Get a guided tour of Tetra OS, the Operating System for Scientific Intelligence: designed to simplify how you find, manage, and use your scientific data.
In these short videos, you’ll learn how to:
- Search, preview, and access scientific files and metadata
- Understand file categories like raw, processed, and IDS
- Work with schemas and artifacts to standardize your data
- Configure agents and pipelines to automate ingestion and processing
- Use the Data and AI Workspace to extract insights and collaborate
Whether you're new to TetraScience or just need a refresher, this demo covers the core features of the platform. Following this is a glossary of key terms, with links to additional information.
There are two views available when logging into Tetra through your browser: a streamlined experience for scientists to quickly find their relevant data and applications; and an IT-focused mode for administrators, developers, and other roles to build, manage, and monitor the components of the Tetra OS.
Introduction: Scientist Experience
Introduction: Build & Manage
Glossary
| Term | Definition | More Information |
|---|---|---|
| Agent | On-premise Windows application that aggregates and transfers data to the platform from a particular instrument, software source, or storage location. |
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| Artifact | One of several types of digital object in the TDP with a namespace, slug, and version. Common artifacts include the protocols executed by pipelines, and schema definitions (IDSs). |
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| Connector | Containerized application that communicates with a specific network-facing data source or target system. Can run in the cloud or on-premise. |
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| IDS | “Intermediate Data Schema,” a schema that is applied to raw instrument data or report files which maps vendor-specific information to vendor-agnostic information. The IDS standardizes naming, data type, data range, and data hierarchy. | Intermediate Data Schemas |
| IDS file | A file which conforms to a common or custom Intermediate Data Schema (IDS). IDS File data is indexed and available for downstream consumption via SQL, API, pipeline integrations, and other methods. | File Types |
| Label | Mutable annotations on files as name-value pairs, used for searching and triggering pipelines. Can be set on ingestion, and generated dynamically by pipelines based on file contents, metadata, and external sources. |
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| Namespace | A namespace defines a set of organizations within the TDP where only those who have the appropriate access can use the artifacts. | Namespaces |
| Organization | A segment of data, users, and infrastructure in the TDP corresponding to your company and usage. Every organization (“org”) has a unique slug. Users may have access to one or more organizations and can switch between them, such as development, test, and production orgs. | Tenants & Organizations |
| Pipeline | Defines one or more actions automatically performed based on an input file. A pipeline consists of the trigger conditions, protocol, notification settings, and execution configuration details. | Tetra Data Pipelines |
| Processed file | Any file produced as output by a workflow which is not an IDS file, such as CSV files or the extracted contents of an archive. | File Types |
| Protocol | The business logic of a pipeline, containing one or more steps with corresponding configuration options. Protocols consist of a namespace, slug, version, and the definition of those steps as a YAML file. The steps invoke functions in task scripts. | Protocol YAML Files |
| Raw file | A file which was uploaded directly to the platform from an outside source and not created directly by a pipeline, such as instrument data files ingested by an Agent. | File Types |
| Slug | A unique identifier that also denotes a reference to a unique identifier (pointer). In TDP, any concept that needs to be uniquely referenced is assigned a slug. | Slugs |
| Task script | The Python code (and associated files) which is executed according to a protocol definition and pipeline configuration, when a workflow runs. | Task Script Files |
| Tetra | “Tetra” refers to the platform you log into from your browser. This is the gateway to the system that centralizes scientific data from instruments, CRO/CMOs, and software systems in a single, cloud-based scientific data lakehouse. It provides a flexible and powerful data pipeline system for you to perform extract-transfer-load (ETL), data transformation, data publication, scientific application hosting to work with the data, and custom actions with self-service deployment tooling. | Tetra Overview |
| Tetra OS | The complete ecosystem for converting the raw data materials of science into compounding Scientific Intelligence through four integrated capabilities: the Scientific Data Foundry, the Scientific Use Case Factory, Tetra AI, and Tetra Sciborgs. | Tetra OS: The Operating System for Scientific Intelligence |
| Workflow | A unique execution of a pipeline, with a specific file as input. Output files, execution status and timing, and log files are associated with each workflow. | Monitor Pipeline File Processing |
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