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207 results for “github”
Vignette dataset for RWireX (GitHub)
<div> <p>This repository contains a dataset for a vignette of the RWireX software, which is available via Github at <a href="https://github.com/RippeLab/RWireX">https://github.com/RippeLab/RWireX</a>. RWireX is a computational tool for analyzing co-accessibility in single-cell ATAC-seq data, implemented as an extension of the ArchR package. It offers two workflows: a single-cell approach to identify autonomous links of co-accessibility (ACs) and a metacell approach to detect domains of contiguous co-accessibility (DCs). With the dataset provided in this archive, both workflows and the associated visualization can be conducted as described in the <a href="https://github.com/RippeLab/RWireX/wiki">RWireX wiki</a>.</p> </div>
Structured Information on State and Evolution of Dockerfiles on GitHub
<p>Docker containers are standardized, self-contained units of applications, packaged with their dependencies and execution environment. The environment is defined in a Dockerfile that specifies the steps to reach a certain system state as infrastructure code, with the aim of enabling reproducible builds of the container. To lay the groundwork for research on infrastructure code, we collected structured information about the state and the evolution of Dockerfiles on GitHub and release it as a PostgreSQL database archive (over 100,000 unique Dockerfiles in over 15,000 GitHub projects). Our dataset enables answering a multitude of interesting research questions related to different kinds of software evolution behavior in the Docker ecosystem.</p>
Commits modifications from 61 projects hosted in Github's repositories
<p>This dataset contains data from 61 opensource projects that can be found in github repositories.The data contains informations about:</p> <ul> <li>committer name</li> <li>committer email</li> <li>modification type - changed, removed or created</li> <li>modified files</li> <li>commit comments</li> <li>commit date</li> </ul> <p> </p> <p>This dataset references 299 major version software, developed in five different programming languages. These languages are:</p> <ul> <li>C++</li> <li>Java</li> <li>Javascript</li> <li>Python</li> <li>Ruby</li> </ul> <p>The dataset was used in my undergraduate thesis</p>
Conceptual Coupling Values - Github Projects Dataset
<p>A Dataset that contains conceptual coupling measures obtained on 61 opensource projects hosted in github.The conceptual coupling measure was calculated by 18 diferent ways. A way is called model. There are 299 software major version colected to calculate these values.</p> <p>To understand how each model was applyed, see the label meaning:</p> <ul> <li>Yco: Use code comment</li> <li>Nco: No use code comment</li> <li>Yca: Divide tokens by CamelCase</li> <li>Nca: no divide tokens by CamelCase</li> <li>Ns: no stemming tokens low frequency occurrence</li> <li>Ys: stemming tokens low frequeny occurrence</li> <li>Qth: remove third quantile </li> <li>Qfi: remove first quantile</li> <li>Lsi: Latent Semantic Indexing</li> <li>TFIDF: term frequency - inverse document frequency</li> <li>Naive: Term-Frequency</li> </ul> <p>All projects are from github and developed in the following language:</p> <ul> <li>C++</li> <li>Java</li> <li>Javascript</li> <li>Python</li> <li>Ruby</li> </ul> <p>This dataset also contains the following resources:</p> <ul> <li>Commits between those project versions</li> <li>List of versions used in this study</li> <li>Scripts used in some works</li> </ul>
Code diffs and commit messages from top1000-2000 Java projects in GitHub
<p>This dataset contains pairs <code changes, commit message> from top 1000-2000 Java projects in GitHub via <a href="https://developer.github.com/v3/">GitHub Developer API</a>.</p> <p>The structure of files is as follows:</p> <ul> <li><repository-id>.<repository-name>/ # Each directory contains commits of one project. <ul> <li><commit-id>.<commit-sha>/ # Each directory contains one commit information, and<br> # the commit-sha is the primary key of this commit in GitHub. <br> # The children in this directory have 3 types: the file<br> # "commit_msg", multiple directories "<filename>", and the file<br> # "error". <ul> <li>commit_msg # This file contains one line, representing the commit<br> # message. </li> <li><filename>/ # The name of this directory is the changed file name. <ul> <li>patch # The code diffs of this <filename>, describing<br> # which lines are added and which lines are<br> # removed with some same lines context.</li> </ul> </li> <li><filename>/ <ul> <li>patch</li> </ul> </li> <li>error # This file contains the error message when crawling<br> # from GitHub. If this file exists, the directories<br> # <filename> will not exist. </li> </ul> </li> </ul> </li> </ul>
Macro-scale analysis of biodiversity-ecosystem functioning relationships in lakes (github repository)
<p>National Lakes Assessment biodiversity-ecosystem functioning relationships across the continental United States. This is an archived version of a github repository, containing data and R code. The repository can also be found online https://github.com/cont-limno/NLA-Diversity-.</p>
Data supporting CeresBarros/TrophicNetRobWF GitHub repo
Open the record for dataset details and reuse information.
Introduction to Publishing Quarto Pages on GitHub
<p>This session walks through how to publish quarto pages using Visual Studio Code and GitHub. Several concepts are walked through, including webpage basics, branches, and pull requests. The demonstration also includes an overview of how TOPTSCHOOL promotes open science, the data science lifecycle, and how to apply what is learned in the TOPSTSCHOOL modules to different topics. The video can also be accessed through YouTube by clicking <a href="https://www.youtube.com/watch?v=t-rsP_xG5Bo" target="_blank" rel="noopener">here</a>.</p>
GitHub Pull Request Demonstration by Alexandr Smagin
<p>In this demonstration, NLU student Alexandr Smagin walks us through pull requests for the TOPS SCHOOL GitHub repository. You can watch the video below or find a link in the 'Additional details' section.</p>
MCMC Traces for GitHub repo "JuBiotech/petase-paper"
<p>Additional MCMC data to re-run analyses from https://github.com/JuBiotech/petase-ts-paper. To use the existing notebooks, please clone the GitHub repository and download and unzip this folder. Afterwards, merge this dataset and its folder structure with the existing folder "data_analysis" of the repo.</p>
Repository contents of 5 GitHub Collections
<p><a href="https://github.com/collections" target="_blank" rel="noopener">Github Collections</a> are a section within the GitHub platform where repositories are collected and organized into thematic collections curated by GitHub.</p> <p>These collections group popular or prominent projects within specific areas such as Machine Learning, Web Development, Data Science, Computer Security, and many other categories of technological interest. The goal is to provide users with repository data that allows to analyze different metrics, such as repository size, filetype used or popularity, among others.</p>
GREED: GitHub Repositories and Descriptions
<p><strong>GREED </strong>is dataset with metadata extracted from GitHub repositories (data collected through GitHub REST API) with Python and Jupyter Notebook as programming languages, more than five stars, and created in 2019 and 2020. In the <strong>final </strong>folder, the repositories descriptions are cleaned and filtered using natural language processing (NLP) techniques. The data is separated by programming language and year.</p> <p>In summary, this dataset contains:</p> <ul> <li><strong>102,358 </strong>repositories (<strong>original</strong>); <strong>58,911 </strong>repositories (<strong>final</strong>)</li> <li><strong>2 </strong>programming languages</li> <li><strong>2 </strong>creation years </li> </ul>
DocMine: A Software Documentation-Related Dataset of 950 GitHub Repositories
<p>DocMine dataset consists of textual information collated from multiple software artifacts, across 950 GitHub Repositories. It also consists of probable percentage contribution of text in each software artifact towards different documentation types in each repository, accompanied by metadata information about the repository such as stargazer count, number of pull requests, commits, issues and other files analyzed.</p>
Replication Package for the paper: "Caracterizando a evolução de software de contratos inteligentes: Um estudo exploratório-descritivo utilizando GitHub e Etherscan"
<p>This is the replication package for the paper "Caracterizando a evolução de software de contratos inteligentes: Um estudo exploratório-descritivo utilizando GitHub e Etherscan". The paper was originally published in the VEM workshop, the prime Brazilian workshop for software Visualisation, Evolution and Maintance.</p> <p> </p> <p>The source code we used to perform the study is available at: https://github.com/gesid/smart-contracts-software-evolution. Please note that the current version of the code on Github may have evolved from the time we first ran the study. For the exact version of the code used to run the study, please refer to the code provided in this replication package.</p> <p> </p> <p>We provide a single .zip file with a few folders inside.</p> <p>- etherscan_contracts: the source code files for the smart contracts as obtained from Etherscan. We provde both the original versions and the ones without comments;</p> <p>- github_data: the source code files as extracted from the smart contracts' repositories on github;</p> <p>- repositories_insights: our data analysis and main results;</p> <p>- research_source_code: the code we used to run our mining, preprocessing, analyses etc;</p> <p>- results_combinations: the similarity values for the combination of .sol files compared to the Etherscan files.</p>
Readme files in 16,000,000 public GitHub repositories (October 2016)
<p>Format</p> <p>index.csv.gz - CSV comma separated file with 3 columns: <repository name>, <flag>,<readme file name> For example: src-d/go-git,s,README.md</p> <p>The flag is either "s" (readme found) or "r" (readme does not exist on the root directory level). Readme file name may be any from the list:</p> <p>"README.md", "readme.md", "Readme.md", "README.MD", "README.txt", "readme.txt", "Readme.txt", "README.TXT", "README", "readme", "Readme", "README.rst", "readme.rst", "Readme.rst", "README.RST"</p> <p>100 part-r-00xxx files are in "new" Hadoop API format with the following settings:</p> <ol> <li> <p>inputFormatClass is org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat</p> </li> <li> <p>keyClass is org.apache.hadoop.io.Text - repository name</p> </li> <li> <p>valueClass is org.apache.hadoop.io.BytesWritable - gzipped readme file</p> </li> </ol>
Sets of mutually similar public GitHub repositories (October 2016)
<p>The format is JSON, the list of lists. Each list is the group of very similar repositories (Weighted Jaccard Similarity threshold 0.8~0.9).</p>
Programming language keyword frequencies extracted from 16,000,000 public GitHub repositories (October 2016)
<p>Origin</p> <p>16,000,000 repositories on GitHub as of October 2016, classified with github/linguist and parsed with Pygments. Token.Keyword tokens were filtered and MapReduce-d. Fuzzy duplicate repositories were discarded.</p> <p>Some languages, e.g. Haskell, are parsed wrong, resulting in <strong>many</strong> keywords. Still they were not removed since we are not familiar with such languages.</p> <p>Format</p> <p>Triples [language name]\t[keyword]\t[frequency]</p> <p>Tabs and new lines in keywords are escaped as \t and \n respectively.</p>
Do Developers Present Proficient Code Snippets in Their README Files? An Analysis of PyPI Libraries in GitHub
<p><strong>Abstract:</strong><br> A README file plays an essential role as the face of a software project and the initial point of contact for developers in Open Source Software (OSS) projects. The code snippet is one of the most important contents in the README file for demonstrating the usage of software and APIs. Presenting easy to comprehend code snippets are preferred by developers to quickly understand the usage and features of the software. However, README files may contain proficient code snippets due to readability and performance reasons. In this paper, we first investigate the prevalence of each competency level of Python code snippets in the README files. Then, we analyze the relationships between the usage of proficient code snippets and topics of libraries. From our empirical study on 1,620 README files of PyPI libraries, we find that developers mainly present 92% of basic elements and 7% of independent elements in code snippets of the README files. However, developers may present proficient elements in code snippets for Application Framework, Quality Assurance, and User Interface libraries. Thus, we encourage developers to (i) write basic README files to attract more newcomers or (ii) write proficient README files to introduce effective usage of PyPI libraries.</p>
[Github Sample Dataset] Microbiota dictate T cell clonal selection to promote graft-vs-host disease after stem cell transplantation
<p>[Github Sample Dataset]. This is an accompanying data set for the github repository "<em>https://github.com/acyeh-lab/2023/tree/main/tcr-simulation" referred to in "</em>Non-genetic determinants of clonotypic T cell expansion following stem cell transplantation" by Yeh AC et al.</p>
Open-source Software Governance Documentation Dataset on GitHub
<p>This dataset contains 710 GitHub-hosted OSS projects, which contain a governance file in the root directory of the project. It also contains commits, issues, and comments on each project.</p>
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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.