Active GO FAIR Implementation Network
DiscoveryIN: Open User Interfaces for Increased Visibility of Research Results
Research data are among the fastest growing openly accessible scientific outputs on the web. While we have made great strides when it comes to accessibility of scientific data, discoverability is seriously lacking. Many frontends are designed from the systems’ rather than the users’ perspective and fail to cover use cases and requirements of researchers and other stakeholders of research. Moreover, several new entrants to the data discovery market are following a closed and proprietary model, which means that these services and interfaces cannot be reused, preventing innovation and possibly causing high costs and new paywalls down the line. The Discovery IN sets out to change this.
Main purpose and objectives
The main purpose of the Discovery IN is to provide interfaces and other user-facing services for data discovery across disciplines. We explore new and innovative ways of enabling discovery, including visualizations, recommender systems, semantics, content mining, annotation, and responsible metrics. We apply user involvement and participatory design to increase usability and usefulness of the solutions. We go beyond academia, involving users from all stakeholders of research data. We create FAIR and open infrastructures, following the FAIR principles complemented by the principles of open source, open data, and open content, thus enabling reuse of interfaces and user-facing services and continued innovation.
Our main objectives are:
- Improve visibility and discoverability of research data across disciplines
- Increase reuse of FAIR data and therefore efficiency and effectiveness of research
- Provide open alternatives to closed and proprietary infrastructures for data discovery
We see the FAIR principles as a precondition to discoverability. Nevertheless, discoverability is an attribute of the infrastructure rather than the data themselves.
Deliverables / Primary Tasks
- Stocktaking: We will identify relevant open indices and innovative open source interfaces and user-facing services to be (re-)used, as well as the main use cases we want to address.
- Structuring: We will define the standards and structure of an an open ecosystem of services and interfaces for data discovery that fulfils the use cases identified above.
- Implementation: We will work towards implementation of the ecosystem laid out above.
All results will be made accessible to the GO FAIR community and published under an open license. Reports and deliverables will be published on Zenodo.
Download the manifesto as a pdf-file here.
Peter Kraker, Open Knowledge Maps
Are you interested in joining the DiscoveryIN? Please express your interest by filling in the form below. Your request will be forwarded to the GO BUILD pillar coordinator and he will get in touch as soon as possible.
Tina Heger – University of Potsdam and Technical University of Munich (Germany)
Organisational members (with contact persons)
Peter Kraker – Open Knowledge Maps (Austria, lead)
Christian Pietsch, Jochen Schirrwagen – BASE (Germany)
Robert Jäschke – Berlin School of Library and Information Science, HU Berlin (Germany)
Ron Dekker, Carsten Thiel – CESSDA ERIC (Norway)
Petr Knoth, Nancy Pontika – CORE (UK)
Helena Cousijn – DataCite (US)
Heinrich Widmann, Mark van de Sanden – EUDAT (EU)
Nataliia Sokolovska – HIIG (Germany)
Dan Whaley – Hypothes.is (US)
Jonathan Jeschke – IGB – Leibniz-Institute of Freshwater Ecology and Inland Fisheries (Germany)
Jason Priem, Heather Piwowar – Impactstory (US/Canada)
Elisabeth Lex – Know-Center (Austria)
Francesca Di Donato – Net7 (Italy)
Antica Culina – NIOO-KNAW (Netherlands)
Claudio Atzori, Alessia Bardi – OpenAIRE (EU)
Suzanne Dumouchel – OPERAS (EU)
Girija Goyal – ReFigure (US)
Egon Willighagen – Scholia (EU/US)
Lambert Heller, TIB – Leibniz Information Centre for Science and Technology (Germany)
Isabella Peters – ZBW – Leibniz Information Centre for Economics (Germany)