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Article

Lessons Learnt from Engineering Science Projects Participating in the Horizon 2020 Open Research Data Pilot

by 1,*,†,‡, 2,†, 2,† and 2,*,†
1
Joint Research Centre (JRC), European Commission, 1755 LE Petten, The Netherlands
2
Data Science Group, Innovation in Research & Engineering Solutions (IRES), Rue Koningin Astridlaan 59B, 1780 Wemmel, Belgium
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
The views expressed are purely those of the author and may not in any circumstances be regarded as stating an official position of the European Commission.
Academic Editor: Juanle Wang
Received: 14 July 2021 / Revised: 13 August 2021 / Accepted: 17 August 2021 / Published: 6 September 2021
(This article belongs to the Section Information Systems and Data Management)
Trends in the sciences are indicative of data management becoming established as a feature of the mainstream research process. In this context, the European Commission introduced an Open Research Data pilot at the start of the Horizon 2020 research programme. This initiative followed the success of the Open Access pilot implemented in the prior (FP7) research programme, which thereafter became an integral component of Horizon 2020. While the Open Access phenomenon can reasonably be argued to be one of many instances of web technologies disrupting established business models (namely publication practices and workflows established over several centuries in the case of Open Access), initiatives designed to promote research data management have no established foundation on which to build. For Open Data to become a reality and, more importantly, to contribute to the scientific process, data management best practices and workflows are required. Furthermore, with the scientific community having operated to good effect in the absence of data management, there is a need to demonstrate the merits of data management. This circumstance is complicated by the lack of the necessary ICT infrastructures, especially interoperability standards, required to facilitate the seamless transfer, aggregation and analysis of research data. Any activity aiming to promote Open Data thus needs to overcome a number of cultural and technological challenges. It is in this context that this paper examines the data management activities and outcomes of a number of projects participating in the Horizon 2020 Open Research Data pilot. The result has been to identify a number of commonly encountered benefits and issues; to assess the utilisation of data management plans; and through the close examination of specific cases, to gain insights into obstacles to data management and potential solutions. Although primarily anecdotal and difficult to quantify, the experiences reported in this paper tend to favour developing data management best practices rather than doggedly pursue the Open Data mantra. While Open Data may prove valuable in certain circumstances, there is good reason to claim that managed access to scientific data of high inherent intellectual and financial value will prove more effective in driving knowledge discovery and innovation. View Full-Text
Keywords: Horizon 2020; data management plan; advanced characterisation; interoperability; materials properties; digitisation Horizon 2020; data management plan; advanced characterisation; interoperability; materials properties; digitisation
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MDPI and ACS Style

Austin, T.; Bei, K.; Efthymiadis, T.; Koumoulos, E.P. Lessons Learnt from Engineering Science Projects Participating in the Horizon 2020 Open Research Data Pilot. Data 2021, 6, 96. https://doi.org/10.3390/data6090096

AMA Style

Austin T, Bei K, Efthymiadis T, Koumoulos EP. Lessons Learnt from Engineering Science Projects Participating in the Horizon 2020 Open Research Data Pilot. Data. 2021; 6(9):96. https://doi.org/10.3390/data6090096

Chicago/Turabian Style

Austin, Timothy, Kyriaki Bei, Theodoros Efthymiadis, and Elias P. Koumoulos. 2021. "Lessons Learnt from Engineering Science Projects Participating in the Horizon 2020 Open Research Data Pilot" Data 6, no. 9: 96. https://doi.org/10.3390/data6090096

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