🧬 Flask Track Docs

Overview

Flask Track is a laboratory operations platform built for biological research, regulated workflows, and traceable experimental execution.

The platform manages laboratory work end-to-end — from scientific catalogs and protocol authoring through execution, compliance, reporting, and long-term audit retention.

Flask Track is designed around how real laboratories operate:

Unlike generic workflow tools or spreadsheet-driven systems, Flask Track models laboratory operations directly.

Built-In Scientific Catalog (Start Immediately)

Flask Track ships with a large curated scientific catalog so laboratories can begin operating immediately without building foundational data from scratch.

The built-in catalog includes:

This allows organizations to:

All catalog entities remain fully editable and organization-aware.

Labs can override defaults, add proprietary records, and maintain operational traceability while still benefiting from preloaded scientific data.

Core Concepts

Flask Track organizes laboratory work around a small set of interconnected operational entities.

Samples

Samples represent the biological entities being worked on.

Examples include:

Each sample maintains a complete operational history, including:

Samples are the foundation of traceability throughout the platform.

Batches

Batches allow laboratories to execute workflows across groups of related samples.

A batch represents:

Batch execution enables laboratories to scale operational work while still maintaining individual sample traceability.

Flask Track supports:

Protocols

Protocols define how laboratory work is performed.

Protocols are structured, versioned procedures consisting of ordered steps and operational requirements.

Protocols may define:

Protocols support reproducible execution and operational consistency across teams.

Workflows

Workflows combine protocols into complete experimental pipelines.

Examples include:

Workflows support:

Workflows are reusable across laboratories, species, and experimental contexts.

Catalogs

Catalog entities define the operational resources used during laboratory work.

These include:

Catalog modeling improves:

Compliance

Compliance is integrated directly into laboratory execution.

Flask Track includes built-in systems for:

Compliance requirements can be attached directly to protocols, workflows, materials, tools, and operational events.

This ensures compliance becomes part of execution rather than a disconnected administrative process.

Automation, APIs & Integrations

Flask Track is designed for both human-driven laboratory operations and programmable infrastructure.

The platform supports structured APIs, automation systems, and integration workflows that allow organizations to connect Flask Track to their broader operational environment.

API Access

Flask Track APIs support operations such as:

This allows Flask Track to operate as a central laboratory system while remaining interoperable with existing infrastructure.

Automation Features

Automation capabilities include:

Automation reduces repetitive manual work while improving consistency and operational visibility.

Structured Data Capture

Protocol steps may include structured forms and schema-driven data collection.

Examples include:

Structured capture enables:

Reporting & Data Infrastructure

Flask Track includes a modern operational reporting and analytical architecture.

Reporting capabilities include:

The platform also supports structured data infrastructure for long-term analysis and interoperability.

Supported capabilities include:

This supports both operational reporting and large-scale historical analysis.

End-to-End Workflow

The following sections describe a typical operational workflow using Flask Track.

1. Review or Extend the Catalog

Most organizations begin by reviewing the preloaded scientific catalog.

Laboratories may optionally extend or customize:

Each entity supports operational metadata, supplier integration, compliance tagging, and attached files.

Ingredients

Ingredients represent:

Ingredients may include:

Tools

Tools represent laboratory equipment and infrastructure such as:

Tools may include:

Suppliers

Suppliers define procurement sources for laboratory materials and equipment.

Supplier tracking includes:

This improves purchasing traceability and reproducibility.

Species

Species define the biological context of laboratory work.

Species records may include:

Plasmids

Plasmids represent genetic constructs used throughout workflows.

Records may include:

Agrobacterium Strains

Agrobacterium strain records define engineered bacterial lines used in transformation workflows.

These records support:

2. Author Protocols

Protocols define how work is executed.

Protocols include:

Only approved protocols should be used in regulated or production workflows.

3. Build a Workflow

Workflows define how protocols progress together over time.

A workflow may:

Workflows are reusable across multiple batches and operational contexts.

4. Create a Batch

A batch represents a single execution of a workflow.

During batch creation:

Flask Track automatically creates linked sample records while preserving individual traceability.

5. Execute Laboratory Work

During execution:

Execution progress is visible at:

This creates a complete operational history.

6. Capture Compliance Evidence

Compliance data is collected directly during execution.

Organizations may:

Compliance events become part of the permanent execution record.

7. Audits & Audit Log

Flask Track maintains immutable audit records across the platform.

Audit records capture:

Auditability extends across:

8. Completion & Reporting

Once execution is complete:

This supports:

Summary

Flask Track connects scientific catalogs, laboratory execution, compliance systems, operational reporting, and automation into a unified laboratory operations platform.

By combining structured workflows, biological traceability, integrated compliance tooling, and programmable infrastructure, Flask Track helps laboratories: