What is IDS for (and not for?)
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.
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.
Our goal is to enable other organizations to overcome the barrier of initial adoption in order to evaluate and explore ADF in real production settings against your use cases. By doing this, such a data format can be truly battle-tested and demonstrate its vitality.
Here is a collection of thought leadership articles about semantic web that we are tracking. This is a question we are asked often, so we thought we'd share.
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...
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.
Despite robotic automation, experiment data sets are still isolated from one another, requiring manual data acquisition and handling. TetraScience’s Data Integration Platform brings automation to the data.
AGU's Sm@rtLine Data Cockpit and TetraScience partner to unify life sciences R&D data in the cloud and automate lab workflows to power Bioprocessing 4.0.