A2 Refereed review article in a scientific journal
Journal research data policies in materials science; 
Authors: Hörmann, Lukas; Myneni, Hemanadhan; Al-Hamd, Rwayda Kh. S.; Batalović, Katarina; Bonfanti, Silvia; Grasselli, Federico; Gražulis, Saulius; Koç, Bahattin; Konstantinou, Konstantinos; Lončarić, Ivor; Lopanitsyna, Nataliya; Oliveira, José Manuel; Pegolo, Paolo; Ramos, Patrícia; Rossi, Kevin; Schwaminger, Sebastian P.; Simmen, Edith; Todorović, Milica; Stricker, Markus; Schmidt, Jonathan
Publisher: Royal Society of Chemistry (RSC)
Publication year: 2026
Journal: Digital Discovery
Volume: 5
Issue: 7
First page : 2795
Last page: 2808
eISSN: 2635-098X
DOI: https://doi.org/10.1039/d6dd00111d
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.1039/d6dd00111d
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/527042488
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
Open and reproducible research in materials science relies on the availability of data, code, and established metadata standards. Journal research data policies (RDPs) are a primary mechanism by which these community norms are enforced. We survey RDPs for 171 materials science journals spanning 17 publishers, using an expanded coding framework that captures both data-and-code sharing behavior as well as refereeing standards. We find clear signs of progress in comparison to earlier research on RDPs: nearly all journals provide an RDP, and most mention data availability statements. However, enforceable requirements remain uncommon, public deposition of underlying data is rarely mandatory, and FAIR publication is typically encouraged rather than required. Expectations for research software are substantially less developed than those for data, with limited attention to versioning and persistent identifiers, dependency disclosure, reproducible execution environments, or software quality practices. Aggregating the findings on policy features into an open research data score reveals pronounced heterogeneity across journals. Neither impact factor nor access model reliably predicts policy strength. Double-coding further shows that more complex policies and stricter policies can be more challenging to interpret consistently, and we highlight challenges in consistent RDP encoding across studies. Lastly, we conclude with recommended best practice directions for the future.
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Funding information in the publication:
This article is a result of joint work in COST Action CA22154 – Data-driven Applications towards the Engineering of functional Materials: an Open Network (DAEMON) supported by COST (European Cooperation in Science and Technology). JS was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program project HERO Grant Agreement No. 810451 and funded by the SNSF Ambizione grant number 233444. LH was supported by a UKRI Horizon grant (MSCA, EP/Y024923/1). KB acknowledges the support by Ministry of Science, Technology and Innovation of the Republic of Serbia via contract no. 451-03- 33/2026-03/200017. MS acknowledges support by the European Union by ERC grant, project no. 101161287. The views and opinions expressed are, however, those of the authors only and do not necessarily reect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. SB acknowledges support from the National Science Center in Poland through the SONATA BIS grant DEC-2023/50/E/ST3/00569 and from the Foundation for Polish Science in Poland through the FENG.02.02-IP.05-0177/23 project. This work was carried out within the “Projektowanie Ulepszonych Szkieł Metalicznych” project (FENG.02.02-IP.05- 0177/23) under the 2.2 First Team programme of the Foundation for Polish Science co-financed by the European Union from the European Funds for Smart Economy 2021–2027 (FENG). All authors gratefully acknowledge Nicola Spaldin, Julian Dederke, and Nicolai Bissantz for helpful and insightful discussions, and the ETH Library for their support.
