How Research Data Management Plans Can Help in Harmonizing Open Science and Approaches in the Digital Economy. Issue 9 (29th December 2022)

• Record Type: Journal Article
• Authors: Salazar, Abel; Wentzel, Bianca; Schimmler, Sonja; Gläser, Roger; Hanf, Schirin; Schunk, Stephan A.
• Is Part Of: Chemistry. Volume 29:Issue 9(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-29
• Subjects: Data Governance Act -- digital economy -- European regulations -- NFDI4Cat -- open science -- research data management plans; Chemistry -- Periodicals; 540
• DOI: 10.1002/chem.202202720
• Languages: English
• ISSNs: 0947-6539

Abstract

Abstract: Within this perspective article, we intend to summarise definitions and terms that are often used in the context of open science and data‐driven R&D and we discuss upcoming European regulations concerning data, data sharing and handling. With this background in hand, we take a closer look at the potential connections and permeable interfaces of open science and digital economy, in which data and resulting immaterial goods can become vital pieces as tradeable items. We believe that both science and the digital economy can profit from a seamless transition and foresee that the scientific outcomes of publicly funded research can be better exploited. To close the gap between open science and the digital economy, and to serve for a balancing of the interests of data producers, data consumers, and an economy around services and the public, we introduce the concept of generic research data management plans (RDMs), which have in part been developed through a community effort and which have been evaluated by academic and industry members of the NFDI4Cat consortium. We are of the opinion that in data‐driven research, RDMs do need to become a vital element in publicly funded projects. Abstract : The integration of open science into a digital economy, while considering legal European regulations, is a balancing act. Here research data management plans play a key role and has the potential to combine the interests of researchers as data producers and the public as data users in publicly funded projects.