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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 continuously reshapes 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 Pistoia Alliance, a non- profit group advocating for better data sharing in life sciences, which 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 at a time when computer infrastructure, knowledge engineering and data generation are finally where they need to be for firms to transition to powerful analytics, enhanced by a semantic and more comprehensive representation of knowledge. One final key element to remember is that the outcomes from implementing the FAIR Principles will be different for each organisation. Some parts of FAIR may be more important to one group than another, for example ‘findable’ might be your priority to begin with, but even small steps will help improve data quality and management, to aid future innovation.


Ted Slater is senior director, product management for PaaS, at Elsevier; James Malone is CTO at SciBite


www.researchinformation.info | @researchinfo


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Data Portability (Interoperability) Polaris OS supports integration with institutional IT systems (LDAP, user accounts, websites, reporting systems) and cross-domain interoperability with bibliographic databases and other digital repositories.


Access & Data Preservation It’s all about long term preservation and access of research outputs. Polaris OS maintains trusted connections


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For more information For a free trial or more information, visit www.mysciencework.com/polaris-os or contact Carolina Sanchez carolina.sanchez@mysciencework.com / +33 6 69 25 46 94


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