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- ItemMoving towards FAIRness in Research Data and Software Management(Meyrin : CERN, 2020-07-03) Kraft, AngelinaPresentation during the Thüringer FDM Tage 2020 within the workshop "FAIR Research Software and Beyond: How to make the most of your code".
- ItemAdvancing Research Data Management in Universities of Science and Technology(Meyrin : CERN, 2020-02-13) Björnemalm, Matthias; Cappellutti, Federica; Dunning, Alastair; Gheorghe, Dana; Goraczek, Malgorzata Zofia; Hausen, Daniela; Hermann, Sibylle; Kraft, Angelina; Martinez Lavanchy, Paula; Prisecaru, Tudor; Sànchez, Barbara; Strötgen, RobertThe white paper ‘Advancing Research Data Management in Universities of Science and Technology’ shares insights on the state-of-the-art in research data management, and recommendations for advancement. A core part of the paper are the results of a survey, which was distributed to our member institutions in 2019 and addressed the following aspects of research data management (RDM): (i) the establishment of a RDM policy at the university; (ii) the provision of suitable RDM infrastructure and tools; and (iii) the establishment of RDM support services and trainings tailored to the requirements of science and technology disciplines. The paper reveals that while substantial progress has been made, there is still a long way to go when it comes to establishing “advanced-degree programmes at our major universities for the emerging field of data scientist”, as recommended in the seminal 2010 report ‘Riding the Wave’, and our white paper offers concrete recommendations and best practices for university leaders, researchers, operational staff, and policy makers. The topic of RDM has become a focal point in many scientific disciplines, in Europe and globally. The management and full utilisation of research data are now also at the top of the European agenda, as exemplified by Ursula von der Leyen addressat this year’s World Economic Forum.However, the implementation of RDM remains divergent across Europe. The white paper was written by a diverse team of RDM specialists, including data scientists and data stewards, with the work led by the RDM subgroup of our Task Force Open Science. The writing team included Angelina Kraft (Head of Lab Research Data Services at TIB, Leibniz University Hannover) who said: “The launch of RDM courses and teaching materials at universities of science and technology is a first important step to motivate people to manage their data. Furthermore, professors and PIs of all disciplines should actively support data management and motivate PhD students to publish their data in recognised digital repositories.” Another part of the writing team was Barbara Sanchez (Head of Centre for Research Data Management, TU Wien) and Malgorzata Goraczek (International Research Support / Data Management Support, TU Wien) who added:“A reliable research data infrastructure is a central component of any RDM service. In addition to the infrastructure, proper RDM is all about communication and cooperation. This includes bringing tools, infrastructures, staff and units together.” Alastair Dunning (Head of 4TU.ResearchData, Delft University of Technology), also one of the writers, added: “There is a popular misconception that better research data management only means faster and more efficient computers. In this white paper, we emphasise the role that training and a culture of good research data management must play.”
- ItemNFDI4Ing - the National Research Data Infrastructure for Engineering Sciences(Meyrin : CERN, 2020-09-25) Schmitt, Robert H.; Anthofer, Verena; Auer, Sören; Başkaya, Sait; Bischof, Christian; Bronger, Torsten; Claus, Florian; Cordes, Florian; Demandt, Évariste; Eifert, Thomas; Flemisch, Bernd; Fuchs, Matthias; Fuhrmans, Marc; Gerike, Regine; Gerstner, Eva-Maria; Hanke, Vanessa; Heine, Ina; Huebser, Louis; Iglezakis, Dorothea; Jagusch, Gerald; Klinger, Axel; Krafczyk, Manfred; Kraft, Angelina; Kuckertz, Patrick; Küsters, Ulrike; Lachmayer, Roland; Langenbach, Christian; Mozgova, Iryna; Müller, Matthias S.; Nestler, Britta; Pelz, Peter; Politze, Marius; Preuß, Nils; Przybylski-Freund, Marie-Dominique; Rißler-Pipka, Nanette; Robinius, Martin; Schachtner, Joachim; Schlenz, Hartmut; Schwarz, Annett; Schwibs, Jürgen; Selzer, Michael; Sens, Irina; Stäcker, Thomas; Stemmer, Christian; Stille, Wolfgang; Stolten, Detlef; Stotzka, Rainer; Streit, Achim; Strötgen, Robert; Wang, Wei MinNFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR – findable, accessible, interoperable, and re-usable. NFDI4Ing has been founded in 2017. The consortium has actively engaged engineers across all five engineering research areas of the DFG classification. Leading figures have teamed up with experienced infrastructure providers. As one important step, NFDI4Ing has taken on the task of structuring the wealth of concrete needs in research data management. A broad consensus on typical methods and workflows in engineering research has been established: The archetypes. So far, seven archetypes are harmonising the methodological needs: Alex: bespoke experiments with high variability of setups, Betty: engineering research software, Caden: provenance tracking of physical samples & data samples, Doris: high performance measurement & computation, Ellen: extensive and heterogeneous data requirements, Frank: many participants & simultaneous devices, Golo: field data & distributed systems. A survey of the entire engineering research landscape in Germany confirms that the concept of engineering archetypes has been very well received. 95% of the research groups identify themselves with at least one of the NFDI4Ing archetypes. NFDI4Ing plans to further coordinate its engagement along the gateways provided by the DFG classification of engineering research areas. Consequently, NFDI4Ing will support five community clusters. In addition, an overarching task area will provide seven base services to be accessed by both the community clusters and the archetype task areas. Base services address quality assurance & metrics, research software development, terminologies & metadata, repositories & storage, data security & sovereignty, training, and data & knowledge discovery. With the archetype approach, NFDI4Ing’s work programme is modular and distinctly method-oriented. With the community clusters and base services, NFDI4Ing’s work programme remains firmly user-centred and highly integrated. NFDI4Ing has set in place an internal organisational structure that ensures viability, operational efficiency, and openness to new partners during the course of the consortium’s development. NFDI4Ing’s management team brings in the experience from two applicant institutions and from two years of actively engaging with the engineering communities. Eleven applicant institutions and over fifty participants have committed to carrying out NFDI4Ing’s work programme. Moreover, NFDI4Ing’s connectedness with consortia from nearby disciplinary fields is strong. Collaboration on cross-cutting topics is well prepared and foreseen. As a result, NFDI4Ing is ready to join the National Research Data Infrastructure.