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  • 标题:Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations
  • 本地全文:下载
  • 作者:Iain Hrynaszkiewicz ; Iain Hrynaszkiewicz ; Varsha Khodiyar
  • 期刊名称:Research Integrity and Peer Review
  • 电子版ISSN:2058-8615
  • 出版年度:2016
  • 卷号:1
  • 期号:1
  • DOI:10.1186/s41073-016-0015-6
  • 语种:English
  • 出版社:Springer
  • 摘要:Sharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles—where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. Using the journal Scientific Data as a case study, we propose and show examples of changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).
  • 关键词:Journal Article;Scientific Data;Data Repository;Open Access Journal;Data Journal
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