Update README.md

README.md

@@ -39,14 +39,12 @@ issues, and the scikit-learn project is well known, openly developed and well do
 
 ## 3. Data collection
 
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 - 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's requests library. Results were paginated, pull requests were filtered out, and the selected fields were assembled into a pandas DataFrame and saved as CSV before being published to the Hugging Face Hub.
-- Collection date: 12/10/2026
-- Authentication: [CONFIRM: e.g. personal access token stored as an environment variable, not in the notebook or this 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.
+- 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 in a Google Colab notebook. Requests used `state=all`, 100 records per page, sorted by creation date in ascending order (oldest first), with a short pause between pages. Pages were collected until 300 genuine issues had been gathered, and the result was trimmed to exactly 300. The records were then assembled into a pandas DataFrame, saved as CSV and Parquet, and published to the Hugging Face Hub.
+- Collection date: 12 October 2026. The collection notebook was re-run on 5 November 2026 and returned the same 300 issues and the same summary figures.
+- Authentication: requests were made to the public GitHub API without a personal access token, within GitHub's public rate limits. No credentials were used or stored.
+- Scope: the dataset contains issues only. The GitHub issues endpoint also returns pull requests, so any record containing a `pull_request` field was removed (about half of the early records returned were pull requests). No record in the dataset has a `/pull/` URL.
+- Selection: because results were sorted oldest first, 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
 
@@ -69,12 +67,13 @@ issues, and the scikit-learn project is well known, openly developed and well do
 
 ## 5. Processing and augmentation
 
-- Raw issue records were assembled into a structured table (one row per issue).
+- Raw issue records were assembled into a structured table (one row per issue), keeping 14 selected fields from the many returned by the API.
 - Pull requests were excluded so that only issues remain.
-- Duplicates: none found (all `issue_id`, `issue_number` and `html_url` values are unique).
+- Text cleaning: leading and trailing whitespace was removed from titles and bodies, a missing body was stored as an empty text, and the list of labels was joined into one comma-separated text field.
+- Duplicates: records were de-duplicated on `issue_id` and records with empty titles were removed. No records needed removing (all `issue_id`, `issue_number` and `html_url` values are unique).
+- Validation: the notebook checks that issue IDs are unique, no title is empty, and the row count is in the expected range.
 - 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`.
-- [CONFIRM / EDIT: any other cleaning, e.g. whitespace stripping, label formatting]
 
 ## 6. Initial data summary
 
@@ -109,12 +108,12 @@ Not intended for: profiling individual contributors, or drawing conclusions abou
 
 ## 9. Ethics, legal and compliance
 
-- **Privacy:** issues are public, but they include public GitHub usernames and can contain links or occasional email-like text. [CONFIRM: usernames were kept / anonymised / removed.] The dataset is not used to identify or profile individuals.
+- **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 within GitHub API access limits. [CONFIRM]
-- **Organisational policy alignment:** [INSERT: name the LMS policy documents on data storage, management and compliance you followed, and how.]
-- **Security:** API tokens are not stored in the notebooks or this repository.
+- **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, no credentials needed or stored, and versioned storage.
+- **Security:** no API token or other credential was used, so none is stored in the notebooks or this repository.
 
 ## 10. Risks and corrective actions
 
@@ -130,9 +129,9 @@ Not intended for: profiling individual contributors, or drawing conclusions abou
 ## 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: [INSERT locations, e.g. `01_data_collection_augmentation.ipynb`, `02_transformers_pipeline.ipynb`, `03_faiss_semantic_search.ipynb`, and where they are stored]
-- Local copy: [INSERT location per organisational policy]
-- Version: v1.0 ([INSERT DATE]). Record any later changes to the data or processing here.
+- 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 and re-verified on 5 November 2026. Any later changes to the data or processing should be recorded here.
 
 ## 12. References