The FAIR data principles for health data analytics

In 2016, the “FAIR Guiding Principles for scientific data management and stewardship” were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data. 

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. 

The four foundational principles:
Findable – The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata is essential for the automatic discovery of datasets and services;
Accessible – Once the user finds the required data, they need to know how they can be accessed, possibly including authentication and authorisation;
Interoperable – The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing;
Reusable – The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. 

The principles are useful because they: 
  • support knowledge discovery and innovation 
  • support data and knowledge integration 
  • promote sharing and reuse of data 
  • are discipline independent and allow for differences in disciplines 
  • move beyond high-level guidance, containing detailed advice on activities that can be undertaken to make data more FAIR 
  • help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets. 
The Competence Centre on Active and Healthy Ageing of the University of Porto, also known as Porto4Ageing, is enrolled in several national and international projects, including the FAIR4Health (Improving Health Research in EU through FAIR Data). FAIR4Health is a project funded by the H2020 Science with and for Society (SwafS-04-2018) call for proposals. It is carried out by 16 partners from 10 different countries. FAIR4Health encourages the application of FAIR principles in data derived from publicly funded health research initiatives so that they can be shared and reused within the European Union (EU) health research community, defining an effective EU-wide strategy for the use of FAIR in Health. 
Thanks to the FAIR Data Principles, organizations can improve their knowledge sharing to foster better collaboration and accelerate their research.