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Long-term preservation of biomedical research data.

Vivek Navale1, Matthew McAuliffe1

  • 1Center for Information Technology, National Institutes of Health, Bethesda, Maryland, 20892, USA.

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Summary

Implementing an Open Archival Information System model and engaging data stewards are crucial for long-term preservation of biomedical research data, ensuring its continued value beyond project lifecycles.

Keywords:
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Area of Science:

  • Biomedical Science
  • Data Science
  • Information Science

Background:

  • Biomedical science is increasingly data-intensive due to advances in genomics, molecular imaging, and translational research.
  • Effective long-term preservation of Big Data sets is essential for researchers to leverage these resources.
  • Current research data management practices often lack strategies for sustained accessibility post-project completion.

Purpose of the Study:

  • To outline actionable strategies for ensuring the long-term preservation and continued resource value of biomedical research data.
  • To advocate for the early involvement of data stewards in the digital data lifecycle.
  • To promote the adoption of standardized practices for enhanced data sharing and integration.

Main Methods:

  • Discussion of an opinion article proposing the use of the Open Archival Information System (OAIS) model.
  • Emphasis on the six functional entities of the OAIS model: Ingest, Access, Data Management, Archival Storage, Administration, and Preservation Planning.
  • Recommendations for data collection strategies, use of common data elements, and engagement with repositories and curators.

Main Results:

  • The OAIS model provides a framework for managing digital data throughout its lifecycle.
  • Early involvement of data stewards and adherence to institutional policies are key to data sustainability.
  • Standardization through common data elements and ontologies improves data interpretation and reuse.
  • Scalable platforms and secure virtual workspaces are needed to support diverse data ingest and facilitate FAIR data principles.

Conclusions:

  • A proactive approach to data preservation, integrating data stewards early, is vital for biomedical Big Data.
  • Implementing robust data management strategies, including provenance tracking and standardization, enhances data quality and reproducibility.
  • Adoption of FAIR data principles through appropriate infrastructure is essential for near- and long-term research needs.