ijoc-data-analysis
brycewang-stanford/Awesome-Journal-Skills
This comprehensive guide provides best practices for designing, running, and reporting computational experiments for academic journals. It teaches authors how to make performance claims scientifically defensible by addressing potential referee criticisms (e.g., tuning artifacts, seed luck). It covers advanced statistical techniques (performance profiles, nonparametric tests) and details the full structure required for a complete, verifiable GitHub data deposit.