IOP Publishing launches series of open access journals dedicated to machine learning and artificial intelligence for the
IOP Publishing (IOPP) is launching the world's first series of open access journals dedicated to the application and development of machine learning (ML) and artificial intelligence (AI) for the sciences. The new multidisciplinary Machine Learning series will collectively cover applications of ML and AI across the physical sciences, engineering, biomedicine and health, and environmental and earth science.
Building on the successful launch of Machine Learning: Science and Technology in 2019 IOPP's Machine Learning series will expand to include three new journals: Machine Learning: Health, Machine Learning: Earth, and Machine Learning: Engineering and will open for submissions later this year. In addition to research articles and reviews the series will also uniquely publish dataset, benchmark and challenge articles to meet the diverse needs of research communities working at the interface of ML, AI and the sciences.
"It's clear that ML and AI have the potential to be transformational in accelerating the advance of new scientific knowledge and discovery," says Dr. Tim Smith, Head of Portfolio Development at IOPP. "Through our new Machine Learning series, we're committed to creating a world-leading publishing home that represents the many areas of science where ML is already playing a critical role. We're excited to introduce even more article formats supporting our open science goals as a publisher and ensuring the reproducibility, integrity and trust of peer-reviewed research."
Authors publishing in IOPP's three new machine learning journals will benefit from free open access publishing throughout 2025, with all article publication charges covered by IOPP.
As part of the Purpose-Led Publishing coalition, researchers publishing with IOPP can be assured that their work will advance knowledge and contributes to the physical sciences community. Profits generated by IOPP are reinvested into the Institute of Physics, supporting efforts to make science accessible for all.
For more information or to submit your ML research, please visit the Machine Learning series page.
Provided by IOP Publishing