Full-Stack AI Intelligence. Zero Noise.The definitive audio briefing for the C-Suite and AI Architects. From Daily News and Strategic Deep Dives to high-density Industrial & Regulatory Intelligence—decoded at the speed of the AI era. . 👉 Start your specialized audio briefing today at Djamgamind.com
I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
Ace the AWS Certified Data Engineer Exam DEA-C01: Mastering AWS Services for Data Ingestion, Transformation, and Pipeline Orchestration.
Unlock the full potential of AWS and elevate your data engineering skills with “Ace the AWS Certified Data Engineer Exam.” This comprehensive guide is tailored for professionals seeking to master the AWS Certified Data Engineer – Associate certification. Authored by Etienne Noumen, a seasoned Professional Engineer with over 20 years of software engineering experience and 5+ years specializing in AWS data engineering, this book provides an in-depth and practical approach to conquering the certification exam.
Inside this book, you will find:
• Detailed Exam Coverage: Understand the core AWS services related to data engineering, including data ingestion, transformation, and pipeline orchestration.
• Practice Quizzes: Challenge yourself with practice quizzes designed to simulate the actual exam, complete with detailed explanations for each answer.
• Real-World Scenarios: Learn how to apply AWS services to real-world data engineering problems, ensuring you can translate theoretical knowledge into practical skills.
• Hands-On Labs: Gain hands-on experience with step-by-step labs that guide you through using AWS services like AWS Glue, Amazon Redshift, Amazon S3, and more.
Are you passionate about AI and looking for your next career challenge? In the fast-evolving world of artificial intelligence, connecting with the right opportunities can make all the difference. We're excited to recommend Mercor, a premier platform dedicated to bridging the gap between exceptional AI professionals and innovative companies.
Whether you're seeking roles in machine learning, data science, or other cutting-edge AI fields, Mercor offers a streamlined path to your ideal position. Explore the possibilities and accelerate your AI career by visiting Mercor through our exclusive referral link:
Your next big opportunity in AI could be just a click away!
• Expert Insights: Benefit from the expertise of Etienne Noumen, who shares valuable tips, best practices, and insights from his extensive career in data engineering.
This book goes beyond rote memorization, encouraging you to develop a deep understanding of AWS data engineering concepts and their practical applications. Whether you are an experienced data engineer or new to the field, “Ace the AWS Certified Data Engineer Exam” will equip you with the knowledge and skills needed to excel.
Prepare to advance your career, validate your expertise, and become a certified AWS Data Engineer. Embrace the journey of learning, practice consistently, and master the tools and techniques that will set you apart in the rapidly evolving world of cloud data solutions.
Get your copy today and start your journey towards AWS certification success!
And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
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:
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 :
Full-Stack AI Intelligence. Zero Noise.The definitive audio briefing for the C-Suite and AI Architects. From Daily News and Strategic Deep Dives to high-density Industrial & Regulatory Intelligence—decoded at the speed of the AI era. . 👉 Start your specialized audio briefing today at Djamgamind.com
I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
In the world of data-driven decision-making, SQL (Structured Query Language) stands out as the lingua franca for managing relational databases. Whether you are preparing to land your first job in data analytics or aiming to advance further in your tech career, proficiency in SQL is often a crucial requirement. This blog post delves into some of the most commonly asked SQL interview questions that you might encounter during a rigorous screening process. From basic queries to complex data manipulation and optimization problems, we cover a comprehensive range of topics that are designed to not only test your technical knowledge but also enhance your understanding of SQL’s powerful features. Prepare to unlock the secrets to acing your SQL interviews with our detailed explanations and insider tips.
How to prepare and Ace SQL Interview
1. Understand SQL Fundamentals
Ensure you have a strong grasp of SQL basics and advanced concepts:
Joins and Subqueries: Know how to use inner, outer, left, and right joins effectively. Subqueries, both correlated and non-correlated, are crucial.
Aggregation and Window Functions: Be proficient with functions like SUM(), AVG(), COUNT(), MIN(), MAX(), and understand window functions like ROW_NUMBER(), RANK(), DENSE_RANK(), and how they differ.
Complex Conditions: Practice writing queries with multiple conditions and understand how to use CASE statements.
Group By and Having Clauses: These are essential for segmentation and conditional aggregates.
2. Practice LeetCode SQL Problems
Daily Practice: Regular practice is key. Start with easier problems and gradually move to medium and hard problems.
Focus on Problem Types: On LeetCode, problems are often categorized by the techniques they require (e.g., joins, window functions). Focus on mastering each category.
Time Yourself: Practicing under time constraints can help simulate the pressures of a real interview.
3. Study Common Patterns and Techniques
Pattern Recognition: Many SQL problems, especially on platforms like LeetCode, follow certain patterns. Identifying and learning these patterns can save time during the interview.
Optimization: Learn how to optimize SQL queries for performance, which includes understanding indexes, avoiding unnecessary columns in SELECT and JOIN clauses, and minimizing subqueries.
4. Mock Interviews
Peer Mock Interviews: Engage with peers or mentors who can conduct mock interviews. Platforms like Pramp or Interviewing.io offer free or paid mock interview services.
Self-Review: If peers aren’t available, write down problems and solve them in a timed setting. Review your solutions against those provided by LeetCode for efficiency and accuracy.
5. Review SQL Theoretically
Books and Online Resources: Books like “SQL Antipatterns” by Bill Karwin or online courses on platforms like Coursera, Udemy, or freeCodeCamp can provide deeper insights into efficient SQL coding practices.
SQL Specifications: Sometimes, interviews can test specific SQL flavors (MySQL, PostgreSQL, SQL Server). Ensure you understand the particular SQL dialect expected in the interview.
6. Understand the Data
Data Schemas: Before solving any problem, thoroughly understand the schema and relationships in the given database. This understanding is crucial for constructing correct and efficient queries.
7. Learn From Others
Discuss Solutions: LeetCode and other forums allow users to discuss their solutions. Reviewing discussions can provide insights into different ways of solving the same problem and help uncover more efficient or elegant solutions.
8. Relax and Strategize During the Interview
Clarify Questions: If a problem statement is unclear, don’t hesitate to ask for clarifications during the interview.
Outline Your Approach: Before you start coding, briefly explain your approach to the interviewer. This can help them understand your thought process and guide you if you’re headed in the wrong direction.
Example Problem: Department Highest Salary
Problem Statement:
The Employee table contains all employees. The Employee table has columns Id, Name, Salary, and DepartmentId. There is also a Department table that holds information about each department.
SQL Interview Questions and Answers: Department Highest Salary
Write an SQL query to find the employees who have the highest salary in each of the departments.
Solution SQL Query:
SQL Interview Questions and Answers: Department Highest Salary – Solution Query
SELECT d.Name AS Department, e.Name AS Employee, e.Salary FROM Employee e JOIN Department d ON e.DepartmentId = d.Id WHERE (e.DepartmentId, e.Salary) IN ( SELECT DepartmentId, MAX(Salary) FROM Employee GROUP BY DepartmentId );
Here’s a breakdown of this query:
Subquery (Ranked): This part computes a rank for each employee within their department based on their salary in descending order. PARTITION BY Department ensures the ranking resets for each department.
Outer Query: The outer query selects the department, employee name, and salary from the ranked results, filtering to include only those entries where Rank is 3 or less, thereby ensuring that only the top three earners per department are selected.
This approach will efficiently retrieve the desired results if your database supports window functions, which is common in systems like PostgreSQL, MySQL (8.0+), SQL Server, and Oracle.
Discussion
This problem tests more advanced SQL concepts such as subqueries and the use of GROUP BY with aggregate functions in conjunction with joins. It’s classified as a medium difficulty problem on LeetCode and helps in understanding how to manipulate and analyze data across multiple tables effectively. This type of query is very common in real-world scenarios where relational database management is required to generate reports or derive insights from the data.
Are you passionate about AI and looking for your next career challenge? In the fast-evolving world of artificial intelligence, connecting with the right opportunities can make all the difference. We're excited to recommend Mercor, a premier platform dedicated to bridging the gap between exceptional AI professionals and innovative companies.
Whether you're seeking roles in machine learning, data science, or other cutting-edge AI fields, Mercor offers a streamlined path to your ideal position. Explore the possibilities and accelerate your AI career by visiting Mercor through our exclusive referral link:
Your next big opportunity in AI could be just a click away!
Example Problem: Department Top 2 Highest Salary
Instead of displaying the top earner by department, display the top 2 earners by department
Solution Query:
You can use the DENSE_RANK() or ROW_NUMBER() window function. This allows you to assign a unique rank to each salary within its respective department, and then you can filter for the top three salaries.
SQL Interview Questions and Answers: DENSE_RANK SQL Query
SELECT Department, Employee, Salary FROM ( SELECT d.Name AS Department, e.Name AS Employee, e.Salary, DENSE_RANK() OVER (PARTITION BY e.DepartmentId ORDER BY e.Salary DESC) AS rank FROM Employee e JOIN Department d ON e.DepartmentId = d.Id ) ranked_employees WHERE rank <= 3;
Subquery (Ranked): This part computes a rank for each employee within their department based on their salary in descending order. PARTITION BY Department ensures the ranking resets for each department.
Outer Query: The outer query selects the department, employee name, and salary from the ranked results, filtering to include only those entries where Rank is 3 or less, thereby ensuring that only the top three earners per department are selected.
This approach will efficiently retrieve the desired results if your database supports window functions, which is common in systems like PostgreSQL, MySQL (8.0+), SQL Server, and Oracle.
Example Problem: Employees Earning More Than Their Managers
Problem Statement: The Employee table holds all employees including their managers. Every employee has an Id, and there is also a column for the manager Id.
And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
The SQL query uses a self-join on the Employee table. It aliases the table as a and b where a represents the employees and b represents the managers.
The WHERE clause matches each employee to their manager using a.ManagerId = b.Id and then checks if the employee’s salary is greater than the manager’s salary using a.Salary > b.Salary.
The result is the name of the employee who earns more than their manager.
Discussion
This question tests understanding of self-joins and basic comparison operations in SQL. It’s a relatively straightforward problem once you’re comfortable with the concept of joining a table to itself to compare records based on a relational key. It’s categorized under the “easy” level on LeetCode, but it encapsulates fundamental skills that can be built upon for more complex queries involving multiple joins, subqueries, and advanced SQL functions.
Full-Stack AI Intelligence. Zero Noise.The definitive audio briefing for the C-Suite and AI Architects. From Daily News and Strategic Deep Dives to high-density Industrial & Regulatory Intelligence—decoded at the speed of the AI era. . 👉 Start your specialized audio briefing today at Djamgamind.com
I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
What are some good datasets for Data Science and Machine Learning?
Finding good datasets for Data Science and Machine Learning can be a challenge. There are a lot of dataset out there, but not all of them are good for machine learning. In order to find a good dataset, you need to consider what you want to use the dataset for. If you want to use the dataset for training a machine learning model, then you need to make sure that the dataset is representative of the real-world data that you want to use the model on.
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
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.
AWS Data Analytics Specialty Certification Practice Exams
Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021
Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.
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.
Are you passionate about AI and looking for your next career challenge? In the fast-evolving world of artificial intelligence, connecting with the right opportunities can make all the difference. We're excited to recommend Mercor, a premier platform dedicated to bridging the gap between exceptional AI professionals and innovative companies.
Whether you're seeking roles in machine learning, data science, or other cutting-edge AI fields, Mercor offers a streamlined path to your ideal position. Explore the possibilities and accelerate your AI career by visiting Mercor through our exclusive referral link:
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.
And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
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.
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.
Quantum Computing – Computing which utilizes quantum mechanics and qubits on quantum computers.
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.
Apache Spark – Unified engine for large-scale data processing.
Qlik – Business intelligence platform for data visualization, analytics, and reporting apps.
Splunk – Platform for searching, monitoring, and analyzing structured and unstructured machine-generated big data in real-time.
Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021
Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.
At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.
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.
CHIP-8 – Virtual computer game machine from the 70s.
Games of Coding – Learn a programming language by making games.
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.
GitHub Actions – Create tasks to automate your workflow and share them with others on GitHub.
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.
NoSQL Guides – Help on using non-relational, distributed, open-source, and horizontally scalable databases.
Contexture – Abstracts queries/filters and results/aggregations from different backing data stores like ElasticSearch and MongoDB.
Database Tools – Everything that makes working with databases easier.
Grakn – Logical database to organize large and complex networks of data as one body of knowledge.
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.
Audiovisual – Lighting, audio and video in professional environments.
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.
Roadmaps – Gives you a clear route to improve your knowledge and skills.
YouTubers – Watch video tutorials from YouTubers that teach you about technology.
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.
Sitecore – .NET digital marketing platform that combines CMS with tools for managing multiple websites.
Silverstripe CMS – PHP MVC framework that serves as a classic or headless CMS.
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.
Plotters – Computer-controlled drawing machines and other visual art robots.
Robotic Tooling – Free and open tools for professional robotic development.
LIDAR – Sensor for measuring distances by illuminating the target with laser light.
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.
From the author: I started with data on roads from naturalearth.com, which also includes some ferry lines. I then calculated the fastest routes (assuming a speed of 90 km/h on roads, and 35 km/h on boat) between each pair of 45 European capitals. The animation visualizes these routes, with brighter lines for roads that are more frequently “traveled”.
In reality these are of course not the most traveled roads, since people don’t go from all capitals to all other capitals in equal measure. But I thought it would be fun to visualize all the possible connections.
The model is also very simple, and does not take into account varying speed limits, road conditions, congestion, border checks and so on. It is just for fun!
In order to keep the file size manageable, the animation only shows every tenth frame.
Is Russia, Turkey or country X really part of Europe? That of course depends on the definition, but it was more fun to include them than to exclude them! The Vatican is however not included since it would just be the same as the Rome routes. And, unfortunately, Nicosia on Cyprus is not included to due an error on my behalf. It should be!
2) This dataset comprises of more than 800 pokemons belonging up to 8 generations.
Using this dataset have been fun for me. I used it to create a mosaic of pokemons taking image as reference. You can find it here and it’s free to use: Couple Mosaic (powered by Pokemons)
Here is the data type information in the file:
Name: Pokemon Name
Type: Type of Pokemon like Grass / Fire / Water etc..,.
ETL pipeline for Facebook’s research project to provide detailed large-scale demographics data. It’s broken down in roughly 30×30 m grid cells and provides info on groups by age and gender.
The GISS Surface Temperature Analysis ver. 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in our publications Hansen et al. (2010) and Lenssen et al. (2019).
Buying a chocolate bar? There are seemingly hundreds to choose from, but its just the illusion of choice. They pretty much all come from Mars, Nestlé, or Mondelēz (which owns Cadbury).
Criteria for choosing a dictionary: – No proper nouns – “Official” source if available – Inclusion of inflected forms – Among two lists, the largest was fancied – No or very rare abbreviations if possible- but hard to detect in unknown languages and across hundreds of thousands of words.
The author found this dataset in a more accessible format upon searching for the keyword “CDPB” (Carcinogenic Potency Database) in the National Library of Medicine Catalog. Check out this parent website for the data source and dataset description. The dataset referenced in OP’s post concerns liver specific carcinogens, which are marked by the “liv” keyword as described in the dataset description’s Tissue Codes section.
DataSet of Tokyo 2020 (2021) Olympics ( details about the Athletes, the countries they representing, details about events, coaches, genders participating in each event, etc.) [1, 2]
Looking for Wildfires Database for all countries by year and month? The quantity of wildfires happening, the acreage, things like that, etc.. [1, 2, 3, ]
Looking for a pill vs fake pill image dataset [1, 2, 3, 4, 5, 6, 7]
In this project, the authors have designed a spatial model which is able to classify urbanity levels globally and with high granularity. As the target geographic support for our model we selected the quadkey grid in level 15, which has cells of approximately 1x1km at the equator.
The author obtained the data from the UK Government website, so unfortunately don’t know the methodology or how they collected the data etc.
The comparison to the general public is a great idea – according to the Government site, 6% of children, 16% of working-age adults and 45% of Pension-age adults are disabled.
According to the author , this animation depicts adult cognitive skills, as measured by the PIAAC study by OECD. Here, the numeracy and literacy skills have been combined into one. Each frame of the animation shows the xth percentile skill level of each individual country. Thus, you can see which countries have the highest and lowest scores among their bottom performers, median performers, and top performers. So for example, you can see that when the bottom 1st percentile of each country is ranked, Japan is at the top, Russia is second, etc. Looking at the 50th percentile (median) of each country, Japan is top, then Finland, etc.
Programme for the International Assessment of Adult Competencies (PIAAC)is a study by OECD to measure measured literacy, numeracy, and “problem-solving in technology-rich environments” skills for people ages 16 and up. For those of you who are familiar with the school-age children PISA study, this is essentially an adult version of it.
The model was built in Stan and was inspired by Andrew Gelman’s World Cup model shown here. These plots show posterior probabilities that the team on the y axis will score more goals than the team on the x axis. There is some redundancy of information here (because if I know P(England beats Scotland) then I know P(Scotland beats England) )
SEDE (Stack Exchange Data Explorer) is a dataset comprised of 12,023 complex and diverse SQL queries and their natural language titles and descriptions, written by real users of the Stack Exchange Data Explorer out of a natural interaction. These pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset. Access it here
Each map size is proportional to population, so China takes up about 18-19% of the map space.
Countries with very far-flung territories, such as France (or the USA) will have their maps shrunk to fit all territories. So it is the size of the map rectangle that is proportional to population, not the colored area. Made in R, using data from naturalearthdata.com. Maps drawn with the tmap package, and placed in the image with the gridExtra package. Map colors from the wesanderson package.
Beneath adds some useful features for shared data, like the ability to run SQL queries, sync changes in real-time, a Python integration, and monitoring. The monitoring is really useful as it lets you check out the write activity of the scraper (no surprise, WSB is most active when markets are open
The scraper (which uses Async PRAW) is open source here
The chart shows the average daily gain in $ if $100 were invested at a date on x-axis. Total gain was divided by the number of days between the day of investing and June 13, 2021. Gains were calculated on average 30-day prices.
Time range: from March 28, 2013, till June 13, 2021
Google Playstore dataset is now available with double the data (2.3 Million) android application data and a new attribute stating the scraped date time in Kaggle.
According to the author: Looking at non-suicide firearms deaths by state (2019), and then grouping by the Guns to Carry rating (1-5 stars), it seems that stricter gun laws are correlated with fewer firearms homicides. Guns to Carry rates states based on “Gun friendliness” with 1 star being least friendly (California, for example), and 5 stars being most friendly (Wyoming, for example). The ratings aren’t perfect but they include considerations like: Permit required, Registration, Open carry, and Background checks to come up with a rating.
The numbers at the bottom are the average non-suicide deaths calculated within the rating group. Each bar shows the number for the individual state.
Interesting that DC is through the roof despite having strict laws. On the flip side, Maine is very friendly towards gun owners and has a very low homicide rate, despite having the highest ratio of suicides to homicides.
Obviously, lots of things to consider and this is merely a correlation at a basic level. This is a topic that interested me so I figured I’d share my findings. Not attempting to make a policy statement or anything.
In 1996 the Australia Government implemented stricter gun control and restrictions. The numbers don’t lie and proves it worked.
Data for word frequency in econ textbooks was compiled by myself by scraping words from 43 undergraduate economics textbooks. For details see Deconstructing Econospeak.
Data Source: from eMarketer, as quoted byJon Erlichman
Purpose according to the author: raw textual numbers (like in the original tweet) are hard to compare, particularly the acceleration or deceleration of a trend. Did for myself, but maybe is useful to somebody.
A few things to notice: It’s dangerous to be a newborn. The same mortality rates are reached again only in the fifties. However, mortality drops after birth very quickly and the safest age is about ten years old. After experiencing mortality jump in puberty – especially high for boys, mortality increases mostly exponentially with age. Every thirty years of life increase chances of dying about ten times. At 80, chance of dying in a year is about 5.8% for males and 4.3% for females. This mortality difference holds for all ages. The largest disparity is at about twenty three years old when males die at a rate about 2.7 times higher than females.
Data from:iposcoop.com From the author u/nobjos: The full article on the above analysis can be found here I have sub r/market_sentiment where I do a comprehensive deep-dive on one investment strategy/topic every week! Some of the author popular articles are a. Performance of Jim Cramer’s stock picks b. Performance of buy and sell recommendations made by financial analysts in the last decade c. Benchmarking performance of Motely fool against SP500 Funko IPO is considered to have the worst first-day return for an IPO in the last two decades. Out of the top 10 list, only 3 Investment banks had below-average returns. On average, IPOs did make money for the investor. But the amount is significantly different if you got allocated the IPO at offer price vs you get the IPO at market price. Baidu.com made a whopping 354% on its listing day. Another interesting observation is 6 out of 10 companies in the list were listed in 200 (just before the dot com crash)
Check out the FAS site for notes and caveats about their estimates. Governments don’t just print this stuff on their websites. These are evidence-based estimates of tightly-guarded national secrets.
Of particular note – Here’s what the FAS says about North Korea: “After six nuclear tests, including two of 10-20 kilotons and one of more than 150 kilotons, we estimate that North Korea might have produced sufficient fissile material for roughly 40-50 warheads. The number of assembled warheads is unknown, but lower. While we estimate North Korea might have a small number of assembled warheads for medium-range missiles, we have not yet seen evidence that it has developed a functioning warhead that can be delivered at ICBM range.”
The author used several sources for this video and article. The first, for the video, is GitHub Archive & CodersRank. For the analysis of the OSCI index data, the author used opensourceindex.io
2021 is straight projections, must be taken with a grain of salt. However, the assumption of continuous rise of murder rate is not a bad one based on recent news reports, such as: here
This image was generated for my research mapping the privacy research field. The visual is a combination of network visualisation and manual adding of the labels.
The data was gathered from Scopus, a high-quality academic publication database, and the visualisation was created with Gephi. The initial dataset held ~120k publications and over 3 million references, from which we selected only the papers and references in the field.
The labels were assigned by manually identifying clusters and two independent raters assigning names from a random sample of publications, with a 94% match between raters.
This is a randomized experiment the author conducted with 450 people on Amazon MTurk. Each person was randomly assigned to one of three writing activities in which they either (a) described their phone, (b) described what they’d do if they received a call from someone they know, or (c) describe what they’d do if they received a call from an unknown number. Pictures of an iPhone with a corresponding call screen were displayed above the text box (blank, “Incoming Call,” or “Unknown”). Participants then rated their anxiety on a 1-4 scale.
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It doesn’t. First, a database is a collection of related data, so I assume you mean DBMS or database language.
Second, pagination is generally a function of the front-end and/or middleware, not the database layer.
But some database languages provide helpful facilities that aide in implementing pagination. For example, many SQL dialects provide LIMIT and OFFSET clauses that can be used to emit up to n rows starting at a given row number. I.e., a “page” of rows. If the query results are sorted via ORDER BY and are generally unchanged between successive invocations, then that can be used to implement pagination.
That may not be the most efficient or effective implementation, though.
So how do you propose pagination should be done?
On context of web apps , let’s say there are 100 mn users. One cannot dump all the users in response.
Cache database query results in the middleware layer using Redis or similar and serve out pages of rows from that.
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What if you have 30, 000 rows plus, do you fetch all of that from the database and cache in Redis?
I feel the most efficient solution is still offset and limit. It doesn’t make sense to use a database and then end up putting all of your data in Redis especially data that changes a lot. Redis is not for storing all of your data.
If you have large data set, you should use offset and limit, getting only what is needed from the database into main memory (and maybe caching those in Redis) at any point in time is very efficient.
With 30,000 rows in a table, if offset/limit is the only viable or appropriate restriction, then that’s sometimes the way to go.
More often, there’s a much better way of restricting 30,000 rows via some search criteria that significantly reduces the displayed volume of rows — ideally to a single page or a few pages (which are appropriate to cache in Redis.)
It’s unlikely (though it does happen) that users really want to casually browse 30,000 rows, page by page. More often, they want this one record, or these small number of records.
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I know for MySQL there is LIMIT offset,size; and for Oracle there is ‘ROW_NUMBER’ or something like that.
But when such ‘paginated’ queries are called back to back, does the database engine actually do the entire ‘select’ all over again and then retrieve a different subset of results each time? Or does it do the overall fetching of results only once, keeps the results in memory or something, and then serves subsets of results from it for subsequent queries based on offset and size?
If it does the full fetch every time, then it seems quite inefficient.
If it does full fetch only once, it must be ‘storing’ the query somewhere somehow, so that the next time that query comes in, it knows that it has already fetched all the data and just needs to extract next page from it. In that case, how will the database engine handle multiple threads? Two threads executing the same query?
something will be quick or slow without taking measurements, and complicate the code in advance to download 12 pages at once and cache them because “it seems to me that it will be faster”.
Answer: First of all, do not make assumptions in advance whether something will be quick or slow without taking measurements, and complicate the code in advance to download 12 pages at once and cache them because “it seems to me that it will be faster”.
YAGNI principle – the programmer should not add functionality until deemed necessary. Do it in the simplest way (ordinary pagination of one page), measure how it works on production, if it is slow, then try a different method, if the speed is satisfactory, leave it as it is.
From my own practice – an application that retrieves data from a table containing about 80,000 records, the main table is joined with 4-5 additional lookup tables, the whole query is paginated, about 25-30 records per page, about 2500-3000 pages in total. Database is Oracle 12c, there are indexes on a few columns, queries are generated by Hibernate. Measurements on production system at the server side show that an average time (median – 50% percentile) of retrieving one page is about 300 ms. 95% percentile is less than 800 ms – this means that 95% of requests for retrieving a single page is less that 800ms, when we add a transfer time from the server to the user and a rendering time of about 0.5-1 seconds, the total time is less than 2 seconds. That’s enough, users are happy.
And some theory – see this answer to know what is purpose of Pagination pattern
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Hello everyone! I am working with `PostgreSQL` and I am using `Alembic` to perform DB migrations by myself. I was just wondering if its possible to like have a shared/collaborative environment where 2 or more people can share and perform DB migrations without any schema conflicts. submitted by /u/Astro_Teeqo [link] [comments]
I started a project a bit ago and I was tracking it on Excel but it seems to be quickly outgrowing that medium. So I'm looking for advice of which database would be best for this project. I want to track the dates and locations of historical figures and military units. Take WW2 for example, I'd plug in where the 4th Infantry was on any given day, and also track the location of their commander for instance if they left the unit for a higher level meeting. On days that they had active combat I'd also like to track those battles in a separate record, preferably so you could later see who they were fighting (eg on X day units A, B, and Z were in combat in city Y). I have a plan to create a world map overlay with this data so you can see where every unit is on any particular date and how they moved throughout time. Any suggestions? submitted by /u/1877KlownsForKids [link] [comments]
I've been using mongodb for a while for projects. Recently I heard from someone saying that if your application needs joins or relationships, you should just use postgreSQL instead. They also mentioned that with JSONB, Postgres can cover most MongoDB use cases anyway. I don't have much experience with Postgres or SQL beyond a few small personal projects, so I'm trying to understand why people say this. In MongoDB, $lookup joins and relations haven’t been a big issue for me so far. The only thing I've struggled with is things like cascade deletes, though it seems like Postgres might also have issues with cascade soft deletes. Are there other problems with modeling relationships and doing joins in MongoDB? And how does Postgres handle this better? submitted by /u/negative_karma_nadeu [link] [comments]
I’m helping a nonprofit set up a better system to manage several programs we run throughout the year. Each year we send out a form to families so they can register for one or more programs, and we want those submissions to automatically connect to the correct program records in a database. We also need to maintain a single household record (so we avoid duplicates) while tracking participation across different programs and years. Sometimes we send follow-up forms later in the year to confirm participation or update information, so the system needs to be able to update existing records rather than creating new ones. I’ll be the one setting the system up, but the staff who will use it regularly are not very tech-savvy, so the interface needs to be simple. Ideally it would support forms, relational tables (households ↔ programs ↔ participation), and basic filtering/reporting. Does anyone have recommendations for software that works well for this type of setup? submitted by /u/rp1load [link] [comments]
EDIT: Thanks for all the ideas. I have a much better sense of what I can -- and what I don't want to get involved in doing, too 😉 I feel like a right idjit asking this, (is this even the right subredit?), but here goes: I have a nonprofit client for whom I've 1) created a Wordpress website and 2) set up a secure CRM that connects with Quickbooks. But now they want to collect a bunch of additional information about their members, information they want to allow all their committee chairs to access, that can't be added to the (intentionally access- limited and secure) CRM. So I'm looking for a free/open source database (if it's not online, I could host it on the server I use) or a spreadsheet for well-intentioned people who are so not tech-savvy that when I initially tried Google forms/spreadsheet for this project, didn't have the wits to sort/filter the spreadsheet by field to find the information they needed. So I'm looking for a database or spreadsheet that allows 1) information to be added by forms and 2) information to be extracted by simple reports or queries. Does such a thing exist? Thanks for your patience. submitted by /u/LilaTovCocktail [link] [comments]
"Give me the 10 best rows" feels easy until you add text search and filters. In Postgres, GIN (inverted) indexes cover text search but can't sort. B-trees sort but break down with text search. This post explains why and how BM25 multi-column indexes can solve TopK with one compound structure that handles equality, sort, and range together. submitted by /u/jamesgresql [link] [comments]
I’m currently learning the basics of database transactions, and I’ve started studying concurrency control. However, I’m struggling to clearly understand the difference between transaction isolation levels and optimistic/pessimistic concurrency control strategies. From what I understand, depending on the isolation level selected (e.g., Read Committed, Repeatable Read, Serializable), different types of locking might be applied to prevent concurrency problems between transactions. At the same time, there are optimistic and pessimistic concurrency control strategies, which also seem to define different approaches to locking and conflict handling. This is where my confusion begins: Are transaction isolation levels and optimistic/pessimistic concurrency control fundamentally different concepts? Are they just different ways of managing concurrency? Or are they complementary concepts, where one operates at a different abstraction level than the other? For example, if I select a specific isolation level, does that already imply a certain concurrency control strategy? Or can optimistic/pessimistic control still be applied independently of the isolation level? I would really appreciate a conceptual clarification on how these ideas relate to each other. Thanks in advance, and apologies if this is a somewhat basic question. I actually submitted a similar question yesterday, but I decided to remove it because it didn’t reflect my doubts correctly. Sorry for the inconvenience! submitted by /u/CardiologistNo5941 [link] [comments]
In a proposed project management software, there are users, and groups that users can host(like a discord server where users can join). When a user makes a project they have the option to have it only associated with their account, or with a group they are an admin of. When users get added to a project, there’s an option to associate them with a group as well. The user to project relationship is many to many; as well as the group to project relationship. Both have their respective join tables. Since association to groups are optional, does it make sense to use: nullable FK on project table to track what group created it if applicable nullable FK on users_projects table to track what group in the project the user is associated with if applicable I’m leaning towards these options for the simplicity, but have seen some arguments that it’s bad practice. I am still “junior” in my database knowledge, so I’m hoping to get some more experienced insight. Edit: I did have the idea of making extra join tables that have those optional fields, and then saving to it if the group connection was needed, but that didn’t seem efficient. submitted by /u/NoJuiceOnlySauce [link] [comments]
For context: I am a IT intern in a medium size org that is currently migrating a legacy system with 150+- .dbo tables into our new system with only 70+- tables. There is clearly a lot of tables and columns to sort through in order to data map and know what Im suppose to migrate. Given this task, what should I be doing to successfully map out all the data I should migrate? Is there any tools that help me automate this process or do I have to 1 man army this task? Currently its all just local files in SQLServer. submitted by /u/JackSzj [link] [comments]
I am trying to run flyway migration script, my sql file is kept in db/migration. But I keep getting " schema system is up to date. No migrations applied" what shall I do. I have attached ss as well. submitted by /u/theinterestingreads [link] [comments]
I have an archive of 300,000 photos from a website, each photo has an accompanying json file withe metadata. I want build a database so I can search it instead of doing a filesystem search each time. I know python and how to work with json, should I just use it or go learn sql/sqlite and use it instead for the database? submitted by /u/Mashic [link] [comments]
I’m building a multi-tenant SaaS using PostgreSQL with a shared-schema approach. Current structure: Users Tenants Roles UserRoleTenant (join table) UserRoleTenant has a composite primary key: (UserId, RoleId, TenantId) This works perfectly for tenant-scoped roles. The problem: I have a Super Admin role that is system-level. Super admins can manage tenants (create, suspend, etc.) They do NOT belong to a specific tenant I want all actors (including super admins) to stay in the same Users table Super admins should not have a TenantId Because TenantId is part of the composite PK, it cannot be NULL, so I can't insert a super admin row. I see two main options: Option 1 – Add surrogate key Add an Id column as primary key to UserRoleTenant and add a unique index on (UserId, RoleId, TenantId). This would allow TenantId to be nullable for super admins. Option 2 – Create a “SystemTenant” Seed a special tenant row (e.g., “System” or “Global”) and assign super admins to that tenant instead of using NULL. My questions: Which approach aligns better with modern SaaS design? Is using a fake/system tenant considered a clean solution or a hack? Is there a better pattern (e.g., separating system-level roles from tenant-level roles entirely)? How do larger SaaS systems typically model this? Would love to hear how others solved this in production systems. submitted by /u/Aawwad172 [link] [comments]
Operations analyst at a manufacturing company and I'm dealing with infor ln as our main erp. If you've worked with infor you know the pain. The data model is complex, the api documentation is sparse, and getting anything out of it in a format thats useful for analysis requires either custom bapi calls or csv exports through their reporting tool which tops out at like 10k rows. Our finance team needs to join infor production data with cost data from a separate budgeting tool and quality metrics from our qms system. Right now someone manually exports from each system weekly and does vlookups in excel to stitch it together. Its error prone and eats up a full day every week. I want to get all of this flowing into a proper database or warehouse automatically so we can build dashboards and do actual analysis instead of spreadsheet gymnastics. But I'm not a developer and our IT team is stretched thin with other priorities. Has anyone successfully extracted data from infor ln into a cloud warehouse? Wondering if there are tools that have prebuilt connectors for infor specifically or if custom development is the only realistic path. submitted by /u/Easy-Affect-397 [link] [comments]
Operations analyst at a manufacturing company and I'm dealing with infor ln as our main erp. If you've worked with infor you know the pain. The data model is complex, the api documentation is sparse, and getting anything out of it in a format thats useful for analysis requires either custom bapi calls or csv exports through their reporting tool which tops out at like 10k rows. Our finance team needs to join infor production data with cost data from a separate budgeting tool and quality metrics from our qms system. Right now someone manually exports from each system weekly and does vlookups in excel to stitch it together. Its error prone and eats up a full day every week. I want to get all of this flowing into a proper database or warehouse automatically so we can build dashboards and do actual analysis instead of spreadsheet gymnastics. But I'm not a developer and our IT team is stretched thin with other priorities. Has anyone successfully extracted data from infor ln into a cloud warehouse? Wondering if there are tools that have prebuilt connectors for infor specifically or if custom development is the only realistic path. submitted by /u/AccountEngineer [link] [comments]
hey fellow devs i need a help to create er diagrams for my projects i have a table which have role attribute of enum datatype each role have diffrente user priviliges like in a event management system a simple user, an admin and an organizer and i am confused in how to represent these entities in my er diagram shall i need to use specialization sorry for my bad english 😅 submitted by /u/MihirMeshram007 [link] [comments]
I wrote up a deep dive into binary JSON encoding internals, showing how databases can achieve ~2,346× faster lookups with indexing. This is also highly relevant to how Parquet in the lakehouse world uses VARIANT. AMA if you are interested in anything database internals! https://floedb.ai/blog/why-json-isnt-a-problem-for-databases-anymore Disclaimer: I wrote the technical blog content. submitted by /u/jincongho [link] [comments]
I found out about Turbopuffer today, which is a search DB backed by object storage. Unfortunately, they don’t currently have any method (that I can find, at least) that allows me to self-host it. I saw Quickwit a while back but they haven’t had a release in almost 2 years, and they’ve since been acquired by Datadog. I’m not confident that they will release a new version any time soon. Are there any alternatives? I’m specifically looking for search databases using object storage. submitted by /u/jgaskins [link] [comments]
I am working on a fast api application with postgres database hosted on RDS. I notice api responses are very slow and it takes time on the UI to load data like 5-8 seconds. How to optimize queries for faster response? submitted by /u/Grand_Syllabub_7985 [link] [comments]
Hey everyone, so I spent the last few weeks going down the KV cache rabbit hole. One thing which is most of what makes LLM inference expensive is the storage and data movement problems that I think database engineers solved decades ago. IMO, prefill is basically a buffer pool rebuild that nobody bothered to cache. So I did this write up using LMCache as the concrete example (tiered storage, chunked I/O, connectors that survive engine churn). Included a worked cost example for a 70B model and the stuff that quietly kills your hit rate. Curious what people are seeing in production. ✌️ submitted by /u/tirtha_s [link] [comments]
Hello all, I am a junior Software Engineer, and after working in the industry for 2 years, I have decided that I should work on some SaaS project to sell for businesses. So I wanted to know what is the right design choice to do for the `User` Table, I have 2 actors in my project: Business Employees and Business Owner that would have email address and password and can sign in to the system. End User that have email address but don't have password since he won't have to sign in to any UI or system, he would just use the system via integration with his phone. So the thing is should: I make them in the same Table and making the password nullable which I don't prefer since this will lead to inconsistent data and would make a lot of problems in the feature. or Create 2 separated tables one for each one of them, but I don't think this is correct since it would lead to having separated table to each role and so on, I know this is the simple thing and it is more reliable but I feel that it is a little bit manual, so if we need to add another role in the future we would need to add some extra table and so on and on. I am confused since I am looking for something that is dynamic without making the DB a mess, and on the other hand something reliable and scalable, so I don't have to join through a lot of tables to collect data, also I don't think that having a GOD table is a good thing. I just can't find the soft spot between them. Please help submitted by /u/Aawwad172 [link] [comments]
I do a lot of consulting work across different stacks and one thing that still surprises me is how fragile database change workflows are in otherwise mature engineering orgs. The patterns I keep seeing: Just drop the SQL file in a folder and let CI pick it up A homegrown script that applies whatever looks new Manual production changes because “it’s safer” Integer-based migration systems that turn into merge-conflict battles on larger teams Rollbacks that exist in theory but not in practice The failure modes are predictable: DDL not being transaction safe A migration applying out of order Code deploying fine but schema assumptions are wrong rollbacks requiring ad hoc scripts at 2am Parallel feature branches stepping on each other’s schema work What I’m looking for in a serious database change management setup: Language agnostic Not tied to a specific ORM SQL first, not abstracted DSL magic Dependency aware Parallel team friendly Clear deploy and rollback paths Auditability of who changed what and when Reproducible environments from scratch I’ve evaluated tools like Sqitch, Liquibase, Flyway, and a few homegrown frameworks. each solves part of the problem, but tradeoffs appear quickly once you scale past 5 developers. one thing that has helped in practice is pairing schema migration tooling with structured test tracking and release visibility. When DB changes are tied to explicit test runs and evidence rather than just merged SQL, risk drops dramatically. We track migrations alongside regression runs and release notes in the same workflow. Tools like Quase, Tuskr or Testiny help on the test tracking side, and having a clean run log per release makes it much easier to prove that a migration was validated under realistic scenarios. Even lightweight test tracking systems can add discipline around what was actually verified before a DB change went live. Curious what others in the database community are using today: Are you all in on Flyway or Liquibase? Still writing custom migration frameworks? Using GitOps patterns for schema changes? Treating schema changes as first class deploy artifacts? submitted by /u/Huge_Brush9484 [link] [comments]
I’d love to find a cheap and simple way of collating client connections- it would preferably be a shared platform that staff can all access and contribute to. It would need to hold basic info such as name, organisation, contact number, general notes. And I’d love to find one that might have an app so staff can access and add to when away from their desktop. Any suggestions?? Thanks so much submitted by /u/strawberry_thief001 [link] [comments]
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