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README.md

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+---
+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
+---
+
+# 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 Files
+
+### 1. `github-trending-projects-full.csv` (19 MB)
+**Complete daily trending data** - All 423,098 entries
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `name` | string | Repository name |
+| `star_count` | integer | Star count (max recorded, may be empty for pre-2020) |
+| `fork_count` | integer | Fork count (max recorded, may be empty for pre-2020) |
+| `repo_owner` | string | Repository owner/organization |
+| `rank` | integer | Position in trending (1-25) |
+| `date` | date | Snapshot date (YYYY-MM-DD) |
+
+**Use cases:**
+- Custom time-series analysis
+- Training ML models on trending patterns
+- Analyzing daily trending dynamics
+- Creating custom aggregations (weekly, yearly, etc.)
+- Studying viral repository behavior
+
+### 2. `github-top-projects-by-month.csv` (211 KB)
+**Monthly top 25 repositories** - Pre-processed with weighted scoring
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `month` | string | Month (YYYY-MM) |
+| `rank` | integer | Monthly rank (1-25) |
+| `repository` | string | Full repository name (owner/name) |
+| `repo_owner` | string | Repository owner |
+| `repo_name` | string | Repository name |
+| `star_count` | integer | Maximum recorded stars |
+| `fork_count` | integer | Maximum recorded forks |
+| `ranking_appearances` | integer | Times appeared in trending that month |
+
+**Use cases:**
+- Quick monthly insights and visualizations
+- Dashboard creation
+- Identifying consistently popular projects
+- Trend analysis without processing overhead
+
+## 🏆 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
+
+**HTML Parsing:**
+- Multiple extraction methods for different page structures (2013-2019, 2020+)
+- Handles changes in GitHub's trending page design
+- Robust error handling for incomplete snapshots
+
+## ⚠️ 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
+- Cause: Different HTML structure in older Wayback snapshots
+- **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
+- Some months: 25 projects, others: 17,446 projects
+- **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
+- **Not temporally aligned** - older projects had more time to accumulate stars
+- **Impact:** Can't fairly compare popularity across eras
+
+### 4. **Multiple Appearances Bias**
+- Top projects appear 1,700-1,900 times
+- 1,129 projects appear only once
+- Favors "evergreen" educational repos
+- **Impact:** Brief viral projects may be undervalued
+
+### 5. **Failed Scrapes**
+- 1,937 URLs failed (10.2%)
+- Mainly 2014-2019 due to SSL/TLS incompatibility
+- Some date ranges completely missing
+- **Impact:** Gaps in temporal coverage
+
+**See DATASET_ISSUES.md for detailed 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 Full Dataset (Python)
+
+```python
+import pandas as pd
+
+# Load complete dataset
+df = pd.read_csv('github-trending-projects-full.csv')
+
+# Filter to 2020+ (with star data)
+df_recent = df[df['date'] >= '2020-01-01']
+
+# Get top trending projects of 2024
+df_2024 = df[df['date'].str.startswith('2024')]
+top_2024 = df_2024.groupby(['repo_owner', 'name']).size().sort_values(ascending=False).head(10)
+print(top_2024)
+```
+
+### Load Monthly Top (Python)
+
+```python
+import pandas as pd
+
+# Load pre-processed monthly rankings
+df_monthly = pd.read_csv('github-top-projects-by-month.csv')
+
+# 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']])
+
+# Find projects that consistently rank #1
+rank_1_projects = df_monthly[df_monthly['rank'] == 1]['repository'].value_counts()
+print(rank_1_projects.head(10))
+```
+
+### Time Series Analysis
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('github-trending-projects-full.csv')
+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()
+```
+
+### Language Trends (requires additional metadata)
+
+```python
+# Note: Language data not included in this dataset
+# Would need to join with GitHub API data or another dataset
+```
+
+## 🔍 Research Applications
+
+This dataset enables analysis of:
+
+1. **Trending Dynamics**
+   - What makes a repository go viral?
+   - How long do projects stay trending?
+   - Seasonal patterns in software development
+
+2. **Technology Adoption**
+   - Rise and fall of programming languages
+   - Framework popularity over time
+   - Shift from monolithic to microservices
+
+3. **Open Source Evolution**
+   - Growth of educational repositories
+   - Corporate open source contributions
+   - Regional trending patterns
+
+4. **Predictive Modeling**
+   - Forecasting future trending projects
+   - Identifying early viral indicators
+   - Star growth prediction models
+
+5. **Developer Behavior**
+   - Community interest shifts
+   - Popular project categories
+   - Documentation and tutorial demand
+
+## 📊 Example Insights
+
+**Most Consistently Trending (2020-2025):**
+1. `jwasham/coding-interview-university` - 1,948 appearances
+2. `TheAlgorithms/Python` - 1,891 appearances
+3. `donnemartin/system-design-primer` - 1,865 appearances
+
+**Trending Patterns:**
+- Educational repositories dominate long-term trending
+- AI/ML projects saw massive spike in 2023-2024
+- Web frameworks remain consistently popular
+
+## 📜 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
+
+**Source Data:** Wayback Machine (public archive)
+**Original Content:** GitHub Trending pages
+
+## 🙏 Acknowledgments
+
+- **GitHub** for maintaining the trending page
+- **Internet Archive** for the Wayback Machine
+- **Open Source Community** for creating amazing projects
+
+## 📧 Contact & Contributions
+
+- **Issues/Questions:** Open an issue on the dataset repository
+- **Data Errors:** Please report any inconsistencies
+- **Contributions:** Additional metadata or corrections welcome
+
+## 📖 Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{github_trending_2013_2025,
+  title={GitHub Trending Projects Dataset (2013-2025)},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/YOUR_USERNAME/github-top-projects}
+}
+```
+
+## 🔗 Related Datasets
+
+- GitHub Archive (gharchive.org) - Complete GitHub event stream
+- GHTorrent - GitHub data for research
+- Libraries.io - Package manager dependency data
+
+## 📅 Updates
+
+- **2025-12:** Initial release (2013-08 to 2025-11)
+- Future updates planned quarterly
+
+## ⚙️ Technical Details
+
+**Scraping Configuration:**
+- Retry attempts: 15 with exponential backoff
+- Delay between requests: 4-6 seconds (randomized)
+- Timeout: 45 seconds per request
+- User-Agent: Mozilla/5.0 (standard browser)
+
+**Data Processing:**
+- Deduplication: By date + repo + rank
+- Sorting: Chronological (newest first)
+- Encoding: UTF-8
+- Format: CSV with headers
+
+## 🐛 Known Issues
+
+See `DATASET_ISSUES.md` for comprehensive list including:
+- Missing data gaps
+- Star count timing issues
+- Temporal distribution variance
+- Recommended usage guidelines
+
+---
+
+**Last Updated:** December 2025
+**Dataset Version:** 1.0
+**Status:** ✅ Complete and ready for use