Abstract
Open research data practices are a relatively new, thus still evolving part of scientific work, and their usage varies strongly within different scientific domains. In the literature, the investigation of open research data practices covers the whole range of big empirical studies covering multiple scientific domains to smaller, in depth studies analysing a single field of research. Despite the richness of literature on this topic, there is still a lack of knowledge on the (open) research data awareness and practices in materials science and engineering. While most current studies focus only on some aspects of open research data practices, we aim for a comprehensive understanding of all practices with respect to the considered scientific domain. Hence this study aims at 1) drawing the whole picture of search, reuse and sharing of research data 2) while focusing on materials science and engineering. The chosen approach allows to explore the connections between different aspects of open research data practices, e.g. between data sharing and data search. In depth interviews with 13 researchers in this field were conducted, transcribed verbatim, coded and analysed using content analysis. The main findings characterised research data in materials science and engineering as extremely diverse, often generated for a very specific research focus and needing a precise description of the data and the complete generation process for possible reuse. Results on research data search and reuse showed that the interviewees intended to reuse data but were mostly unfamiliar with (yet interested in) modern methods as dataset search engines, data journals or searching public repositories. Current research data sharing is not open, but bilaterally and usually encouraged by supervisors or employers. Project funding does affect data sharing in two ways: some researchers argue to share their data openly due to their funding agency’s policy, while others face legal restrictions for sharing as their projects are partly funded by industry. The time needed for a precise description of the data and their generation process is named as biggest obstacle for data sharing. From these findings, a precise set of actions is derived suitable to support Open Data, involving training for researchers and introducing rewards for data sharing on the level of universities and funding bodies.
Highlights
Introduction and motivationThe availability of research data affects the work of every researcher to a greater or lesser extent
Two example types of data will be named, which were mentioned by four researchers each, all of them working on granular materials
In this study, detailed interviews were conducted on the current state of open research data practices of 13 researchers in the field of granular materials, which is part of materials science and engineering
Summary
Introduction and motivationThe availability of research data affects the work of every researcher to a greater or lesser extent. With background in mathematics started working in a new field, granular materials, and faced the problems arising from the lack of openly available data. In the literature, both experimental and computational data was published in processed form, but it was the raw data, which would have facilitated the own scientific work. From the author’s experiences, this funding scheme was very attractive, and a project was funded, which included to generate and share all research data needed for a specific purpose: the validation of Discrete Element Models for granular materials. It was decided to publish research (experimental) data accompanied with classical research articles to minimise the needed work for the description of the data (experiments). Three article/data set pairs are published [1,2,3,4,5,6], including 3D scan data or direct shear tests and uniaxial compression tests of two types of railway ballast
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