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.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “Git commits”
452,000,000 public Git commits on GitHub (October 2016)
<p>What's inside</p> <p>part-000xx.lzo - LZO archives with the data (refer to "Format").</p> <p>part-000xx.lzo.index - LZO index files so that the archives are splittable in Hadoop.</p> <p>stats.csv.gz - GZIP-ed CSV file with some repository statistics related to the commits.</p> <p>Format</p> <p>part-000xx - text, one line per repository, every line is JSON with the following scheme:</p> <p>{ "r": "repository name", "c": [{ "h": "git hash", "a": "author's email hash", "t": "date and time commit was created", "m": "commit message" }, ...] }</p> <p>Date and time format is <em>mostly</em> Go language's time.Time.String(), I recommend to use dateutil.parse() to parse it with Python.</p> <p>Commit message contains explicit \r and \n symbols in order to be a single line.</p> <p>stats.csv has 4 columns: repository name, number of commits, number of contributors, average length of the commit messages.</p>
GAP: Forecasting Commit Activity in git Projects
<p>This entry contains the replication package for our paper <em>GAP: Forecasting Commit Activity in git Projects</em> accepted for publication in Journal of Systems and Software.</p> <p>Abandonment of active developers poses a significant risk for many open source software projects. This risk can be reduced by forecasting the future activity of contributors involved in such projects. Focusing on the commit activity of individuals involved in git repositories, this paper proposes a practicable probabilistic forecasting model based on the statistical technique of survival analysis. The model is empirically validated on a wide variety of projects accounting for 7,528 git repositories and 5,947 active contributors. We found that a model based on the last 20 observed days of commit activity per contributor provides the best concordance. We also found that the predictions provided by the model are generally close to actual observations, with slight underestimations for low probability predictions and slight overestimations for higher probability predictions. This model is implemented as part of an open source tool, called gap, that predicts future commit activity.</p> <p><strong>Replication package</strong></p> <p>The model is explained and defined in <em>"notebooks/Survival analysis.ipynb"</em>. This is a Jupyter notebook created with Jupyter Lab. The dependencies required to run this notebook are listed in <em>requirements.txt</em> and can be automatically installed using <code>pip install -r requirements.txt</code>. Consider making use of a virtual environment to ensure a proper replication of the analyses.</p> <p>The data used to validate the model can be found in <em>data/cargo.csv.gz</em>. They were produced with the script <em>data/convert.py</em> that requires file <em>data-raw/cargo_all_proj_commits_id.csv.gz</em>. This file was created by retrieving all the commits of all projects hosted on github that are related to a project distributed on Cargo. To identify such projects, we relied on libraries.io dataset. GitHub API was then queried to obtain the username of each author (if available) to allow some basic identity merging task. Data about repositories were extracted from libraries.io 1.4.0 dataset and can be found in <em>data/repositories.csv.gz</em>.</p> <p><strong>The GAP tool</strong></p> <p>GAP is made available on <a href="https://github.com/AlexandreDecan/gap">https://github.com/AlexandreDecan/gap</a></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.