How an IDS Complements Raw Experimental R&D Data in the Digital Lab
We use an intuitive analogy to help explain the purpose and use cases of our Intermediate Data Schema (IDS) and its relationship with data archival in a life sciences context.
Listen to the New Matter podcast episode, "Helping Scientists Do More," with SLAS's Scientific Director, Marshall Brennan, Ph.D., and Tetra's Chief Scientific Officer, Mike Tarselli, Ph.D. where they discuss the challenge of "Big Data" in life science R&D.
Specialization and compatibility are possible with the Leaf Node - scientists, engineers, and data scientists can rapidly iterate on the data model to create and query standardized data sets so ontology experts can focus on semantics.
We believe one of the key building blocks of a Digital Lab is the flow of Data. Without the flow of information, data is siloed, fragmented, and nearly impossible to drive action. To enable scalable data flow, Digital Lab needs an Intermediate Data Schema...
Use case illustrating how experimental data from a cell counter is automatically collected, centralized in a data lake, and converted into an ADF-compatible format, saving time, reducing error, and making the data accessible and actionable.
Once your Empower data is in the cloud - harmonized, structured, and connected to your data science tools - what meaningful analyses can your data scientists perform? We've collected several obvious and non-obvious data science use cases from our network.