What used to be primarily an internal process for organizations (where their work centered around utilizing internal applications and data sources) has transformed to demand more extensive access to data and applications. 

Science is all about expanding your view, not limiting it. SciBite is here to support this. We recognize that access to more usable data is crucial to ongoing success and progress, which makes the ability to access public and third party domain sources (PubMed, Patents, full-text journal literature, ClinicalTrials.gov, FDA etc.), commercial intelligence (Sitetrove, Pharmaprojects, Pharmapremia etc), and data provided by contract research organizations (CROs) pretty much a non-negotiable. 

‘Findable’ means having data that’s assigned a unique and persistent identifier, described with rich and machine-readable metadata and searchable and easy to find. 

‘Accessible’ ensures that data and metadata can be retrieved by their identifier, read and accessed via a standardized communications protocol. Access to this data should be as open as possible and as restricted as necessary for more sensitive data, with metadata being accessible even after the data is no longer available. 

‘Interoperable’ refers to data and metadata are presented with standardized, documented and accessible semantic descriptions. They should use standardized vocabularies, terminologies and ontologies and be described with references to others so that it’s possible to understand the relations between data. 

‘Reusable’ means data and metadata should contain multiple types of contextual information, like its scientific purpose. This should also be associated with detailed provenance information and structured/documented in accordance with applicable domain-relevant standards and formats. 

Maintaining FAIR data has so many benefits to organizations but the economic benefits are particularly important. In May 2018 the EU published a report (Cost-benefit analysis for FAIR research data) in which they estimated the cost of not having FAIR research data across the EU data market and EU data economy.  The EU report found that the annual cost of not having FAIR research data costs the European economy at least €10.2bn every year. By drawing a rough parallel with the European open data economy, they concluded that the downstream inefficiencies arising from not implementing FAIR could account for further €16bn annually.  

Gartner research also supports this by highlighting the average financial impact of poor data quality on organizations is $15 million per year. 

Change and growth isn’t always easy, but it’s usually worth it. So, in order to create a world that benefits scientists and researchers by giving them access to huge repositories of FAIR data, we’re going to need to work together to resolve some major issues.  

Our biggest challenges come from unstructured legacy data stored in electronic lab notebooks, proprietary databases, PDFs and SharePoint folders. Data silos often limit access to critical information, and historical data may be lost due to mergers or inadequate storage. The biological complexity of documents and poor ontology management can further hinder the transition to FAIR data.