Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud

The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the breadth of these interpretations. In observing this creeping spread of interpretation, several of the original authors felt it was now appropriate to revisit the Principles, to clarify both what FAIRness is, and is not

FROM POTENTIAL TO REALIZED IMPACTS: THE BRIDGING ROLE OF DIGITAL INFRASTRUCTURES IN FAIR DATA

Science funders, research institutions and policymakers have been investing heavily in building digital infrastructures that will enable findable, accessible, interoperable and reusable (FAIR) data. Despite the enthusiasm for these infrastructures, many scientists still do not understand how they can be leveraged to advance their research goals. To address this gap, we conduct an inductive qualitative study of a digital infrastructure supporting FAIR data and propose a framework exploring how these infrastructures enable new capabilities in researchers’ workflows. First, we find that these infrastructures facilitate the seamless transition between the storage and analysis of data for new insights. Second, we find that these infrastructures enable researchers to extend their typical individual research workflow to larger scales of collaboration. By exploring the bridging role of digital infrastructures in FAIR, our study hopes to inform the scientific community and policymakers on how to accelerate the adoption of FAIR practices and maximize the future impact of these infrastructures currently under development

U.S. Department of the Interior: Sharing FAIR Data Fairly

Government-produced data are consumed by thousands of scientists, researchers, industries, and students around the world daily, but are often difficult to locate because they are collected and stored in a duplicative state at varying levels of quality, inhibiting their usefulness for data science investigations and analysis. To address these challenges, the United States Department of the Interior bureaus have been implementing FAIR Data Principles into their data sharing strategies since 2016. Differing interpretations of the FAIR Data Principles are leading to data that are not documented uniformly and are not properly integrated for reuse. In order to establish a FAIR baseline, analysis of select datasets is being performed with peer-reviewed FAIR assessment tools. Delphi panels are being conducted with DOI Chief Data Officers and DOI Federal data consumers to gain insights as to how to affordably deliver this data according to the FAIR Data Principles

Recommendations for services in a FAIR data ecosystem

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

FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability.

The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we have liberated it from the tables and figures of journal articles, making it more accessible and usable by immunologists. Recently, the principles of Findability, Accessibility, Interoperability and Reusability have been formulated as goals that data repositories should meet to enhance the usefulness of their data holdings. We here examine how the IEDB complies with these principles and identify broad areas of success, but also areas for improvement. We describe short-term improvements to the IEDB that are being implemented now, as well as a long-term vision of true 'machine-actionable interoperability', which we believe will require community agreement on standardization of knowledge representation that can be built on top of the shared use of ontologies

How FAIR are CMC Corpora?

In recent years, research data management has also become an important topic in the less data-intensive areas of the Social Sciences and Humanities (SSH). Funding agencies as well as research communities demand that empirical data collected and used for scientific research is managed and preserved in a way that research results are reproducible. In order to account for this the FAIR guiding principles for data stewardship have been established as a framework for good data management, aiming at the findability, accessibility, interoperability, and reusability of research data. This article investigates 24 European CMC corpora with regard to their compliance with the FAIR principles and discusses to what extent the deposit of research data in repositories of data preservation initiatives such as CLARIN, Zenodo or Metashare can assist in the provision of FAIR corpora

How FAIR is MARC?: FAIR Data Principles applied to a bibliographic data standard

FAIR Data Principles provide a framework for considering how best to make data available in a way that is 1) findable, 2) accessible, 3) interoperable, and 4) reusable. Designed to be simple to understand and machine-actionable, FAIR principles support data use and reuse. This conceptual paper investigates the application of FAIR principles to bibliographic data through an examination of the current standard for encoding library records, MARC. To this end, this paper begins by describing the FAIR principles. It then looks to understand the MARC standard and applies the FAIR principles to the data affordances provided by the MARC encoding itself. In doing so, it probes the question of the extent to which MARC, as a standard, is FAIR. Ultimately, MARC is historically designed for machine-readability, not machine-actionability; although it is well suited to the description of bibliographic materials and is widely used, it does not adhere fully to any of the four FAIR principles. Even so, this examination suggests that FAIR principles could be useful in assessing specific MARC record datasets, particularly as bibliographic data is more widely shared and reused

A comprehensive comparison of automated FAIRness Evaluation Tools

The FAIR Guiding Principles (Findable, Accessible, Interop- erable, and Reusable) have been widely endorsed by the scientific community, funding agencies, and policymakers. However, the FAIR principles leave ample room for different implementations, and several groups have worked towards manual, semi-automatic, and automatic approaches to evaluate the FAIRness of digital objects. This study compares and con- trasts three automated FAIRness evaluation tools namely F-UJI, the FAIR Evaluator, and FAIR Checker. We examine three aspects: 1) tool characteristics, 2) the evaluation metrics, and 3) metrics tests for three public datasets. We find significant differences in the evaluation results for tested resources, along with differences in the design, implementation, and documentation of the evaluation metrics and platforms. While auto- mated tools do test a wide breadth of technical expectations of the FAIR principles, we put forward specific recommendations for their improved utility, transparency, and interpretability