** This GO FAIR Implementation Network is no longer active. Visit this webpage for an overview of current Implementation Networks. **
GO Inter: Cross-Domain Interoperability of Heterogeneous Research Data
Despite the existence of widely accepted standards for data representation and linking, research data are often described in heterogeneous and unstandardized ways. This results in domain-specific, disconnected “data silos” which makes discovery, linking and reuse of data across community borders challenging tasks. A key challenge of a cross-disciplinary implementation of the FAIR principles lies in the complexities of interoperability, whose different layers, ranging from encoding up to structural and semantic specifications of data, are yet to be fully understood. The GO Inter Implementation Network sets out to tackle this exactly, the “I” in FAIR. Its focus is on the specific question of semantic interoperability. This is especially needed when it comes to linking data from different communities. The implementation network will be research-driven from the beginning, i.e. it will be based on cross- community research use cases for which reference applications will be implemented.
Main purpose and objectives
The Implementation Network aims at applying, developing and evaluating methods, tools and guidelines for implementing and assessing semantic interoperability of heterogeneous research data across discipline borders. By this cross-domain interoperability framework GO Inter intends to foster data sharing and discovery across traditional discipline boundaries.
GO Inter’s main objectives are:
- To provide a cross-domain interoperability framework consisting of methods, tools and guidelines for implementing and assessing semantic interoperability of heterogeneous research data across discipline borders
- To develop and evaluate reference implementations of interoperability for real-world cross-domain research uses case
- To engage with other GO FAIR implementation networks and related initiatives to disseminate and exchange best practice solutions for cross-domain interoperability
Main tasks
- Review of existing technologies and standards as well as past and ongoing initiatives and projects which address the interoperability aspect of the FAIR principles
- Explore cross-domain use cases to better understand differences of interoperability, in particular the different layers of interoperability
- Identify the main hurdles for concerted and coordinated actions to implement measures to enhance interoperability
- Derive requirements for cross-domain interoperability from real-world cross-domain research use cases
- Provide assistance services that guide data providers in bringing (meta)data into common representation formats and schemes (such as schema.org, DCAT), in mapping their data to existing vocabularies and in making data available via standard protocols
- Provide an ontologies lookup service that work as a gatekeeper across different standards and domains and overcomes incongruences between different vocabularies
- Apply existing or provide novel measures and gradational maturity models for assessing cross-domain interoperability (see fairmetrics.org and fairsfair.eu)
- Develop and evaluate reference implementations for real-world use cases that link data from different communities, by applying common Web (W3C) standards and technologies as well as solutions proposed by initiatives like RDA, such as the Digital Object Interface Protocol (DOIP)
- Publish guidelines for implementing and assessing cross-domain interoperability
- Share results with the GO-FAIR community and other related initiatives and networks through common workshops.
Manifesto
Download the manifesto here
Contact
Peter Mutschke, GESIS
Reports
Results of the Kick-off Workshop (1-2 October 2019, GESIS, Cologne)
Report of the FAIR Digital Object hackathon (18-19 May 2020, GESIS, Cologne)
Report of the 2nd GO INTER hackathon (1-2 July 2020, virtual)
News
GO Inter established working group on semantics (published 8 July 2021)
Partners
- Peter Mutschke, Felix Bensmann, Benjamin Zapilko, GESIS – Leibniz Institute for the Social Sciences (GESIS), Cologne, Germany
- Atif Latif, Leibniz Information Centre for Economics (ZBW), Kiel, Germany
- Marc Rittberger, Daniel Schiffner, German Institute for International Educational Research (DIPF), Frankfurt, Germany
- Gotthard Meinel, Sujit Kumar Sikder, Leibniz Institute of Ecological Urban and Regional Development (IOER), Dresden, Germany
- Michael Bosnjak, Roland Ramthun, Tanja Burgard, Leibniz Institute for Psychology Information (ZPID), Trier, Germany
- York Sure-Vetter, Michael Färber, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
- Andrea Scharnhorst, Kathleen Gregory, Gerard Coen, Ronald Siebes, Herbert van de Sompel, Data Archiving and Networked Services (DANS), Den Haag, The Netherlands
- Richard P. Smiraglia, Institute for Knowledge Organization and Structure, Lake Oswego, Oregon, USA
- Robert Pergl, Czech Technical University in Prague (CTU), Czech Republic
- Yann le Franc, e-Science Data Factory, Paris, France
- Giancarlo Guizzardi, Free University of Bozen-Bolzano, Italy
- Tiago Prince Sales – University of Trento, Italy
- Michel Dumontier, Ricardo de Miranda Azevedo, Vincent Emonet, Chris Evelo, Maastricht University, The Netherlands
- Sören Auer, Markus Stocker, Oliver Koepler, TIB Hannover, Germany
- Oscar Corcho, Maria Poveda, Universidad Politécnica de Madrid, Spain
- Stefan Dietze, Heinrich-Heine-University Düsseldorf, GESIS – Leibniz Institute for the Social Sciences (GESIS), Germany
- Mathieu Daquin, Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
- John Domingue, Knowledge Media Institute, The Open University, Milton Keynes, UK
- Javad Chamanara, L3S Research Center, Leibniz University Hanover, Germany
- Paul Groth, University of Amsterdam, The Netherlands
- Julia Lane, New York University, USA
- Wouter Beek, Triply B.V. & guest researcher at VU University Amsterdam, The Netherlands
- Paolo Manghi, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” – CNR, Pisa, Italy
- Tobias Kuhn, VU University Amsterdam, The Netherlands
- Peter McQuilton, Oxford University, UK
- Carole Goble, The University of Manchester, UK
- John Graybeal, Stanford University, USA
- Ruth Duerr, Ronin Institute for Independent Scholarship
- Milan Ojsteršek, University of Maribor, Slovenia
- Barbara Magagna, Environment Agency Austria
- Beyza Yaman, ADAPT Centre (Dublin) and Dublin City University, Ireland
- Simon Hodson, CODATA (Committe on Data, Internatioal Science Council), Paris, France
- Stuart Chalk, University of North Florida, USA
- Paul Libbrecht, International University of Applied Sciences (IUBH Distance Learning), Bad Honnef, Germany
- Daniel Garijo, Information Sciences Institute, University of Southern California, USA
- Pierre Larmande, IRD – French Institute for Sustainable Development, Marseille, France
- Clement Jonquet, Montpellier, LIRMM (Laboratoire d’informatique, de Robotique et de Microélectronique de Montpellier)
- Biswanath Dutta, Indian Statistical Institute, Bangalore, India
- Guang Yuan Sun, Nanyang Technological University, Singapore
- Zachary Trautt, National Institute of Standards and Technology, Gaithersburg, USA
- Nikolay Garabedian, Karlsruhe Institute of Technology, IAM-CMS, Karlsruhe, Germany