The University of California has halted all further subscriptions with one of the world’s largest scholarly publishers, Amsterdam-based Elsevier. The move follows more than six months of negotiations which failed to reach a substantial agreement on securing universal open access to UC research. The split is part of growing push in the machine learning community for free and open public access to its research.
The UC’s 10 campuses produce a massive amount of research papers, and some 18 percent of these end up in Elsevier publications. The UC also pays millions of dollars annually in subscription fees for Elsevier journals.
Print vs Digital Publishing
Print publishers traditionally own the rights to the articles in their journals, and charge anyone who wants to read or use them via subscription or other fees. However, with the rise of the Internet the game has changed. It no longer makes sense for researchers to pay for a publishing platform that locks out many potential readers.
The machine learning community has been at the forefront of the emerging movement for free and open access to research. The UC’s Elsevier subscriptions expired on Dec 31, and when the school announced it would not renew unless a new deal could be struck, Facebook AI Chief Yann LeCun showed his support in a Facebook post: “It’s easy for us in CS/AI/ML to avoid for-profit publishers *and* non-profit publishers that insist on keeping their publications behind a paywall: we have NeurIPS, ICML, ICLR, and JMLR (and, of course, ArXiv). All these venues are not only open access (free for readers), they are also free for authors (though conference proceedings are funded by registrations and sponsorships).”
Is the burgeoning movement for free and open access to research unstoppable? Has Elsevier finally met its Waterloo? On January 10, the entire editorial board of the Elsevier-owned Journal of Informetrics resigned in protest against high fees and restricted access to citation data. Days later the same team launched a new open-access journal to fill the gap, Quantitative Science Studies. (In a similar scenario in 2015, the editorial board of Elsevier linguistics journal Lingua left and launched a rival open-access publication called Glossa.)
Open Access Movement Frontrunners: CS, ML, and AI
Elsevier is not the only for-profit publisher affected by the open access movement. In late 2017 popular scientific journal Nature came under fire after announcing plans to debut a new online-only publication, Nature Machine Intelligence (NMI), ”for research and perspectives from the fast-moving fields of artificial intelligence, machine learning, and robotics.” Thousands of machine learning researchers signed a petition pledging to boycott the new journal: “We see no role for closed access or author-fee publication in the future of machine learning research and believe the adoption of this new journal as an outlet of record for the machine learning community would be a retrograde step.” Prominent AI researchers Yoshua Bengio, Samy Bengio, Geoffrey Hinton, Jeff Dean, Ian Goodfellow, Gary Marcus, Sergey Levine and others said they would not submit any papers, reviews or editorial services to NMI in protest over its paper submission fees and individual and institutional subscription fees.
The first issue of NMI went live in January free-of-cost, and Nature says it will remain free through 2019.
There are a growing number of non-profit publishers now providing public access for machine learning research, and Synced has compiled a list for your reference.
“Open access to 1,509,050 e-prints in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. ArXiv is owned and operated by Cornell University, a private not-for-profit educational institution. arXiv is funded by Cornell University, the Simons Foundation and by the member institutions.”
Journal of Machine Learning Research (JMLR)
“The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. ”
International Conference on Learning Representations (ICLR)
“ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.”
International Conference on Machine Learning (ICML)
“ICML is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.”
Conference on Neural Information Processing Systems (NeurIPS)
“NeurIPS is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.”
The push for open access has united ML researchers and the broader AI and tech community, many of whom are passionate in their rejection of submission and subscription fee based publishing models. As AI pioneer Professor Yoshua Bengio declared in a Facebook post: “Making profits on the back of access to science has to stop.”
The UC’s termination of subscriptions with Elsevier may be a major turning point in the fight.
Journalist: Fangyu Cai | Editor: Michael Sarazen