The course Data Intelligence 4 Librarians was developed by 3TU.Datacentrum at the end of 2011 to provide online resources and training for digital preservation practitioners, specifically for library staff. The course is intended to overcome the insecurity and perceived lack of knowledge about data management which prevents library staff from proactively providing support to research staff with the management, storage and sharing of their research data. The course objectives are: 1) to transfer and exchange knowledge about data management, and 2) to provide participants with the skills required to advise researchers or research groups on efficient and effective ways of adding value to their data. The course is an additional service provided by 3TU.Datacentrum44 a digital repository for research data set up by the research libraries of the three Dutch Universities of Technology (3TU): Delft University of Technology, Eindhoven University of Technology, and the University of Twente. The paper describes the process of creating the course, the methodology, and the results of the two courses in 2012. There were three phases to creating the course: 1) an investigation of the training needs, 2) the design of the course and 3) the development of the training materials. The training needs could be divided into hard skills (such as data management basics, data citation and knowledge of the data browser interface) and soft skills (such as acquisition skills, advisory skills and a reflective attitude towards their profession). This has led to a course with a competency-based modular design, consisting of four modules. The project team consisted of 3TU.Datacentrum staff. The team designed a blended learning course, composed of a didactical mix of group meetings, online study and homework assignments, including the website Data Intelligence45 that provides online reference material. By using a modern collaboration environment and social media, the course meets the needs of the modern scientific community. The training methodology proved effective in creating a solid base for digital preservation and a network of starting data librarians. In the first half of 2012, 14 librarians participated in the pilot course and provided feedback. The feedback has been used to improve the next cycle of the course, which started in September 2012. In February 2013 the third cycle starts. This paper demonstrates the choices made during the design process. Finally, future plans are discussed. They include expanding the course to make the materials also suitable to researchers and other data repositories in cooperation with organisations from The Netherlands.@en

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