Long-term preservation of biomedical research data

Abstract

Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning. We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier. The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.