Datasets:

ronantakizawa
/

github-top-projects

license: mit
task_categories:
  - text-classification
  - time-series-forecasting
language:
  - en
tags:
  - github
  - trending
  - repositories
  - software-engineering
  - popularity
  - time-series
size_categories:
  - 100K<n<1M
configs:
  - config_name: full
    data_files: full/data.csv
  - config_name: monthly
    data_files: monthly/data.csv

GitHub Trending Projects (2013-2025)

A comprehensive dataset of 423,098 GitHub trending repository entries spanning 12+ years (August 2013 - November 2025), scraped from Wayback Machine snapshots of GitHub's trending page.

🎯 Dataset Overview

This dataset captures the evolution of GitHub's trending repositories over time, providing insights into:

Software development trends across programming languages and domains
Popular open-source projects and their trending patterns
Community interests and shifts in developer focus over 12 years
Viral repository dynamics and sustained popularity patterns

Key Statistics:

📊 423,098 trending repository entries
🗂️ 14,500 unique repositories
📅 128 months of coverage (2013-08 to 2025-11)
⭐ 89.8% scraping success rate from Wayback Machine
🏆 Pre-processed monthly rankings with weighted scoring

🔧 Dataset Configurations

This dataset has two configurations defined in the YAML header:

Configuration: `full` (Default)

Complete daily trending data with 423,098 entries

from datasets import load_dataset
ds = load_dataset('ronantakizawa/github-top-projects', 'full')

Columns:

name (string): Repository name
star_count (int): Star count (may be empty for pre-2020)
fork_count (int): Fork count (may be empty for pre-2020)
repo_owner (string): Repository owner/organization
rank (int): Position in trending (1-25)
date (string): Snapshot date (YYYY-MM-DD)

Configuration: `monthly`

Top 25 repositories per month with 3,200 entries

from datasets import load_dataset
ds = load_dataset('ronantakizawa/github-top-projects', 'monthly')

Columns:

month (string): Month (YYYY-MM)
rank (int): Monthly rank (1-25)
repository (string): Full repository name (owner/name)
repo_owner (string): Repository owner
repo_name (string): Repository name
star_count (int): Maximum recorded stars
fork_count (int): Maximum recorded forks
ranking_appearances (int): Times appeared in trending that month

🏆 Scoring Methodology

Monthly rankings use a weighted frequency and position-based scoring system:

Score = Σ (25 - rank + 1) for each trending appearance

Where:
- Rank 1 → 25 points
- Rank 2 → 24 points
- ...
- Rank 25 → 1 point

Example:

Project appears 10 times at rank #1 → 250 points
Project appears 20 times at rank #10 → 320 points (higher ranked!)

This rewards both consistency (frequent appearances) and position (higher ranks).

📊 Data Collection

Source: GitHub Trending page via Wayback Machine (web.archive.org) Period: August 21, 2013 - November 30, 2025 Method: Python web scraping with BeautifulSoup Snapshots: 17,127 successfully scraped from 19,064 available Retry Logic: Up to 15 retries with exponential backoff

⚠️ Known Limitations

1. Missing Star/Fork Data (Pre-2020)

100% of 2013-2019 entries lack star/fork counts
Only 67.8% of dataset has popularity metrics
Impact: Cannot compare absolute popularity for historical projects

2. Uneven Temporal Distribution

Snapshot frequency: 1 to 31 per month (31x variance)
2019-2020 heavily over-represented
Impact: Monthly scores favor periods with more snapshots

3. Star Count Timing Inconsistency

Star counts are "maximum ever recorded" across all snapshots
A 2015 project's stars might be from 2025 scraping
Impact: Can't fairly compare popularity across eras

See DATASET_ISSUES.md for comprehensive analysis.

📈 Data Quality by Era

Era	Quality	Star Data	Snapshot Density	Grade
2013-2019	Limited	❌ 0%	Low-Medium	C+
2020-2025	Excellent	✅ 100%	High	A-

Recommendation: Use 2020-2025 data for analyses requiring star/fork counts

💡 Usage Examples

Load with Hugging Face Datasets (Recommended)

from datasets import load_dataset

# Load complete daily dataset (423,098 entries)
ds_full = load_dataset('ronantakizawa/github-top-projects', 'full')
df_full = ds_full['train'].to_pandas()

# Load monthly top 25 dataset (3,200 entries)
ds_monthly = load_dataset('ronantakizawa/github-top-projects', 'monthly')
df_monthly = ds_monthly['train'].to_pandas()

# Filter to 2020+ (with star data)
df_recent = df_full[df_full['date'] >= '2020-01-01']

# Get November 2025 top 10
nov_2025 = df_monthly[df_monthly['month'] == '2025-11'].head(10)
print(nov_2025[['rank', 'repository', 'star_count', 'ranking_appearances']])

Time Series Analysis

import pandas as pd
import matplotlib.pyplot as plt
from datasets import load_dataset

ds = load_dataset('ronantakizawa/github-top-projects', 'full')
df = ds['train'].to_pandas()
df['date'] = pd.to_datetime(df['date'])

# Analyze a specific project over time
project = 'microsoft/vscode'
project_df = df[(df['repo_owner'] == 'microsoft') & (df['name'] == 'vscode')]

# Plot trending frequency over time
monthly_counts = project_df.groupby(project_df['date'].dt.to_period('M')).size()
monthly_counts.plot(title=f'{project} Trending Frequency')
plt.ylabel('Days in Trending')
plt.show()

🔍 Research Applications

This dataset enables analysis of:

Trending Dynamics - What makes a repository go viral?
Technology Adoption - Rise and fall of programming languages
Open Source Evolution - Growth of educational repositories
Predictive Modeling - Forecasting future trending projects
Developer Behavior - Community interest shifts

📊 Example Insights

Most Consistently Trending (2020-2025):

jwasham/coding-interview-university - 1,948 appearances
TheAlgorithms/Python - 1,891 appearances
donnemartin/system-design-primer - 1,865 appearances

📜 License

MIT License

This dataset is released under the MIT License. You can:

✅ Use for commercial purposes
✅ Modify and distribute
✅ Use in research (attribution appreciated!)
✅ Include in proprietary software

🙏 Acknowledgments

GitHub for maintaining the trending page
Internet Archive for the Wayback Machine
Open Source Community for creating amazing projects

📖 Citation

If you use this dataset in your research, please cite:

@dataset{github_trending_2013_2025,
  title={GitHub Trending Projects Dataset (2013-2025)},
  author={Ronan Takizawa},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/ronantakizawa/github-top-projects}
}

📅 Updates

2025-12: Initial release (2013-08 to 2025-11)
Future updates planned quarterly

Last Updated: December 2025 Dataset Version: 1.0 Status: ✅ Complete and ready for use