Tools and research on scientific reliability, research software, and scholarly communication.
🧪 Replication and computational reliability: Comparing statistical results across R and Python, Tracing numerical changes across software releases, Analyzing replication outcomes, Examining research code.
📦 Measuring software use: Software references in replication packages, Python package imports across GitHub.
🤝 Software credit and support: Keeping citation metadata synchronized, Finding funding links for Python dependencies, Encouraging software citations.
📚 Citations and scientific error: Checking bibliographies for retractions and corrections, Citations after retraction, Responses to publicized errors, Miscitations in review articles.
📝 Publishing and research communication: Production and readership of political science articles, Journal prices, Precision in numerical summaries, Summarizing publications through citation contexts.