Achieving a F.A.I.R. Data Management Strategy
Pharmaceutical companies generate a wealth of data and, with most investing in technologies to enable high throughput experimentation and automation, the speed at which new data is generated is rapidly increasing. However, most organizations are unable to gain actionable insights from this valuable resource and realize its true potential. In fact, it is estimated that over 50% of the cumulative knowledge generated by pre-clinical research is not reproducible. Considering the US alone, this translates into tens of billions of dollars of R&D investment that cannot be exploited effectively.
IDBS has been working to make R&D data findable, accessible, interoperable, and reusable long fore the acronym and principles were defined – 27 years prior, in fact.
E-WorkBook was developed with the F.A.I.R. principles in mind. With a diverse customer list including R&D-driven organizations in biotechnology, agricultural sciences, chemicals, consumer goods, food and beverage, energy, and healthcare, E-WorkBook helps teams to drive science through effective data management. Similarly, Polar is a cloud-based platform that enables biopharma companies to more efficiently design, scale and run their processes. An embedded integration layer simplifies the curation of a process data backbone that powers advanced analytics.
In this whitepaper, we explore what the F.A.I.R. principles mean in practical terms for your R&D data management strategy before describing how IDBS enables organizations to make their scientific data Findable, Accessible, Interoperable, and Reusable and mitigate the costs of not being F.A.I.R.
- The F.A.I.R. principles are guidelines with the potential to transform your data management
- These principles have gained traction due to to challenges arising from legacy data management systems
- Data management needs to improve in order to leverage advanced computational tools such as artificial intelligence and machine learning
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