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A finance company is storing paid invoices in an Amazon S3 bucket. After the invoices are uploaded, an AWS Lambda function uses Amazon Textract to process the PDF data and persist the data to Amazon DynamoDB. Currently, the Lambda execution role has the following S3 permission:
Append “s3:GetObject” to the Action. Append the bucket name to the Resource.
Modify the Action to be “s3:GetObjectAttributes.” Modify the Resource to be only the bucket name.
Append “s3:GetObject” to the Action. Modify the Resource to be only the bucket ARN.
Modify the Action to be: “s3:GetObject.” Modify the Resource to be only the bucket ARN.
Practice Quiz 1 – Correct Answer: D.
According to the principle of least privilege, permissions should apply only to what is necessary. The Lambda function needs only the permissions to get the object. Therefore, this solution has the most appropriate modifications.
A data engineer is designing an application that will transform data in containers managed by Amazon Elastic Kubernetes Service (Amazon EKS). The containers run on Amazon EC2 nodes. Each containerized application will transform independent datasets and then store the data in a data lake. Data does not need to be shared to other containers. The data engineer must decide where to store data before transformation is complete.
Which solution will meet these requirements with the LOWEST latency?
Containers should use an ephemeral volume provided by the node’s RAM.
Containers should establish a connection to Amazon DynamoDB Accelerator (DAX) within the application code.
Containers should use a PersistentVolume object provided by an NFS storage.
Containers should establish a connection to Amazon MemoryDB for Redis within the application code.
Practice Quiz 2 – Correct Answer: A.
Amazon EKS is a container orchestrator that provides Kubernetes as a managed service. Containers run in pods. Pods run on nodes. Nodes can be EC2 instances, or nodes can use AWS Fargate. Ephemeral volumes exist with the pod’s lifecycle. Ephemeral volumes can access drives or memory that is local to the node. The data does not need to be shared, and the node provides storage. Therefore, this solution will have lower latency than storage that is external to the node.
Free beginner level courses from AWS Skill builder.
Fundamentals of Data Analytics on AWS – the current Skillbuilder course linked from the certification site is ending in Feb and is being split into 2 new courses :
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Deciphering the Marketing Landscape: Latest Insights & Trends for 2023
In the dynamic world of marketing, trends evolve at a breakneck speed. As consumers become more discerning and digitally connected, their preferences and behavior patterns shift, requiring marketers to stay ahead of the curve. With each passing year, some strategies solidify their ground, while others wane. Dive into our curated compilation of the latest marketing insights and trends for 2023. Whether you’re a seasoned marketer or a curious entrepreneur, these findings offer a snapshot of the changing consumer landscape and emerging marketing frontiers. Get ready to recalibrate, reimagine, and reshape your strategies!
1. The Eroding Value of “Sustainability” Recent research on Palm Oil reveals a surprising trend – consumers favor products labeled as “free-from palm oil” over those stamped with “sustainably produced palm oil.” This shift stems from the overused term “sustainable,” which seems to be losing its weight in the marketplace. This raises concerns, especially as WWF emphasizes that abandoning palm oil isn’t the right solution.
2. Packaging – The Silent Salesperson Kerry’s latest research underscores that 72% of consumers believe brands can help them reduce waste by enhancing the shelf life of food through better packaging. This trend is not just isolated. European publication Amcor’s findings align, showing a growing demand for improved packaging. In the future, marketers must spotlight their packaging efforts more prominently.
3. Cars and Consumers: A Telling Connection Recent data from the 2023 GWI Commerce Report showcases a peculiar trend – 40% of recent car purchasers also invested in a domestic vacation. In another intriguing find, consumers tend to make impulse purchases post physical activities. While not a new revelation, it’s worth noting for potential marketing strategies.
4. Prime Day vs. Black Friday Amazon’s Prime Day is carving out its niche, with 4% of consumers favoring it over the traditional Black Friday. But with the US Consumer Confidence fluctuating in October, it’ll be intriguing to monitor Amazon’s trajectory in the coming year.
5. Rethinking Boomer Representation in Ads? Gen-Z and Millennials’ financial concerns are largely attributed to the Baby Boomer generation, as per OnePoll data. With Gen-Z’s growing bias against Baby Boomers, marketers might need to reevaluate the representation of this age group in advertising campaigns.
6. The UK’s Growing Love for Loyalty Discounts A significant portion of consumers in the UK is trading brand loyalty for alluring discounts. Findings from the Data & Marketing Association and American Express emphasize the importance of loyalty schemes. Given the current political and economic landscape, loyalty schemes could be the game-changer for retailers in the UK.
7. Snapshots from Other Reports:
A whopping $80B is lost to Ad Fraud, as per new insights from Juniper Research.
Mobile advertising is booming in the UK, with over 60% of companies planning to ramp up their budgets.
Gen-X feels overlooked in TV advertising, says Wavermaker Studio.
The beauty industry take note: consumers crave educational content, says a report from Happi.
Italy’s consumer spending expected to dip by approximately $3.7B, data from Ansa suggests.
Conclusion: Staying updated with the ever-evolving marketing landscape is vital for businesses to make informed decisions. From the waning trust in sustainability claims to the UK’s growing penchant for loyalty schemes, marketers need to remain agile and receptive to these shifts.
Welcome to the Djamgatech Marketing podcast, your go-to source for the latest trends and insights in the world of marketing. In today’s episode, we’ll cover the latest marketing insights and trends for 2023, including consumer preferences, improved packaging, investments in vacations, the popularity of Prime Day, generational differences, loyalty discounts, the rise of mobile ad budgets, neglected Gen-X in TV ads, the demand for educational beauty content, and the expected decrease in Italy’s consumer spending. Additionally, we’ll highlight the importance of staying updated in marketing for informed decisions on sustainability claims and UK loyalty schemes.
In the fast-paced world of marketing, trends come and go faster than you can say “advertise.” As consumers get pickier and more plugged in, their tastes and habits shift, forcing marketers to keep up with the times. Each year brings new opportunities and challenges, with some strategies becoming tried and true, while others fade into obscurity. But fear not, because we’ve got you covered. Take a deep dive into our meticulously curated collection of the freshest marketing insights and trends for 2023. Whether you’re a seasoned marketing guru or just starting out, these findings will give you a great snapshot of what’s happening in the ever-changing world of consumers and marketing. So get ready to adapt, think outside the box, and reshape your strategies to stay ahead of the game. It’s time to embrace the future!
So, let’s dive right into some interesting research findings that shed light on important consumer trends. First up, recent studies on Palm Oil reveal that consumers now prefer products labeled as “free-from palm oil” rather than those labeled as “sustainably produced palm oil.” It seems that the term “sustainable” has become so overused that it’s losing its impact in the marketplace. However, we need to be cautious about completely abandoning palm oil, as organizations like WWF emphasize. They argue that the solution lies not in abandoning palm oil, but in finding sustainable ways to produce it. Now let’s talk about the power of packaging. Kerry’s latest research shows that a whopping 72% of consumers believe that brands can help them reduce waste by improving the packaging of food and extending its shelf life. And this trend is not just limited to one study. European publication Amcor’s findings align with Kerry’s research, revealing a growing demand for better packaging. So, moving forward, marketers need to highlight their packaging efforts more prominently in order to cater to this consumer demand. Next, let’s take a look at an interesting connection between car purchases and consumer behavior. Data from the 2023 GWI Commerce Report shows that 40% of recent car purchasers also invested in a domestic vacation. This finding uncovers a possible pattern of consumers making impulse purchases following physical activities. While this may not be a groundbreaking revelation, it’s definitely worth noting for potential marketing strategies. We can’t talk about consumer trends without mentioning the impact of major shopping events.
Amazon’s Prime Day, which has gained popularity in recent years, now has 4% of consumers favoring it over the traditional Black Friday. However, with US Consumer Confidence fluctuating in October, it’ll be intriguing to see how Amazon’s trajectory plays out in the coming year. Moving on to demographics, recent data suggests that Gen-Z and Millennials have significant financial concerns that are often attributed to the Baby Boomer generation. OnePoll data reveals a growing bias among Gen-Z towards Baby Boomers. With this in mind, marketers might need to reevaluate the representation of this age group in their advertising campaigns in order to better resonate with younger consumers. Let’s now shift our focus to the UK, where loyalty discounts are gaining popularity among consumers. A significant portion of UK consumers is trading brand loyalty for attractive discounts.
The Data & Marketing Association, along with American Express, emphasizes the importance of loyalty schemes in the current political and economic landscape. It seems that loyalty schemes could be the game-changer for retailers in the UK. Now, let’s take a quick look at some snapshots from other reports: First, new insights from Juniper Research reveal that a staggering $80 billion is lost to ad fraud. This highlights the need for stricter measures to combat fraudulent advertising practices. Second, mobile advertising is booming in the UK, with over 60% of companies planning to increase their budgets in this area. This showcases the growing importance of mobile platforms in reaching targeted audiences.
Third, Wavermaker Studio reports that Gen-X feels overlooked in TV advertising. This demographic segment is seeking more representation and targeted messaging in TV ads for better engagement. Fourth, a report from Happi emphasizes that consumers in the beauty industry crave educational content. This highlights the opportunity for beauty brands to create informative and educational content to better connect with consumers. Finally, data from Ansa suggests that Italy’s consumer spending is expected to dip by approximately $3.7 billion. This indicates a potential shift in consumer behavior and purchasing power in the country. That wraps up our exploration of some recent research findings and their implications for marketers. It’s fascinating how consumer trends evolve and shape the strategies businesses need to adopt to stay relevant. Stay tuned for more insights and updates in the ever-changing world of marketing and consumer behavior.
So, here’s the thing. In today’s fast-paced world, staying on top of the latest trends and developments in marketing is absolutely crucial. Why? Well, because it allows businesses to make smart and informed decisions that can ultimately lead to success. Trust me, you don’t want to be left in the dust while your competitors are flourishing. One interesting observation that has been made is the growing skepticism around sustainability claims. Consumers are becoming more discerning and are not just going to blindly believe every green marketing message they come across. This means that businesses need to be extra careful and make sure their sustainability efforts are truly authentic and transparent. Now, let’s talk about loyalty schemes. Apparently, the UK has been going crazy for them. People just can’t seem to get enough of those reward programs and discounts. And you know what? Marketers need to take notice of this.
Loyalty schemes can be a powerful tool to not only retain existing customers but also to attract new ones. By the way, I came across some interesting resources that might pique your interest. It seems that a Redditor by the name of lazymentors has gathered a treasure trove of marketing papers from the subreddit r/Marketing. I’m talking about over 100 papers! So, if you’re looking to expand your knowledge and stay in the loop, you might want to check it out. In conclusion, my friend, the marketing landscape is constantly evolving, and it’s our job to stay agile and receptive. Trust is fading in sustainability claims, and loyalty schemes are all the rage in the UK. So, let’s keep our eyes peeled and make sure we’re on top of these shifts.
In this episode, we covered the latest marketing insights and trends for 2023, including strategies to recalibrate in the evolving consumer landscape, the importance of improved packaging, the rising popularity of Prime Day, and the impact of ad fraud on mobile ad budgets. Stay informed and make informed decisions in marketing with our recap of top items covered. Thank you for joining us on the Djamgatech Marketing podcast, where we delve into the latest marketing trends and provide insightful information – be sure to subscribe and stay tuned for our next episode!
Deciphering the Marketing Landscape: What’s the most wanted digital marketing skills?
Data story telling. Don’t just share data, share “why” it’s important and what to do with it. A big reason why I got the last few jobs is being able to show that I can translate data and what to do with it.
It boggles my mind sometimes that many agencies don’t do this correctly. Follow the McKinsey model:
Data synthesis
Summary
“Why this data matters/what it means”
What to do with it
How data can become your best sales strategy coupled with a string message focusing on user outcomes they are hiring the product/service for ( jobs-to-be-done theory)? Link
Here is the TLDR for the best tips without knowing your case in more detail (feel free to read the deep dive if you want):
Share multiple data points but keep it focused
Don’t overdo it on the number of decks
Remember that you’ll probably have to pivot at least once
Detectives don’t solve cases off one single data point and neither should marketing decisions be made (in my humble opinion)
Point Number 1: 2-3 data points is enough to make a solid case (ex: if you’re trying to share which topics/content ideas their audience resonates with, look at engagement rates on topics across different channels). If it’s SEO, use 2 different softwares and find the patterns. Those are the most obvious bleeds.
Point number 2: Early in my career I made the mistake of creating 50+ power point slides which was great research but we ended up using only 20% of that data. Huge waste of time, energy, not to mention incredibly inefficient.
Point number 3: The reality is, pivots are bound to happen unless you’re working with a team that’s super patient for a strategy to come to fruition or if you make the right decision based on the data (business acumen happens as you grow in your career.)
The most important skill is one that you can prove an ROI. For that I say Lead Gen.
Organic is:
“local” SEO (when you see a local company appear on the ‘map’ in search result near you)
regular SEO (regular search results under the map)
email marketing to an established email list
growing social media accounts
Paid is:
Google PPC ads
FB ads
any other… Tiktok, instagram etc.
I focus on Google PPC with Local SEO.
Pick a path and watch as much educational content on it as you can. Work for free initially. Then go wild.
SEO is highly wanted, and Google ads and Facebook ads are also highly wanted. I choose two things to become an expert in, and everything else just know enough to be able to do it. It also depends on where you get hired. Whatever u decide you want to do, become an expert in it, as there is a huge shortage of experts out there.
After 23 years in the industry and quite high demand as an independent consultant advisor I would say what people want is you solve them their problems. and in digital marketing and growth problems are very complex and multidisciplinary. Ok, they want ads to run smoothly and cheaply, but you need to make the data stack good so you track everything, and you need to make the Conversion Rate higher, but that involves like six tools plus the web, and you need to orchestrate everything to out-optimize your competitors. It is the T-shaped knowledge but with many deep knowledge areas. And understanding how everything interacts with each other. Like how page speed increases conversions, decreases CPA on paid, increases SEO, and how you can improve it. I think that is what is lacking in most growth agencies. They see stuff as silos, they take 2yo experienced specialists on PPC or SEO or whatever, but they have no clue about how the rest is important.
I think you only can gain that knowledge if you have been running your own sites or webapps, from creation to monetization, etc. That gives you a great understanding on the orchestration of things. And above all you need to be able to move seamlessly between strategy, tactical and operational. And communicate equally good with CEO’s and developers with poor social skills.
Deciphering the Marketing Landscape: One-Minute Daily Marketing News
Deciphering the Marketing Landscape: What Happened In Marketing October 17th 2023
Meta launches new formats and updates for Reels Ads.
Google launches new tool to manage first-party data easily.
Youtube launches Audio Descriptions & Pronouns for Creators.
FTC proposes a new bill to fight against hidden fees in Product Prices.
Google’s multiple security updates focused on user privacy.
EU warns all Social Media Apps to do better moderation of content.
TikTok
TikTok partners with Disney to introduce Disney Content and Elements.
Update to API, allowing better Direct Posting for Third-party apps.
TikTok shares more facts about user data privacy.
TikTok expands Effect House Rewards Program to more regions.
New Reports about TikTok rewarding creators to pump live shopping.
Instagram & Threads
IG set to bring back Creator Cash Bonuses.
Instagram shares new tips for E-commerce shops in a Post.
Threads App gets new post editing and Voice notes feature.
Meta
Meta’s AI Chatbots are not working in the best way possible.
Facebook UK sales surged ahead of Ad Downturn.
WhatsApp testing Event Creation for Groups.
Twitter (X)
X aims to fight substack says Elon to allow article publishing.
X’s efforts to launch live-streaming features are coming together.
Expanded Bios are live on X Desktop.
New Feature &. Updates to X’s Security & Content Reporting.
X launches new updates to Community Notes to increase reliability.
Google
Google SGE AI now helps to create Images and Content Drafts.
Google Demand Gen Ads roll out to all advertisers.
Disabling Third-party cookies for 1% Chrome Users.
Updating their Ads Policy later this month.
Google Search stops intended search results.
Expands access to Social Media Links for Business profiles.
Agency News
WFA & MediaSense launch “Future of media Agency” Report.
Stagwell acquires Left Field Labs, A digital Agency.
Publicis Groupe Posts 5.3% growth in Q3.
Dentsu partners with VideoAmp for Ad buying.
Virgin Voyages gives its Global Media Account to Hearts & Science.
Idris Elba’s agency launches first campaign for Sky Cinema.
Wavemaker & Merlin Entertainment extend their partnership.
GroupM Betas Walmart Retail Media Certification Program.
Brands & Ads
Taco Bell & Deutsch LA partner with Pete Davidson for new campaign.
Lloyds Banking Group appoints new CMO.
N26 Bank launches new global brand campaign.
Doc Martens launch new Brand Platform “Made Strong”.
Netflix to open retail sites in 2025 as Brand move.
ASICS & City of Paris’s latest campaign launched on Mental Health Day.
4/ Gen-Z doesn’t like to get called, mostly prefer online chat & WhatsApp to connect with friends and others, data from The Sun.
5/ 19% of US adults aged 18-34 are actively saving in case of layoffs, compared to only 13% of older adults.
6/ Black Gen-Zers are hiding names for job applications and being more private shares new data.
7/ 83% of Gen-Z workers are job hoppers. (CNBC)
8/ Gen-Z wants feminine care products to become more blunt and clear in their Ad Copies. (NY Post)
9/ Majority of Gen-Z Students trust College Education, shares new report exposing online gurus. (Gallup)
10/ 73% of Gen-Z Americans have changed their spending habits over inflation causes. 43% now prefer to home cook, 40% spend less on clothes and 33% limiting spend to Essential shopping. (Bank of America)
11/ Gen-Zers are struggling to find third places to network and make friends. Many are paying for multiple memberships to make friends.
12/ Harvard’s research suggests that Gen-Z 27% more likely to buy from sustainable brands. However new research from Kantar shares distrust of Gen-Z in Sustainability advertising.
13/ Gen-Z & Millennials are making impulse purchases of social media suggestions shares new data from Bankrate.
Deciphering the Marketing Landscape: What Happened In Marketing October 16th 2023
Tiktok
TikTok launches Search Ads Toggle, allowing brands to display ads in search results.
TikTok enhances data security and localized storage in US, Singapore, and Malaysia.
TikTok unveils Direct Post feature for smoother third-party platform content sharing.
Meta
Meta shared photos of the business onboarding steps for MetaVerified for Business
Instagram new “Avatar interactions” setting lets you control who can interact with your avatar
Instagram is working on a new sticker: Music Pick
Facebook is killing its Notes feature on Nov 13th
Facebook Messenger added a tab called Channels
Threads now showing the “Suggested for you” section in feed.
X (Twitter)
X rolls out new ad format that can’t be reported, blocked
X is working on giving streamers options on who can join their chat before the start of the stream
Google
Google tests generative AI in Search for creating imagery and drafting text.
Passkeys introduced for secure, fingerprint-based login on eBay, Uber, and WhatsApp
Others
Twitch update empowers streamers to block banned users from viewing their livestreams.
Duolingo will launch language learning lessons through Duolingo Music and Duolingo Math in the EU as well
CapCut added a new AI-based feature, AI model
Twitter
Early preview unveiled for ‘X calling’ feature.
Facebook
Facebook seeks feedback from Meta Verified subscribers on service quality.
Facebook starts showing the page name in the app header, and it sticks to the header when scrolling through the page.
Tiktok
TikTok enables mentioning videos via audio page in user-created content.
TikTok update removes auto-generated captions from post, privacy settings.
TikTok launches AI meme generation for user-taken or selected photos.
Instagram 🔥
Instagram introduces option for page linking within user accounts.
Instagram extends account activity access to desktop platforms.
Meta
Meta offers business support option beyond Meta Verified service.
Whatsapp
WhatsApp developing date-specific message search for web client.
WhatsApp Web rolls out ‘Create Channel’ feature for users.
Ai
Box unveils Box Hubs, streamlining document access with AI integration.
CharacterAI debuts ‘Character Group Chat’ for multi-user, multi-AI interactions.
Others
Mozilla teams with Fastly, Divvi Up for enhanced Firefox privacy tech.
Elgato introduces web Marketplace, upgrading digital assets exchange for creators.
Search Engine Land Awards 2023 finalists announced, winners to be revealed Oct. 17.
Snapchat encourages gifting Snapchat+ to friends on upcoming birthdays.
Spotify trials top playback controls during in-app scrolling.
I analyze over 200 headlines per week. Here’s a well-known psychological bias you can use to drive a tonne of clicks
“Harvard psychologist: 7 things the most passive-aggressive people always do—and the No. 1 way to respond”
This article is trending hard on CNBC Make It.
Sure, it’s good content.
But the headline clearly plays a huge role in its success.
Confirmation bias is a psychological effect where people seek information to validate their pre-existing beliefs.
To effectively use confirmation bias in headlines:
– Identify behaviors your audience likely has strong beliefs or opinions about
– Write a headline that appears to confirm or challenge that belief
In this headline, passive aggression is the behavior many have encountered or been accused of.
A lot of people have pre-existing beliefs about what it looks like.
The headline suggests there are definitive behaviors that passive-aggressive people exhibit.
Readers want to know whether their own beliefs will be confirmed or challenged.
So they click to find out.
It’s brilliant.
Other psychological effects that make this headline an absolute click magnet:
Authority Bias – “Harvard Professor”. Readers are more likely to click when a headline implies endorsement from an expert.
Social Identity Theory: People will always want to identify with certain groups (in-groups) and distance themselves from others (out-groups).
They’ll seek out content to determine which “bucket” they fall into.
Do people they know fall into the “passive-aggressive” bucket? Do they themselves fall into that bucket?
They can’t help but click to find out.
Examples from different niches:
Productivity: “The 7 App Habits Of Highly Productive People”
Pre-existing belief – Productive people do or do not use apps a certain way.
Personal Finance – “The Actual Impact Of Cutting Out Coffee On Your Savings”
Pre-existing belief – Cutting out a daily coffee will or will not have a meaningful impact on savings.
Parenting – “Does Strict Parenting Actually Lead To Academic Success?”
Pre-existing belief – Strict parenting does or does not lead to academic success.
——————————————————–
*Disclaimer* – The content needs to match the expectations set by the title.
That’s what makes a title clickworthy as opposed to clickbait.
Also, the content shouldn’t be written with the sole purpose of being provocative. It should solve real problems and provide real value.
Giving it a juicy title is just how you make sure it’s actually read and that value is delivered.
As Ogilvy says:
“On the average, five times as many people read the headline as read the body copy. When you have written your headline, you have spent eighty cents out of your dollar.”
Deciphering the Marketing Landscape: What Happened In Marketing October 01-07 2023
X is looking to launch Ad-free Premium Tier for users.
Instagram announces option to share instagram stories only to a certain no. of followers in lists.
Reddit expands its learning hub with new courses and updates.
Google releases October 2023 Brand Core Update.
Deutsch New York plans to lays off about 19% of staff.
Youtube Testing New Community Notes Feed on Mobile.
DDB WorldWide names Alex Lubar as global CEO.
Snapchat announces “Phantom House” new activision for halloween.
X has ruined everything for link sharing with new Link Preview UI.
VMLY&R Named Lead Creative Agency for World of Hyatt.
GA4 adds new features to improve data security and report accuracy.
BEReal launches a new global campaign, trying to get back attention.
Meta rolls out AI Tools for Advertisers.
X is testing a new Ad format that you can’t report or fight back against.
M&S Appoints Mother as Creative Agency for UK Business.
Non-Alcohol Brands are testing Sober October campaigns, Ritual biggest one so far.
Netflix global Ad president departs after 13 months. Now, Amy Reinhard is the new Ad President.
Mullenlowe retains US Military Account for Recruiting Marketing, Account worth $450M.
US Ad Employement grew by 3k Jobs in Sep 2023.
Google Spam October 2023 Core Update also launched.
IG testing Ad Carousels with tag “you might like” with 5 Different Ads side by side.
Watched 8 hours of MrBeast’s content. Here are 7 psychological strategies he’s used to get 34 billion views
MrBeast can fill giant stadiums and launch 8-figure candy companies on demand.
He’s unbelievably popular.
Recently, I listened to the brilliant marketer Phil Agnew being interviewed on the Creator Science podcast.
The episode focused on how MrBeast’s near-academic understanding of audience psychology is the key to his success.
Better than anyone, MrBeast knows how to get you:
– Click on his content (increase his click-through rate)
– Get you to stick around (increase his retention rate)
He gets you to click by using irresistible thumbnails and headlines.
I watched 8 hours of his content.
To build upon Phil Agnew’s work, I made a list of 7 psychological effects and biases he’s consistently used to write headlines that get clicked into oblivion.
Even the most aggressively “anti-clickbait” purists out there would benefit from learning the psychology of why people choose to click on some content over others.
Ultimately, if you don’t get the click, it really doesn’t matter how good your content is.
1. Novelty Effect
MrBeast Headline: “I Put 100 Million Orbeez In My Friend’s Backyard”
MrBeast often presents something so out of the ordinary that they have no choice but to click and find out more.
That’s the “novelty effect” at play.
Our brain’s reward system is engaged when we encounter something new.
You’ll notice that the headline examples you see in this list are extreme.
MrBeast takes things to the extreme.
You don’t have to.
Here’s your takeaway:
Consider breaking the reader/viewer’s scrolling pattern by adding some novelty to your headlines.
How?
Here are two ways:
Find the unique angle in your content
Find an unusual character in your content
Examples:
“How Moonlight Walks Skyrocketed My Productivity”.
“Meet the Artist Who Paints With Wine and Chocolate.”
Headlines like these catch the eye without requiring 100 million Orbeez.
2. Costly Signaling
MrBeast Headline: “Last To Leave $800,000 Island Keeps It”
Here’s the 3-step click-through process at play here:
MrBeast lets you know he’s invested a very significant amount of time and money into his content.
This signals to whoever reads the headline that it’s probably valuable and worth their time.
They click to find out more.
Costly signaling is all amount showcasing what you’ve invested into the content.
The higher the stakes, the more valuable the content will seem.
In this example, the $800,000 island he’s giving away just screams “This is worth your time!”
Again, they don’t need to be this extreme.
Here are two examples with a little more subtlety:
“I built a full-scale botanical garden in my backyard”.
“I used only vintage cookware from the 1800s for a week”.
Not too extreme, but not too subtle either.
3. Numerical Precision
MrBeast knows that using precise numbers in headlines just work.
Almost all of his most popular videos use headlines that contain a specific number.
“Going Through The Same Drive Thru 1,000 Times”
“$456,000 Squid Game In Real Life!”
Yes, these headlines also use costly signaling.
But there’s more to it than that.
Precise numbers are tangible.
They catch our eye, pique our curiosity, and add a sense of authenticity.
“The concreteness effect”:
Specific, concrete information is more likely to be remembered than abstract, intangible information.
“I went through the same drive thru 1000 times” is more impactful than “I went through the same drive thru countless times”.
4. Contrast
MrBeast Headline: “$1 vs $1,000,000 Hotel Room!”
Our brains are drawn to stark contrasts and MrBeast knows it.
His headlines often pit two extremes against each other.
It instantly creates a mental image of both scenarios.
You’re not just curious about what a $1,000,000 hotel room looks like.
You’re also wondering how it could possibly compare to a $1 room.
Was the difference wildly significant?
Was it actually not as significant as you’d think?
It increases the audience’s *curiosity gap* enough to get them to click and find out more.
Here are a few ways you could use contrast in your headlines effectively:
Transformational Content:
“From $200 to a $100M Empire – How A Small Town Accountant Took On Silicon Valley”
Here you’re contrasting different states or conditions of a single subject.
Transformation stories and before-and-after scenarios.
You’ve got the added benefit of people being drawn to aspirational/inspirational stories.
2. Direct Comparison
“Local Diner Vs Gourmet Bistro – Where Does The Best Comfort Food Lie?”
5. Nostalgia
MrBeast Headline: “I Built Willy Wonka’s Chocolate Factory!”
Nostalgia is a longing for the past.
It’s often triggered by sensory stimuli – smells, songs, images, etc.
It can feel comforting and positive, but sometimes bittersweet.
Nostalgia can provide emotional comfort, identity reinforcement, and even social connection.
People are drawn to it and MrBeast has it down to a tee.
He created a fantasy world most people on this planet came across at some point in their childhood.
While the headline does play on costly signaling here as well, nostalgia does help to clinch the click and get the view.
Subtle examples of nostalgia at play:
“How this [old school cartoon] is shaping new age animation”.
“[Your favorite childhood books] are getting major movie deals”.
6. Morbid Curiosity
MrBeast Headline: “Surviving 24 Hours Straight In The Bermuda Triangle”
People are drawn to the macabre and the dangerous.
Morbid curiosity explains why you’re drawn to situations that are disturbing, frightening, or gruesome.
It’s that tension between wanting to avoid harm and the irresistible desire to know about it.
It’s a peculiar aspect of human psychology and viral content marketers take full advantage of it.
The Bermuda Triangle is practically synonymous with danger.
The headline suggests a pretty extreme encounter with it, so we click to find out more.
7. FOMO And Urgency
MrBeast Headline: “Last To Leave $800,000 Island Keeps It”
“FOMO”: the worry that others may be having fulfilling experiences that you’re absent from.
Marketers leverage FOMO to drive immediate action – clicking, subscribing, purchasing, etc.
The action is driven by the notion that delay could result in missing out on an exciting opportunity or event.
You could argue that MrBeast uses FOMO and urgency in all of his headlines.
They work under the notion that a delay in clicking could result in missing out on an exciting opportunity or event.
MrBeast’s time-sensitive challenge, exclusive opportunities, and high-stakes competitions all generate a sense of urgency.
People feel compelled to watch immediately for fear of missing out on the outcome or being left behind in conversations about the content.
Creators, writers, and marketers can tap into FOMO with their headlines without being so extreme.
“The Hidden Parisian Cafe To Visit Before The Crowds Do”
“How [Tech Innovation] Will Soon Change [Industry] For Good”
(Yep, FOMO and urgency are primarily responsible for the proliferation of AI-related headlines these days).
Why This All Matters
If you don’t have content you need people to consume, it probably doesn’t!
But if any aspect of your online business would benefit from people clicking on things more, it probably does.
“Yes, because we all need more clickbait in this world – *eye-roll emoji*” – Disgruntled Redditor
I never really understood this comment but I seem to get it pretty often.
My stance is this:
If the content delivers what the headline promises, it shouldn’t be labeled clickbait.
I wouldn’t call MrBeast’s content clickbait.
The fact is that linguistic techniques can be used to drive people to consume some content over others.
You don’t need to take things to the extremes that MrBeast does to make use of his headline techniques.
If content doesn’t get clicked, it won’t be read, viewed, or listened to – no matter how brilliant the content might be.
While “clickbait” content isn’t a good thing, we can all learn a thing or two from how they generate attention in an increasingly noisy digital world.
Little trick on how I use Quora to grow my business
This really doesn’t cost a lot of time and can be helpful for every business.
In order to leverage Quora effectively for your business, you need relevant questions to answer in the best possible way.
This can be tedious and a lot of work, while your answers can get buried quickly. To maximize the impact, I use this approach:
Look for Quora questions with many views but few answers.
Type in Google:
site:quora.com keyword “1 answer” “k views”
For example, I founded Simple Analytics, a GA4 alternative. So I’m interested in keywords like Google Analytics, Ga4, privacy-friendly analytics etc:
site:quora.com google analytics “1 answer” “k views”
It will find questions related to your keyword with just one answer but with many views (you can play around with the variables here)
But this is essentially where you want to be! Now provide a thoughtful answer and even mention your business if it fits the context. You’ll be the top rated answer and get many views.
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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.
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.
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.
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:
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.
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.
Like, I think he’s a more talented actor than people give him credit for. I feel like a lot of the criticism of his ability comes from people who have only seen Morbius and Suicide Squad But it’s well known that he seems like a huge pain in the ass. His method thing seems really annoying for his costars, and also just wastes a lot of time. Leto seems to have a lot of confidence in his formula, not realizing most of the roles that made him famous were just relatively normal dudes. Now he wants to prove himself as some kind of “eccentric method genius!” After a while, why would producers and directors still want to pick him up? Especially if his movies haven’t been very successful recently. Also, I know David Ayer seemed to like his weird process, which I suppose makes sense considering Ayer got along with Shia Labeouf very well. However, what I find funny is when you look at the behind the scenes for the Suicide Squad, the cast seemed legitimately very close. There’s a lot of nice pictures with them… the one caveat being that Leto is usually not in any of those pictures submitted by /u/Odd_Advance_6438 [link] [comments]
For me it's memory reboot -------> Blade runner 2049. Absolute beast of the movie, even though I haven't watched the og blade runner ( I know the plot though). Love Ryan Gosling's top notch acting and one scene is particularly my favourite where he says "God Damn it" , it was beautiful if you have already watched the movie. What movie is it for you submitted by /u/Brilliant_Knee_7542 [link] [comments]
Ever since the assassination of the United Health Care CEO this past week, I haven't been able to get this movie out of my mind and I saw it many years ago. It's about a guy whose wife gets sick, their health insurance reaches their limit and he tries to cash in on his pension from the military, but most of it was put into stocks and this film's setting was the 2008-2009 Wall Street crash. The wife feels guilty, commits suicide and the husband goes off the edge and starts going on a killing spree against stock brokers and CEOs. It's directed by Uwe Boll (Of course, yes, that Uwe Boll) but as far as his movies go, I don't remember this one being half bad. I remember Dominic Purcell from Prison Break as the husband and John Heard as the main antagonist. I see it's on Tubi, I might rewatch it this weekend. Update: Wild to see that this made it to the front page of the sub, thanks all! submitted by /u/jblanch3 [link] [comments]
I've seen very little talk about this trope but I've noticed it in quite a bit of media (Star Wars Rogue One (entire cast), Halo Reach (video game, entire cast), Young Sheldon (TV Show, George Cooper)). I know in some situations writing this into the story of the prequel is basically required but I was just wondering if there was a name or term for it. submitted by /u/nubnub4455 [link] [comments]
PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY PLAY…Continue reading on Medium »
The big budget should have been used to correct a narrative between African American community and the PoliceContinue reading on TeePee Entertainment Hub »
Which movie after you saw it did you start seeing tons of references to it in other pop culture? I started watching through old movies on tcm and now I see references to them everywhere. The one that got me was north by northwest. I didn't even particularly enjoy it but I've seen it referenced in at least 2 of my kid's shows and the simpsons. This happens everything I watch something considered a classic. What are the must watch movies to catch the most of these? submitted by /u/pnwsnuggles [link] [comments]
World’s Top 10 Youtube channels in 2022
T-Series, Cocomelon, Set India, PewDiePie, MrBeast, Kids Diana Show, Like Nastya, WWE, Zee Music Company, Vlad and Niki
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What are some ways to increase precision or recall in machine learning?
What are some ways to Boost Precision and Recall in Machine Learning?
Sensitivity vs Specificity?
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.
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).
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).
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).
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).
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.
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.
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.
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.
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.
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!
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!
Hello folks, I've been working on an agentic solution where you can have an autonomous agent taking live calls. We're using a pipeline of Speech to Text, LLM for generating responses and then Text to Speech. In this pipeline, Speech to text is causing some issues because it's difficult to determine when exactly a sentence is over since the user can take pauses. Moreover, when multiple inputs go into LLM, multiple responses are generated and they queue up for Text to speech. How would you solve this problem? How would you also handle cases where the user interrupts the agent? submitted by /u/Leo2000Immortal [link] [comments]
I built a website to compare EVERY AI Model, from LLMs to image models etc. Basically, it’s a free tool that lets you compare different AI models—LLMs, vision models, and so on—in one spot. Why did I make it? Because sorting through all the token limits, pricing, and features across multiple docs and websites can drive you nuts. I was tired of jumping between a million tabs just to figure out which model fit my needs. With Countless.dev, you can: Compare models based on price, token limits, and special features (like vision support). Use a built-in cost calculator to estimate what you’d spend with different models. Do side-by-side comparisons to narrow down your choices fast. I originally built this while testing out AI code editors. After I shared an early version on Twitter and got some positive feedback, I decided to open it up for everyone. It’s free, open-source, and (I hope) pretty straightforward to use. If this sounds useful, feel free to give it a try. I’m all ears for feedback—good, bad, or otherwise. If something’s off, I’d love to hear it so I can make it better. Cheers! Live on Product Hunt rn (if you like the website please help me get #1!): https://www.producthunt.com/posts/countless-dev submitted by /u/ahmett9 [link] [comments]
We all saw in class the trade off between bias and variance, that we don't want our train loss to keep going down and our test loss go up. But in practice I feel like doing hyperparameter tuning for classic ML models with GridSearchCV / BayesSearchCV is not enough. Even though I do cross validation, the search.best_model obtained at the end is almost always overfitting. How can you actually perform a search that will give you a robust generalized model with higher chances ? submitted by /u/desslyie [link] [comments]
Head to head of meme-interpretability with the same image and text prompt! Anecdotal but interesting responses. Also clear winner! submitted by /u/No_Cartoonist8629 [link] [comments]
In my academic field (social sciences) I deal with the problem of bias in SA models. My previous work showed that deep learning SA systems inherit bias (e.g. nonrepresentative of the population political bias) from annotators: https://arxiv.org/abs/2407.13891 Now I devised a solution that used a technique I call semantic blinding to provide only the bare necessary information for the model to predict emotions in text, leaving no signal for the model to overfit and produce bias from: https://arxiv.org/abs/2411.12493 Interested to hear your thoughts before I publish the SProp Gnn. Do you think it could be useful beyond the academia? submitted by /u/Hub_Pli [link] [comments]
When would the phase 2 decision come out? I know the date is December 9th, but would there be chances for the result to come out earlier than the announced date? or did it open the result at exact time in previous years? (i.e., 2024, 2023, 2022 ....) Kinda make me sick to keep waiting. submitted by /u/No-Style-7975 [link] [comments]
Few months ago, I migrated from TF 2.0 to Jax. I found that jax is significantly faster than Tf. I noticed in the official documentation that it relies on XLA default that uses JIT compilation which makes execution faster. I also noticed that TF graphs also have option to enable JIT compilation with XLA. But still jax dominates TF with XLA. I just want to know why. submitted by /u/Odd-Detective289 [link] [comments]
Multimodal AI is changing the game by combining text, images, and even video into a single, cohesive system. It’s being talked about as a major leap in AI capabilities. What industries do you think will benefit the most from this tech? And are there any challenges you see in integrating these models into everyday use? Would love to hear everyone's thoughts! submitted by /u/Frosty_Programmer672 [link] [comments]
Say you’ve selected the best classifier for a particular problem, using threshold invariant metrics such as AUROC, Brier score, or log loss. It’s now time to choose the classification threshold. This will clearly depend on the use case and the cost/ benefits associated with true positives, false positives, etc. Often I see people advising to choose a threshold by looking at metrics such precision and recall. What I don’t see very often is people explicitly defining relative (or absolute, if possible) costs/ benefits of each cell in the confusion matrix (or more precisely the action that will be taken as a result). For example a true positive is worth $1000, a false positive -$500 and the other cells $0. You then optimise the threshold based on maximum benefit using a cost-threshold curve. The precision and recall can also be reported, but they are secondary to the benefit optimisation and not used directly in the choice. I find this much more intuitive and is my go-to. Does anyone else regularly use this approach? In what situations might this approach not make sense? submitted by /u/hazzaphill [link] [comments]
Hello everyone, I am looking for methods that can automatically categorize and select layers from for transfer learning. If you know any such methods or research please let me know or share. Thanks submitted by /u/reshail_raza [link] [comments]
Imagine a customer support chatbot for an e-commerce platform that retrieves relevant product details from its knowledge base and performs web searches for additional information. Furthermore, it remembers past conversations to deliver a seamless and personalized experience for returning users. Here is how it works: - Store your own data in the knowledge base—in our case, a Website URL. - Convert the data into embeddings and save it in the Qdrant Vector Database. - Use phidata Agentic Workflow to combine Tools, LLM, Memory, and the Knowledge Base. Code Implementation Video: https://www.youtube.com/watch?v=CDC3GOuJyZ0 submitted by /u/External_Ad_11 [link] [comments]
New paper and code for the scale-wise transformer for fast text-to-image generation from our team at Yandex Research Switti outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being faster than distilled diffusion models. Code with checkpoints: https://github.com/yandex-research/switti Generation examples submitted by /u/_puhsu [link] [comments]
Hello everyone, I've been working on a project for some time now and wanted to share a concept I'm exploring. As we know, decision tree-based models typically split the feature space using certain metrics like MSE, entropy, etc. I started thinking about an alternative approach: instead of splitting individual features, what if we could split the entire space directly? However, this seemed quite difficult, as determining boundaries and regions in the space is challenging. Then I had an idea—what if I project the data onto a line within the feature space, and then split that line, like how trees are typically built on individual features? In essence, I’m thinking of projecting points onto a line and then using tree-based methods to split them progressively. Here's a high-level view of the algorithm: Fit a linear regression model to the dataset (normalized values). Project the data onto the line defined by the regression. Apply a decision tree on this projection, effectively splitting one feature (the projection axis). Calculate the residuals and fit another linear model on the residuals, applying boosting in the process. Since the new linear regressions fitted on the residuals will define separate lines, I assume that through boosting, the model will gradually divide the data in the desired manner over time. You can read a more detailed description of the algorithm here: Algorithm PDF. To visualize how the decision boundaries are formed in a 2D dataset: SpaceBoostingRegressor Note: If you want to see a visual example, uploading high-dimensional GIFs can sometimes be an issue. You can check out the example here: Gif on GitHub. Also you can check the code in the repository: Repository This approach is simple because it assumes linearity, and it works in scenarios where there is a high linear correlation between the target and features while also allowing for some non-linear relationships. You can see an example in the repo,example.ipynb file. However, I’m not sure how well it would perform on real-world datasets, as the linear assumption may not always hold. I want to take this algorithm further, but speed is important for scaling. Techniques like PCA don't seem to help because I need the line to reflect the variance in both the target and feature space, rather than just feature variance. I tried using MLPs and extracting the embeddings from a hidden layer before the output layer, which works better since we're evaluating the target in a larger space, but this approach becomes too slow and isn’t feasible in practice. I think this project has great potential, and I’m looking for feedback, ideas, or anyone interested in collaborating. Any comments or suggestions are welcome! submitted by /u/zedeleyici3401 [link] [comments]
Hoping to see if I can find any recommendations or suggestions into deploying R alongside other code (probably JavaScript) for commercial software. Hard to give away specifics as it is an extremely niche industry and I will dox myself immediately, but we need to use a Bayesian package that has primary been developed in R. Issue is, from my perspective, the package is poorly developed. No unit tests. poor/non-existent documentation, plus practically impossible to understand unless you have a PhD in Statistics along with a deep understanding of the niche industry I am in. Also, the values provided have to be "correct"... lawyers await us if not... While I am okay with statistics / maths, I am not at the level of the people that created this package, nor do I know anyone that would be in my immediate circle. The tested JAGS and untested STAN models are freely provided along with their papers. It is either I refactor the R package myself to allow for easier documentation / unit testing / maintainability, or I recreate it in Python (I am more confident with Python), or just utilise the package as is and pray to Thomas Bays for (probable) luck. Any feedback would be appreciated. submitted by /u/Sebyon [link] [comments]
Edit: I have misworded the title as if OpenAI would confirm how O1 was implemented. I have changed the text to reflect what I meant say. I really want to deep dive into the technicals of how the O1 models perform better than previous models. Have researchers come to any definitive agreement as to what OpenAI could have possible done to achieve O1? From reading online I hear about MCTS, COT... etc, but are any of these methods in large agreement by researhers? submitted by /u/Daveboi7 [link] [comments]
Hey everyone, I'm working on a pipeline to encode over 100 million rows into embeddings using SentenceTransformers, PySpark, and Pandas UDF on Dataproc Serverless. Currently, it takes several hours to process everything. I only have one column containing sentences, each under 30 characters long. These are encoded into 64-dimensional vectors using a custom model in a Docker image. At the moment, the job has been running for over 12 hours with 57 executors (each with 24GB of memory and 4 cores). I’ve partitioned the data into 2000 partitions, hoping to speed up the process, but it's still slow. Here’s the core part of my code: F.pandas_udf(returnType=ArrayType(FloatType())) def encode_pd(x: pd.Series) -> pd.Series: try: model = load_model() return pd.Series(model.encode(x, batch_size=512).tolist()) except Exception as e: logger.error(f"Error in encode_pd function: {str(e)}") raise The load_model function is as follows: def load_model() -> SentenceTransformer: model = SentenceTransformer( "custom_model", device="cpu", cache_folder=os.environ['SENTENCE_TRANSFORMERS_HOME'], truncate_dim=64 ) return model I tried broadcasting the model, but I couldn't refer to it inside the Pandas UDF. Does anyone have suggestions to optimize this? Perhaps ways to load the model more efficiently, reduce execution time, or better utilize resources? submitted by /u/nidalap24 [link] [comments]
Has anyone had experience using an ML model to recognize handwriting like this? The notebook contains important information that could help me decode a puzzle I’m solving. I have a total of five notebooks, all from the same person, with consistent handwriting patterns. My goal is to use ML to recognize and extract the notes, then convert them into a digital format. I was considering Google API after knowing that Tesseract might not work well with illegible samples like this. However, I’m not sure if Google API will be able to read it either. I read somewhere that OCR+ CNN might work, so I’m here asking for suggestions. Thanks! Any advice/suggestions are welcomed! submitted by /u/SpaceSheep23 [link] [comments]
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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.
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.
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.
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.
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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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 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.
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.
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.
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.
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
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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.
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.
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.
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).
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?
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:
Is machine learning appropriate for this problem, and why or why not?
What is the ML problem if there is one, and what would a success metric look like?
What kind of ML problem is this?
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.
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.
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?
Success would be the purchase of a product that was advertised.
This is a supervised learning problem because we have a labeled data point, our success metric, which is the purchase of a product.
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.
ML is appropriate because of the scale and variability.
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.
There isn’t a specific outcome variable. There’s no target or label. So this is an unsupervised problem.
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.
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.).
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.
The problem is drivers and customers are having poor experiences connecting for pickup, which is pushing customers away from the platform.
Success would be properly identifying appropriate pickup locations so they can be integrated into the feature.
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.
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.”
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
df = pd.read_csv(“xxxxx_data_2.csv”) df.head()
Output:
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
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.
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.
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.
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.
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).
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.
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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.
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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.
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
U.S. National Highway Traffic Safety Administration – Fatalities since […]
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
Design systems – Collection of reusable components, guided by rules that ensure consistency and speed.
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.
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
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.
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.
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.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
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.
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.
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
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.
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.
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
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.
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
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
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 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
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
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
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.
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.
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
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.
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.
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.