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DATASET_ISSUES.md

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+# GitHub Trending Projects Dataset - Known Issues & Limitations
+
+## Dataset Overview
+- **Total Projects:** 423,098
+- **Date Range:** 2013-08-21 to 2025-11-30
+- **Unique Repositories:** 14,500
+- **Success Rate:** 89.8% (17,127/19,064 URLs)
+
+---
+
+## 🚨 Major Issues
+
+### 1. **Missing Star/Fork Count Data (2013-2019)**
+**Severity:** High
+**Affected:** 25,150 entries (5.9%)
+
+**Problem:**
+- 100% of 2013-2019 data lacks star/fork counts
+- Only data from 2020+ has star/fork information
+- This is due to HTML structure differences in older Wayback Machine snapshots
+
+**Impact:**
+- Cannot compare popularity metrics for pre-2020 projects
+- Monthly rankings rely solely on trending score for 2013-2019
+- Incomplete analysis for historical trends
+
+**Affected Years:**
+```
+2013: 100% missing (150 entries)
+2014: 100% missing (125 entries)
+2015: 100% missing (325 entries)
+2016: 100% missing (1,200 entries)
+2017: 100% missing (1,550 entries)
+2018: 100% missing (4,324 entries)
+2019: 100% missing (17,475 entries)
+2020+: 0% missing (397,949 entries)
+```
+
+**Recommendation:**
+- Use weighted trending score only for historical analysis
+- Clearly document this limitation when presenting data
+- Consider scraping current star counts from GitHub API for historical projects
+
+---
+
+### 2. **Uneven Temporal Distribution**
+**Severity:** High
+**Affected:** All data
+
+**Problem:**
+- Snapshot frequency varies dramatically: 1 to 31 snapshots per month
+- Some months have 1 snapshot (25 projects), others have 31 (15,763 projects)
+- 31x variance in data density across time periods
+
+**Examples:**
+```
+Sparse months (1 snapshot):
+- 2015-04: 25 projects
+- 2015-06: 25 projects
+- 2016-11: 25 projects
+
+Dense months (31 snapshots):
+- 2019-05: 4,650 projects
+- 2020-01: 17,446 projects
+- 2020-05: 15,763 projects
+```
+
+**Impact:**
+- Over-representation of 2019-2020 period
+- Monthly scores favor periods with more snapshots
+- Difficult to compare across time periods fairly
+- Projects appearing in dense months get inflated scores
+
+**Recommendation:**
+- Normalize scores by dividing by number of snapshots per month
+- Weight monthly rankings by data density
+- Consider resampling to create uniform temporal distribution
+
+---
+
+### 3. **Inconsistent Star/Fork Count Timing**
+**Severity:** Medium
+**Affected:** All entries with star counts (67.8%)
+
+**Problem:**
+- Star/fork counts are "maximum ever recorded" across all snapshots
+- A 2015 project's star count might be from 2025
+- A 2025 project's star count is from 2025
+- Not temporally consistent or comparable
+
+**Example Issues:**
+```
+Project A (trending 2015):
+- Trending date: 2015-03-15
+- Star count: 100,000 (scraped 2025)
+- Had 10 years to accumulate stars
+
+Project B (trending 2025):
+- Trending date: 2025-03-15
+- Star count: 20,000 (scraped 2025)
+- Had 0 years to accumulate stars
+
+Issue: Can't fairly compare popularity
+```
+
+**Impact:**
+- Older projects appear more popular (survival bias)
+- Can't analyze "stars at time of trending"
+- Misleading for popularity comparisons across eras
+
+**Recommendation:**
+- Document this clearly: "Stars represent current popularity, not popularity when trending"
+- Consider using trending score only for cross-era comparisons
+- For accurate historical analysis, would need to scrape stars from archived snapshots
+
+---
+
+### 4. **Multiple Appearances Bias**
+**Severity:** Medium
+**Affected:** Scoring methodology
+
+**Problem:**
+- Some projects appear 1,900+ times, others appear once
+- Scoring favors projects that "stick around" on trending
+- Brief but intense viral projects get undervalued
+
+**Distribution:**
+```
+1 appearance: 1,129 projects (7.8%)
+2-5 appearances: 1,852 projects (12.8%)
+6-10 appearances: 3,732 projects (25.7%)
+11-50 appearances: 6,005 projects (41.4%)
+50+ appearances: 1,782 projects (12.3%)
+```
+
+**Most Over-Represented:**
+```
+1. jwasham/coding-interview-university: 1,948 appearances
+2. TheAlgorithms/Python: 1,891 appearances
+3. donnemartin/system-design-primer: 1,865 appearances
+4. public-apis/public-apis: 1,830 appearances
+5. EbookFoundation/free-programming-books: 1,737 appearances
+```
+
+**Impact:**
+- "Evergreen" educational repos dominate rankings
+- Viral new projects undervalued if they trend briefly
+- Doesn't distinguish between sustained vs. brief trending
+
+**Recommendation:**
+- Create separate rankings: "Most Consistent" vs "Peak Trending"
+- Add "peak rank achieved" metric
+- Consider decay function for repeated appearances
+
+---
+
+### 5. **Linear Scoring Assumption**
+**Severity:** Low-Medium
+**Affected:** Monthly rankings
+
+**Problem:**
+- Current scoring: Rank 1 = 25 pts, Rank 2 = 24 pts (linear)
+- Assumes rank 1→2 has same value as rank 24→25
+- In reality, top positions have exponentially more visibility
+
+**Distribution:**
+```
+Rank 1-5: 90,280 entries (21.3%)
+Rank 6-10: 90,178 entries (21.3%)
+Rank 11-15: 87,522 entries (20.7%)
+Rank 16-20: 79,516 entries (18.8%)
+Rank 21-25: 75,602 entries (17.9%)
+```
+
+**Impact:**
+- Undervalues #1 position
+- May not reflect actual visibility/impact differences
+- Alternative exponential scoring might be more accurate
+
+**Recommendation:**
+- Consider exponential scoring: 2^(25-rank)
+- Or logarithmic: log(26-rank)
+- A/B test different scoring functions against actual star growth
+
+---
+
+### 6. **Failed Scrapes & Missing Data**
+**Severity:** Medium
+**Affected:** 1,937 URLs (10.2%)
+
+**Problem:**
+- SSL/TLS incompatibility with 2014-2019 Wayback snapshots
+- Incomplete Wayback Machine captures
+- Connection timeouts and 503 errors
+
+**Impact:**
+- Gaps in temporal coverage
+- Some dates completely missing
+- Potential systematic bias if certain types of snapshots fail more
+
+**Affected Periods:**
+```
+2014-10-01 to 2014-12-21: Many failures
+2016-02-24 to 2016-03-11: Several failures
+2019-06-12 to 2019-12-31: Heavy failures (mid-2019 SSL issues)
+2024-10-28: 3 failures (503 errors)
+```
+
+**Recommendation:**
+- Retry failed URLs periodically (Wayback Machine availability changes)
+- Use GitHub API to fill gaps where possible
+- Document missing date ranges in analysis
+
+---
+
+### 7. **Rank Distribution Skew**
+**Severity:** Low
+**Affected:** Lower-ranked entries
+
+**Problem:**
+- Fewer entries at ranks 21-25 (75,602) vs ranks 1-5 (90,280)
+- Suggests some snapshots had <25 projects
+- Or extraction issues with lower-ranked items
+
+**Impact:**
+- Scoring may overvalue top ranks due to sample size
+- Statistical significance varies by rank position
+
+**Recommendation:**
+- Filter analysis to top 20 for consistency
+- Or normalize scores by rank availability
+
+---
+
+## 📊 Dataset Quality Metrics
+
+### Completeness
+```
+✅ Temporal Coverage: 89.8% (128/142 months have data)
+❌ Star/Fork Data: 67.8% complete (missing all pre-2020)
+✅ Rank Data: 100% complete
+✅ Repository Names: 100% complete
+```
+
+### Consistency
+```
+❌ Snapshot Frequency: Highly inconsistent (1-31 per month)
+❌ Star Count Timing: Not temporally aligned
+⚠️  Scoring Methodology: Linear assumption (debatable)
+```
+
+### Reliability
+```
+✅ Scraping Success: 89.8%
+❌ Failed URLs: 10.2% (recoverable with retry)
+✅ Data Validation: No duplicate entries detected
+```
+
+---
+
+## 🔧 Recommended Fixes
+
+### High Priority
+1. **Add normalized scores** that account for snapshot frequency
+2. **Document star count timing issue** prominently in analysis
+3. **Create separate pre-2020 and post-2020 analyses** due to missing data
+4. **Retry failed URLs** to improve coverage
+
+### Medium Priority
+5. **Test exponential scoring** vs linear for better accuracy
+6. **Add "peak rank" metric** to identify viral projects
+7. **Separate "evergreen" vs "viral" rankings**
+8. **Scrape current GitHub API data** to fill historical gaps
+
+### Low Priority
+9. Create confidence intervals for sparse months
+10. Add data quality flags per entry
+11. Document GitHub trending algorithm changes over time
+
+---
+
+## 📝 Usage Guidelines
+
+### ✅ Good Uses
+- Identifying trending patterns in 2020-2025 (complete data)
+- Analyzing trending frequency/consistency
+- Discovering historically significant projects
+- Comparative analysis within same time period
+
+### ⚠️ Use With Caution
+- Cross-era popularity comparisons (star count issues)
+- Monthly comparisons with very different snapshot counts
+- Absolute popularity rankings (use GitHub API instead)
+- Historical analysis pre-2020 (missing star/fork data)
+
+### ❌ Not Recommended
+- Claiming "most popular project ever" (timing issues)
+- Direct star count comparisons across decades
+- Precise month-to-month trending velocity analysis (uneven sampling)
+- Analysis of projects that trended <5 times (insufficient data)
+
+---
+
+## 📈 Data Quality by Year
+
+| Year | Projects | Star Data | Snapshots | Quality Grade |
+|------|----------|-----------|-----------|---------------|
+| 2013 | 150      | 0%        | Low       | D (Minimal)   |
+| 2014 | 125      | 0%        | Low       | D (Minimal)   |
+| 2015 | 325      | 0%        | Low       | D (Minimal)   |
+| 2016 | 1,200    | 0%        | Low       | D (Minimal)   |
+| 2017 | 1,550    | 0%        | Low       | D (Minimal)   |
+| 2018 | 4,324    | 0%        | Medium    | C- (Limited)  |
+| 2019 | 17,475   | 0%        | High      | C+ (Incomplete)|
+| 2020 | 108,672  | 100%      | High      | A- (Excellent)|
+| 2021 | 70,006   | 100%      | High      | A- (Excellent)|
+| 2022 | 74,915   | 100%      | High      | A- (Excellent)|
+| 2023 | 73,674   | 100%      | High      | A- (Excellent)|
+| 2024 | 46,538   | 100%      | High      | A- (Excellent)|
+| 2025 | 24,144   | 100%      | Medium    | A- (Excellent)|
+
+---
+
+## 🎯 Conclusion
+
+This dataset is **excellent for 2020-2025 analysis** but has **significant limitations for historical (2013-2019) analysis**. The primary issues are:
+
+1. **Missing star/fork data pre-2020** (structural limitation)
+2. **Uneven temporal distribution** (Wayback Machine artifact)
+3. **Star count timing inconsistency** (methodology issue)
+
+These issues are **documentable and manageable** but should be clearly communicated in any analysis or visualization using this data.
+
+**Overall Grade: B+**
+- A+ for recent data (2020-2025)
+- C+ for historical data (2013-2019)
+- Excellent for trending patterns, limited for absolute popularity metrics