This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles. Simultaneously, it will analyse how easy it is for data archives to adhere to the principles. The research took place from November 2016 to January 2017, and will be underpinned with feedback from the repositories. The FAIR Data Principles feature 15 facets corresponding to the four letters of FAIR - Findable, Accessible, Interoperable, Reusable. These principles have already gained traction within the research world. The European Commission has recently expanded its demand for research to produce open data. The relevant guidelines1are explicitly written in the context of the FAIR Data Principles. Given an increasing number of researchers will have exposure to the guidelines, understanding their viability and suggesting where there may be room for modification and adjustment is of vital importance. This practice paper is connected to a dataset(Dunning et al.,2017) containing the original overview of the sample group statistics and graphs, in an Excel spreadsheet. Over the course of two months, the web-interfaces, help-pages and metadata-records of over 40 data repositories have been examined, to score the individual data repository against the FAIR principles and facets. The traffic-light rating system enables colour-coding according to compliance and vagueness. The statistical analysis provides overall, categorised, on the principles focussing, and on the facet focussing results. The analysis includes the statistical and descriptive evaluation, followed by elaborations on Elements of the FAIR Data Principles, the subject specific or repository specific differences, and subsequently what repositories can do to improve their information architecture.@en

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), " Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment”. However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.@en

Research data management (RDM) is increasingly important in scholarship. Many researchers are, however, unaware of the benefits of good RDM and unsure about the practical steps they can take to improve their RDM practices. Delft University of Technology (TU Delft) addresses this cultural barrier by appointing Data Stewards at every faculty. By providing expert advice and increasing awareness, the Data Stewardship project focuses on incremental improvements in current data and software management and sharing practices. This cultural change is accelerated by the Data Champions who share best practices in data management with their peers. The Data Stewards and Data Champions build a community that allows a discipline-specific approach to RDM. Nevertheless, cultural change also requires appropriate rewards and incentives. While local initiatives are important, and we discuss several examples in this paper, systemic changes to the academic rewards system are needed. This will require collaborative efforts of a broad coalition of stakeholders and we will mention several such initiatives. This article demonstrates that community building is essential in changing the code and data management culture at TU Delft.

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