| Votes | By | Price | Discipline | Year Launched |
| OpenML | OPEN SOURCE | Machine Learning |
OpenML is a collaborative, open-science platform designed to make machine learning research more transparent, reproducible, and shareable. It serves as a global hub where researchers can upload datasets, publish benchmarks, share model results, and compare algorithms on standardized tasks. By organizing data, code, and experimental outcomes into a unified structure, OpenML simplifies reproducibility—one of the biggest challenges in modern ML research. Its API-friendly design integrates seamlessly with Python, R, Java, and major ML libraries, allowing users to automate experiments and contribute results programmatically. OpenML’s strength lies in its community-driven approach: datasets become living resources enriched with metadata, versioning, and performance records from thousands of contributors. While it is primarily used by academic ML researchers and benchmarking enthusiasts, its emphasis on openness and standardized evaluation makes it a valuable tool for anyone interested in systematic machine learning experimentation.