Biopharmaceutical industry R&D continues the shift from being application-centric to being data-centric in recognition of the idea that, while technologies and applications may come and go, it is the data assets from internal and external sources that really drive drug discovery and development.
Therefore, it is critically important for organisations to manage data and metadata effectively so that they may be used and reused to provide maximum value, and those organisations that do this best will have a significant and enduring advantage over their competitors.
The ultimate goal of the FAIR Data Principles is to help researchers increase the reusability of data. In particular, increasing the interoperability of data using the formal, standardised methods suggested by FAIR lays the foundation for establishing a shared understanding of the meaning of the data. Ontologies are key to good interoperability, which enables data and metadata integration and allows researchers to leverage more of what is known in their scientific domains to make higher quality decisions faster. Knowledge graphs, increasingly relied upon by researchers to help them understand the complex relationships between biomedical entities, are impossible to implement without ontologies.
Ontologies can be challenging to manage and deploy, but those activities are much more successful when started with data and metadata that are FAIR. Use of the FAIR Principles, conversely, can help make better ontologies. One major challenge today, however, is the limited availability of expertise in knowledge engineering and the FAIR Data Principles across biopharmaceutical industry R&D, which can hinder efforts to build and leverage knowledge graphs and other ways of making the best use and reuse of data. We have a assembled a panel of experts to help you navigate this space, to help you understand why it’s valuable to leverage the interplay of ontologies and FAIR, and what a difference it can make in R&D decision making.
Ted Slater, Senior Director, Product Management PaaS, Elsevier
Jane Lomax, Head of Ontologies, SciBite
Peter McQuilton, Product Owner, GSK
Nathalie Conte, Data infrastructure Lead (Omic Data), AstraZeneca
Sabine Schefzick, Director, Science and Clinical Analytics and Informatics, Pfizer
In the first of this two-part blog, I describe what ontologies are and how you can use them to make the best use of scientific data within your organisation.Read
This blog post focuses on mapping, building, and managing ontologies. In my previous blog, I described what ontologies are and how you can use them to make the best use of scientific data within your organization. Here I’ll expand upon this and focus on mapping, building, and managing ontologies.Read
Get in touch with us to find out how we can transform your data
© Copyright © 2024 Elsevier Ltd., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies.