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  • 标题:Systematic archiving and access to health research data: rationale, current status and way forward
  • 作者:Manju Rani ; Brian S Buckley
  • 期刊名称:Bulletin of the World Health Organization
  • 印刷版ISSN:0042-9686
  • 出版年度:2012
  • 卷号:90
  • 期号:12
  • 页码:932-939
  • DOI:10.2471/BLT.12.105908
  • 语种:English
  • 出版社:World Health Organisation
  • 摘要:Systematically archiving data from health research and large-scale surveys and ensuring access to databases offer economic benefits and can improve the accountability, efficiency and quality of scientific research. Recently, interest in data archiving and sharing has grown and, in developed countries, research funders and institutions are increasingly adopting data-sharing policies. In developing countries, however, there is a lack of awareness of the benefits of data archiving and little discussion of policy. Many databases, even those of large-scale surveys, are not preserved systematically and access for secondary use is limited, which reduces the return on research investment. Several obstacles exist: organizational responsibility is unclear; infrastructure and personnel with appropriate data management and analysis skills are scarce; and researchers may be reluctant to share. This article considers recent progress in data sharing and the strategies and models used to encourage and facilitate it, with a focus on the World Health Organization Western Pacific Region. A case study from the Philippines demonstrates the benefits of data sharing by comparing the number and type of publications associated with two large-scale surveys with different approaches to sharing. Advocacy and leadership are needed at both national and regional levels to increase awareness. A step-by-step approach may be the most effective: initially large national databases could be made available to develop the methods and skills needed and to foster a data-sharing culture. Duplication of costs and effort could be avoided by collaboration between countries. In developing countries, interventions are required to build capacity in data management and analysis.
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