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

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.

Data sharing attitudes and practices of researchers in Korean government research institutes: a survey-based descriptive study

“…High-level bodies and funding agencies are the primary contributors to the culture and practice of data sharing [1]. For example, the European Commission implemented the Open Research Data (ORD) pilot in Horizon 2020 (H2020), which requires H2020-funded projects to develop data management plans (DMPs) and provide open access to research data [2]. The principle of ORD is that research data should be "as open as possible, as closed as necessary, " and it emphasizes sound data management rather than forcing all research data to be open [3].…”

“…The research questions were as follows: (1) To what extent do the researchers share their research data? (2) In what ways do they share research data? (3) What challenges do they perceive in relation to data sharing?…”

“…As shown in Table 3, sharing data with principal investigators or coinvestigators involved in collecting or creating data received the greatest proportion of the responses (n = 94, 36.3%), followed by sharing data with all participants in research projects where data were collected/created (n = 86, 33.2%). The findings indicate that restricted data sharing-"the exchange of data be-tween the members of a specific group or project consortium to their mutual benefit" [2]-was more common than data sharing on request (n = 60, 23.2%) or making data publicly accessible (n = 19, 7.3%).…”

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

“…The knowledge space depends on data accessibility. The European Commission directives in Europe require that data are as open as possible and shared (as closed as possible) within the interconnected users via the web [ 60 ]. A major initiative towards this direction is the European Horizon 2020 Open Research Data Pilot.…”

“…Hence, in Section 4 , a threefold pattern in the prediction of their biological activity profile is presented based on data-driven representations, which could be prone to the availability of data and bias due to the applied procedures for sample preparation (contamination, purification), including biological systems containing human/algae cells, as well as in the bionanomaterial–cell interfaces. As presented in Section 5 , the foundation for this scope is data mining, management, and curation to enrich new or existing databases, as it is commonly accepted that the catalyst for AI is the accessibility to structured and big data [ 59 , 60 ]. Consequently, the existing methods can be supported by the computational theory that focuses on data, which holds a lot of promise to greatly reduce the computational and experimental pressure for innovation.…”