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BMC Res Notes. 2012 Sep 7;5:494. doi: 10.1186/1756-0500-5-494.

Open by default: a proposed copyright license and waiver agreement for open access research and data in peer-reviewed journals.

Author information

1
BioMed Central Ltd, London, UK. iain.hrynaszkiewicz@biomedcentral.com

Abstract

Copyright and licensing of scientific data, internationally, are complex and present legal barriers to data sharing, integration and reuse, and therefore restrict the most efficient transfer and discovery of scientific knowledge. Much data are included within scientific journal articles, their published tables, additional files (supplementary material) and reference lists. However, these data are usually published under licenses which are not appropriate for data. Creative Commons CC0 is an appropriate and increasingly accepted method for dedicating data to the public domain, to enable data reuse with the minimum of restrictions. BioMed Central is committed to working towards implementation of open data-compliant licensing in its publications. Here we detail a protocol for implementing a combined Creative Commons Attribution license (for copyrightable material) and Creative Commons CC0 waiver (for data) agreement for content published in peer-reviewed open access journals. We explain the differences between legal requirements for attribution in copyright, and cultural requirements in scholarship for giving individuals credit for their work through citation. We argue that publishing data in scientific journals under CC0 will have numerous benefits for individuals and society, and yet will have minimal implications for authors and minimal impact on current publishing and research workflows. We provide practical examples and definitions of data types, such as XML and tabular data, and specific secondary use cases for published data, including text mining, reproducible research, and open bibliography. We believe this proposed change to the current copyright and licensing structure in science publishing will help clarify what users - people and machines - of the published literature can do, legally, with journal articles and make research using the published literature more efficient. We further believe this model could be adopted across multiple publishers, and invite comment on this article from all stakeholders in scientific research.

PMID:
22958225
PMCID:
PMC3465200
DOI:
10.1186/1756-0500-5-494
[Indexed for MEDLINE]
Free PMC Article

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