Hylke Koers’s scientific contributions

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


Recommendations for Services in a FAIR Data Ecosystem
  • Article
  • Full-text available

September 2020

·

39 Reads

·

6 Citations

Patterns

Hylke Koers

·

·

Emilie Hermans

·

[...]

·

Mustapha Mokrane

This article puts forward recommendations for data and infrastructure service providers to support findable, accessible, interoperable, and reusable (FAIR) research data within the scholarly ecosystem. Formulating such recommendations is important to coordinate progress in realizing a FAIR data ecosystem in which research data can be easily shared and optimally reused, with the aim of driving down inefficiencies in the current academic system and enabling new forms of data-driven discovery. Key recommendations—ranked by their perceived urgency—resulting from an extensive community consultation process include that (1) funders and institutions should consider FAIR alignment and data sharing as part of research assessment, among other criteria; (2) services should support domain-specific ontologies by identifying disciplines that lack ontologies and enriching existing registries of ontologies; (3) repositories should support FAIR data by developing tools, such as APIs, sharing best practices, and undergoing FAIR-aligned certification; and (4) institutions should support FAIR awareness and implementation by establishing data stewardship programs providing simple and intuitive training for researchers. The recommendations outlined in this article are meant to help guide the way forward to putting into practice the FAIR guiding principles for data management.

Download

Figure 1. A Model for FAIR Digital Objects, which Lies at the Heart of the Notion of a FAIR Ecosystem as Proposed in the ''Turning FAIR into Reality'' Report Figure reused from this report. 7
Figure 2. Approach to Prioritizing Recommendations
Figure 3. Prioritized Recommendations
Figure 4. Audience Input in the Form of Word Clouds on Possible Action Owners for Two of the Actions Defined by the Breakout Groups
Figure 5. Mapping of the Clustering of Recommendations Presented Here to the Categories Introduced in the ''Turning FAIR into Reality'' Report
Recommendations for Services in a FAIR Data Ecosystem

July 2020

·

729 Reads

·

46 Citations

Patterns

The development and growing adoption of the FAIR data principles and associated standards as a part of research policies and practices place novel demands on research data services. This article highlights common challenges and priorities and proposes a set of recommendations on how data infrastructures can evolve and collaborate to provide services that support the implementation of the FAIR data principles, in particular in the context of building the European Open Science Cloud (EOSC). The recommendations cover a broad area of topics, including certification, infrastructure components, stewardship, costs, rewards, collaboration, training, support, and data management. These recommendations were prioritized according to their perceived urgency by different stakeholder groups and associated with actions as well as suggested action owners. This article is the output of three workshops organized by the projects FAIRsFAIR, RDA Europe, OpenAIRE, EOSC-hub, and FREYA designed to explore, discuss, and formulate recommendations among stakeholders in the scientific community. While the results are a work-in-progress, the challenges and priorities outlined provide a detailed and unique overview of current issues seen as crucial by the community that can sharpen and improve the roadmap toward a FAIR data ecosystem.

Citations (1)


... Depending on the context and use case of data, all facets of FAIR Data Principles may not be required in data to be reusable. Dunning et al. [8] suggest including at least PID, machine-readable metadata, explicit licences, and access protocols for ease of sharing and optimal reuse of data [9]. Stakeholders in the STEM community should sustainably support FAIR Data Principles awareness, implementation, and practices for improving the FAIRness of digital materials and resources related to teaching, learning, and research. ...

Reference:

FAIRifying STEM Data Ecosystem to Enhance Data Reuse
Recommendations for Services in a FAIR Data Ecosystem

Patterns