--- license: cc-by-nc-4.0 pretty_name: GitHub Stars in Context language: - en tags: - github - mining-software-repositories - stars - repositories size_categories: - 10M ## Files | File | Rows | Contents | | --- | ---: | --- | | `users.parquet` | 3,671,497 | Account IDs, login, retrieval status, profile-availability flag, follower count | | `repositories.parquet` | 53,990,808 | Owner key, GitHub repository ID, name, creation time, stars | | `portfolios.parquet` | 3,671,497 | Per-account repository count, total and maximum stars, h-index, concentration | | `data-dictionary.md` / `.json` | | Field meanings, types and NULL rules | | `release.json` | | Version, licences, counts, collection dates and file checksums | | `crosswalk.sql` | | View mapping local keys to GitHub IDs, names and URLs | | `PARQUET-SHA256SUMS` | | Checksums of the files above | `repositories.user_id` joins `users.id`. Both are local keys. `users.github_id` and `repositories.repository_id` are GitHub's numeric IDs and join to other resources such as GH Archive. ## Quick start Download the files once (1.57 GiB). This needs the Hugging Face CLI, or use the `curl` commands in the code repository's README: ```sh hf download NaiveNeuron/github-stars-in-context --repo-type dataset --revision v1.1.0 --local-dir data (cd data && shasum -a 256 -c PARQUET-SHA256SUMS) ``` Then query the local files, for example with DuckDB (`pip install duckdb`): ```python import duckdb print(duckdb.sql(""" SELECT count(*) FILTER (WHERE r.stars >= 10) AS kept, count(*) AS total FROM 'data/repositories.parquet' r JOIN 'data/users.parquet' u ON r.user_id = u.id WHERE u.retrieval_status = 'complete' """).fetchall()) # [(1560798, 53987867)] ``` DuckDB can also read the files remotely through `hf://` paths, but a query over the large table sends several thousand requests and can exceed Hugging Face's limit for anonymous access. Download the files, or log in first. ## How it was collected - **Accounts** were found on 8–9 December 2025 through GitHub GraphQL user search, split by follower count (1–9 up to 10,000+) and registration date to stay under the 1,000-result search limit. Organizations are excluded. Accounts with no followers were not searched - **Repositories** were fetched per account from about December 2025 to early January 2026, by the collector author's estimate. The query selects owned, public, non-fork repositories, including archived ones, and follows pagination. Per-request times were not stored - **Follower counts** were fetched from 28 January to 2 February 2026 ## What to know before using it - **Not a random sample of GitHub.** Discovery searched for accounts with at least one follower. Follower counts fetched later can be zero (1,719 accounts). Search cells that still exceeded 1,000 results on a single registration day were skipped, and a failed search request could drop a cell. The number of missed accounts is unknown - **Retrieval status.** `retrieval_status` is `complete` for 3,615,479 accounts (372,821 of them own no stored repository), `failed` for 56,012 and `unknown` for 6. The classes are derived from the collector's logic. They are not an independent check against GitHub - **Missing follower counts.** `profile_available` is false for 1,151,092 accounts whose profile fields are missing or ambiguous. Their `followers_count` is NULL. The rule masks NULL biographies and profiles with only default values, which can include genuinely empty profiles. In a sample of 300 such accounts of the complete class checked on 10 November 2026, 298 still existed and 296 had followers. This supports treating the stored zeros as missing, but it does not show their values in January 2026. The raw stored value is kept in `followers_count_raw` and must not be read as zero followers. Follower analyses cover only the accounts with available profiles, measured after the repositories - **Historical observations.** Star counts are as of retrieval. Per-request times were not stored, so this is not a star history - **Repository IDs are not unique keys.** Twenty repository IDs appear under more than one account - **Stars are not a measure of people.** Do not use this dataset to rank or evaluate individuals ## Personal data The dataset contains only fields that were public on GitHub: account logins and numeric IDs, repository names, creation times and star counts. Logins and numeric IDs can identify people. Numeric IDs are needed to join the tables to other datasets, and logins allow lookup by name. Biographies, company, location and external links were collected but are **not** included. To have an account removed, write to marek@suppa.sk or open an issue in the code repository. Please do not post personal documents in a public issue. We remove the account's rows from all three tables in a new release. Copies that others have already downloaded cannot be recalled. ## Licence The data are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). The licence covers the rights the authors hold in the compilation. It does not grant rights held by GitHub users or other third parties. The code is MIT-licensed. ## Versions - **1.1.0** (2026-11-10): documentation only. The data dictionary is rewritten in plain language, and `release.json` replaces `analysis-manifest.json`. The three Parquet files are byte-identical to 1.0.0 - **1.0.0** (2026-11-10): first public release Each version is a Git tag of this repository (`v1.1.0`, `v1.0.0`). The code repository has matching tags. ## Citation A paper describing the dataset is under review. Until it is published, please cite this dataset page: ```bibtex @misc{pavlov2026githubstars, author = {Pavlov, Mykhailo and {\v{S}}uppa, Marek}, title = {{GitHub} Stars in Context: A Dataset of Repository Popularity Across Accounts}, year = {2026}, version = {1.1.0}, howpublished = {\url{https://huggingface.co/datasets/NaiveNeuron/github-stars-in-context}} } ```