How Research Data Management Plans Can Help in Harmonizing Open Science and Approaches in the Digital Economy

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.

Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use

“…To take advantage of the new opportunities arising from the synergies between data value chains and data transparency, reliable regulations must be created. It has been proposed that mandatory “research data management plans” (RDMPs) should be established for publicly funded research projects to align the interests of data producers in terms of control over and responsibility for their data with FAIR principles [8b] …”

“…A standardized data structure for catalysis data is completely lacking, and the archived data are most frequently not released, for example, with a Digital Object Identifier or any other unique identifier (ID) [8b] . For kinetic data, it should be possible to come to a standard data structure as is common, for example, for structural data in the Inorganic Crystal Structure Database [61] or for the evaluation of surface area and pore size distribution [62] .…”

“…The conceptualization summarizes the objects and concepts that are assumed to be of interest in a particular domain and describes the relationships that exist between them (e.g., that the composition of a material is related to its electronic or magnetic properties, or the crystal structure to structural porosity) [58a, 66] . No ontology of catalysis science exists, but there is an awareness that development is necessary [8b, 48, 58a, 67] . Efforts to develop common ontologies are currently led, for example, by the NFDI4Cat consortium in Germany, [8d, 68] but the catalysis community must increase their involvement and get into action to make the proposal a reality and develop community standards.…”