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Dataset results
207 results for “github”
Raw data for the Github repository "Collection of scripts to download & process hydropower generation data in Argentina, Bolivia, Brazil, Uruguay"
<p>This is the raw data downloaded from the websites of the following system operators:</p> <p> - CNDC, Comité Nacional de Despacho de Carga (Bolivia): https://www.cndc.bo/home/index.php<br> - ONS, Operador Nacional do Sistema Elétrico (Brazil): https://www.ons.org.br/<br> - UTE, Usinas y Trasmisiones Eléctricas (Uruguay): https://www.ute.com.uy/<br> - CAMMESA, Compañía Administradora del Mercado Mayorista Eléctrico Sociedad Anónima (Argentina): https://cammesaweb.cammesa.com</p> <p>Hydropower generation data is extracted using the R scripts available here: https://github.com/matteodefelice/hydro-sam</p>
GitHub Repository: Datasets, Experimental Setups, and Code for Exploiting Relations Between Commits
<p>This release contains all previously missing results and notebooks.</p>
Wasmizer: Curating WebAssembly-driven Projects on GitHub
<p>This is the dataset and artifact that accompanies the MSR 2023 paper titled: "Wasmizer: Curating WebAssembly-driven Projects<br> on GitHub"</p>
Dataset for "Deep Dive into the Verifiability of Code Generated by GitHub Copilot"
<p>A collection of GitHub-Copilot-generated Python solutions and their translations to Dafny with verification attempts.</p>
Data accompanying the GitHub repository bartonlab/paper-clonal-dynamics
<p>This dataset contains data that accompany the GitHub repository bartonlab/paper-covariance-estimation. The GitHub repository contains code and scripts for reproducing results described in the manuscript "Correlated allele frequency changes reveal clonal structure and selection in temporal genetic data." See the GitHub repository for details on the interpretation and analysis of this data.</p>
Data to support publication figures and animation scripts at GitHub: Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana)
Open the record for dataset details and reuse information.
Are developers losing interest on my project? An initial study on unstar events on GitHub platform
<p>GitHub is the most popular social coding platform and the world's largest collection of open source software. In this platform, users can star repositories to manifest their interest or satisfaction with the hosted project. However, it is not possible to maintainers of these projects to know whether they are losing the interest of developers from this platform (i.e., developers are removing their stars). In this paper, we present an initial investigation on how often developers remove stars from GitHub projects and the impact on their popularity. Our results show that GitHub projects frequently lose stars, however these events do not have a major influence on their overall popularity since most of them gain more stars than lose stars. We also show that some programming languages retain the interest from developers for more time.</p>
GitHub developer behavior and repository evolution dataset
<p>In this work, based on GitHub Archive project and repository mining tools, we process all available data into concise and structured format to generate GitHub developer behavior and repository evolution dataset. With the self-configurable interactive analysis tool provided by us, it will give us a macroscopic view of open source ecosystem evolution.</p>
ecPoint_GitHub_TestData
<p>One day of test data, with input and output files to run ecPoint (https://github.com/ecmwf/ecPoint).</p>
Dataset for "The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub"
<p>Supplementary data to "The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub" accepted for publication at MSR 2020.</p> <p>The data included in this package were used to conduct analyses to characterize the AI & ML software development community hosted on GitHub. Please read the paper for a full understanding of what data was collected and how it was used.</p> <p>Questions and comments can be directed to Danielle Gonzalez dng2551@rit.edu</p>
Mining the Technical Roles of GitHub Users
<p>This dataset contains the scripts and dataset used in the study reported at <em>Mining the Technical Roles of GitHub Users</em> paper. The files are described in more detailed below:</p> <ul> <li>processed_ground_truth.csv: A CSV file with the information of the developers considered in the study. Due to privacy issues, we already preprocessed the dataset to remove identification clues. Please contact the authors in case you need the original one.</li> <li>processed_ground_truth_fullstack.csv: Same CSV file but with fullstack developers.</li> <li>script.ipynb, utils.py: Source code of the script used in our study.</li> <li>Dockerfile, docker-compose.yml, requirements.txt: Files to replicate the code environment used in this study.</li> <li>BoW-tuning.csv: List of classifications results for different bag of words parameters.</li> </ul>
GitHub Public Pull Request Comments
<p>Over 13 MILLION pull request comments</p><p>Dataset used for the master's thesis "LLMs for Code Comment Consistency." Covers the languages Go, Java, JavaScript, TypeScripp, and Python. All data is mined from permissively-licensed GitHub public projects with at least 25 stars and 25 pull requests submitted at the time of access.</p><p> </p><p>This dataset pertains specifically to **pull request comments that are made on files.** In other words, every comment in this dataset is linked to a specific file in a pull request.</p><p> </p><p>### What can I do with this data?</p><p>Anything you want, of course, but here are some starter ideas:</p><p>- Sentiment analysis of comments, is there a correlation between number of contributions and positivity of reviews?</p><p>- Pull request comment generation: can we automatically make code review comments?</p><p>- PR text mining: can we mine out examples of a specific type of comment? (in my project, this was comments about function documentation)</p><p> </p><p>The mining code is publicly accessible.</p><p> </p><p>Each file is a JSON object where each key is a Github repository, and each value is a pull request comment in that repository.</p>
GitHub Public Repository Metadata
<p>Metadata (i.e. no code) of all public repositories with 5+ stars on GitHub</p><p> </p><p>This dataset is obtained from the Github API and contains only public repository-level metadata.<br>It may be useful for anyone interested in studying the Github ecosystem. It contains approximately 3.1 million entries.</p><p>The Github API Terms of Service apply.</p><blockquote><p>You may not use this dataset for spamming purposes, including for the purposes of selling GitHub users' personal information, such as to recruiters, headhunters, and job boards.</p></blockquote><p><br>The data format is a JSON array of entries, an example of which is given below.</p><p><i>Redacted for anonymity.</i></p>
github_1000k_developer_info
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BSO-GBD:Behavior Sequence-Oriented GitHub Bot Detection model
<p>Our dataset is primarily divided into three parts. Firstly, there are human accounts (Human) and bot accounts (Bot). Both of these types appear similar to regular developer accounts, lacking any distinctive markings. The second category comprises self-bot accounts, typically formed by GitHub apps. These accounts generally bear an explicit "bot" label, and we exclude them in our classification.</p> <p>The dataset is structured as follows: a total of 4786 ordinary accounts, with 4325 being Human accounts and 461 being Bot accounts. Each account internally consists of a CSV table with the following fields: user_name; repo_name; event_type_L1; event_type_L2; event_type_L3; timestamp. These data constitute our raw dataset.</p>
Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot
<p>Replication Package for "Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot"</p>
ROS2 Python code repository from Github
<p>The dataset comprises a curated assemblage of GitHub repositories housing ROS2 source code scripted in Python. Acquired in November 2023, this compilation is organized within a compressed file. Included alongside the repositories is a CSV file mapping each directory within the archive to its respective repository on GitHub, facilitating streamlined navigation and utilization.</p>
Replication Package for Data Quality Assessment in the Wild: Findings from GitHub
<p>This contains the data and code used in the EASE 2014 paper "Data Quality Assessment in the Wild: Findings from GitHub."</p>
Github code reviewer
Open the record for dataset details and reuse information.
CofeXHug: A curated dataset of HuggingFace pre-trained models exploited in the GitHub ecosystem
<p> Pre-trained models (PTMs) are becoming increasingly popular in the software engineering community. Their usage is facilitated by model repositories, e.g., HuggingFace, which collect, store, and maintain a wide range of PTMs. However, the actual adoption of these models in real-world projects is still an open question. In particular, many of them are used in toy projects or simply as a mirror for the HF repository. Thus, we see the need for a curated codebase related to PTMs to support developers and practitioners who are interested in using them in their projects.<br>This artifact contains CodeXHug, a curated dataset of HuggingFace PTMs exploited in the GitHub ecosystem. Starting from the latest HF dump, we first conduct a data curation to collect PTMs with a tag and a model card. Then, the GitHub platform has been queried to find actual usages of the identified PTMs, resulting in 7,325 different models and 372,063 Python files. We also present a statistical analysis of the dataset, highlighting the most popular PTMs and the most common tasks for which they are used. Finally, we discuss the research opportunities enabled by CodeXHug and the implications of our findings for the software engineering community.</p>
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.