Webinar Recap: Fostering a New Data Culture with APL Machine Learning

APL Machine Learning is committed to publishing the highest quality and most useful research in the research areas of machine learning (ML) and data-driven approaches for physical sciences and the development of better ML and artificial intelligence (AI) technologies. The journal aims to champion authors, not only through the publication of their research but also through increasing the reusability and reproducibility of their published research.

By removing “Data available on request from the authors,” APL Machine Learning is proud to be one of the first journals in the AIP Publishing portfolio to transition open data from “nice to have” to “need to have.” Data sharing can speed up the pace and improve science, encourage collaborations, and advance researchers’ careers through increased discoverability, reuse, and even increased citations to papers.

Moderated by Editor-in-Chief Adnan Mehonic, “Fostering a New Data Culture with APL Machine Learning” explores trends, challenges, and opportunities in open data and machine learning research with expert speakers. Below are key takeaways from each panelist.