The Importance of FAIR Data Principles in Healthcare & Life Sciences

What is FAIR data?

A group of scientists published in 2016 a paper in Nature magazine, discussing the need for a set of principles to govern the discovery, management and reuse of scientific data. Dozens of prominent scientists who contributed to the paper came up with the FAIR Data concept of describing the principles that make data valuable to researchers and scientists. The authors of the paper wrote:

The FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.

These fundamental principles state that all research objects should be Findable, Accessible, Interoperable and Reusable (FAIR) both for machines and for people. The emphasis on making data understandable to machines or ‘machine-actionable’ data, as the paper on FAIR Data Principles says, helps data management, data sharing and data reuse by third parties.

Each of the four FAIR principles calls for data and metadata to be easily found, accessed, understood, exchanged and reused.

• Findable is such data in which data and metadata are assigned a globally unique and persistent identifier so that computers can easily find it. In this principle, metadata and data are registered or indexed as a searchable resource.
• Accessible are data that can be retrieved by the identifier via a standardized protocol that is open, free and universally implementable.
• Interoperable data refers to formal, accessible, shared and broadly applicable language for knowledge representation, which allows for data integration with other data sources without ambiguity.
• Reusable data are such that can be further used or repurposed by machines, therefore it needs detailed provenance and rich description of metadata attributes.

Why Is FAIR Data Important?

The FAIR Data Principles make data more valuable as it is easier to find through unique identifiers and easier to combine and integrate thanks to the formal shared knowledge representation. Such data is easier to reuse, repurpose and share because machines have the means to understand where data comes from and what it is about. It also accelerates research, boosts cooperation and facilitates reuse in scientific research. Policymakers and stakeholders have seen its value in driving innovation and many have embraced these principles.

As early as in 2016, the leaders of the G20 voiced their support to research based on open science and the FAIR principles. The European Union has also embraced them and had an expert group report on how to turn FAIR into reality.

In the United States, the Office of Science at the Department of Energy announced in April 2020 a total of US$8.5 million for new research aimed at advancing the FAIR Data Principles in Artificial Intelligence (AI) research and development.

How Ontotext Applies FAIR Data Principles

By applying FAIR Data Principles, Ontotext helps companies in the pharmaceutical, biotech, agro-chemical, healthcare and health insurance industries gain insights from all their proprietary data in knowledge graph powered AI solutions

We help our clients quickly develop knowledge graphs by picking up relevant public datasets from our large LOD inventory, loading highly normalized and semantically interlinked data in your custom knowledge graph, ingesting their proprietary data and feeding the AI and analytical applications with high-quality data with traceable provenance. Some of the most important benefits for our clients are:

• unlocking information hidden in unstructured documents stored in regulated DMS (Pharma, Agro-chemical, Consumer goods, etc.);
• consolidating all relevant information for a business process into a highly interconnected network of knowledge (Pharma, Agro-chemical, Consumer goods, etc.);
• easily monitoring big players and competitors, their R&D activities, new products, filed patents, mentions in news, etc. (Pharma, Medical devices manufacturers, etc.);
• automatically processing diverse textual data streams to identify drug safety signals (Pharma);
• maintaining referential knowledge base of known medical facts for checking inconsistencies in health insurance claims.

Thanks to the FAIR Data Principles, these organizations can improve their knowledge sharing to foster better collaboration and accelerate their research.

Do you want to learn more about how Ontotext can help you turn your data into knowledge?