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Common challenges to FAIR implementation


While the ideas behind the FAIR principles have been around for some time, implementation in the life sciences has been slow, because the path to adoption is neither finite nor predetermined. FAIRification is the long-term overhaul


of how data are created and used in an organisation, and this process is continuously influenced by an ever- changing knowledge landscape. When organisations begin the FAIRification journey, they face some common challenges, including: • Unstructured legacy data – often data are not tagged, contain haphazard names or identifiers and lack common terminology;


• Data silos and trapped historical data – technologies used in previous research are likely obsolete or no longer supported; often personnel responsible for creating original datasets have moved on, leading to data becoming inaccessible or uninterpretable;


• Scientific complexity – machine- readable representations of biological information can quickly become extremely complex;


• Ontology management – there are multiple competing ontologies and vocabularies, often even in a single organisation, with little standardisation across the industry; and


• Cultural barriers – changing the culture of an organisation can be one of the most challenging tasks; researchers and organisations are typically very protective of even non-proprietary data. Incentivising all parties to do their parts in generating high-quality FAIR data will require valuing efforts to that end, as much as the marketable output of a drug development pipeline.


Path to implementation FAIRification does not happen immediately and comprehensively. Making data FAIR is an evolving and progressive process. This is especially true for the pharmaceutical industry, where data production is continuous and new knowledge is always reshaping the information landscape for research questions. However complex FAIRification may


seem, it is critical to start the process and allow for an agile, test-and-learn adoption. Helpfully, companies do not


need to go it alone. There is a large and growing network of organisations offering assistance, expertise and tools to help FAIRify data.


This includes the Analytical Information


Markup Language (AnIML), which is an emerging ASTM XML standard for analytical chemistry and biological data. AnIML was instigated to solve problems with existing data standards and take advantage of the recent development of the eXtensible Markup Language (XML). The goal of the ASTM working group is to develop an analytical data standard that can be used to store data from any analytical instrument. The Pistoia Alliance, a non-profit group


advocating for better data sharing in life sciences, also offers a free FAIR toolkit for implementation. The FAIR movement is not the first


attempt at merging data from disparate sources. But it is gathering pace, due to the combination of increased computing power, data availability and data generation that are making AI a reality for many organisations. To make effective use of this shift to advanced analytics, life science organisations should take the first steps towards FAIR data.


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