Recommendations & Outputs Catalogue

RDA Outputs are the technical and social infrastructure solutions developed by RDA Working Groups or Interest Groups that enable data sharing, exchange, and interoperability.

These Outputs have an important impact in two areas: solving problems, and incorporation and/or adoption in infrastructure environments by individuals, projects and organisations. As an organisation, RDA’s goal is to expand awareness and adoption of these Outputs, and hence their impact, within all regions of the world. RDA Outputs are products of the respective Working or Interest Group and should be demonstrably developed and endorsed by the group. Each Output should have the respective Working or Interest Group listed as an author where appropriate.

RDA Outputs are classified as RDA Recommendations (official, endorsed results of RDA Groups), Supporting Outputs (useful solutions from our RDA Working and Interest Groups) or other Outputs – more information can be found at https://www.rd-alliance.org/what-are-recommendations-and-outputsThey are all listed below and can be searched according to their focus, scientific domain, or by status using the filters on the right. Filters can be combined, too (if more than one filter is selected, results sum up).

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1-4 of 4 Results
Interest Group Output
Ten principles to improve dataset discoverability
Review Status: Endorsed
Authors: Mingfang Wu, Kathleen Gregory, Felicitas Löffler, Brigitte Mathiak, Fotis Psomopoulos, Uwe Schindler, Amir Aryani, Jordi BODERA SEMPERE, Leyla Jael Castro, Antica Culina, Andreas Czerniak, Christopher Erdmann, Jeffrey Grethe, Maggie Hellström, Christin Henzen, Christopher Hunter, Nick Juty, Live Kvale, Allyson Lister, Ying-Hsang Liu, Bénédicte Madon, Andrea Medina-Smith, Graham Parton, Samantha Kanza, Andrea Pörsch, Emanuel Soeding, Dimitri Szabo, Lucas van der Meer, Nina Weisweiler, Heinrich Widmann, CJ Woodford
The  FAIR (meta) data principles provide overarching guidelines to make metadata and data Findable, Accessible, Interoperable and Reusable. While significant effort has been dedicated to specific recommendations that enable best practices in implementing FAIR principles, particularly from a data curation perspective, this document focuses primarily on enhancing the discoverability of data from the perspectives of…
July 15, 2024
Interest Group Output
RDA Value for the Evaluation of Research
Review Status: Endorsed
Authors: Francoise Genova, Emma Crott, Devika Madalli, Amy Nurnberger
The evaluation of research is evolving from being mostly bibliographic index-based to a broader context, which is now recognised as indispensable for enabling Open Research. In the same way that Open Research promotes the open sharing of FAIR data and other research outputs, evaluators must also value and consider these outputs as part of the…
July 3, 2024
Interest Group Output
Recommendations on Open Science Rewards and Incentives: Guidance for multiple stakeholders in Research.
Review Status: Endorsed
Authors: Laurence Mabile, Hanna Shmagun, Christopher Erdmann, Anne Cambon-Thomsen, Mogens Thomsen, Florencia Grattarola
Open Science contributes to the collective building of scientific knowledge and societal progress. However, academic research currently fails to recognise and reward efforts to share research outputs. Yet it is crucial that such activities be valued, as they require considerable time, energy, and expertise to make scientific outputs usable by others, as stated by the…
June 25, 2024
Interest Group Output
FAIR Principles for Research Software (FAIR4RS Principles)
Review Status: Endorsed
Authors: Paula Andrea Martinez
FAIR for Research Software (FAIR4RS) WG Group co-chairs: Michelle Barker, Paula Andrea Martinez, Leyla Garcia, Daniel S. Katz, Neil Chue Hong, Jennifer Harrow, Fotis Psomopoulos, Carlos Martinez-Ortiz, Morane Gruenpeter Recommendation Title: FAIR Principles for Research Software (FAIR4RS Principles) This output has been superseded by the FAIR Principles for Research Software (FAIR4RS Principles) DOI: 10.15497/RDA00068 Authors: Neil P. Chue Hong*, Daniel S. Katz*, Michelle Barker*;…
June 10, 2021