Why just store your data? Leverage it today and future-proof it for Scientific AI.

Let data scientists create models. Automatically aggregate and prepare scientific data they can use for algorithms to deliver meaningful scientific outcomes.

Get access to the full knowledge kit, a collection of 6 resources curated for AI and data science leaders. You'll learn:

How to access large-scale scientific datasets centralized in the cloud

Strategies to automate data transformation into analytics- and AI-ready data

Approaches to Scientific AI from top data and science experts

Are you prepared for the paradigm shift in scientific data management and analysis?

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.

Access the full Knowledge Kit

Please fill out the form below to get access to the full knowledge kit, a collection of 8 resources curated for heads of research and scientific leaders.

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.

The 15 scientific use cases illustrating the promise of AI

Scientific data is expected to exceed 30 million petabytes in 2030. Virtually every stage in the pharmaceutical value chain, from discovery to commercialization, has the potential to be transformed by AI.

The Scientific AI Gap

The vast volumes of data that biopharma companies generate and collect are not ready for advanced analytics, AI, or machine learning applications. Moving forward will require embracing a new path in scientific data management.

Learn:

  • The promise of Scientific AI in biopharma
  • 3 primary obstacles to Scientific AI
  • How to close the AI gap

The key to unlocking the value of large-scale, complex datasets

See how a leading biopharmaceutical company found a data solution to accelerate biologics discovery and development, improve time-to-insight, and bring therapies to patients faster.

This case study explores how they:

  • Automated CRO data ingestion
  • Eliminated time-consuming manual data cleansing
  • Accelerated .CZI image processing
  • Streamlined HTS workflows

Quickly aggregate and analyze scientific data with AI

This demo video shows the AI-powered tool transforming how scientists analyze their data. With simple conversational prompts, they can retrieve data and generate visualizations—in just seconds. Save time, eliminate errors, and gain deeper insights into their data.

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.