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github-scikit-learn-issues-dataset

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metadata 
pretty_name: GitHub Scikit-learn Issues Dataset
language:
  - en
license: other
size_categories:
  - n<1K
task_categories:
  - text-classification
  - sentence-similarity
tags:
  - github
  - scikit-learn
  - issue-tracking
  - software-engineering
  - open-data
  - text

GitHub Scikit-learn Issues Dataset

1. Overview

This dataset contains 300 issues collected from the public GitHub repository scikit-learn/scikit-learn. It was created for an AI/data assessment (Weeks 9-12) to demonstrate dataset creation, a Transformers pipeline and FAISS semantic search, with documentation of sourcing, limitations and compliance.

2. Why this dataset

GitHub issues are real, human-written technical text (bug reports, feature requests, documentation problems) paired with structured metadata (labels, dates, state, comment counts). This makes them suitable for text classification, sentiment/zero-shot analysis and semantic search for similar issues, and the scikit-learn project is well known, openly developed and well documented.

3. Data collection

  • Platform: GitHub (public issues of scikit-learn/scikit-learn)
  • Collection method: issues were retrieved from the GitHub REST API (https://api.github.com/repos/scikit-learn/scikit-learn/issues) using Python and the requests library, then assembled into a pandas DataFrame, saved as CSV and published to the Hugging Face Hub in Parquet format.
  • Collection date: 12 October 2026
  • Authentication: requests were authenticated with a GitHub personal access token. The token is not included in this dataset or repository.
  • Scope: the dataset contains issues only. No pull requests are included (no record has a /pull/ URL).
  • Selection: the 300 records are the oldest issues in the repository (issue numbers 1 to 768, created between 2010-08-31 and 2012-04-13). They are not a random sample.

4. Dataset structure

Column Type Description
issue_id int GitHub's global issue ID
issue_number int Issue number within the repository
repository string scikit-learn/scikit-learn
title string Issue title
body string Issue description (9 records are empty)
state string closed (295) or open (5)
labels string Comma-separated GitHub labels (empty for 235 records)
created_at datetime When the issue was opened (UTC)
closed_at datetime When the issue was closed (empty for open issues)
comments int Number of comments on the issue (count only; comment text is not included)
author string Public GitHub username of the issue author
html_url string Link to the original issue
text_length int Derived field: length of the issue text (see Section 5)
time_to_close_hours float Derived field: hours between created_at and closed_at (empty for open issues)

5. Processing and augmentation

  • Raw issue records were assembled into a structured table (one row per issue).
  • Pull requests were excluded so that only issues remain.
  • Duplicates: none found (all issue_id, issue_number and html_url values are unique).
  • Derived fields added: text_length and time_to_close_hours.
  • Missing values were kept, not imputed: 9 empty bodies, 235 records without labels, and 5 open issues without closed_at / time_to_close_hours.

6. Initial data summary

Item Value
Rows / columns 300 / 14
Date range 2010-08-31 to 2012-04-13
State 295 closed, 5 open
Issues with labels 65 of 300 (22%)
Most common labels New Feature (21), Enhancement (11), Bug (7)
Comments per issue median 4, mean 7.2, max 59
Time to close median about 1,195 hours (about 50 days); highly skewed (max about 125,700 hours)
Distinct authors 84; the top 3 (amueller, fabianp, mblondel) wrote 132 of the 300 issues

7. Intended uses

  1. Transformers pipeline: apply a pretrained model (e.g. sentiment or zero-shot classification) to titles/bodies and examine the outputs.
  2. Semantic search: embed issue text and build a FAISS index to retrieve similar issues for natural-language queries.

Not intended for: profiling individual contributors, or drawing conclusions about the modern scikit-learn project.

8. Limitations and bias

  • Temporal bias: all issues are from 2010-2012, the earliest days of the project. Topics, labels and writing style differ from current issues, and many early issues were written by a handful of core developers.
  • Small sample: 300 rows, not representative of the repository's full history.
  • Author concentration: a few authors account for a large share of records.
  • Sparse labels: 78% of issues have no label, so label-based analysis covers a minority.
  • Short or empty text: some bodies are empty or very short, which weakens classification and embedding quality.
  • Open vs closed: only 5 issues are open, so resolution-time analysis is dominated by closed issues.
  • Model limitations: general-purpose sentiment models often misread technical text; semantic search quality depends on the embedding model.
  • No comment text: only the number of comments is included.

9. Ethics, legal and compliance

  • Privacy: issues are public, but they include public GitHub usernames and can contain links or occasional email-like text. Usernames have been kept as they appear publicly on GitHub. The dataset is not used to identify or profile individuals.
  • Attribution: content originates from the scikit-learn GitHub project and its contributors; every record links back to its original issue via html_url.
  • Licensing: the scikit-learn software is BSD-3-Clause. This licence is for the software repository and is not assumed to cover user-written issue text, so this dataset is marked license: other and is shared for educational and research purposes only. Users should check GitHub's Terms of Service and the original project's terms before reuse.
  • Terms of service: data was collected from publicly available content through the official GitHub API.
  • Organisational policy alignment: the dataset was collected, stored and documented in line with the organisation's data storage, management and compliance policies provided for this assessment: public source data only, no private data, attribution to the original source, secure handling of credentials, and versioned storage.
  • Security: API tokens are not stored in the notebooks or this repository.

10. Risks and corrective actions

Risk Mitigation
Personal data in public issue text Keep only public handles; document handling; remove or anonymise author if required; do not target individuals
Temporal / sampling bias Document the 2010-2012 window; in future, collect a random or time-stratified sample
Licence ambiguity of user content Mark as other, link back to originals, educational use only
Missing labels / bodies Keep missing values visible, report counts, avoid over-interpreting
Misleading model output Manually review sample predictions and state model limits
Data loss Dataset on the Hugging Face Hub with version history; notebooks and outputs backed up per policy

11. Storage, versioning and reproducibility

  • Published on the Hugging Face Hub (git-based, so every commit is versioned): https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
  • Notebooks: three notebooks (data collection and augmentation, Transformers pipeline, FAISS semantic search) are submitted with this assessment as evidence.
  • Local copy: the CSV and notebooks are kept as a backup copy alongside the project files.
  • Version: v1.0, collected 12 October 2026. Any later changes to the data or processing should be recorded here.

12. References