- Implementing FAIR Data Infrastructures (Dagstuhl Perspectives Workshop 18472). Natalia Manola, Peter Mutschke, Guido Scherp, Klaus Tochtermann, and Peter Wittenburg. In Dagstuhl Reports, Volume 8, Issue 11, pp. 91-111, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)
- Implementing FAIR Data Infrastructures (Dagstuhl Perspectives Workshop 18472). Natalia Manola, Peter Mutschke, Guido Scherp, Klaus Tochtermann, Peter Wittenburg, Kathleen Gregory, Wilhelm Hasselbring, Kees den Heijer, Paolo Manghi, and Dieter Van Uytvanck. In Dagstuhl Manifestos, Volume 8, Issue 1, pp. 1-34, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)
“Open Science” reflects a fundamental paradigm shift in making scientific research more accessible and reusable. The Open Science movement has recently gained a strong momentum worldwide with an increasing demand for reliable and sustainable research data infrastructures that enable researchers to cooperate on data and share results. On the European level, for example, the European Open Science Cloud is being developed. In this context the so-called FAIR principles seem to become a common and widely accepted conceptual basis for future research data infrastructures.
These principles describe the core characteristics of data use, but they do not define or suggest any technological implementations. Thus, there is still a great lack of models, infrastructures and services available showing how FAIR research data infrastructures can be implemented. Computer science can greatly contribute to this important field.
The interdisciplinary Dagstuhl Perspectives Workshop “Implementing FAIR Data Infrastructures” aims at bringing together computer scientists with digital infrastructure experts from different domains. The central goal is to discuss the key elements required for the transition of scientific e-infrastructures and services to Open Science from the perspective of computer science as well as from different stakeholder perspectives. The workshop will discuss requirements, common mechanisms, and best practices for FAIR data management platforms and services, with a particular focus on innovative tools and methods for sharing research data and on principles of how research data can be represented in an interoperable way to foster linking and reusing data across community borders.
The Workshop will publish a manifesto of recommendations and an inventory of tools & services for implementing FAIR data principles and other targeted aspects of Open Science in future research data infrastructures and data management services. It will further shape the role that the field of computer science has to play in advancing Open Science practices.
The Dagstuhl Perspectives Workshop on "Implementing FAIR Data Infrastructure" aimed at bringing together computer scientists and digital infrastructure experts from different domains to discuss challenges, open issues, and technical approaches for implementing the so-called FAIR Data Principles in research data infrastructures. Moreover, the workshop aimed to shape the role of and to develop a vision for computer science for the next years in this field, and to work out the potentials of computer science in advancing Open Science practices.
In the context of Open Science, and the European Open Science Cloud (EOSC) in particular, the FAIR principles seem to become a common and widely accepted conceptual basis for future research data infrastructures. The principles consist of the four core facets that data must be Findable, Accessible, Interoperable, and Reusable in order to advance the discoverability, reuse and reproducibility of research results. However, the FAIR principles are neither a specific standard nor do they suggest specific technologies or implementations. They describe the core characteristics of data use. Thus, the FAIR principles cover a broad range of implementation solutions. This certainly incorporates the risk of having a highly fragmented set of solutions at the end of the day.
Given this, and in view of the "need for a fast track implementation initiative [of the EOSC]", it is strongly needed to turn the principles into practice. Therefore, the workshop took the recommendations of the European Commission Expert Group on FAIR Data "Turning FAIR into reality" as a starting point and discussed what can be done next from the perspective of computer science to enable data providers to make their data FAIR.
The workshop started with three ignition talks on the wider background and context of the FAIR principles (given by Peter Wittenburg), the relationship of FAIR to Open Data (given by Natalia Manola) and the role of the principles within the EOSC (given by Klaus Tochtermann). Based on these talks as well as inputs from all participants in the forefront of the workshop, we have split the discussion into three working groups addressing, for each of the four principles, the main key challenges for implementing FAIR and the question what and how computer science can contribute to these key challenges. Based on the results of these three initial working groups we furthermore split into more focused groups addressing the problem of licenses w.r.t. data use, (self)improvement of FAIRification, and the relation of FAIR and data intensive science.
Finally, we identified three major areas to be addressed in the manifesto which we discussed in three further working groups:
- Infrastructures & Services Aspects: This group focused on the question by which technical means research data infrastructures and data services can be advanced to better address and fulfil the FAIR principles.
- Computer Science Research Topics: The working group discussed the relationship of research areas in computer science and topics relevant to implement FAIR data infrastructures.
- FAIR Computer Science Research: While the other two groups mainly focused on the contribution of computer science to implement FAIR, this working group addressed the question how the FAIR principles are currently adopted by computer science research itself and what should be improved.
The participants will continue their work in the aforementioned issues, and a manifesto is foreseen to be ready by mid May 2019.
- Marcel R. Ackermann (LZI Schloss Dagstuhl & dblp Trier) [dblp]
- Luiz Dlavo Bonino da Silva Santos (GO FAIR - Leiden, NL)
- Timothy W. Clark (University of Virginia, US) [dblp]
- Ron Dekker (CESSDA ERIC, NO) [dblp]
- Kees den Heijer (TU Delft, NL)
- Michel Dumontier (Maastricht University, NL) [dblp]
- Marie Farge (ENS - Paris and CNRS, FR) [dblp]
- Sascha Friesike (VU University of Amsterdam, NL) [dblp]
- Carole Goble (University of Manchester, GB) [dblp]
- Kathleen Gregory (NL) [dblp]
- Gregor Hagedorn (Museum für Naturkunde - Berlin, DE) [dblp]
- Wilhelm Hasselbring (Universität Kiel, DE) [dblp]
- Oliver Kohlbacher (Universität Tübingen, DE) [dblp]
- Paolo Manghi (ISTI-CNR - Pisa, IT) [dblp]
- Natalia Manola (University of Athens, GR) [dblp]
- Daniel Mietchen (University of Virginia, US) [dblp]
- Peter Mutschke (GESIS - Köln, DE) [dblp]
- Heike Neuroth (FH Potsdam, DE) [dblp]
- Andreas Rauber (TU Wien, AT) [dblp]
- Marc Rittberger (DIPF - Frankfurt am Main, DE) [dblp]
- Raphael Ritz (Max Planck Computing and Data Facility - Garching, DE) [dblp]
- Guido Scherp (ZBW-Leibniz-Informationszentrum Wirtschaft - Kiel, DE) [dblp]
- Birgit Schmidt (SuB - Göttingen, DE) [dblp]
- Achim Streit (KIT - Karlsruher Institut für Technologie, DE) [dblp]
- Klaus Tochtermann (ZBW-Leibniz-Informationszentrum Wirtschaft - Kiel, DE) [dblp]
- Dieter Van Uytvanck (CLARIN ERIC - Utrecht, NL) [dblp]
- Tobias Weigel (DKRZ Hamburg, DE) [dblp]
- Mark D. Wilkinson (Polytechnic University of Madrid, ES) [dblp]
- Peter Wittenburg (Max Planck Computing and Data Facility - Garching, DE) [dblp]
- data bases / information retrieval
- semantics / formal methods
- Open Science
- Open Data
- FAIR principles
- Research Data Infrastructures