| # GitHub Trending Projects Dataset - Known Issues & Limitations |
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| ## 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) |
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| --- |
|
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| ## 🚨 Major Issues |
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|
| ### 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 |
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| --- |
|
|
| ### 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 |
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|
| **Recommendation:** |
| - Normalize scores by dividing by number of snapshots per month |
| - Weight monthly rankings by data density |
| - Consider resampling to create uniform temporal distribution |
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| --- |
|
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| ### 3. **Inconsistent Star/Fork Count Timing** |
| **Severity:** Medium |
| **Affected:** All entries with star counts (67.8%) |
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|
| **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 |
|
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| **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 |
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| --- |
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| ### 4. **Multiple Appearances Bias** |
| **Severity:** Medium |
| **Affected:** Scoring methodology |
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|
| **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 |
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|
| **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 |
|
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| **Recommendation:** |
| - Create separate rankings: "Most Consistent" vs "Peak Trending" |
| - Add "peak rank achieved" metric |
| - Consider decay function for repeated appearances |
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| --- |
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| ### 5. **Linear Scoring Assumption** |
| **Severity:** Low-Medium |
| **Affected:** Monthly rankings |
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|
| **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 |
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|
| **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 |
|
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| **Recommendation:** |
| - Consider exponential scoring: 2^(25-rank) |
| - Or logarithmic: log(26-rank) |
| - A/B test different scoring functions against actual star growth |
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| --- |
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| ### 6. **Failed Scrapes & Missing Data** |
| **Severity:** Medium |
| **Affected:** 1,937 URLs (10.2%) |
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| **Problem:** |
| - SSL/TLS incompatibility with 2014-2019 Wayback snapshots |
| - Incomplete Wayback Machine captures |
| - Connection timeouts and 503 errors |
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| **Impact:** |
| - Gaps in temporal coverage |
| - Some dates completely missing |
| - Potential systematic bias if certain types of snapshots fail more |
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| **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) |
| ``` |
|
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| **Recommendation:** |
| - Retry failed URLs periodically (Wayback Machine availability changes) |
| - Use GitHub API to fill gaps where possible |
| - Document missing date ranges in analysis |
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| --- |
|
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| ### 7. **Rank Distribution Skew** |
| **Severity:** Low |
| **Affected:** Lower-ranked entries |
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| **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 |
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| **Impact:** |
| - Scoring may overvalue top ranks due to sample size |
| - Statistical significance varies by rank position |
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| **Recommendation:** |
| - Filter analysis to top 20 for consistency |
| - Or normalize scores by rank availability |
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| --- |
|
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| ## 📊 Dataset Quality Metrics |
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| ### 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 |
| ``` |
|
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| ### Consistency |
| ``` |
| ❌ Snapshot Frequency: Highly inconsistent (1-31 per month) |
| ❌ Star Count Timing: Not temporally aligned |
| ⚠️ Scoring Methodology: Linear assumption (debatable) |
| ``` |
|
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| ### Reliability |
| ``` |
| ✅ Scraping Success: 89.8% |
| ❌ Failed URLs: 10.2% (recoverable with retry) |
| ✅ Data Validation: No duplicate entries detected |
| ``` |
|
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| --- |
|
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| ## 🔧 Recommended Fixes |
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| ### 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 |
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| ### 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 |
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| ### Low Priority |
| 9. Create confidence intervals for sparse months |
| 10. Add data quality flags per entry |
| 11. Document GitHub trending algorithm changes over time |
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| --- |
|
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| ## 📝 Usage Guidelines |
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| ### ✅ Good Uses |
| - Identifying trending patterns in 2020-2025 (complete data) |
| - Analyzing trending frequency/consistency |
| - Discovering historically significant projects |
| - Comparative analysis within same time period |
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| ### ⚠️ 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) |
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| ### ❌ 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) |
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| --- |
|
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| ## 📈 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)| |
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| --- |
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| ## 🎯 Conclusion |
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| This dataset is **excellent for 2020-2025 analysis** but has **significant limitations for historical (2013-2019) analysis**. The primary issues are: |
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| 1. **Missing star/fork data pre-2020** (structural limitation) |
| 2. **Uneven temporal distribution** (Wayback Machine artifact) |
| 3. **Star count timing inconsistency** (methodology issue) |
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| These issues are **documentable and manageable** but should be clearly communicated in any analysis or visualization using this data. |
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| **Overall Grade: B+** |
| - A+ for recent data (2020-2025) |
| - C+ for historical data (2013-2019) |
| - Excellent for trending patterns, limited for absolute popularity metrics |
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