KAUST Library signed a read and publish deal with IOPP for 2023 to 2025.

Research papers, special issue papers, and letters accepted in the listed IOPP journals where a KAUST affiliated author is the corresponding author can be published Open Access at no additional charge (free APC).

Please follow these steps:

• While submitting articles, corresponding authors must ensure their primary affiliation is set to KAUST to be identified by IOPP. The authors do not need to select open access during submission.
• Identified authors will receive notification of the OA arrangement during peer review, or around acceptance. Identified articles will be opted in to open access under the agreement automatically.
• The author will need to agree to the CC-BY open access license.
• Authors can opt-out of the open-access agreement, and should contact the journal after receiving notification of identification if they wish to do so.
• In both cases, KAUST authors will not be subject to any additional Article Publication Charges (APC).
• If an author has not been notified of the agreement but believes they qualify, they should contact the journal to confirm eligibility. 

Important:

This agreement will only support KAUST authors to make their article Open Access, without any extra payment of APCs or administrative process. Author(s) are still responsible for all publishing-related activities, including preparing the manuscript and correspondence during the publication process with the publisher, reviewers or co-authors, as usual.

NOTE: The number of free tokens/APCs is unlimited.

If you need further details, or a meeting, or presentation to your team - please email us at library@kaust.edu.sa

IOP Publishing launches series of open access journals dedicated to machine learning and artificial intelligence for the sciences

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. The new journals 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.  

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.  

Use IOPP Journal Finder to discover relevant, high-quality  IOPP journals related to your research.

Go to IOPP Journal Finder

You can also read IOPP Press Release