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.

Addressing barriers in FAIR data practices for biomedical data

“…To define which properties need to be collected about a dataset, a schema defines the set of field names within the data and what they represent (i.e., description (dataset description), creator (author(s) who generated and/or processed the data), measurementTechnique (experimental technique(s) used to collect the data), etc. 1 ). More detailed schemas also define the allowable values (controlled vocabularies, or ontologies, which are formal representations of allowed values and their relationship to each other) and constraints such as type or expected number for each property.…”

“…Often, data standards, tools, software, platforms, and resources are developed as pilot projects or as side effects of hypothesis-driven scientific grants. For example, the NIAID Systems Biology Data Dissemination Working Group developed and implemented an infectious disease-specific Dataset and ComputationalTool schema, increasing the FAIRness of nearly 400 datasets and computational tools using it 1 . The schema is straightforward, yet has potential to exponentially enhance biological and biomedical dataset accessibility and reuse via increased exposure through dataset aggregation projects like Google Dataset Search.…”