Manual no more: Automating the scientific lifecycle
Leading biopharma organizations are reimagining research, development, and delivery of life-saving therapeutics through automated data acquisition, wrangling, and transformation.
This white paper — Paving the Way to Automated Data Acquisition and Transformation — describes how this world of data science automation has become a reality.
You will learn:
- The benefits of findable, accessible, interoperable, reusable (FAIR) data in the cloud
- The importance of multi-point productized integrations
- The steps to automate the scientific data lifecycle
Why End-to-end Data Lifecycle Automation Is Critical
With an automated end-to-end data lifecycle, harmonized scientific data flows freely throughout the biopharma data pipeline to accelerate innovation and support quality. Decoupling instrument control from data publishing enables scaled-up data science automation.
We discuss these benefits in detail in our white paper:
- With an automated and remote control interface to interact with instruments, organizations don't need to worry about collecting data with the heterogeneous and fragmented nature of lab data systems.
- With automated data acquisition across different instruments and lab systems, scientists no longer have to manually locate, transfer, and clean the data.
- With data pipelines for automated processing and interactive analysis, the data is automatically prepared, analyzed, and presented to the researcher, driving faster decision-making.
The Tetra Scientific Data and AI Cloud automates the collection and transformation of data in a format that is compliant, harmonized, liquid, and actionable. See what a modern-day end-to-end data pipeline looks like with TetraScience. Read our entire white paper now.
