Researchers expect datasets to be FAIR, and they assert that their data are FAIR even more than they claim that their data are conclusive. But creating FAIR data is hard, and the process depends on authoring metadata that adheres to community standards. CEDAR provides technology that can help investigators to create FAIR data. The platform uses biomedical ontologies stored in the BioPortal repository and a user-managed library of machine-readable reporting guidelines that provide standardized metadata schemas. CEDAR is being used internationally in a range of projects to make FAIR data not just a slogan but an achievable reality.

Open life science data is critically important for the research community - it is extensively reused, as experimental reference and for novel science. ELIXIR, a European infrastructure connecting 23 countries, is rolling out good data management, a common toolkit, and building an expert network that supports life science projects with FAIR practices - creating a data federation that allows users to extract large, interoperable datasets across national boundaries. In this talk I will describe our common toolkit, our training and capacity building, and the foundation of core data resources with examples on how this supports diverse life science communities.

The flow of SAR data curated from literature and patents into ~ChEMBL, BindingDB, Guide to Pharmacology, and PubChem is central to drug discovery informatics. While these sources have made 2.8 million structures FAIR, commercial sources indicate a legacy extraction shortfall of 4 million. Another impediment is journal authors assuming that depositing bioactivity results as supplementary files into repositories such as Figshare magically makes it FAIR, despite not being machine-readable. Approaches to ameliorate both these legacy and current FAIR limitations will be outlined.

FAIR principles are good in principle, but how do we get FAIR data in practice. All too often, issues of FAIRness are considered at the end of the data life-cycle, just prior to publication or meeting funder's requirements. In this presentation, we consider the advantages of integrating FAIR principles into the complete lifecycle of data associated with a research investigation.  

Adoption of FAIR has emerged as a solution to optimize data-driven Life Sciences R&D. Digital transformation initiatives are now relying on FAIR principles to accelerate drug design and clinical trials. However, complex data, such as medical images, present additional challenges that require unique solutions. In this presentation, we will discuss the soup-to-nuts approach of implementing FAIR across large teams to improve R&D efficiency and project outcomes.

The US Government and legislative bodies across the globe have started to mandate the use of fast healthcare interoperability resources (FHIR) in clinical systems. These mandates leave a clear gap with the research community, causing organizations to determine their own interoperability path. This presentation will overview why these organizations should consider FHIR data in their FAIR data strategies, the implications for life sciences organizations, and other standards to consider.

With a mission to enable scientific discoveries that improve human health, UK Biobank has become a tremendously significant database for science and medicine and is currently being used by 30,000 researchers in over 90 countries around the world. The resource is unique given the combination of its scale, richness, duration, and accessibility. This session will focus on UK Biobank’s accessibility and the steps taken to ensure this petabyte-scale database is made available to bona fide researchers to improve diagnosis, treatment, and prevention strategies for the most devastating diseases, benefiting millions of people in the UK and around the world.

After combining data of various data types into a single schema-less database, we find that novel insights can be derived from the data relatively easily by domain experts and that for complex analyses, they can be presented to users easily in an extensible way.  We will show two specific examples, one concerning metagenomic analysis of individuals with colorectal cancer, and another concerning ECG and subsequent analysis from a randomized cohort of individuals from the UKBiobank with cardiac arrhythmias.  We are particularly excited to demonstrate that explainable machine learning results can be loaded alongside primary data, pointing (not directing) clinicians and researchers to data-driven indications.  

Realizing the promise of FAIR principles to enable interoperability between datasets is crucial to supporting novel analyses that drive discovery. FAIRness is usually considered at the end of the research process, and public repositories suffer from considerable variability in metadata quality. Leveraging explainable machine learning to enhance the FAIRness of both existing and new data leads to significant improvements in the ability to use integrated data to answer novel questions. In this talk, approaches to automated integration and specific examples of its impact will be discussed and future impacts will be outlined.