Fix schema mismatch with YAML configs

README.md

@@ -14,6 +14,11 @@ tags:
 - 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)
@@ -35,66 +40,43 @@ This dataset captures the evolution of GitHub's trending repositories over time,
 - ⭐ **89.8%** scraping success rate from Wayback Machine
 - 🏆 **Pre-processed monthly rankings** with weighted scoring
 
-## 🔧 Dataset Structure
+## 🔧 Dataset Configurations
 
-This dataset is organized into **two subdirectories**:
+This dataset has **two configurations** defined in the YAML header:
 
-### `full/` - Complete Daily Data
-423,098 trending entries (2013-2025)
+### Configuration: `full` (Default)
+Complete daily trending data with 423,098 entries
 
 ```python
 from datasets import load_dataset
-ds = load_dataset('ronantakizawa/github-top-projects', data_dir='full')
+ds = load_dataset('ronantakizawa/github-top-projects', 'full')
 ```
 
-### `monthly/` - Monthly Top 25
-3,200 entries (top 25 per month with weighted scoring)
+**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
 
 ```python
 from datasets import load_dataset
-ds = load_dataset('ronantakizawa/github-top-projects', data_dir='monthly')
+ds = load_dataset('ronantakizawa/github-top-projects', 'monthly')
 ```
 
-## 📁 Dataset Files
-
-### `full/data.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
-
-### `monthly/data.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
+**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
 
@@ -124,44 +106,24 @@ This rewards both **consistency** (frequent appearances) and **position** (highe
 **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**
+See `DATASET_ISSUES.md` for comprehensive analysis.
 
 ## 📈 Data Quality by Era
 
@@ -180,11 +142,11 @@ This rewards both **consistency** (frequent appearances) and **position** (highe
 from datasets import load_dataset
 
 # Load complete daily dataset (423,098 entries)
-ds_full = load_dataset('ronantakizawa/github-top-projects', data_dir='full')
+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', data_dir='monthly')
+ds_monthly = load_dataset('ronantakizawa/github-top-projects', 'monthly')
 df_monthly = ds_monthly['train'].to_pandas()
 
 # Filter to 2020+ (with star data)
@@ -195,28 +157,15 @@ nov_2025 = df_monthly[df_monthly['month'] == '2025-11'].head(10)
 print(nov_2025[['rank', 'repository', 'star_count', 'ranking_appearances']])
 ```
 
-### Load Directly from CSV
-
-```python
-import pandas as pd
-
-# Download files from the dataset page, then:
-df_full = pd.read_csv('full/data.csv')
-df_monthly = pd.read_csv('monthly/data.csv')
-
-# Get top trending projects of 2024
-df_2024 = df_full[df_full['date'].str.startswith('2024')]
-top_2024 = df_2024.groupby(['repo_owner', 'name']).size().sort_values(ascending=False).head(10)
-print(top_2024)
-```
-
 ### Time Series Analysis
 
 ```python
 import pandas as pd
 import matplotlib.pyplot as plt
+from datasets import load_dataset
 
-df = pd.read_csv('github-trending-projects-full.csv')
+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
@@ -230,41 +179,15 @@ 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
+1. **Trending Dynamics** - What makes a repository go viral?
+2. **Technology Adoption** - Rise and fall of programming languages
+3. **Open Source Evolution** - Growth of educational repositories
+4. **Predictive Modeling** - Forecasting future trending projects
+5. **Developer Behavior** - Community interest shifts
 
 ## 📊 Example Insights
 
@@ -273,11 +196,6 @@ This dataset enables analysis of:
 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**
@@ -288,21 +206,12 @@ This dataset is released under the MIT License. You can:
 - ✅ 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:
@@ -310,46 +219,18 @@ 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},
+  author={Ronan Takizawa},
   year={2025},
   publisher={Hugging Face},
-  url={https://huggingface.co/datasets/YOUR_USERNAME/github-top-projects}
+  url={https://huggingface.co/datasets/ronantakizawa/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