Making data Findable, Accessible, Interoperable, and Reusable (FAIR) is a necessity for modern R&D teams navigating digital transformation. A recent study in Chemical Science introduced a fully open-source, end-to-end workflow to "FAIRify" R&D data.
Novo Nordisk's journey in ontology-based data management for Research & Early Development showcases the power of this approach in enhancing data accessibility, consistency, and governance.
A brief overview of the new NiFi 2 Python Extensions. The ability to use Python's rich ecosystem of libraries and tools within NiFi is a game-changer, especially for complex data manipulation, machine learning, and AI tasks.
Data is the backbone of R&D operations and data quality is undoubtedly important to R&D organizations. This article is a brief discussion on data quality in R&D, including why it is important, what are the key factors and how to measure data quality.
