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207 results for “github”
Fire in US lake watersheds (1500 m lake buffers) from 1984-2015 (github repository)
<p>This repository contains lake-specific watershed (1500 m buffers) fire histories for US lakes >= 4 ha based on Monitoring Trends in Burn Severity (fire type and burn severity class). 8702 lake watersheds experienced at least one fire from 1984-2015. This repository contains analysis scripts (R) and data products associated with the manuscript McCullough et al. (2019) “Do lakes feel the burn? Ecological consequences of increasing exposure of lakes to fire in the continental US”. Global Change Biology. The repository is an April 2019 snapshot of the github repository https://github.com/cont-limno/DoLakesFeelTheBurn for when the manuscript was accepted.</p>
Replication Package for "Catching Smells in the Act: A GitHub Actions Workflow Investigation" - Thesis
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Subset of The Most Popular Kotlin Apps on Github
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Dataset for GitHub repository AlexanderKroll/SPOT_prediction_function
<p>Dataset for GitHub repository AlexanderKroll/SPOT_prediction_function</p>
An Investigation on Usage of Code References in Pull Requests on GitHub
<p>Review for ICPC 2025</p>
Big Data = Big Insights? Operationalising Brooks' Law in a Massive GitHub Data Set (Data Sets)
<p>Data sets containing fine-grained information on edits and co-edits in 201 GitHub projects published with the article <em>"Big Data = Big Insights? Operationalising Brooks' Law in a Massive GitHub Data Set"</em>.</p>
Code review regression analysis of open source GitHub projects
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Need for Tweet: How Open Source Developers Talk About Their GitHub Work on Twitter
<p>This dataset contains 70,427 cross-linked Twitter-[GHTorrent](http://ghtorrent.org) user pairs identified as likely belonging to the same users. The dataset accompanies our research paper:</p> <p>@inproceedings{fang2020tweet,</p> <p> author = {Fang, Hongbo and Klug, Daniel and Lamba, Hemank and Herbsleb, James and Vasilescu, Bogdan},</p> <p> title = {Need for Tweet: How Open Source Developers Talk About Their GitHub Work on Twitter},</p> <p> booktitle = {International Conference on Mining Software Repositories (MSR)},</p> <p> year = {2020},</p> <p> pages = {to appear},</p> <p> publisher = {ACM},</p> <p>}</p> <p> </p> <p>The data <strong>cannot</strong> be used for any purpose other than conducting research. </p> <p>Due to privacy concerns, we only release the user IDs in Twitter and GHTorrent, respectively. We expect that users of this dataset will be able to collect other data using the Twitter API and GHTorrent, as needed. Please see below for an example.</p> <p>To query the Twitter API for a given user_id, you can:</p> <ul> <li> <p>Apply for Twitter developer account <a href="https://developer.twitter.com/en/apply-for-access">here</a>.</p> </li> <li> <p>Create an APP with your Twitter developer account, and create “API key” and “API secret key”.</p> </li> <li> <p>Obtain an access token. Given the previous <API key>:<API secret key>, run: </p> </li> </ul> <p>curl -u "<API key>:<API secret key>" --data "grant_type=client_credentials" "https://api.twitter.com/oauth2/token"</p> <p>The response looks like this: {"token_type":"bearer","access_token":"<...>"} </p> <p>Copy the "access_token".</p> <ul> <li> <p>Given the previous access token, run:</p> </li> </ul> <p>curl --request GET --url "https://api.twitter.com/1.1/users/show.json?user_id=<user_id>" --header "authorization: Bearer <access_token>"</p> <p>The GHTorrent user ids map to the users table in the MySQL version of GHTorrent. To use GHTorrent, please follow instructions on the <a href="https://ghtorrent.org">GHTorrent website</a>.</p>
How Has Forking Changed in the Last 20 Years? A Study of Hard Forks on GitHub
<p>The notion of forking has changed with the rise of distributed version control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent development branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community. Today, in social coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hard forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive non- competitive alternative to the original project.</p>
Shell Aliases on GitHub
<p>A dataset of 2,204,199 shell alias definitions found on GitHub, broken down into 2,534,167 commands and 3,630,423 arguments. Compiled from 844,140 files from 304,361 GitHub repositories, collected between December 20th 2019 and January 8th 2020. This represents 94.09 % of the estimated population of files under 29 KB on GitHub written in Shell language and containing the word “alias”.</p>
DeepPull: Deep Learning-Based Approach for Predicting Reopening, Decision, and Lifetime of Pull Requests on GitHub Open-Source Projects
<p>This dataset is from the paper titled "DeepPull: A Deep Learning-Based Approach for Predicting Reopening, Decision, and Lifetime of Pull Requests in GitHub Open-Source Projects," authored by P. Banyongrakkul and S. Phoomvuthisarn.</p><p>The dataset includes 288,121 pull requests from 83 open-source GitHub projects, spanning 6 different programming languages: Python, R, Java, Ruby, PHP, and C++. These pull requests were submitted between the periods of August 2010 and September 2023. The dataset consists of 30 tabular features, 2 textual features, 3 target features, and 5 metadata properties.</p>
Data Package for "Stories Complicate Things: A Qualitative Analysis of Coding Problems (Un)solved by GitHub"
<div> <pre>This package is part of the paper "Stories Complicate Things: A Qualitative Analysis of Coding Problems (Un)solved by GitHub Copilot" by Julian Oertel, Jil Klünder and Regina Hebig. <br>It contains unprocessed data, a complete list of codes for coding problems and solutions, and generated images.</pre> </div>
Dataset of the Paper: Practices and Challenges of Using GitHub Copilot: An Empirical Study
<p>This dataset collected from Stack Overflow (SO) and GitHub Discussions was used to conduct an empirical study on the practices and challenges of using GitHub Copilot. A brief description of each document in the dataset is provided below:</p> <p><strong>1. Dataset (SO).xlsx</strong></p> <p>contains the IDs and URLs of the labelled posts which are related to Copilot from Stack Overflow, and the data extracted from these related SO posts.</p> <p><strong>2. Dataset (GitHub).xlsx</strong></p> <p>contains the discussion IDs and discussion URLs in the Copilot category of GitHub Discussions, and the data extracted from all the discussions.</p> <p><strong>3. Extracted Data (SO+GitHub).xlsx</strong></p> <p>contains the final results of data extracted from SO posts and GitHub discussions.</p>
Supplementary Website, Data, and Scripts for the Paper "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Supplementary website containing result plots, pseudonymized input data, resulting pseudonymized classification data, analysis scripts, and Dockerfile used to produce the results of the paper "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized input data is also availble separately here: https://zenodo.org/record/7775078</p> <p>The resulting pseudonymized classification data is also available separately here: https://zenodo.org/record/7775385</p>
Origin and evolutionary malleability of T cell receptor α diversity (zipped files for GitHub code)
<p>Lymphocytes of vertebrate adaptive immune systems acquired the capability to</p> <p>assemble, from split genes in the germline, billions of functional antigen receptors.</p> <p>These receptors show specificity; unlike the broadly tuned receptors of the innate</p> <p>system, antibodies (Ig) expressed by B lymphocytes, for instance, can faithfully</p> <p>distinguish between the two enantiomers of organic acids4 whereas T cell receptors</p> <p>(TCRs) reliably recognize single amino acid replacements in their peptide antigens5.</p> <p>In developing lymphocytes, antigen receptor genes (AgRs) are assembled from a</p> <p>comparatively small set of germline-encoded genetic elements in a process referred</p> <p>to as V(D)J recombination6,7. Potential self-reactivity of some AgRs arising from the</p> <p>quasi-random somatic diversification is suppressed by several robust control</p> <p>mechanisms. For decades, scientists have puzzled over the evolutionary origin of</p> <p>somatically diversifying antigen receptors. It has remained unclear how, at the</p> <p>inception of this mechanism, immunologically beneficial expanded receptor diversity</p> <p>was traded against the emerging risk of destructive self-recognition. Here, we explore</p> <p>the hypothesis that sequence microhomologies marking the ends of recombining</p> <p>elements are the crucial targets of selection determining the outcome of nonhomologous</p> <p>end joining-based repair of DNA double-strand breaks generated during</p> <p>RAG-mediated recombination in early vertebrates. We find that, across the main clades</p> <p>of jawed vertebrates, TCR α repertoire diversity is best explained by species-specific</p> <p>extents of such sequence microhomologies. Thus, selection of germline sequence</p> <p>composition of rearranging elements emerges as a major factor determining the</p> <p>degree of diversity of somatically generated AgRs.</p>
VolcanoSV Github test data
<p>test example datasets for VolcanoSV github https://github.com/maiziezhoulab/VolcanoSV</p>
Dataset and Code Package for Studying OSS Governance on GitHub
<h2>Introduction</h2> <p>This data and code package accompanies the research paper, providing all necessary scripts for replicating the study's findings and facilitating further exploration. The package includes a series of Python and R scripts, each designed to perform specific tasks in the data analysis process. Below is a detailed description of each file included in the package:</p> <h3>Usage Instructions</h3> <p>To replicate the study's results, users should run the scripts in the order provided. Each script is designed to build upon the previous one, ensuring a seamless data processing workflow. Detailed comments within each script offer further guidance on their specific functionalities and dependencies.</p> <h3>Dependencies</h3> <ul> <li><strong>Python 3.12</strong>: Required for running the Python scripts.</li> <li><strong>R</strong>: Required for running the <code>11_effects_analysis.R</code> script.</li> <li><strong>Python Libraries</strong>: <code>pandas</code>, <code>numpy</code>, <code>nltk</code>, and other standard libraries as specified in the scripts.</li> <li><strong>R Packages</strong>: <code>dplyr</code>, <code>ggplot2</code>, and other necessary packages as specified in the R script.</li> </ul> <h3>Dataset Description</h3> <h4>Under gov_md_files</h4> <p>This folder contains markdown files related to governance documentation from various GitHub projects. Each file includes detailed governance rules and policies, which are analyzed to understand the evolution and impact of governance structures.</p> <h4>comment-list.csv</h4> <p>This file contains a list of comments extracted from the GitHub projects. Each entry includes details such as the comment text, author, date, and associated rule or issue, providing context for qualitative analysis.</p> <h4>governance-change-commit.csv</h4> <p>This file lists commits related to governance changes in the projects. Each entry includes commit metadata such as commit ID, author, date, and a brief description, allowing for tracking and analysis of governance modifications over time.</p> <h4>governance-change-content.csv</h4> <p>This file provides the content of governance changes associated with each commit. It includes the actual changes made to governance documentation, offering detailed insights into how governance rules evolve.</p> <h4>issue-list.csv</h4> <p>This file contains a list of issues reported in the GitHub projects. Each issue includes details such as issue ID, title, description, author, date, and status, enabling analysis of issues in relation to governance and rule changes.</p> <h4>repo-list.csv</h4> <p>This file lists the repositories included in the study. Each entry includes repository metadata such as repository name, owner, and other relevant information, serving as a reference for the scope of the analysis.</p> <h4>latest-governance-file-content.csv</h4> <p>This file contains the latest content of governance files from the projects. It captures the current state of governance rules and policies, providing a snapshot for comparison with historical data.</p>
Dataset and Code Package for Studying OSS Governance on GitHub
<h2>Introduction</h2> <p>This data and code package accompanies the research paper, providing all necessary scripts for replicating the study's findings and facilitating further exploration. The package includes a series of Python and R scripts, each designed to perform specific tasks in the data analysis process. Below is a detailed description of each file included in the package:</p> <h3>Usage Instructions</h3> <p>To replicate the study's results, users should run the scripts in the order provided. Each script is designed to build upon the previous one, ensuring a seamless data processing workflow. Detailed comments within each script offer further guidance on their specific functionalities and dependencies.</p> <h3>Dependencies</h3> <ul> <li><strong>Python 3.12</strong>: Required for running the Python scripts.</li> <li><strong>R</strong>: Required for running the <code>11_effects_analysis.R</code> script.</li> <li><strong>Python Libraries</strong>: <code>pandas</code>, <code>numpy</code>, <code>nltk</code>, and other standard libraries as specified in the scripts.</li> <li><strong>R Packages</strong>: <code>dplyr</code>, <code>ggplot2</code>, and other necessary packages as specified in the R script.</li> </ul> <h3>Dataset Description</h3> <h4>Under gov_md_files</h4> <p>This folder contains markdown files related to governance documentation from various GitHub projects. Each file includes detailed governance rules and policies, which are analyzed to understand the evolution and impact of governance structures.</p> <h4>comment-list.csv</h4> <p>This file contains a list of comments extracted from the GitHub projects. Each entry includes details such as the comment text, author, date, and associated rule or issue, providing context for qualitative analysis.</p> <h4>governance-change-commit.csv</h4> <p>This file lists commits related to governance changes in the projects. Each entry includes commit metadata such as commit ID, author, date, and a brief description, allowing for tracking and analysis of governance modifications over time.</p> <h4>governance-change-content.csv</h4> <p>This file provides the content of governance changes associated with each commit. It includes the actual changes made to governance documentation, offering detailed insights into how governance rules evolve.</p> <h4>issue-list.csv</h4> <p>This file contains a list of issues reported in the GitHub projects. Each issue includes details such as issue ID, title, description, author, date, and status, enabling analysis of issues in relation to governance and rule changes.</p> <h4>repo-list.csv</h4> <p>This file lists the repositories included in the study. Each entry includes repository metadata such as repository name, owner, and other relevant information, serving as a reference for the scope of the analysis.</p> <h4>latest-governance-file-content.csv</h4> <p>This file contains the latest content of governance files from the projects. It captures the current state of governance rules and policies, providing a snapshot for comparison with historical data.</p>
Architecture Decisions in Quantum Software Systems: An Empirical Study on Stack Exchange and GitHub
<p>This dataset was collected from GitHub and Stack Exchange (including Stack Overflow, Quantum Computing Stack Exchange, and Computer Science Stack Exchange) to conduct an empirical study on architecture decisions in quantum software systems. We provide below a brief description of each file:</p><p>1. Dataset (GitHub).xlsx</p><p>contains selected quantum software projects from GitHub with project names, issue IDs, and issue URLs and the data extracted from the GitHub issues that are related to architecture decisions in quantum software development.</p><p>2. Dataset (SO).xlsx</p><p>contains the IDs and URLs of Stack Overflow (SO) labeled posts and the extracted data from the Stack Overflow posts that are related to architecture decisions in quantum software development.</p><p>3. Dataset (QC).xlsx</p><p>contains the IDs and URLs of Quantum Computing (QC) Stack Exchange labeled posts and the extracted data from the Quantum Computing Stack Exchange posts that are related to architecture decisions in quantum software development.</p><p>4. Dataset (CS).xlsx</p><p>contains the IDs and URLs of Computer Science (CS) Stack Exchange labeled posts and the extracted data from the Computer Science Stack Exchange posts that are related to architecture decisions in quantum software development.</p><p>5. Extracted Data (GitHub+SO+QC+CS).xlsx</p><p>provides the final results of data extracted from the related GitHub issues, SO posts, QC posts, and CS posts.</p>
LLM-Generated Software Requirements from GitHub Issues
<p>This dataset contains software requirements automatically generated from bug reports and feature requests extracted from the three most popular machine learning repositories on GitHub: <strong>Scikit-learn, TensorFlow, and Transformers</strong>. The dataset is structured into issue data, generated requirements, and evaluations based on three well-defined criteria.</p> <h4><strong>Dataset Structure</strong></h4> <ul> <li><strong><code>issues.csv</code></strong>: Contains issue titles along with their corresponding repository names and unique identifiers.</li> <li><strong>Requirements Files</strong>: These files store the requirements generated by LLMs for each issue, categorized by different prompting methods: <ul> <li><code>few_shot_requirements.csv</code></li> <li><code>zero_shot_requirements.csv</code></li> <li><code>expert_requirements.csv</code></li> <li><code>expert_few_shot_requirements.csv</code></li> </ul> </li> <li><strong>Evaluation Files</strong>: These files contain the assessment of the generated requirements based on three key quality criteria: <strong>Unambiguity, Understandability, and Singularity</strong>. The evaluations are also divided by prompting methods: <ul> <li><code>few_shot_evaluation.csv</code></li> <li><code>zero_shot_evaluation.csv</code></li> <li><code>expert_evaluation.csv</code></li> <li><code>expert_few_shot_evaluation.csv</code></li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.