acm-sigkdd-conference-on-knowledge-discovery-and-data-mining
brycewang-stanford/Awesome-Journal-Skills
A comprehensive guide for authors submitting data mining papers to the ACM SIGKDD Conference. It helps authors assess the manuscript's fit, reframe the contribution (e.g., from journal to conference style), and strengthens the evidence bar by focusing on novelty, scale, reproducibility, and clear real-world impact required by top-tier data science venues.