7 Works

Tromsø recommendations for citation of research data in linguistics

Helene N. Andreassen, Andrea L. Berez-Kroeker, Lauren Collister, Philipp Conzett, Christopher Cox, Koenraad De Smedt, Bradley McDonnell &

WDS/RDA Assessment of Data Fitness for Use WG Outputs and Recommendations

WDS/RDA Assessment of Data Fitness for Use WG
This statement describes the background, efforts and outputs of the WDS/RDA Assessment of Data Fitness for Use Working Group. This group was chartered to develop criteria, procedures for assessment of research data fitness for use, along with a means to communicate this assessment to others. It concluded with the development of a) criteria for research dataset fitness for use compared against the CoreTrustSeal requirements and FAIR principles, and b) a checklist for evaluation of dataset...

39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition

Agrisemantic Working Group
This document presents the recommendations of the RDA Agrisemantics Working Group (WG) to promote the use of semantics for agricultural data for the purpose of enhancing data interoperability in agriculture. These recommendations are high-level, to encourage researchers and practitioners to extend them according to their area of expertise.

RDA Outputs Overview Presentation

Anthony Juehne
Page containing RDA Outputs overview presentations.

Matrix of use cases and functional requirements for research data repository platforms

The matrix describes the functional requirements identified for research data repository platforms. The functional requirements are based on eleven use cases that describe their requirements for digital repository platforms. The matrix provides a description for forty-four requirements and, for each one, provides a functional requirement score that is based on the use cases. Functional requirements of greater importance are identified with higher functional requirement scores. The functional requirement scores can be used to assess research...

RDA DMP Common Standard for Machine-actionable Data Management Plans

Paul Walk, Tomasz Miksa & Peter Neish

A curriculum for foundational Research Data Science skills for Early Career Researchers

This recommendation describes the curriculum and example materials to give Early Career Researchers (ECR’s) the foundational skills in Data Science to work with their data. This curriculum combines technical skills, such as Software Carpentry with responsible research practices such as Open and Responsible Research.

Registration Year

  • 2019
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Resource Types

  • Text
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