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. (1) H2020 Guidelines on FAIR Data Management:http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf

One of the biggest challenges for multidisciplinary research institutions which provide data management support to researchers is addressing disciplinary differences1. Centralised services need to be general enough to cater for all the different flavours of research conducted in an institution. At the same time, focusing on the common denominator means that subject-specific differences and needs may not be effectively addressed. In 2017, Delft University of Technology (TU Delft) embarked on an ambitious Data Stewardship project, aiming to comprehensively address data management needs across a multi-disciplinary campus. In this practice paper, we describe the principles behind the Data Stewardship project at TU Delft, the progress so far, we identify the key challenges and explain our plans for the future.

One of the biggest challenges for multidisciplinary research institutions which provide data management support to researchers is addressing disciplinary differences (Akers and Doty,2013). Centralised services need to be general enough to cater for all the different flavours of research conducted in an institution. At the same time, focusing on the common denominator means that subject-specific differences and needs may not be effectively addressed. In 2017, Delft University of Technology (TU Delft) embarked on an ambitious Data Stewardship project, aiming to comprehensively address data management needs across a multi-disciplinary campus. In this article we describe the principles behind the Data Stewardship project at TU Delft, the progress so far, identify the key challenges and explain our plans for the future.

Founded in 2008 as an initiative of the libraries of three of the four technical universities in the Netherlands, the 4TU.Centre for Research Data (4TU.Research Data) provides since 2010 a fully operational, cross-institutional, long-term archive that stores data from all subjects in applied sciences and engineering. Presently, over 90% of the data in the archive is geoscientific data coded in netCDF (Network Common Data Form) – a data format and data model that, although generic, is mostly used in climate, ocean and atmospheric sciences. In this practice paper, we explore the question of how 4TU.Research Data can stay relevant and forward-looking in a rapidly evolving research data management landscape. In particular, we describe the motivation behind this question and how we propose to address it.

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