IOP Publishing (IOPP) has launched an open access journal series dedicated to the applications and developments of Machine Learning (ML) and Artificial Intelligence (AI) in the scientific domain. The new interdisciplinary Machine Learning series provides comprehensive coverage of ML and AI applications across physical sciences, engineering, biomedicine and health, and environmental and geosciences.
Building on the success of Machine Learning: Science and Technology In 2019, IOPP’s Machine Learning series will expand to three new journals. Machine Learning: Health, Machine Learning: Earthand Machine Learning: Engineering. The new journal will begin accepting submissions later this year. In addition to research articles and reviews, the series will uniquely publish datasets, benchmarks and challenge papers to address the diverse needs of the research community working at the interface of ML, AI and science.
“It's clear that ML and AI have the transformative potential to accelerate the advancement of new scientific knowledge and discovery,” said Dr Tim Smith, Head of Portfolio Development at IOPP. “Through our new Machine Learning series, we are committed to creating a world-leading publishing hub representing the many areas of science where ML already plays a key role. We are pleased to introduce even more article formats that support our open science goals as a publisher and ensure the reproducibility, completeness and reliability of peer-reviewed research.”
Authors submitting papers to IOPP's three new machine learning journals will benefit from free open access publishing throughout 2025, with all article publication costs covered by IOPP.
As part of the purpose-driven publishing coalition, researchers who publish with IOPP can be confident that their research will advance knowledge and contribute to the physical sciences community: profits generated by IOPP are reinvested in the Institute of Physics, supporting efforts to make science accessible to all.
For more information about the machine learning series, see https://ioppublishing.org/publications/journals/machine-learning-series.
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