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An automated solution for measuring the progress toward FAIR research data

ABSTRACT: Summary With a rising number of scientific datasets published and the need to test their Findable, Accessible, Interoperable, and Reusable (FAIR) compliance repeatedly, data stakeholders have recognized the importance of an automated FAIR assessment. This paper presents a programmatic solution for assessing the FAIRness of research data. We describe the translation of the FAIR data principles into measurable metrics and the application of the metrics in evaluating FAIR compliance of research data through an open-source tool we developed. For each metric, we conceptualized and implemented practical tests drawn upon prevailing data curation and sharing practices, and the paper discusses their rationales. We demonstrate the work by evaluating multidisciplinary datasets from trustworthy repositories, followed by recommendations and improvements. We believe our experience in developing and applying the metrics in practice and the lessons we learned from it will provide helpful information to others developing similar approaches to assess different types of digital objects and services. Highlights • A set of core metrics for assessing the FAIR compliance of scientific data• An automated assessment tool for applying the metrics in practice• FAIR assessments are drawn upon widely adopted data sharing practices• Tested and used in the context of diverse data repositories The bigger picture In recent years, there has been a strong recommendation from funders, publishers, and research organizations on adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles to maximize scientific data availability and reuse. However, measuring the FAIRness of research data in practice is challenging. The number of datasets has proliferated in scientific research, and new datasets are emerging from various sources. This paper aims to contribute to the FAIR data assessment through a set of core metrics elaborating the principles and an automated tool that supports FAIR evaluation based on the metrics. The metrics are built on established work and consider standard data practices. The tool development is collaborative and iterative. The consultative process has motivated the repositories to refine their data services. Further pilots are planned with the repositories in the European Open Science Cloud. The broader goal is to adapt the solution to assess other digital objects such as vocabularies and software. One of the open questions around the FAIR assessment is how to measure FAIRness of the scientific data in practice. We found that explicit and machine-assessable metrics drawn upon prevailing data sharing practices are vital to evaluating FAIR data programmatically. An automated FAIR assessment is continuous development and must go beyond the object itself. FAIR-enabling repositories should evolve in parallel to support continued access to and long-term preservation of FAIR data.

SUBMITTER: Devaraju A

PROVIDER: S-EPMC8600246 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. OBJECTIVE:This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. METHODS:The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. RESULTS:The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. CONCLUSIONS:To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

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An automated solution for measuring the progress toward FAIR research data

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