emeryberger/CSrankings

A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide...

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Summary Information

Updated 21 minutes ago
Added to GitGenius on September 21st, 2026
Created on May 3rd, 2016
Open Issues & Pull Requests: 1,114 (+2)
GitHub issues: Enabled
Number of forks: 3,886
Total Stargazers: 3,211 (+1)
Total Subscribers: 37 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 6.5 days
Mean response time: 18.0 days
90th percentile: 43.7 days
Tracked items: 2,159

How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "rate-limited" is answered fastest, typically in about 18 hours, while "pr-created" waits about 8 days. 62% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. 68% of issues opened in the past year have been closed, leaving a working backlog.

Charts & Analytics

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Issue Activity (beta)

Open issues: 949
New in 7 days: 79
Closed in 7 days: 2
Avg open age: 109 days
Stale 30+ days: 746
Stale 90+ days: 590

Recent activity

Opened in 7 days: 50
Closed in 7 days: 2
Comments in 7 days: 4
Events in 7 days: 12

Top labels

  • pr-created (2,330)
  • CSrankings form submission (2,181)
  • auto-batched (131)
  • rate-limited (129)
  • validation-failed (107)
  • anonymous-profile (49)
  • sponsor (12)
  • invalid-name-format (4)

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Detailed Description

CS Rankings is a web application that ranks computer science departments by their research output in selective venues and identifies active faculty across research areas.

The tool addresses the problem of evaluating academic computer science departments through a metrics-based approach. It aggregates publication data from selective conferences and venues, then calculates department rankings based on the research contributions of faculty members. Users can search for active researchers within specific subfields and view which institutions are producing the most research in particular areas.

The application suits anyone evaluating computer science programs, whether for hiring decisions, program selection, or understanding research landscape distribution across institutions. It works best for those seeking data-driven comparisons grounded in publication records rather than reputation alone. The tool focuses on selective venues rather than exhaustive publication counts, which means it emphasizes research quality through venue selectivity. Faculty can be found and filtered by research area, making it useful for identifying expertise clusters within and across departments.

The project maintains an active codebase with regular updates to its ranking data and faculty information. The tool continues to expand its coverage of research areas and venues included in its rankings. Development includes ongoing refinement of the methodology for how publications are weighted and attributed to institutions.