DLMar 11

Journal Research Data Policies in Materials Science

arXiv:2603.1930112.2h-index: 12
Predicted impact top 87% in DL · last 90 daysOriginality Synthesis-oriented
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This work addresses the problem of inconsistent and weak open data practices in materials science research, which is incremental as it builds on prior surveys to highlight ongoing challenges.

The study surveyed research data policies across 171 materials science journals, finding progress in policy adoption but limited enforceable requirements for data sharing and software practices, with heterogeneity in policy strength not predicted by impact factor or access model.

Open and reproducible research in materials science relies on the availability of data, code, and common metadata standards. Journal research data policies (RDPs) remain a primary mechanism by which publication norms are defined and 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.

Foundations

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