are great with words but can't handle visual tasks like understanding images. Teaching them to use visual tools could make them much more capable. A new paper introduces GPT4Tools - a method to efficiently teach existing LLMs to invoke tools for visual tasks without proprietary data. My highlights from the paper: Uses ChatGPT as a "teacher" to generate instructional data for other LLMs Fine-tunes LLMs like Vicuna on this data using selective weight tuning (keeps base model frozen) Allows smaller 13B LLM to match 175B GPT-3.5 on seen tools after tuning Data augmentation with negative/context samples was found to be the secret sauce to get this to work Can generalize to brand new visual tools in a zero-shot way This is big because it shows we may not need hyper-expensive training of massive models to impart visual capabilities to LLMs. They seems to be generalizable enough that they can be taught to work with images. Some examples shown include counting objects or segmenting items in pictures using other tools. With this approach, existing models can be made multi-modal! Pretty cool. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]

  • [D] Tools to gather and collaborate on fine-tuning datasets?
    by /u/zeJaeger (Machine Learning) on September 24, 2023 at 6:42 pm

    Hey all, I run a small team & we are collaborating on a few data sets that we use to fine-tune GPT3.5, We are currently using Google Sheets and I'm wondering if there is a tool where we can organize our data preferably with version control Any ideas? submitted by /u/zeJaeger [link] [comments]

  • [D] Colored Point Cloud Completion
    by /u/bySmily (Machine Learning) on September 24, 2023 at 6:17 pm

    Hello, I have created point clouds from images using Point-E. Sadly they are very sparse (for example wehn inputting an image of a house, the roof has very few points in it) and I was searching for other Models, that could make the PC more dense and predict the color of every point. Point-E outputs xyz and rgb vectors for every point. Do some of you have advise for me here? submitted by /u/bySmily [link] [comments]

  • [P]Just published my second blog on medium about feature scaling in machine learning please have a look
    by /u/indusop (Machine Learning) on September 24, 2023 at 6:15 pm

    submitted by /u/indusop [link] [comments]

  • [D][P] how to create a 3D gymnasium environment for mujoco env?
    by /u/rakk109 (Machine Learning) on September 24, 2023 at 5:32 pm

    Hi I'm a student and working on a RL project for the university and need some guidance. I have created a 3d model with mujoco (I have the xml file) how do I create an environment in gymnasium with this xml file? for the sake of an example let's say I have the xml file of the humanoid model how do I load this in gymnasium so that I could train it to walk? (this is just an example because the current project is harder to explain, but will use the humanoid model in the project) or is the approach that I'm trying is not appropriate at all? I came across this stackoverflow post where they say mujoco is itself good for this but was hard for me to understand due to lack of examples. would really appreciate some advice and guidance thank you. submitted by /u/rakk109 [link] [comments]

  • [P] Made a simple semantic segmentation annotation tool with segment-anything masks support in PyQt5
    by /u/Divelix (Machine Learning) on September 24, 2023 at 3:37 pm

    I just open-sourced (MIT License) semantic segmentation annotation tool powered by segment-anything model that I used for a while in my projects. Hopefully it will help someone as it seems to me that it is more suitable for small projects than popular huge web based annotation tools. Link to the project: SAMAT (any feedback in Discussions section on GitHub is appreciated) Features: Brush annotation (opposed to polygons) Magic Wand (like in Photoshop) powered by segment-anything masks (it is optional, if you don’t have cool GPU to prepare masks) samat showcase Why yet another annotation tool? Before starting this project I tried supervisely, segments.ai, roboflow and several others, but found them not convenient for my tasks. Their cons, I tried to fix with my tool: Latency: they are web based, hence burden with irritating latency during labeling (SAMAT is snappy because it is a local desktop app) Complicated: too much features, hence overloaded UI (SAMAT is just a colored brush) P.S. there is another labeling tool called SALT on github which also uses segment-anything model, but it follows different approach to UI/UX, may be it will be more suitable for you, so take a look at it too. submitted by /u/Divelix [link] [comments]

  • [R] Generative AI in Mafia-like game simulation
    by /u/BorderAffectionate81 (Machine Learning) on September 24, 2023 at 3:00 pm

    Paper: https://arxiv.org/abs/2309.11672 Abstract: In this research, we explore the efficacy and potential of Generative AI models, specifically focusing on their application in role-playing simulations exemplified through Spyfall, a renowned mafia-style game. By leveraging GPT-4's advanced capabilities, the study aimed to showcase the model's potential in understanding, decision-making, and interaction during game scenarios. Comparative analyses between GPT-4 and its predecessor, GPT-3.5-turbo, demonstrated GPT-4's enhanced adaptability to the game environment, with significant improvements in posing relevant questions and forming human-like responses. However, challenges such as the model;s limitations in bluffing and predicting opponent moves emerged. Reflections on game development, financial constraints, and non-verbal limitations of the study were also discussed. The findings suggest that while GPT-4 exhibits promising advancements over earlier models, there remains potential for further development, especially in instilling more human-like attributes in AI. Key Findings Generative AI and its Potential in Games: • Generative AI has shown promise in simulating human-like interactions, and we show its potential by playing Spyfall, a Mafia-like social deduction game. • In the research, GPT-4’s basic performance surpasses its predecessor, GPT-3.5-turbo. GPT-4’s Adaptation in the Gaming Scenario: • We chose Spyfall for its demand for understanding, decision-making, and psychological elements. • Results highlighted GPT-4’s ability to form natural questions, and its proficiency as a player. Constraints and Limitations in the Study: • GPT-4 shows limitations when playing as non-spies. The absence of non-verbal cues and the flaws of the rules are also supposed to affect the balance between spies and non-spies. • Also, we suppose the initial setting of the model to avoid violation may affect its performance. The Evolution and Future of GPT Models and Generative AI: • From GPT-2 to GPT-4, there has been significant advancement in decision-making, explainability, and problem-solving abilities. • Future directions point towards not just imitating human behavior but infusing “human-like” at- tributes into AI, making them more versatile and widely accessible. • Addressing misconceptions about the models and interdisciplinary collaboration is also vital to foster its growth and broader application. submitted by /u/BorderAffectionate81 [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on September 24, 2023 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • [D] insights on the arsenal tool, AI Security
    by /u/Agile_Temperature678 (Machine Learning) on September 24, 2023 at 11:54 am

    For those who have tried Microsoft's Arsenal tool in conjunction with MITRE's offerings, how does it compare to other AI security tools you've used? submitted by /u/Agile_Temperature678 [link] [comments]

  • [D] help with RVC mode training!
    by /u/mannequin7412 (Machine Learning) on September 24, 2023 at 11:07 am

    so I've had this problem where I try to train the model in RVC but the training stops after: INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration. Does anyone know why is it happening and how can I fix it? this is what it shows to me and idk what to do... any help would be appreciated thank you submitted by /u/mannequin7412 [link] [comments]

Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers

What are some good datasets for Data Science and Machine Learning?

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

As a data scientist, it's important to understand the difference between simple linear regression, multiple linear regression, and MANOVA. This will come in handy when you're working with different datasets and trying to figure out which one to use. Here's a quick overview of each method:

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Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

As a data scientist, it’s important to understand the difference between simple linear regression, multiple linear regression, and MANOVA. This will come in handy when you’re working with different datasets and trying to figure out which one to use. Here’s a quick overview of each method:


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A Short Overview of Simple Linear Regression, Multiple Linear Regression, and MANOVA

Simple linear regression is used to predict the value of a dependent variable (y) based on the value of one independent variable (x). This is the most basic form of regression analysis.

Multiple linear regression is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.). This is more complex than simple linear regression but can provide more accurate predictions.


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MANOVA is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.), while also taking into account the relationships between those variables. This is the most complex form of regression analysis but can provide the most accurate predictions.

So, which one should you use? It depends on your dataset and what you’re trying to predict. If you have a small dataset with only one independent variable, then simple linear regression will suffice. If you have a larger dataset with multiple independent variables, then multiple linear regression will be more appropriate. And if you need to take into account the relationships between your independent variables, then MANOVA is the way to go.

In data science, there are a variety of techniques that can be used to model relationships between variables. Three of the most common techniques are simple linear regression, multiple linear regression, and MANOVA. Although these techniques may appear to be similar at first glance, there are actually some key differences that set them apart. Let’s take a closer look at each technique to see how they differ.

Simple Linear Regression

Simple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and a single independent variable. The dependent variable is the variable that is being predicted, while the independent variable is the variable that is being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Linear Regression Basics for Absolute Beginners | by Benjamin Obi Tayo Ph.D. | Towards AI

Multiple Linear Regression

Multiple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. As with simple linear regression, the dependent variable is the variable that is being predicted. However, in multiple linear regression, there can be multiple independent variables that are being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide\
Multiple Linear Regression from scratch using only numpy | by Debidutta Dash | Analytics Vidhya | Medium

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MANOVA

MANOVA (multivariate analysis of variance) is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. Unlike simple linear regression or multiple linear regression, MANOVA can only be used when the dependent variable is continuous. Additionally, MANOVA can only be used when there are two or more dependent variables.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

When it comes to data modeling, there are a variety of different techniques that can be used. Simple linear regression, multiple linear regression, and MANOVA are three of the most common techniques. Each technique has its own set of benefits and drawbacks that should be considered before deciding which technique to use for a particular project.We often encounter data points that are correlated. For example, the number of hours studied is correlated with the grades achieved. In such cases, we can use regression analysis to study the relationships between the variables.

Simple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the value of an independent variable (x). In other words, we can use simple linear regression to find out how much y will change when x changes.

Multiple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the values of multiple independent variables (x1, x2, …, xn). In other words, we can use multiple linear regression to find out how much y will change when any of the independent variables changes.

Multivariate analysis of variance (MANOVA) is a statistical method that allows us to compare multiple dependent variables (y1, y2, …, yn) simultaneously. In other words, MANOVA can help us understand how multiple dependent variables vary together.

Simple Linear Regression vs Multiple Linear Regression vs MANOVA: A Comparative Study
The main difference between simple linear regression and multiple linear regression is that simple linear regression can be used to predict the value of a dependent variable based on the value of only one independent variable whereas multiple linear regression can be used to predict the value of a dependent variable based on the values of two or more independent variables. Another difference between simple linear regression and multiple linear regression is that simple linear regression is less likely to produce Type I and Type II errors than multiple linear regression.

Both simple linear regression and multiple linear regression are used to predict future values. However, MANOVA is used to understand how present values vary.

Conclusion:

In this article, we have seen the key differences between simple linear regression vs multiple linear regression vs MANOVA along with their applications. Simple linear regression should be used when there is only one predictor variable whereas multiple linear regressions should be used when there are two or more predictor variables. MANOVA should be used when there are two or more response variables. Hope you found this article helpful!

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Data Lakes, S3, Kinesis, Lake Formation, Athena, Kibana, Redshift, EMR, Glue, Kafka, Apache Spark, SQl, NoSQL, Python,DynamoDB, DocumentDB,  linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, Data cleansing, ETL, Data Science and Analytics Cheat Sheets

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Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

As a data scientist, it’s important to understand the difference between simple linear regression, multiple linear regression, and MANOVA. This will come in handy when you’re working with different datasets and trying to figure out which one to use. Here’s a quick overview of each method:

A Short Overview of Simple Linear Regression, Multiple Linear Regression, and MANOVA

Simple linear regression is used to predict the value of a dependent variable (y) based on the value of one independent variable (x). This is the most basic form of regression analysis.

Multiple linear regression is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.). This is more complex than simple linear regression but can provide more accurate predictions.

MANOVA is used to predict the value of a dependent variable (y) based on the values of two or more independent variables (x1, x2, x3, etc.), while also taking into account the relationships between those variables. This is the most complex form of regression analysis but can provide the most accurate predictions.

So, which one should you use? It depends on your dataset and what you’re trying to predict. If you have a small dataset with only one independent variable, then simple linear regression will suffice. If you have a larger dataset with multiple independent variables, then multiple linear regression will be more appropriate. And if you need to take into account the relationships between your independent variables, then MANOVA is the way to go.

In data science, there are a variety of techniques that can be used to model relationships between variables. Three of the most common techniques are simple linear regression, multiple linear regression, and MANOVA. Although these techniques may appear to be similar at first glance, there are actually some key differences that set them apart. Let’s take a closer look at each technique to see how they differ.

Simple Linear Regression

Simple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and a single independent variable. The dependent variable is the variable that is being predicted, while the independent variable is the variable that is being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Linear Regression Basics for Absolute Beginners | by Benjamin Obi Tayo Ph.D. | Towards AI

Multiple Linear Regression


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Multiple linear regression is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. As with simple linear regression, the dependent variable is the variable that is being predicted. However, in multiple linear regression, there can be multiple independent variables that are being used to make predictions.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide\
Multiple Linear Regression from scratch using only numpy | by Debidutta Dash | Analytics Vidhya | Medium

MANOVA

MANOVA (multivariate analysis of variance) is a statistical technique that can be used to model the relationship between a dependent variable and two or more independent variables. Unlike simple linear regression or multiple linear regression, MANOVA can only be used when the dependent variable is continuous. Additionally, MANOVA can only be used when there are two or more dependent variables.

Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist's Guide
Simple Linear Regression vs. Multiple Linear Regression vs. MANOVA: A Data Scientist’s Guide

When it comes to data modeling, there are a variety of different techniques that can be used. Simple linear regression, multiple linear regression, and MANOVA are three of the most common techniques. Each technique has its own set of benefits and drawbacks that should be considered before deciding which technique to use for a particular project.We often encounter data points that are correlated. For example, the number of hours studied is correlated with the grades achieved. In such cases, we can use regression analysis to study the relationships between the variables.

Simple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the value of an independent variable (x). In other words, we can use simple linear regression to find out how much y will change when x changes.

Multiple linear regression is a statistical method that allows us to predict the value of a dependent variable (y) based on the values of multiple independent variables (x1, x2, …, xn). In other words, we can use multiple linear regression to find out how much y will change when any of the independent variables changes.

Multivariate analysis of variance (MANOVA) is a statistical method that allows us to compare multiple dependent variables (y1, y2, …, yn) simultaneously. In other words, MANOVA can help us understand how multiple dependent variables vary together.

Simple Linear Regression vs Multiple Linear Regression vs MANOVA: A Comparative Study
The main difference between simple linear regression and multiple linear regression is that simple linear regression can be used to predict the value of a dependent variable based on the value of only one independent variable whereas multiple linear regression can be used to predict the value of a dependent variable based on the values of two or more independent variables. Another difference between simple linear regression and multiple linear regression is that simple linear regression is less likely to produce Type I and Type II errors than multiple linear regression.

Both simple linear regression and multiple linear regression are used to predict future values. However, MANOVA is used to understand how present values vary.

Conclusion:

In this article, we have seen the key differences between simple linear regression vs multiple linear regression vs MANOVA along with their applications. Simple linear regression should be used when there is only one predictor variable whereas multiple linear regressions should be used when there are two or more predictor variables. MANOVA should be used when there are two or more response variables. Hope you found this article helpful!

Get Certified with the AWS Data analytics DAS-C01 Exam Prep PRO App:
Very Similar to real exam, Countdown timer, Score card, Show/Hide Answers, Cheat Sheets, FlashCards, Detailed Answers and References
No ADS, Access All Quiz Detailed Answers, Reference and Score Card

Hundreds of Quizzes covering Quiz and Brain Teaser for AWS Data analytics DAS-C01, Data Science, Various Practice Exams covering Data Collection, Data Security, Data processing, Data Analysis, Data Visualization, Data Storage and Management,
Data Lakes, S3, Kinesis, Lake Formation, Athena, Kibana, Redshift, EMR, Glue, Kafka, Apache Spark, SQl, NoSQL, Python,DynamoDB, DocumentDB,  linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, Data cleansing, ETL, Data Science and Analytics Cheat Sheets

What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

Summary of Machine Learning and Artificial Intelligence Capabilities

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What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

Machine Learning (ML) is a field of Artificial Intelligence (AI) that enables computers to learn from data, without being explicitly programmed. Machine learning algorithms build models based on sample data, known as “training data”, in order to make predictions or decisions, rather than following rules written by humans. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also focuses on prediction-making through the use of computers. Machine learning can be applied in a wide variety of domains, such as medical diagnosis, stock trading, robot control, manufacturing and more.


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Problem Formulation in Machine Learning
What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

The process of machine learning consists of several steps: first, data is collected; then, a model is selected or created; finally, the model is trained on the collected data and then applied to new data. This process is often referred to as the “machine learning pipeline”. Problem formulation is the second step in this pipeline and it consists of selecting or creating a suitable model for the task at hand and determining how to represent the collected data so that it can be used by the selected model. In other words, problem formulation is the process of taking a real-world problem and translating it into a format that can be solved by a machine learning algorithm.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

There are many different types of machine learning problems, such as classification, regression, prediction and so on. The choice of which type of problem to formulate depends on the nature of the task at hand and the type of data available. For example, if we want to build a system that can automatically detect fraudulent credit card transactions, we would formulate a classification problem. On the other hand, if our goal is to predict the sale price of houses given information about their size, location and age, we would formulate a regression problem. In general, it is best to start with a simple problem formulation and then move on to more complex ones if needed.


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Some common examples of problem formulations in machine learning are:
Classification: given an input data point (e.g., an image), predict its category label (e.g., dog vs cat).
Regression: given an input data point (e.g., size and location of a house), predict a continuous output value (e.g., sale price).
Prediction: given an input sequence (e.g., a series of past stock prices), predict the next value in the sequence (e.g., future stock price).
Anomaly detection: given an input data point (e.g., transaction details), decide whether it is normal or anomalous (i.e., fraudulent).
Recommendation: given information about users (e.g., age and gender) and items (e.g., books and movies), recommend items to users (e.g., suggest books for someone who likes romance novels).
Optimization: given a set of constraints (e.g., budget) and objectives (e.g., maximize profit), find the best solution (e.g., product mix).

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Problem Formulation: What this pipeline phase entails and why it’s important

The problem formulation phase of the ML Pipeline is critical, and it’s where everything begins. Typically, this phase is kicked off with a question of some kind. Examples of these kinds of questions include: Could cars really drive themselves?  What additional product should we offer someone as they checkout? How much storage will clients need from a data center at a given time?

The problem formulation phase starts by seeing a problem and thinking “what question, if I could answer it, would provide the most value to my business?” If I knew the next product a customer was going to buy, is that most valuable? If I knew what was going to be popular over the holidays, is that most valuable? If I better understood who my customers are, is that most valuable?

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However, some problems are not so obvious. When sales drop, new competitors emerge, or there’s a big change to a company/team/org, it can be easy to say, “I see the problem!” But sometimes the problem isn’t so clear. Consider self-driving cars. How many people think to themselves, “driving cars is a huge problem”? Probably not many. In fact, there isn’t a problem in the traditional sense of the word but there is an opportunity. Creating self-driving cars is a huge opportunity. That doesn’t mean there isn’t a problem or challenge connected to that opportunity. How do you design a self-driving system? What data would you look at to inform the decisions you make? Will people purchase self-driving cars?

Part of the problem formulation phase includes seeing where there are opportunities to use machine learning.

In the following practice examples, you are presented with four different business scenarios. For each scenario, consider the following questions:

  1. Is machine learning appropriate for this problem, and why or why not?
  2. What is the ML problem if there is one, and what would a success metric look like?
  3. What kind of ML problem is this?
  4. Is the data appropriate?’

The solutions given in this article are one of the many ways you can formulate a business problem.

I)  Amazon recently began advertising to its customers when they visit the company website. The Director in charge of the initiative wants the advertisements to be as tailored to the customer as possible. You will have access to all the data from the retail webpage, as well as all the customer data.

  1. ML is appropriate because of the scale, variety and speed required. There are potentially thousands of ads and millions of customers that need to be served customized ads immediately as they arrive to the site.
  2. The problem is ads that are not useful to customers are a wasted opportunity and a nuisance to customers, yet not serving ads at all is a wasted opportunity. So how does Amazon serve the most relevant advertisements to its retail customers?
    1. Success would be the purchase of a product that was advertised.
  3. This is a supervised learning problem because we have a labeled data point, our success metric, which is the purchase of a product.
  4. This data is appropriate because it is both the retail webpage data as well as the customer data.

II) You’re a Senior Business Analyst at a social media company that focuses on streaming. Streamers use a combination of hashtags and predefined categories to be discoverable by your platform’s consumers. You ran an analysis on unique streamer counts by hashtags and categories over the last month and found that out of tens of thousands of streamers, almost all use only 40 hashtags and 10 categories despite innumerable hashtags and hundreds of categories. You presume the predefined categories don’t represent all the possibilities very well, and that streamers are simply picking the closest fit. You figure there are likely many categories and groupings of streamers that are not accounted for. So you collect a dataset that consists of all streamer profile descriptions (all text), all the historical chat information for each streamer, and all their videos that have been streamed.

  1. ML is appropriate because of the scale and variability.
  2. The problem is the content of streamers is not being represented by the existing categories. Success would be naturally grouping the streamers into categories based on content and seeing if those align with the hashtags and categories that are being commonly used.  If they do not, then the streamers are not being well represented and you can use these groupings to create new categories.
  3. There isn’t a specific outcome variable. There’s no target or label. So this is an unsupervised problem.
  4. The data is appropriate.

III) You’re a headphone manufacturer who sells directly to big and small electronic stores. As an attempt to increase competitive pricing, Store 1 and Store 2 decided to put together the pricing details for all headphone manufacturers and their products (about 350 products) and conduct daily releases of the data. You will have all the specs from each manufacturer and their product’s pricing. Your sales have recently been dropping so your first concern is whether there are competing products that are priced lower than your flagship product.

  1. ML is probably not necessary for this. You can just search the dataset to see which headphones are priced lower than the flagship, then compare their features and build quality.

IV) You’re a Senior Product Manager at a leading ridesharing company. You did some market research, collected customer feedback, and discovered that both customers and drivers are not happy with an app feature. This feature allows customers to place a pin exactly where they want to be picked up. The customers say drivers rarely stop at the pin location. Drivers say customers most often put the pin in a place they can’t stop. Your company has a relationship with the most used maps app for the driver’s navigation so you leverage this existing relationship to get direct, backend access to their data. This includes latitude and longitude, visual photos of each lat/long, traffic delay details, and regulation data if available (ie- No Parking zones, 3 minute parking zones, fire hydrants, etc.).

  1. ML is appropriate because of the scale and automation involved. It’s not feasible to drive everywhere and write down all the places that are ok for pickup. However, maybe we can predict whether a location is ok for pickup.
  2. The problem is drivers and customers are having poor experiences connecting for pickup, which is pushing customers away from the platform.
    1. Success would be properly identifying appropriate pickup locations so they can be integrated into the feature.
  3. This is a supervised learning problem even though there aren’t any labels, yet. Someone will have to go through a sample of the data to label where there are ok places to park and not park, giving the algorithms some target information.
  4. The data is appropriate once a sample of the dataset has been labeled. There may be some other data that could be included too. What about asking UPS for driver stop information? Where do they stop?

In conclusion, problem formulation is an important step in the machine learning pipeline that should not be overlooked or underestimated. It can make or break a machine learning project; therefore, it is important to take care when formulating machine learning problems.”

AWS machine Learning Specialty Exam Prep MLS-C01
AWS machine Learning Specialty Exam Prep MLS-C01

Step by Step Solution to a Machine Learning Problem – Feature Engineering

Feature Engineering is the act of reshaping and curating existing data to make patters more apparent. This process makes the data easier for an ML model to understand. Using knowledge of the data, features are engineered and  tuned to make ML algorithms work more efficiently.

 

For this problem, imagine a scenario where you are running a real estate brokerage and you want to predict the selling price of a house. Using a specific county dataset and simple information (like the location, total square footage, and number of bedrooms), let’s practice training a baseline model, conducting feature engineering, and tuning a model to make a prediction.

First, load the dataset and take a look at its basic properties.

# Load the dataset
import pandas as pd
import boto3

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df = pd.read_csv(“xxxxx_data_2.csv”)
df.head()

housing dataset example
housing dataset example: xxxxx_data_2.csv

Output:

feature_engineering_dataset_example
feature_engineering_dataset_example

This dataset has 21 columns:

  • id – Unique id number
  • date – Date of the house sale
  • price – Price the house sold for
  • bedrooms – Number of bedrooms
  • bathrooms – Number of bathrooms
  • sqft_living – Number of square feet of the living space
  • sqft_lot – Number of square feet of the lot
  • floors – Number of floors in the house
  • waterfront – Whether the home is on the waterfront
  • view – Number of lot sides with a view
  • condition – Condition of the house
  • grade – Classification by construction quality
  • sqft_above – Number of square feet above ground
  • sqft_basement – Number of square feet below ground
  • yr_built – Year built
  • yr_renovated – Year renovated
  • zipcode – ZIP code
  • lat – Latitude
  • long – Longitude
  • sqft_living15 – Number of square feet of living space in 2015 (can differ from sqft_living in the case of recent renovations)
  • sqrt_lot15 – Nnumber of square feet of lot space in 2015 (can differ from sqft_lot in the case of recent renovations)

This dataset is rich and provides a fantastic playground for the exploration of feature engineering. This exercise will focus on a small number of columns. If you are interested, you could return to this dataset later to practice feature engineering on the remaining columns.

A baseline model

Now, let’s  train a baseline model.

People often look at square footage first when evaluating a home. We will do the same in the oflorur model and ask how well can the cost of the house be approximated based on this number alone. We will train a simple linear learner model (documentation). We will compare to this after finishing the feature engineering.

import sagemaker
import numpy as np
from sklearn.model_selection import train_test_split
import time

t1 = time.time()

# Split training, validation, and test
ys = np.array(df[‘price’]).astype(“float32”)
xs = np.array(df[‘sqft_living’]).astype(“float32”).reshape(-1,1)

np.random.seed(8675309)
train_features, test_features, train_labels, test_labels = train_test_split(xs, ys, test_size=0.2)
val_features, test_features, val_labels, test_labels = train_test_split(test_features, test_labels, test_size=0.5)


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# Train model
linear_model = sagemaker.LinearLearner(role=sagemaker.get_execution_role(),
instance_count=1,
instance_type=’ml.m4.xlarge’,
predictor_type=’regressor’)

train_records = linear_model.record_set(train_features, train_labels, channel=’train’)
val_records = linear_model.record_set(val_features, val_labels, channel=’validation’)
test_records = linear_model.record_set(test_features, test_labels, channel=’test’)

linear_model.fit([train_records, val_records, test_records], logs=False)

sagemaker.analytics.TrainingJobAnalytics(linear_model._current_job_name, metric_names = [‘test:mse’, ‘test:absolute_loss’]).dataframe()

 

If you examine the quality metrics, you will see that the absolute loss is about $175,000.00. This tells us that the model is able to predict within an average of $175k of the true price. For a model based upon a single variable, this is not bad. Let’s try to do some feature engineering to improve on it.

Throughout the following work, we will constantly be adding to a dataframe called encoded. You will start by populating encoded with just the square footage you used previously.

 

encoded = df[[‘sqft_living’]].copy()

Categorical variables

Let’s start by including some categorical variables, beginning with simple binary variables.

The dataset has the waterfront feature, which is a binary variable. We should change the encoding from 'Y' and 'N' to 1 and 0. This can be done using the map function (documentation) provided by Pandas. It expects either a function to apply to that column or a dictionary to look up the correct transformation.

Binary categorical

Let’s write code to transform the waterfront variable into binary values. The skeleton has been provided below.

encoded[‘waterfront’] = df[‘waterfront’].map({‘Y’:1, ‘N’:0})

You can also encode many class categorical variables. Look at column condition, which gives a score of the quality of the house. Looking into the data source shows that the condition can be thought of as an ordinal categorical variable, so it makes sense to encode it with the order.

Ordinal categorical

Using the same method as in question 1, encode the ordinal categorical variable condition into the numerical range of 1 through 5.

encoded[‘condition’] = df[‘condition’].map({‘Poor’:1, ‘Fair’:2, ‘Average’:3, ‘Good’:4, ‘Very Good’:5})

A slightly more complex categorical variable is ZIP code. If you have worked with geospatial data, you may know that the full ZIP code is often too fine-grained to use as a feature on its own. However, there are only 7070 unique ZIP codes in this dataset, so we may use them.

However, we do not want to use unencoded ZIP codes. There is no reason that a larger ZIP code should correspond to a higher or lower price, but it is likely that particular ZIP codes would. This is the perfect case to perform one-hot encoding. You can use the get_dummies function (documentation) from Pandas to do this.

Nominal categorical

Using the Pandas get_dummies function,  add columns to one-hot encode the ZIP code and add it to the dataset.

encoded = pd.concat([encoded, pd.get_dummies(df[‘zipcode’])], axis=1)

In this way, you may freely encode whatever categorical variables you wish. Be aware that for categorical variables with many categories, something will need to be done to reduce the number of columns created.

One additional technique, which is simple but can be highly successful, involves turning the ZIP code into a single numerical column by creating a single feature that is the average price of a home in that ZIP code. This is called target encoding.

To do this, use groupby (documentation) and mean (documentation) to first group the rows of the DataFrame by ZIP code and then take the mean of each group. The resulting object can be mapped over the ZIP code column to encode the feature.

Nominal categorical II

Complete the following code snippet to provide a target encoding for the ZIP code.

means = df.groupby(‘zipcode’)[‘price’].mean()
encoded[‘zip_mean’] = df[‘zipcode’].map(means)

Normally, you only either one-hot encode or target encode. For this exercise, leave both in. In practice, you should try both, see which one performs better on a validation set, and then use that method.

Scaling

Take a look at the dataset. Print a summary of the encoded dataset using describe (documentation).

encoded.describe()

Scaling  - summary of the encoded dataset using describe
Scaling – summary of the encoded dataset using describe

One column ranges from 290290 to 1354013540 (sqft_living), another column ranges from 11 to 55 (condition), 7171 columns are all either 00 or 11 (one-hot encoded ZIP code), and then the final column ranges from a few hundred thousand to a couple million (zip_mean).

In a linear model, these will not be on equal footing. The sqft_living column will be approximately 1300013000 times easier for the model to find a pattern in than the other columns. To solve this, you often want to scale features to a standardized range. In this case, you will scale sqft_living to lie within 00 and 11.

Feature scaling

Fill in the code skeleton below to scale the column of the DataFrame to be between 00 and 11.

sqft_min = encoded[‘sqft_living’].min()
sqft_max = encoded[‘sqft_living’].max()
encoded[‘sqft_living’] = encoded[‘sqft_living’].map(lambda x : (x-sqft_min)/(sqft_max – sqft_min))

cond_min = encoded[‘condition’].min()
cond_max = encoded[‘condition’].max()
encoded[‘condition’] = encoded[‘condition’].map(lambda x : (x-cond_min)/(cond_max – cond_min))]

Read more here….

Amazon Reviews Solution

Predicting Credit Card Fraud Solution

Predicting Airplane Delays Solution

Data Processing for Machine Learning Example

Model Training and Evaluation Examples

Targeting Direct Marketing Solution

What are some good datasets for Data Science and Machine Learning?

What are some good datasets for Data Science and Machine Learning?

You can translate the content of this page by selecting a language in the select box.

What are some good datasets for Data Science and Machine Learning?

Finding good datasets for Data Science and Machine Learning can be a challenge. There are a lot of dataset out there, but not all of them are good for machine learning. In order to find a good dataset, you need to consider what you want to use the dataset for. If you want to use the dataset for training a machine learning model, then you need to make sure that the dataset is representative of the real-world data that you want to use the model on.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

The dataset should also be large enough to train a robust model. Another important consideration is whether or not the dataset is open source. Open source datasets are typically better because they have been vetted by the community and are more likely to be of high quality. However, open source datasets can also be more difficult to find. A good place to start looking for datasets is on websites like Kaggle and UC Irvine Machine Learning Repository. These websites contain a variety of high-quality datasets that are free to download and use.

What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions
AWS Data Analytics Specialty Certification Practice Exams

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses


Amplify Your Brand's Exposure with the AI Unraveled Podcast - Elevate Your Sales Today! [200K downloads per Month]

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Earth’s population reaches 8 billion

Earth's population reaches 8 billion
Earth’s population reaches 8 billion

The most used words on every country’s Wikipedia Page

What are some good datasets for Data Science and Machine Learning?
The most used words on every country’s Wikipedia Page

Who works from home in 2022? Rates by industry 

Who works from home in 2022? Rates by industry
Who works from home in 2022? Rates by industry

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

If you are looking for an all-in-one solution to help you prepare for the AWS Cloud Practitioner Certification Exam, look no further than this AWS Cloud Practitioner CCP CLFC01 book

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

What are some good datasets for Data Science and Machine Learning?
What are some good datasets for Data Science and Machine Learning?

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses

Djamgatech: Build the skills that’ll drive your career into six figures: Get Djamgatech.

 
 

The Biggest Source of Power in Every US and Canadian State and Province 

The Biggest Source of Power in Every State and Province
The Biggest Source of Power in Every State and Province

Top 10 largest oil fields by 2021 production

Top 10 largest oil fields by 2021 production
Top 10 largest oil fields by 2021 production

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

The Largest Entertainment Streaming Companies
The Largest Entertainment Streaming Companies

The F word in Popular Movies

r/dataisbeautiful - [OC] The F word in Popular Movies

The easiest words to rhyme – Words that have the most rhymes

Post image

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices


2023 Azure Fundamentals Exam Prep Book: Pass the AZ900 Cert Ace the Microsoft Azure Fundamentals AZ-900 Certification Exam: Pass the Azure Fundamentals Exam with Ease: Master the AZ-900 Certification with this Comprehensive Exam Preparation Guide!

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

Suicide rate among countries with the highest Human Development Index

Suicide rate among countries with the highest Human Development Index
Suicide rate among countries with the highest Human Development Index

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Amazon Omics

Store, query, analyze, and generate insights from genomic and other omics data.

Amazon Omics
Amazon Omics

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

 
 
Behshad Behzadi on LinkedIn: Partnering with iCAD to improve breast cancer screening
 

From AI Research to Real world Clinical Practice:
After a pivotal moment in 2020 to show our AI technology performed better than radiologists in a retrospective study at identifying signs of breast cancer, today a new important milestone is achieved: Google Health announces our first commercial agreement to license our mammography AI research model to be integrated in real-world clinical practice.

This can make healthcare AI to be more accessible and eventually saves more lives.

#ai #research #google #health #healthcare #breastcancer #mammography

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Node.js – Async non-blocking event-driven JavaScript runtime built on Chrome’s V8 JavaScript engine.
  • Frontend Development
  • iOS – Mobile operating system for Apple phones and tablets.
  • Android – Mobile operating system developed by Google.
  • IoT & Hybrid Apps
  • Electron – Cross-platform native desktop apps using JavaScript/HTML/CSS.
  • Cordova – JavaScript API for hybrid apps.
  • React Native – JavaScript framework for writing natively rendering mobile apps for iOS and Android.
  • Xamarin – Mobile app development IDE, testing, and distribution.
  • Linux
    • Containers
    • eBPF – Virtual machine that allows you to write more efficient and powerful tracing and monitoring for Linux systems.
    • Arch-based Projects – Linux distributions and projects based on Arch Linux.
  • macOS – Operating system for Apple’s Mac computers.
  • watchOS – Operating system for the Apple Watch.
  • JVM
  • Salesforce
  • Amazon Web Services
  • Windows
  • IPFS – P2P hypermedia protocol.
  • Fuse – Mobile development tools.
  • Heroku – Cloud platform as a service.
  • Raspberry Pi – Credit card-sized computer aimed at teaching kids programming, but capable of a lot more.
  • Qt – Cross-platform GUI app framework.
  • WebExtensions – Cross-browser extension system.
  • RubyMotion – Write cross-platform native apps for iOS, Android, macOS, tvOS, and watchOS in Ruby.
  • Smart TV – Create apps for different TV platforms.
  • GNOME – Simple and distraction-free desktop environment for Linux.
  • KDE – A free software community dedicated to creating an open and user-friendly computing experience.
  • .NET
    • Core
    • Roslyn – Open-source compilers and code analysis APIs for C# and VB.NET languages.
  • Amazon Alexa – Virtual home assistant.
  • DigitalOcean – Cloud computing platform designed for developers.
  • Flutter – Google’s mobile SDK for building native iOS and Android apps from a single codebase written in Dart.
  • Home Assistant – Open source home automation that puts local control and privacy first.
  • IBM Cloud – Cloud platform for developers and companies.
  • Firebase – App development platform built on Google Cloud Platform.
  • Robot Operating System 2.0 – Set of software libraries and tools that help you build robot apps.
  • Adafruit IO – Visualize and store data from any device.
  • Cloudflare – CDN, DNS, DDoS protection, and security for your site.
  • Actions on Google – Developer platform for Google Assistant.
  • ESP – Low-cost microcontrollers with WiFi and broad IoT applications.
  • Deno – A secure runtime for JavaScript and TypeScript that uses V8 and is built in Rust.
  • DOS – Operating system for x86-based personal computers that was popular during the 1980s and early 1990s.
  • Nix – Package manager for Linux and other Unix systems that makes package management reliable and reproducible.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • JavaScript
  • Swift – Apple’s compiled programming language that is secure, modern, programmer-friendly, and fast.
  • Python – General-purpose programming language designed for readability.
    • Asyncio – Asynchronous I/O in Python 3.
    • Scientific Audio – Scientific research in audio/music.
    • CircuitPython – A version of Python for microcontrollers.
    • Data Science – Data analysis and machine learning.
    • Typing – Optional static typing for Python.
    • MicroPython – A lean and efficient implementation of Python 3 for microcontrollers.
  • Rust
  • Haskell
  • PureScript
  • Go
  • Scala
    • Scala Native – Optimizing ahead-of-time compiler for Scala based on LLVM.
  • Ruby
  • Clojure
  • ClojureScript
  • Elixir
  • Elm
  • Erlang
  • Julia – High-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science.
  • Lua
  • C
  • C/C++ – General-purpose language with a bias toward system programming and embedded, resource-constrained software.
  • R – Functional programming language and environment for statistical computing and graphics.
  • D
  • Common Lisp – Powerful dynamic multiparadigm language that facilitates iterative and interactive development.
  • Perl
  • Groovy
  • Dart
  • Java – Popular secure object-oriented language designed for flexibility to “write once, run anywhere”.
  • Kotlin
  • OCaml
  • ColdFusion
  • Fortran
  • PHP – Server-side scripting language.
  • Pascal
  • AutoHotkey
  • AutoIt
  • Crystal
  • Frege – Haskell for the JVM.
  • CMake – Build, test, and package software.
  • ActionScript 3 – Object-oriented language targeting Adobe AIR.
  • Eta – Functional programming language for the JVM.
  • Idris – General purpose pure functional programming language with dependent types influenced by Haskell and ML.
  • Ada/SPARK – Modern programming language designed for large, long-lived apps where reliability and efficiency are essential.
  • Q# – Domain-specific programming language used for expressing quantum algorithms.
  • Imba – Programming language inspired by Ruby and Python and compiles to performant JavaScript.
  • Vala – Programming language designed to take full advantage of the GLib and GNOME ecosystems, while preserving the speed of C code.
  • Coq – Formal language and environment for programming and specification which facilitates interactive development of machine-checked proofs.
  • V – Simple, fast, safe, compiled language for developing maintainable software.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Flask – Python framework.
  • Docker
  • Vagrant – Automation virtual machine environment.
  • Pyramid – Python framework.
  • Play1 Framework
  • CakePHP – PHP framework.
  • Symfony – PHP framework.
  • Laravel – PHP framework.
    • Education
    • TALL Stack – Full-stack development solution featuring libraries built by the Laravel community.
  • Rails – Web app framework for Ruby.
  • Phalcon – PHP framework.
  • Useful .htaccess Snippets
  • nginx – Web server.
  • Dropwizard – Java framework.
  • Kubernetes – Open-source platform that automates Linux container operations.
  • Lumen – PHP micro-framework.
  • Serverless Framework – Serverless computing and serverless architectures.
  • Apache Wicket – Java web app framework.
  • Vert.x – Toolkit for building reactive apps on the JVM.
  • Terraform – Tool for building, changing, and versioning infrastructure.
  • Vapor – Server-side development in Swift.
  • Dash – Python web app framework.
  • FastAPI – Python web app framework.
  • CDK – Open-source software development framework for defining cloud infrastructure in code.
  • IAM – User accounts, authentication and authorization.
  • Chalice – Python framework for serverless app development on AWS Lambda.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

  • Big Data
  • Public Datasets
  • Hadoop – Framework for distributed storage and processing of very large data sets.
  • Data Engineering
  • Streaming
  • Apache Spark – Unified engine for large-scale data processing.
  • Qlik – Business intelligence platform for data visualization, analytics, and reporting apps.
  • Splunk – Platform for searching, monitoring, and analyzing structured and unstructured machine-generated big data in real-time.

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ years of experience

r/dataisbeautiful - [OC] 35% of "entry-level" jobs on LinkedIn require 3+ years of experience

Source: LinkedIn data  (see original post)

Tool: Photoshop from my colleague

Latest complete Netflix movie dataset

Created from 4 APIs. 11K+ rows and 30+ attributes of Netflix (Ratings, earnings, actors, language, availability, movie trailers, and many more)

Dataset on Kaggle.

Explore this dataset using FlixGem.com (this dataset is powering this webapp)

Dataset on Google Sheets.

Common Crawl

A corpus of web crawl data composed of over 50 billion web pages. The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions.

AWS CLI Access (No AWS account required)

aws s3 ls s3://commoncrawl/ --no-sign-request

s3://commoncrawl/crawl-data/CC-MAIN-2021-17 – April 2021

 Dataset on protein prices

Data on Primary Commodity Prices are updated monthly based on the IMF’s Primary Commodity Price System.

Excel Database

 CPOST dataset on suicide attacks over four decades

The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.

Credit Card Dataset – Survey of Consumer Finances (SCF) Combined Extract Data 1989-2019

You can do a lot of aggregated analysis in a pretty straightforward way there.

Drone imagery with annotations for small object detection and tracking dataset

11 TB dataset of drone imagery with annotations for small object detection and tracking

Download and more information are available here

Dataset License: CDLA-Sharing-1.0

Helper scripts for accessing the dataset: DATASET.md

Dataset Exploration: Colab

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

Registry of Open Data on AWS

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS.

See all usage examples for datasets listed in this registry.

See datasets from Digital Earth AfricaFacebook Data for GoodNASA Space Act AgreementNIH STRIDESNOAA Big Data ProgramSpace Telescope Science Institute, and Amazon Sustainability Data Initiative.

Textbook Question Answering (TQA)

1,076 textbook lessons, 26,260 questions, 6229 images

Documentation: allenai.org/data/tqa

Download

Harmonized Cancer Datasets: Genomic Data Commons Data Portal

The GDC Data Portal is a robust data-driven platform that allows cancer
researchers and bioinformaticians to search and download cancer data for analysis.

Genomic Data Commons Data Portal
Genomic Data Commons Data Portal

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.

AWS CLI Access (No AWS account required)

aws s3 ls s3://tcga-2-open/ --no-sign-request

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams.  TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads

SQuAD (Stanford Question Answering Dataset)

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.

PubMed Diabetes Dataset

The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.

Download Link

Drug-Target Interaction Dataset

This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link

Pharmacogenomics Datasets

PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.

Pancreatic Cancer Organoid Profiling

The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://gdc-organoid-pancreatic-phs001611-2-open/ --no-sign-request

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation

AWS CLI Access (No AWS account required)

aws s3 ls s3://afsis/ --no-sign-request

Dataset for Affective States in E-Environments

DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.

NatureServe Explorer Dataset

NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.

The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here

Flight Records in the US

Airline On-Time Performance and Causes of Flight Delays – On_Time Data.

This database contains scheduled and actual departure and arrival times, reason of delay. reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

FlightAware.com has data but you need to pay for a full dataset.

The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:

  • flights: all flights that departed a given airport in a given year and month
  • weather: hourly meterological data for a given airport in a given year and month
  • airports: airport names, FAA codes, and locations
  • airlines: translation between two letter carrier (airline) codes and names
  • planes: construction information about each plane found in flights

Airline On-Time Statistics and Delay Causes

The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here

Worldwide flight data

Open flights: As of January 2017, the OpenFlights Airports Database contains over 10,000 airports, train stations and ferry terminals spanning the globe

Download: airports.dat (Airports only, high quality)

Download: airports-extended.dat (Airports, train stations and ferry terminals, including user contributions)

Bureau of Transportation:

Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.

flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.

 

2019 Crime statistics in the USA

Dataset with arrest in US by race and separate states. Download Excel here

Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

Source – Summary – Paper – IBM Blog

100 million protein structures Dataset by DeepMind

DeepMind creates ‘transformative’ map of human proteins drawn by AI. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,

Here’s a good article about this topic

Google Dataset Search

Google Dataset Search

Malware traffic dataset

Comprises 1914081 records created from all malware traffic analysis .net PCAP files, from 2013 to 2021. The logs are generated using Suricata and Zeek.

Originator: ali_alwashali

Percent of “foreign-born” population in each US and EU state or country.

For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state 🇺🇸🇪🇺

Author: Here

Percent of “foreign-born” population in each US and EU state or country. For the EU, “foreign-born” mean being born outside of any of the EU countries. For the US, “foreign-born” mean being born outside of any US state.

Examples of “foreign-born” in this context:

  • Person born in Spain and living in France is NOT “foreign-born”

  • Person born in Turkey and living in France is “foreign-born”

  • Person born in Florida and living in Texas is NOT “foreign-born”

  • Person born in Mexico and living in Texas is “foreign-born”

  • Person born in Florida and living in France is “foreign-born”

  • Person born in France and living in Florida is “foreign-born”

🇺🇸🇪🇺🗺️

Note: Poland, Ireland, Germany, Greece, Cyprus, Malta, Portugal uses Eurostat 2010 Migration data and Croatia has no data at all

Link1

Link2

Link3

Tools: MS Office

Source: Here

35% of “entry-level” jobs on LinkedIn require 3+ year