Data Communities: A New Model for Supporting STEM Data Sharing

Authors: Danielle Cooper, Rebecca Springer

Abstract: There is a growing perception that science can progress more quickly, more innovatively, and more rigorously when researchers share data with each other. Policies and supports for data sharing within the STEM (science, technology, engineering, and mathematics) academic community are being put in place by stakeholders such as research funders, publishers, and universities, with overlapping effects. Additionally, many data sharing advocates have embraced the FAIR data principles – holding that data must be findable, accessible, interoperable, and reusable, by both humans and machines – as the standard benchmark for data sharing success. There is also an emerging scholarly literature evaluating the efficacies of some of these policies, although this literature tends to either focus on discrete disciplines or particular journal or funder initiatives.


Citation: Cooper, D., & Springer, R. (2019, May 13). Data Communities: A New Model for Supporting STEM Data Sharing.


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