How to integrate, engineer and analyze the world's most important data: Your scientific data
Find the path to large-scale, AI-ready scientific datasets in the cloud. Get access to the full knowledge kit, a collection of 8 resources curated for Scientific IT leaders. You'll learn:
How to free your scientific data from silos so users can access it when and where they need it
Strategies to reduce costs with a cloud-native solution that offers a lower total cost of ownership and better scalability
How to automate data transfer with industrialized integrations
Start your journey to creating a self-service, automated transfer of scientific data while reducing costs
A traditional scientific data management system (SDMS) merely stores your data, keeping it stagnant and locked up in just another data silo. This impedes its use for predictive analytics and AI. Pharmaceutical companies are realizing they have mishandled this goldmine of data, which holds the key to improving and accelerating scientific outcomes. Explore the resources below to understand strategies and approaches to get the most out of your scientific data.
Derive value at every step in the scientific data and AI journey
Focus on scientific outcomes
Take a holistic approach with end-to-end data workflows that encompass collection, contextualization, and harmonization—all aimed at driving scientific outcomes.
Seamlessly access your data for analytics and AI
Construct dashboards, perform advanced analytics, and apply AI algorithms with the tools of your choice for data-driven scientific insights and decision making.
Free your scientific data from silos
See how the Tetra Scientific Data and AI Cloud automatically assembles and centralizes data from disconnected sources for easy access, sharing, collaboration, and management.
Tap into the largest library of integrations
Leverage the world’s largest, fastest-growing, purpose-built library of industrialized integrations and data schemas that covers 100% of the highest-priority instruments in top biopharmas.
The 8 Trends Redefining Scientific Data Management in Biopharma
Discover how the Tetra Scientific Data and AI Cloud provides the world’s largest, fastest-growing, purpose-built library of data integrations and data models that drive scientific outcomes.
Legacy data architectures are slowing your AI journey
Traditional SDMSs were designed to store and archive data for regulatory compliance, not to prepare data for AI applications. See the 5 obstacles.
Get both: lab connectivity and a data foundation for analytics & AI
Learn more about the middleware approach and how it is a dead end for scientific data. It is the opposite of “data centric.”
View the largest library of scientific data integrations
TetraScience hosts the world’s largest, fastest-growing, purpose-built library of industrialized integrations and data schemas. It covers 100% of the highest-priority instruments in top biopharma organizations.
Accelerate innovation and establish the foundation for analytics and AI with a pluggable connector framework
Learn how a Pluggable Connector Framework accelerates the connector release cycle, offers rapid iteration, and scales to the growing needs of your organization.
Advanced in silico model predicts cell line formulations faster
See how this in silico model helps reduce wet lab experiments by 88% with media formulation optimization. See how to radically accelerate high throughput screening.
An AI model significantly streamlines and improves the accuracy of in vitro testing
See how AI helped for Charles River Laboratories Hungary (SOLVO) after replatforming and engineering scientific data with an in silico model optimizing sampling and improving calculations.
Accelerate Scientific Workflows with Bidirectional Data Movement
See how to collect data from an electronic lab notebook (ELN), transform that data into a standardized format, and write a new sample set method, which can be used to run an experiment in a chromatography data system.