Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
207
datasets available to search
ShareScore release 0.7.1
Dataset results
207 results for “github”
88.6 Million Developer Comments from GitHub
<p><em><strong>Description</strong></em></p> <p>This is a collection of developer comments from GitHub issues, commits, and pull requests. We collected 88,640,237 developer comments from 17,378 repositories. In total, this dataset includes:</p> <ul> <li>54,252,380 issue comments (from 13,458,208 issues)</li> <li>979,642 commit comments (from 49,710,108 commits)</li> <li>33,408,215 pull request comments (from 12,680,373 pull requests)</li> </ul> <p><strong>Warning:</strong> The uploaded dataset is compressed from <strong>185GB</strong> down to 25.1GB.</p> <p> </p> <p><em><strong>Purpose</strong></em></p> <p>The purpose of this dataset (corpus) is to provide a large dataset of software developer comments (natural language) for research. We intend to use this data in our own research, but we hope it will be helpful for other researchers.</p> <p> </p> <p><em><strong>Collection Process</strong></em></p> <p>Full implementation details can be found in the following publication:</p> <ul> <li>Benjamin S. Meyers. <a href="https://scholarworks.rit.edu/theses/11609/">Human Error Assessment in Software Engineering.</a> Rochester Institute of Technology. 2023.</li> </ul> <p>Data was downloaded using <a href="https://docs.github.com/en/graphql">GitHub's GraphQL API</a> via requests made with Python's <a href="https://docs.python-requests.org/en/master/">requests</a> library. We targeted 17,491 repositories with the following criteria:</p> <ul> <li>At least 850 stars.</li> <li>Primary language in the Top 50 from the <a href="https://www.tiobe.com/tiobe-index/">TIOBE Index</a> and/or listed as "popular" in GitHub's advanced search. Note that we collected the list of languages on August 31, 2021.</li> </ul> <p>Due to design decisions made by GitHub, we could only get a list of at most 1,000 repositories for each target language. Comments from 113 repositories could not be downloaded for various reasons (failing API queries, <a href="https://docs.python.org/3/library/json.html#json.JSONDecodeError">JSONDecoderErrors</a>, etc.). Eight target languages had no repositories matching the above criteria.</p> <p>After collection using the GraphQL API, data was written to CSV using Python's <a href="https://docs.python.org/3/library/csv.html?highlight=csv%20writer#csv.writer">csv.writer</a> class. We <strong>highly recommend</strong> using Python's <a href="https://docs.python.org/3/library/csv.html?highlight=csv%20writer#csv.reader">csv.reader</a> to parse these CSV files as <strong>no newlines</strong><strong> have been removed</strong> from developer comments.</p> <p> </p> <p><em><strong>88_million_developer_comments.zip</strong></em></p> <p>This zip file contains 135 CSV files; 3 per language. CSV names are formatted <em><code>_<language>.csv</em>, with <em><language></em> being the name of the primary language and <em><code></em> being one of <em>co</em> (commits), <em>is</em> (issues), or <em>pr</em> (pull requests).</p> <p>Languages included are: ABAP, Assembly, C, C# (C-Sharp), C++ (C-PlusPlus), Clojure, COBOL, CoffeeScript, CSS, Dart, D, DM, Elixir, Fortran, F# (F-Sharp), Go, Groovy, HTML, Java, JavaScript, Julia, Kotlin, Lisp, Lua, MATLAB, Nim, Objective-C, Pascal, Perl, PHP, PowerShell, Prolog, Python, R, Ruby, Rust, Scala, Scheme, Scratch, Shell, Swift, TSQL, TypeScript, VBScript, and VHDL.</p> <p>Details on the columns in each CSV file are described in the provided <em>README.md</em>.</p> <p> </p> <p><em><strong>Detailed_Breakdown.ods</strong></em></p> <p>This spreadsheet contains specific details on how many repositories, commits, issues, pull requests, and comments are included in <em>88_million_developer_comments.zip</em>.</p> <p> </p> <p><em><strong>Note On Completeness</strong></em></p> <p>We make no guarantee that <em><strong>every</strong></em> commit, issue, and/or pull request for each repository is included in this dataset. Due to the nature of the GraphQL API and data decoding difficulties, sometimes a query failed and that data is not included here.</p> <p> </p> <p><em><strong>Versioning</strong></em></p> <ul> <li><strong>v1.1:</strong> The original corpus had duplicate header rows in the CSV files. This has been fixed.</li> <li><strong>v1.0:</strong> Original corpus.</li> </ul> <p> </p> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p> </p> <p><em><strong>Acknowledgments</strong></em></p> <ul> <li>Collection of this data has been sponsored in part by the National Science Foundation grant 1922169, and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</li> <li>This data was collected using the compute resources from the <a href="https://www.rit.edu/researchcomputing/">Research Computing</a> department at the Rochester Institute of Technology. <a href="https://doi.org/10.34788/0S3G-QD15">doi:10.34788/0S3G-QD15</a></li> </ul>
Mutation Testing in the Wild: Findings from GitHub
<p>Supplementary material for the articule "Mutation Testing in the Wild: Findings from GitHub" submitted to the Empirical Software Engineering Journal. It includes:</p> <ul> <li>Literature_search_for_mutation_tools_2018-2021.xlsx. This document contains the papers found in the search for mutation testing tools performed between 2018 and 2021.</li> <li>Mutation_tools.xlsx: This document includes the 127 mutation testing tools identified in our study along with tables and graphs.</li> <li>Repositories_raw_data.xlsx: All repositories data mined from Github as evidence of use of the top 10 mutation tools analyzed in our study. In addition, tables with calculations and graphs used in our work are included.</li> <li>Search_strings_mutation_tools.xlsx: The search strings used to look for repositories using each mutation tool in Github.</li> </ul>
An Empirical Study on the Survival Rate of GitHub Projects
<p>This zip provides the replication package for the paper titled <em>An Empirical Study on the Survival Rate of GitHub Projects</em> sent to the <em>Mining Software Repositories 2022</em> conference, currently under review.</p>
GitHub project dataset for license analysis
<p>This dataset consists of a number of GitHub repositories that cover the following programming languages: PHP, Java, JavaScript, C, C++, C#, Python, Visual Basic. The repositories were used by license extraction tools, i.e. FOSSology Nomos, Ninka, to see which open source software licenses exist in the source code. They were also used to perform analysis on the README.md file and discover licenses used in libraries as described in the README.</p>
SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 2b Introduction to Github
<p>Teaching data for practical session: "2b Introduction to GitHub" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022).</p> <p>See: <a href="https://spaam-community.github.io/wss-summer-school/#/2022/">https://spaam-community.github.io/wss-summer-school/#/2022/</a> or <a href="https://doi.org/10.5281/zenodo.6976711">https://doi.org/10.5281/zenodo.6976711</a> for slides.</p> <p>Once downloaded, run:</p> <pre><code class="language-bash">tar xvfz <session>.tar.gz</code></pre> <p> to decompress the data directory for the session.</p> <p> </p>
Autonomy Is An Acquired Taste: Exploring Developer Preferences for GitHub Bots
<p>Software bots are an important part of software development, and the rise of AI-based code tools will make them even more important. Software bots can do many tasks, and can exhibit a range of interaction behaviors in a project. But in order for the bot to be effective, it must be accepted by the developers and project community with which the bot interacts. The main purpose of this study is to enumerate some of these factors, and to explain what leads to perceived bad behavior in the context of a bot's autonomous actions and persona. We find developers prefer bots which are personable but show little autonomy. This dataset contains the replication package of the study. For Phase I, we shared the full and detailed interview guide, anonymized interview transcripts, and the result of the data analysis, i.e., codebook. For Phase II, the survey design and data. <br> </p>
GitHub repositories for Enterprise Resource Planning (ERP) systems
<p>This dataset contains the main data and results of an analysis of open source ERPs (Enterprise Resource Planning) found in GitHub repositories.</p> <ul> <li><em>erp-repositories-ALL-short-filtered-v2.csv</em> is the main dataset file containing information collected for repositories using the GitHub Search API (11,500 relevant repositories after applying filtering criteria). It is also the main file that can be used for replication purposes of the reserch work.</li> <li>The remaining files contain results from the analysis on the dataset.</li> </ul> <p>The relevant paper from the 2024 International Conference on Software and Systems Reuse (ICSR) can be used for citing this work: The current status of open source ERP systems: a GitHub analysis, by Georgia M. Kapitsaki and Maria Papoutsoglou.</p>
Data to accompany the outlier-waveform-detection Github repository (internal globus pallidus, GPi)
<p>This repository contains data based on neuronal recordings from two monkeys (G and I, in the pre- and post-MPTP states) that serve as input to the code provided at <a href="https://github.com/turner-lab-pitt/outlier-waveform-detection">https://github.com/turner-lab-pitt/outlier-waveform-detection</a>. Text files located within that Github repository provide detailed instructions on how these data may be used with that code. As described in those text files, extra data are provided for Monkey G, in the pre-MPTP state.</p> <p>The data-description.txt file provides detailed information regarding the contents of each zipped tar archive. Briefly, the most important components of the files are the "snips" (individual spike waveforms) from the two monkeys and MPTP states, as extracted for each of a series of single sorted units from the internal globus pallidus (GPi). The additional G-Pre data provides examples of the high-pass filtered voltage signals from which these snips were extracted. All data are stored in the Matlab .mat format.</p> <p>All zipped files can be decompressed with 7-zip: <a href="https://www.7-zip.org/" target="_blank" rel="noopener">https://www.7-zip.org/</a></p> <p>These data and the associated Github code were used for analyses reported in an in-preparation manuscript (Kase et al., "Movement-related activity in the internal globus pallidus of the parkinsonian macaque"), and also with a preprint that is currently under review:</p> <div> <div>Detecting rhythmic spiking through the power spectra of point process model residuals</div> </div> <div>Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner</div> <div>bioRxiv 2023.09.08.556120; doi: <a href="https://doi.org/10.1101/2023.09.08.556120" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.08.556120</a></div> <div> </div> <p>This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset. </p>
Replication Package for "Catching Smells in the Act: A GitHub Actions Workflow Investigation" (SCAM 2024)
<p>Welcome to our artifact! In here we provide additional information on how to retrace our steps performed during the research. We have split up our content into four sections based on the RQ's we have answered. Below you can find a quick summary of the contents of each folder, each folder also contains additional information regarding any data and scripts present.</p> <ul> <li>RQ1 + 2: Contains excel files with the commits we have analyzed and the scripts we have used to automate this process.</li> <li>RQ3: Contains our smell detector and evaluation of the detector</li> <li>RQ4: Contains the data on our contribution study</li> </ul>
hscScore github repository input files
<p><strong>Summary:</strong> Data and annotation files for input to the hscScore github repository https://github.com/fionahamey/hscScore</p> <p><strong>File descriptions</strong><strong>:</strong></p> <ul> <li>ensembl_gene_table_81.txt - Table downloaded from http://jul2015.archive.ensembl.org/index.html with gene name, gene ID and gene type information for mouse genes</li> <li>gene_conversion_10x.csv - Table mapping gene name to ensembl ID for 10x genomics single-cell RNA-sequencing data</li> <li>nestorowa_htseq_counts_all_cells_gene_names_renamed.txt - Gene expression counts for cells from Nestorowa et al (2016) (doi: https://doi.org/10.1182/blood-2016-05-716480)</li> <li>table_S3_model_selection_cv_scores.txt - Results of GridSearchCV for https://github.com/fionahamey/hscScore/blob/master/hsc_score_parameter_search.py</li> <li>wilson_HTSEQ_results.txt - HTSeq count results for data from Wilson et al (2015) (https://doi.org/10.1016/j.stem.2015.04.004)</li> <li>wilson_nomo_molo_genes.csv - List of MolO and NoMO genes from Wilson et al (2015) (https://doi.org/10.1016/j.stem.2015.04.004)</li> <li>wilson_rna_seq_hsc_scores.csv - MolO score prediction from Wilson et al (2015) (https://doi.org/10.1016/j.stem.2015.04.004) using random forest classifier</li> </ul>
No lake left behind: Lake protection in the continental US (github repository)
<p>This is a snapshot of a github repository (https://github.com/cont-limno/FreshwaterConservation) associated with a manuscript on representation of lakes in US protected areas with respect to international conservation targets. In 2010, the Convention on Biological Diversity stated that by 2020 "at least 17 percent of terrestrial and inland water areas, and 10 percent of coastal and marine areas, especially areas of particular importance for biodiversity and ecosystem services, are conserved through effectively and equitably managed, ecologically representative and well connected systems of protected areas and other effective area-based conservation measures, and integrated into the wider landscapes and seascapes." (Aichi target 11)</p>
Investigando o Uso da Inteligência Artificial em Projetos Python Hospedados no GitHub
<p>This dataset was utilized in the research paper titled "Investigando o Uso da Inteligência Artificial em Projetos Python Hospedados no GitHub," accepted for publication in the 12th Workshop on Software Visualization, Evolution, and Maintenance. This study aims to explore the presence and usage of artificial intelligence libraries in Python projects hosted on GitHub.</p> <p>To achieve this, we developed a Python script that analyzes repositories listed in a CSV file, checking for dependencies related to AI. The script utilizes GitHub’s API to access repository data and inspect `requirements.txt` files for mentions of AI libraries. The dataset includes information on repositories that use specific AI libraries, such as TensorFlow, PyTorch, and scikit-learn. By examining these dependencies, the study provides insights into how frequently and in what contexts these libraries are used in real-world Python projects. This research contributes to understanding the adoption of AI technologies in software development and supports practitioners in identifying relevant AI tools in the Python ecosystem.</p>
MACHINE LEARNING ALGORITHMS FOR ANOMALY DETECTION IN PUBLIC DATA USING GITHUB AS AN EXAMPLE
<p>This study explores the application of machine learning algorithms for detecting anomalies in GitHub data to enhance the evaluation of technological projects. The research aims to develop a robust methodology for identifying data anomalies, such as artificial activity spikes, that can distort project assessments. Methods such as Isolation Forest, One-Class SVM, and advanced deep learning techniques like autoencoders and GANs are employed to analyze and identify irregular patterns in GitHub repositories. The findings demonstrate that these algorithms effectively detect both obvious and subtle anomalies, offering reliable insights into project authenticity. The proposed conceptual model integrates these methods into a scalable system, enhancing transparency and accuracy in technological project evaluation. The novelty of this work lies in its comprehensive approach to analyzing GitHub data, combining traditional and deep learning techniques to improve the reliability of assessments, making it a significant contribution to the field.</p>
GitHub Open Source Survey 2024
<p>Data and accompanying documentation for the Open Source Survey fielded by GitHub and collaborators in 2024. Respondents were sourced via random sampling from traffic to licensed open source repositories on GitHub.com. A report based on the subset of responses sampled from GitHub.com is available at http://opensourcesurvey.org/2024/. </p> <p>See the README for details on sampling methodology, and notes and questionnaire files for question wordings, response options, branching logic, and recoded variables.</p>
GitHub Issue Dataset From Top Repositories of Top Languages
<p>GitHub issue dataset from top 200 most popular repositories associated with top 55 programming languages. The language ranking used for this work is available at: <a href="https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2020">https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2020</a></p> <p>Original work utilizes this dataset to classify issue reports into respective categories: Available at <a href="https://github.com/ansnadeem/aic">https://github.com/ansnadeem/aic</a></p> <p>The original work also appeared in ISSRE'21 titled '<strong>Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports</strong>'. Please consider citing our work if you use this dataset.</p>
Empirical Study on Test Generation Using GitHub Copilot --- Replication Package
<p>This replication package contains the data and scripts used in the "Empirical Study on Test Generation Using GitHub Copilot" thesis. </p>
Quality Assurance Awareness in Open Source Software Projects on GitHub Analysis Dataset
<p>Dataset for the paper "Quality Assurance Awareness in Open Source Software Projects on GitHub", submitted to the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM), 2023.</p>
Angular GitHub Commits Object-centric Event Log
<p><strong>Overview</strong></p> <p>This real-world object-centric event log in the OCEL 2.0 standard contains an extraction of the commit information from the <a href="https://github.com/angular/angular">GitHub repository</a> used to developed the <a href="https://www.angular.io/">Angular</a> platform. A single code commit in the repository is abstracted to one event in the log. The dataset contains essential information for each commit, such as the timestamp and the contributor's details. Crucially, commit information is connected to two classes of objects: the file(s) affected by the commit, and the branch(es) in the repository containing the commit.</p> <p><strong>Description</strong></p> <p>GitHub, a popular platform for developers offering the functionalities of the Git versioning system, allows to record single modifications to software projects by contributors; such modifications are grouped in units called <strong>commits</strong>. Commits contain all details of the edits operated on a group of files in the projects. Therefore, all commits of a project constitute a ledger, that allows to rewind or fast-forward all contributions in the project.</p> <p>Commits in a project are arranged in <strong>branches</strong>, which form a tree-like structure. A contributor may create a new branch, essentially a copy of the project, in order to commit modifications safely. Once the contributor is satisfied with the edits, they may <strong>merge</strong> their new branch back into the pre-existing branch (realized by applying the modifications of all the new commits sequentially, and then solving the conflicts that may arise).</p> <p>This log contains an extraction of the commit information of the <a href="https://www.angular.io/">Angular</a> project on <a href="https://github.com/angular/angular">GitHub</a>. The abstraction level is such that every commit corresponds to an event in the log.</p> <p>For each event, the following information is recorded:</p> <ul> <li>a unique identifier (<strong>hash</strong>)</li> <li>the author's timestamp of the commit (includes timezone information)</li> <li>an <strong>activity label</strong>: the Angular project conforms to the <a href="https://www.conventionalcommits.org/">Conventional Commits</a> initiative, which mandates commit messages containing an initial identifier. This helps to reconstruct a clean activity notion. Some of the labels have been cleaned by hand (for instance, in case of typos)</li> <li>the message of the commit</li> <li>the contributor's name</li> <li>the contributor's email (<strong>resource</strong>)</li> <li>a <strong>merge</strong> flag; <strong>True</strong> if the commit is a merge, <strong>False</strong> otherwise</li> <li>information related to the <strong>files</strong> edited by the commit (in case of renames, we track the new name)</li> <li>information related to the <strong>branches</strong> in which the commit appears</li> </ul> <p>Files and branches are two distinct object types in this log. Note that a commit might not be associated to any file. Conversely, a commit always appears in at least one branch.</p> <p>This event log has been extracted with the help of <a href="https://github.com/ishepard/pydriller">PyDriller</a>.</p> <p><strong>Properties</strong></p> <p>This event log has the following properties:</p> <table> <tbody> <tr> <td><strong>Property</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Events</td> <td>27847</td> </tr> <tr> <td>Activity Labels</td> <td>67</td> </tr> <tr> <td>Object Types</td> <td>2</td> </tr> <tr> <td>Objects (files)</td> <td>35392</td> </tr> <tr> <td>Objects (branches)</td> <td>119</td> </tr> </tbody> </table> <p><strong>Get started</strong></p> <p>Download the dataset, and position it in the folder of your Python script or console.</p> <p><em>pip install pm4py</em></p> <p>To manipulate object-centric logs programmatically, use the functionality of the <em>ocel</em> package <a href="https://pm4py.fit.fraunhofer.de/static/assets/api/2.7.5.1/api.html#object-centric-process-mining-pm4py-ocel">in the PM4Py library</a>. Additionally, check out the <a href="https://www.ocel-standard.org/beta/tool-support/overview/">tool support</a> for object-centric event logs!</p> <p><em>from pm4py import ocel</em></p> <p><strong>Acknowledgements</strong></p> <p>We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p>
QCD Uncertainties in Particle Spectra from Dark Matter Annihilation (updated data can be found in GitHub: https://github.com/ajueid/qcd-dm.github.io.git)
<p>************************************************************************************************</p> <p>QCD Uncertainties on Particle Spectra from Dark Matter Annihilation</p> <p><strong>Please check the updated data at GitHub: https://github.com/ajueid/qcd-dm.github.io.git</strong></p> <p>Authors: Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, and Peter Skands</p> <p>If you use these tables, please cite:</p> <p>S. Amoroso et al. arXiv: 1812.07424 [hep-ph], JCAP05(2019)007</p> <p>************************************************************************************************</p> <p>We provide the spectra of stable particles in dark matter annihilation, in the galactic region or beyond, in a tabulated form using PYTHIA8 version 8235. In addition to the central prediction, we estimate for the first time the QCD uncertainties both due to hadronization as well as to showering. The uncertainties on the spectra are provided in separate tables. A wide range of dark matter masses from 10 GeV to 100 TeV is covered. We consider 11 primary annihilation channels:</p> <p>DM DM -> e+e-, mu+ mu-, tau tau, qq (q=u,d,s), cc, bb, tt, WW, ZZ, gg, and hh.</p> <p>Each file contains 13 columns: the dark matter mass, the fraction x -- defined as the kinetic energy of the particle divided by the DM mass -- in the logarithmic scale, and dN/dLog_10(x) for 11 primary channels. The provided tables correspond to the dN/dLog_10(x) of Standard Model stable particles, i.e. of photons, positrons, electron anti-neutrinos, muon anti-neutrinos and tau anti-neutrinos.</p> <p>The work on the spectra of anti-protons is ongoing (please come back soon). </p> <p>For each particle species, we provide twelve tables which can be found in zip format. The notation of the different tables is given below:</p> <p> 1) The table corresponding to the central prediction for the spectra is denoted by 'AtProduction-Hadronization1-$TYPE.dat' with $TYPE=Nuel, Numu, Nuta, Ga which refers to the three flavours of neutrinos, and photons respectively.</p> <p> 2) There are nine tables corresponding to the different variations of the light quark fragmentation function's parameters. These tables are denoted by 'AtProduction-Hadronization$h-$TYPE.dat' with h=2,..,10.</p> <p> 3) The particle spectra corresponding to the variations of the shower evolution scale (mu_R) are denoted by 'AtProduction-Shower-Var$s-$TYPE.dat' with s=1,2 corresponds to 1/2 mu_R and 2 mu_R. </p> <p><em><strong>IMPORTANT:</strong></em></p> <p> i) Uncertainty on the spectra, from hadronization, is obtained from the envelope of all the variations (including the central prediction).</p> <p> ii) In the variations of the parton shower evolution scale, the parameters of the hadronization function are fixed to their central value.</p> <p> iii) In principle, showering uncertainties are uncorrelated to hadronization uncertainties. To obtain the full uncertainty, one might combine those uncertainties in quadrature.</p> <p><em><strong>If you use the data on the site, please cite:</strong></em></p> <p>Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, Peter Skands, "Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches," <strong>JCAP 05 (2019) 007</strong>, arXiv: 1812.07424.</p> <p><em><strong>In addition, if you use the data corresponding to shower uncertainties, please cite:</strong></em></p> <p>S. Mrenna and P. Skands, "Automated Parton-Shower Variations in Pythia 8,'' <strong>Phys. Rev. D 94 (2016) no.7</strong>, 074005, arXiv:1605.08352 [hep-ph].</p> <p><em><strong>Finally, please cite the paper of M. Cirelli et al. if you use their data for comparison or other tasks:</strong></em></p> <p>M.Cirelli, G.Corcella, A.Hektor, G.Hütsi, M.Kadastik, P.Panci, M.Raidal, F.Sala, A.Strumia, "PPPC 4 DM ID: A Poor Particle Physicist Cookbook for Dark Matter Indirect Detection'', <strong>JCAP 1103 (2011) 051</strong>, arXiv 1012.4515, Erratum: <strong>JCAP 1210 (2012) E01</strong>.</p> <p>Contact: <em>Adil Jueid</em> <adil.hep@gmail.com></p>
Dataset - How do you propose your code changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub
<p>This package contains the raw open data for the study </p> <p>Marco Ortu, Giuseppe Destefanis, Daniel Graziotin, Michele Marchesi, Roberto Tonelli. 2020. How do you propose your code changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub. Under Review.</p> <p>The dataset is based on GHTorrent dataset:</p> <p>Georgios Gousios. 2013. The GHTorent dataset and tool suite. In Proceedings of the 10th Working Conference on Mining Software Repositories (MSR ’13). IEEE Press, 233–236</p> <p>And released with the same license (CC BY-SA 4.0).</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.