[Paper Review] Moving towards FAIR practices in epidemiological research

Montserrat García‐Closas, Thomas U. Ahearn+13 more|arXiv (Cornell University)|Jun 13, 2022

Artificial Intelligence in Healthcare4 citations

TL;DR

This paper advocates for the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles in epidemiological research to enhance reproducibility, collaboration, and data/code sharing. By migrating data and code to the cloud, using open standards and open-source tools, researchers can improve accessibility and reusability, ultimately increasing scientific impact and equity while overcoming barriers related to confidentiality, cost, and incentives.

ABSTRACT

Reproducibility and replicability of research findings are central to the scientific integrity of epidemiology. In addition, many research questions require combiningdata from multiple sources to achieve adequate statistical power. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of sharing resources, both data and code. Epidemiological practices that follow FAIR principles can address these barriers by making resources (F)indable with the necessary metadata , (A)ccessible to authorized users and (I)nteroperable with other data, to optimize the (R)e-use of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to the Cloud, using machine-readable and non-proprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses, and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing resources. But these costs are amply outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the re-use of precious research resources by the scientific community.

Motivation & Objective

• To address barriers in data and code sharing that hinder reproducibility and collaborative research in epidemiology.
• To promote the adoption of FAIR principles—Findable, Accessible, Interoperable, Reusable—as a framework for improving research integrity and efficiency.
• To provide practical implementation strategies for epidemiologists to increase the FAIRness of their data and code.
• To encourage institutional and funder support for training, incentives, and infrastructure to sustain FAIR practices.
• To demonstrate that the long-term benefits of FAIR adoption outweigh the initial costs in terms of research impact, equity, and reusability.

Proposed method

• Migrating data and code from on-site storage to cloud-based platforms to enhance accessibility and scalability.
• Using machine-readable, non-proprietary file formats to improve interoperability across systems and tools.
• Developing and sharing open-source code to ensure transparency and reproducibility of analyses.
• Implementing rich, standardized metadata to make data resources Findable and citable.
• Establishing access controls to ensure authorized access while preserving confidentiality and privacy.
• Aligning FAIR practices with existing data sharing policies from funders and scientific journals to ensure compliance.

Experimental results

Research questions

• RQ1 How can epidemiological research improve reproducibility and reusability of data and code?
• RQ2 What practical steps can researchers take to make their data and code FAIR?
• RQ3 What institutional and systemic barriers currently limit data and code sharing in epidemiology?
• RQ4 How can cloud computing and open standards support the implementation of FAIR principles?
• RQ5 What incentives and support structures are needed to sustain FAIR practices across the research community?

Key findings

• Moving data and code to the cloud enhances accessibility and supports scalable, collaborative research.
• Using machine-readable, non-proprietary formats increases interoperability and reduces dependency on specific software.
• Open-source code improves transparency, reproducibility, and community review of analytical methods.
• Rich metadata significantly improves the Findability and citability of research resources.
• Organizational support, training, and incentives are essential for sustained adoption of FAIR practices.
• The long-term benefits of FAIR adoption—increased research impact, equity, and reusability—far outweigh the initial implementation costs.