Abstract
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.
Highlights
The engineering materials sector has relevance for all industrial domains, from electronics and nanotechnologies to energy production and aerospace
The Open Research Data (ORD) pilot clearly resonates with the European scientific community
The extent of participation in the ORD pilot provides a concrete indicator of its success, the experience of the authors suggests scope for improvement, at the proposal stage
Summary
The engineering materials sector has relevance for all industrial domains, from electronics and nanotechnologies to energy production and aerospace. The FAIR data principles [11] offer a solution to this challenge, whereby findability, accessibility, interoperability and reusability are identified as the core 'attributes which are essential to extract the full scientific value from data resources and to unleash the potential for largescale, machine-driven analysis' [12] In this context, the Horizon 2020 Open Research Data pilot was not an exercise to promote responsible management of the research data but was an initiative with immediate relevance to policy imperatives in the industrial and scientific sectors. To have value and become useful to others, such data needs to meet specific requirements in accordance with the FAIR principles To this end, a DMP allows all stages of the data lifecycle to be documented so that a permanent record exists of the data that are generated, processed or collected throughout the project. The DMP needs to give attention to the dissemination level and whether data access is open or restricted, preferably with justifications
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