Addressing barriers in FAIR data practices for biomedical data

Abstract

AIR data sharing is integral to spur research reproducibility, promote data reuse, and accelerate research. However, the first step in using these data assets -discovering that they exist -is compounded by problems in incentives, standardization, and coordination of efforts. In 2023, the National Institutes of Health (NIH) implemented their updated Data Sharing Policy, which mandates timely data sharing of all NIH-funded data. However, for the policy to shift the data sharing culture, to improve research reproducibility, and to promote data reuse, several changes must happen. In a companion piece 1 , we present a survey of the data sharing landscape for immune-mediated and infectious disease data, combined with our efforts to create a reusable methodology to catalog data we generate. We found that researchers routinely share data, but datasets are still not necessarily findable, accessible, interoperable, or reusable (FAIR) 2 . In the course of these efforts, we identified three interdependent barriers that need to be addressed to maximize the impact of data sharing efforts that are becoming increasingly commonplace: (1) a lack of incentives, (2) little standardization in metadata collection and access, and (3) uncoordinated efforts. Here, we propose solutions to improve the FAIRness of metadata, data, and the experimental conditions used to generate these data. Our solutions include leveraging journals as a key driver of research incentives and consolidating data searching and citation into a centralized platform. While we focus our discussion based on our experience primarily with NIH-funded research, these principles are broadly applicable to other data sharing efforts worldwide.Barrier 1: Without incentives, researchers tend to provide incomplete metadata, which limits data discovery and reuse. Currently, there are few tangible incentives for researchers to share data. There is limited recognition amongst scientists regarding the benefits of data sharing, while the potential for downside effects such as the risk of getting scooped, concerns over protecting participant/patient privacy, a lack of Intellectual Property protection, and the enhanced workload associated with providing good, reusable, and discoverable data are all well recognized. As a result, researchers frequently do the bare minimum to comply with journal and funder mandates. When sharing data, researchers also include metadata which describe the contents of the dataset to aid in searchability, but these metadata are often insufficient to help researchers identify useful datasets. Even if a metadata property is required, researchers can circumvent these requirements by providing short, generic descriptions. Consequently, metadata is often cursory and incomplete, and data is often provided in non-machine readable formats (e.g., .pdf, Microsoft Word, or free text) without associated documentation to support reuse. While there are many projects that attempt to improve the FAIRness of poorly collected metadata 3,4 , it is far easier to collect comple...

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening

“…The latter, combined with the relatively low response rate (42.7%) for this section, suggests a lack of awareness and limited implementation of FAIR principles among the surveyed database stakeholders. This is consistent with other studies that have found low levels of FAIR compliance [20]. Therefore, the results underline the importance and need for targeted interventions to promote FAIR compliance and standardize data sharing practices across different domains.…”

Developing a standardized but extendable framework to increase the findability of infectious disease datasets

“…The primary barriers to widely sharing and reusing data are a set of interrelated challenges (see companion Commentary on Addressing barriers in FAIR data practices for biomedical data 65 ), including: (1) lack of incentives for researchers to share information in a timely and transparent manner; (2) logistical challenges with collecting, sharing, and finding data; and (3) lack of documentation and interoperability conflicts which make it difficult to reuse data. As a result, our main goal in this work was to tackle the logistical barrier: collecting sufficient metadata that promotes easily discovering datasets, the first step in reusing public data.…”

Implementation of One Health surveillance systems: opportunities and challenges - Lessons learned from the OH-EpiCap application

“…Regarding Data sharing, our study revealed a lack of adherence to FAIR data principles (2 [1-2.5]). Despite the recognized importance of FAIR data, practical application remains challenging [36][37][38]. Numerous obstacles, including ethical, organizational, legal, technical, political, and economic issues, hindered timely and effective data sharing between health authorities and stakeholders [2,19,38,39].…”