Projects

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

Add normalized scores that account for snapshot frequency
Document star count timing issue prominently in analysis
Create separate pre-2020 and post-2020 analyses due to missing data
Retry failed URLs to improve coverage

Medium Priority

Test exponential scoring vs linear for better accuracy
Add "peak rank" metric to identify viral projects
Separate "evergreen" vs "viral" rankings
Scrape current GitHub API data to fill historical gaps

Low Priority

Create confidence intervals for sparse months
Add data quality flags per entry
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:

Missing star/fork data pre-2020 (structural limitation)
Uneven temporal distribution (Wayback Machine artifact)
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