A Community Roadmap for Scientific Workflows Research and Development

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together.

2022-11-03T09:58:05+01:00December 28, 2021|Publications|Comments Off on A Community Roadmap for Scientific Workflows Research and Development

FAIR Data Reuse – the Path through Data Citation

Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse

2022-11-03T09:58:09+01:00January 1, 2020|Publications|Comments Off on FAIR Data Reuse – the Path through Data Citation

FAIR Computational Workflows

Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

2022-11-03T09:58:09+01:00January 1, 2020|Publications|Comments Off on FAIR Computational Workflows

FAIR Principles: Interpretations and Implementation Considerations

We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future.

2022-11-03T09:58:09+01:00January 1, 2020|Publications|Comments Off on FAIR Principles: Interpretations and Implementation Considerations

Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.

2022-11-03T09:58:10+01:00January 1, 2020|Publications|Comments Off on Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR
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