Repository visibility
The project evaluates how stars and forks are distributed across repositories and whether a small number of projects dominate attention.
Overview
What this project investigates
Maintenance signals
It studies active lifespan, commit activity, and repository longevity to understand how maintenance patterns vary.
Documentation and quality
It compares README coverage and community health metrics to see how well repositories are documented and structured.
Research Questions
What the analysis is trying to answer
How concentrated is visibility?
Are stars and forks spread broadly across repositories, or concentrated in a small set of highly visible projects and owners?
What does repository maturity look like?
Do most repositories remain early stage, or do they show sustained activity and stronger signs of long-term maintenance?
How strong are documentation signals?
Does README coverage remain high even among lower-impact repositories, and how does that compare with community health measures?
Data
How the datasets are used
Repository metadata source
The main analysis uses the full repository metadata file with 14,644 repositories, which provides stars, forks, commits, README status, and lifecycle fields.
GitHub users source
A separate GitHub users dataset supports the relational schema and helps illustrate joins, normalization, and cross-source entity matching.
Key limitation
Only about 200 repositories match across the two sources, so the public-facing analysis emphasizes repository-level patterns instead of full profile-level integration.
Why This Project Matters
What makes the analysis stronger than a simple repository dashboard
It evaluates drivers, not just totals
The project moves beyond counting stars and asks how documentation, sustained activity, and reuse signals interact with visibility.
It uses normalized and comparative metrics
The analytical framing emphasizes averages, adoption rates, concentration, lifecycle, and impact-tier share rather than only raw aggregates.
It treats open source as a data product
The final output is positioned as a decision-support artifact for understanding repository quality, adoption, and long-term maintenance signals.
Site Map
Explore the project by page
Analysis
Interactive charts, filters, KPI cards, and repository spotlight views built from the cleaned full dataset.
Data Model
Schema design, source integration strategy, SQL view definitions, and methodology notes from the project.
Findings
Main conclusions, data-quality challenges, and future work drawn directly from the final presentation.
Deliverables
SQL scripts, prepared BI datasets, published Power BI link, and supporting documents for presentation or review.