Research Data Strategy
Science has already entered its big data era. Almost any research field is benefiting from the availability of huge amounts of experimental data. Typical examples include, but are not limited to bioinformatics, astronomy, particle physics, and geology. Nevertheless, a lot of innovation in the data field has been happening in the technology sector. Companies such as Google and Amazon have invested heavily in areas such as machine learning and cloud computing. There are also advancements in new ways of working, such as lean and agile development. The adoption of data-intensive technologies remains low even in the industry sector, outside of the tech companies. For large scale organizations like that, the concept of “data strategy” has been developed. Research organizations can benefit a lot from this, and this article aims to introduce its scientific variant : “Research Data Strategy” (RDS).
Angelov, Boyan. 2020. “Research Data Strategy: Framework and Motivating Factors.” OSF Preprints. October 25. doi:10.31219/osf.io/e6ycp.
October 25, 2020