Paradigm shift in scientific data management and analysis

Explore our video series to see how the Tetra Scientific Data and AI Cloud elevates your scientific data management beyond any SDMS.

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Free your data from SDMS

The Tetra Scientific Data and AI Cloud frees your data from the proprietary silos of a scientific data management system (SDMS). It seamlessly integrates and harmonizes data sources throughout the modern laboratory while providing cloud-based storage, archive, restore, and access control functionality that you depend on.

We feel held hostage by [our traditional SDMS]; any time we need to change our workflow, they lack flexibility, and we have to go back to them at additional time and cost for extra validation services.

Vice President of IT from a leading CDMO

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Collecting Data: Files

Collect datasets from scientific instruments and centralize them in the cloud.

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00:09 The challenge
00:24 Automated file collection
02:00 Agent configuration (including metadata)
02:53 Path configuration (including metadata)
05:23 Automated archive & deletion of uploaded files
06:34 Centralized, cloud-based storage
08:48 Automated notifications

Collecting Data: Applications

Collect data from applications, including CDSs, ELNs, LIMSs, and robotics systems.

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00:08 Introduction
01:19 Challenges
02:40 Waters Empower example
03:18 Searching for Empower data in Tetra Scientific Data & AI Cloud (TSDAC)
03:52 Configuring the Tetra Empower Agent
05:17 Archiving
05:33 Integrating with other applications
06:08 Creating custom connectors
06:33 Harmonizing data
07:17 Using data with Analytics, AI & ML applications
07:33 Triggering pipelines

Collecting Data: Version and Lineage

Track and manage data versioning and lineage—including upload details, transformations, and history—enabling precise monitoring of your data’s journey and lifecycle.

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00:40 File Details page (source, workflows created, metadata, ...)
01:36 File Journey visual view of the file processing
03:25 Versioning
05:07 Summary

Contextualization with Metadata

Contextualize data using experimental metadata to make it findable and accessible.

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00:30 Bridging the Gap - Data Replatforming
00:47 Provenance metadata
01:40 Agent level metadata
02:06 Path level metadata
03:24 Manually adding metadata
03:30 Extracting metadata from file name, path, contents
04:00 Addng metadata from external files (ex. Excel)
04:37 Adding metadata from external sources
05:28 Search
07:25 File details
08:00 Summary

Searching for Data

Easily find data via the Tetra Data Platform user interface, SQL queries, or the API.

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00:14 What is indexed for search?
01:17 How can you search for data?
01:38 Searching using the TDP UI
03:47 Tabular view of search results
04:00 Querying in Snowflake
04:48 Querying using Elasticsearch
07:14 Summary