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

Abstract

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt Open Science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

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

“…1b), we developed a minimalistic-but-extendable schema tailored for infectious disease datasets. The NIAID Division of Microbiology and Infectious Diseases (DMID) Systems Biology (NIAID SysBio) Dataset schema contains 7 required fields, 7 recommended fields, and 1 optional field (File 2 available on Zenodo 41 ). The schema uses Schema.org as a base and introduces four main changes: (1) the addition of infectious disease-specific properties, encompassing those pertaining to the pathogen and/or the host; (2) the addition of whether a property is required, recommended, or optional (marginality);…”

“…1 Distribution of Schema.org standards in common data repositories. (a) Schema.org compliance in common biomedical repositories (see File 1 available on Zenodo 41 ). (b) Within Schema.org-compliant repositories, each source used the standard differently.…”

“…While available in Schema.org, funding information is not generally captured by repositories, and some providers like Harvard Dataverse collect funding information 42 but do not standardize or expose it 43 . To promote compatibility with other commonly used schemas, we provide a crosswalk between our NIAID SysBio schema, Schema.org, and Google Dataset Search (File 2 available on Zenodo 41 ), as well as a more extensive table (File 3 available on Zenodo 41 ) which compares our schema to an analysis of data schemas including DataCite and Dublin Core by the RDA Research Metadata Schemas Working Group 44 .…”

“…To facilitate this process, we created a dataset registration flowchart (Fig. 2a) based on our landscape analysis of biological repositories (File 1 available on Zenodo 41 ) that advises data providers how to register their dataset with minimal effort. If the dataset is stored in a Schema.org-compliant repository, the registered metadata can automatically be ingested via pre-existing markup crawlers like Google Dataset Search.…”

“…2b). The data portal compatibility checker includes the more than 200 data repositories and portals commonly used by the biomedical research community we analyzed in our landscape analysis (File 1 available on Zenodo 41 ).…”