2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review

Sharing research data accelerates the exchange of scientific data and the pace of science, by enabling validation of results, access to datasets, and encourages reuse of data for future studies. Towards fostering a culture of open data, APL Machine Learning requires all data, code, methods, and models should be documented and described either in the main text of the article or supporting information to provide the research community with enough transparency and detail to effectively replicate the findings.

This collection highlights papers published in APL Machine Learning in 2023 that showcase well-documented data, code, and supplementary materials or provide comprehensive and structured background review.