icon file Flask Track Docs

Create a Batch

A batch represents a single execution of a laboratory workflow.

Unlike workflows and protocols, which are reusable templates, a batch represents an individual laboratory production run, experiment, or cultivation process.

Each batch maintains its own:

A workflow may be executed an unlimited number of times by creating new batches.

Examples include:

Relationship Between Workflows, Batches and Samples

A workflow defines how laboratory work is performed.

A batch represents one execution of that reusable workflow.

As the batch progresses through its ordered protocols, individual samples are created by promoting the batch through the workflow. Those promoted samples then become independently tracked laboratory objects while remaining associated with their originating batch.

flowchart TB

    W["Reusable Workflow"]

    W --> B["Batch Execution"]

    B --> P["Ordered Protocols"]

    P --> S["Promoted Samples"]

    S --> S1["Sample 1"]
    S --> S2["Sample 2"]
    S --> S3["Sample n"]

This separation allows one reusable workflow to generate many independent batches, while each batch ultimately produces and manages its own collection of individually tracked samples.

Batch Planning

A batch begins as a planned production run.

During creation you specify the planned quantity, representing the number of samples you expect the workflow to ultimately produce.

This value is immediately used for:

As the workflow executes, samples are promoted from the batch and individually tracked throughout the remainder of their lifecycle.

Creating a Batch

Select New Batch from the Batches page.

Creating a batch consists of selecting the reusable workflow to execute and providing basic execution information.

Required information includes:

Optional information includes:

flowchart LR
    Domain --> Species
    Species --> Workflow
    Workflow --> Quantity["Planned Quantity"]
    Quantity --> Batch["Create Batch"]

Domain

Select the laboratory domain.

Examples include:

The selected domain filters both available species and compatible workflows.

Species

Select the species being processed.

Species are filtered by the selected domain.

Choosing a species automatically limits the available workflows to those compatible with that organism, helping prevent execution of incorrect laboratory procedures.

Workflow

Select the workflow the batch will execute.

The selected workflow determines:

The workflow itself is never copied.

Instead, the batch becomes a live execution of that reusable workflow.

flowchart TB

    Workflow["Reusable Workflow"]

    Workflow --> Protocol1["Protocol 1"]
    Workflow --> Protocol2["Protocol 2"]
    Workflow --> Protocol3["Protocol 3"]

    Batch["New Batch"]
    Batch -. Executes .-> Workflow

The same workflow may therefore be executed hundreds or thousands of times while every batch maintains its own independent execution history.

Planned Quantity

The planned quantity represents the intended number of samples that this batch is expected to produce.

Although individual sample records do not necessarily exist immediately after the batch is created, this value allows FlaskTrack to estimate laboratory requirements before work begins.

FlaskTrack automatically uses the planned quantity to:

Example

A workflow contains a protocol whose reference batch is defined for 10 samples.

That protocol requires:

Creating a batch with a planned quantity of 50 samples automatically scales every protocol requirement.

flowchart LR

    A["Reference Batch<br/>10 Samples"]

    A --> B["500 mL Media"]
    A --> C["20 g Sucrose"]
    A --> D["8 g Agar"]

    B --> E["2.5 L Media"]
    C --> F["100 g Sucrose"]
    D --> G["40 g Agar"]

No manual calculations are required.

As the workflow progresses, protocols may promote the batch into individually tracked samples. These samples inherit their execution history while beginning their own independent laboratory lifecycle.

The actual number of samples may increase or decrease during execution as material is:

For this reason, the planned quantity should be considered a planning estimate rather than a fixed limit.

Batch Name

Provide a descriptive name for the production run.

Examples include:

Good naming conventions make batches easier to identify throughout reporting and audit history.

Notes

Optionally record information specific to this execution.

Examples include:

Unlike workflow descriptions, these notes apply only to this individual batch.

Scheduled For

Specify when execution is expected to begin.

Scheduling allows FlaskTrack to:

The scheduled start time becomes the reference point for protocol scheduling throughout the workflow.

Batch Execution

After selecting Create Batch, FlaskTrack creates a new execution of the selected workflow.

The batch immediately inherits:

As protocols are completed, the workflow gradually promotes work into individually tracked samples.

flowchart LR

    Batch
        --> Workflow

    Workflow
        --> Protocols

    Protocols
        --> Promotion["Sample Promotion"]

    Promotion
        --> Samples["Tracked Samples"]

    Samples
        --> Reports["Reporting & Analytics"]

From this point forward:

Completing protocols, recording laboratory data, consuming materials, promoting samples, or generating compliance events affects only this batch and its associated samples. The underlying reusable workflow remains unchanged.

flowchart LR

    W["Reusable Workflow"]

    W --> P1["Protocol 1"]
    P1 --> P2["Protocol 2"]
    P2 --> P3["Protocol 3"]

    W -. creates execution .-> B["Batch"]

    B --> S1["Sample 1"]
    B --> S2["Sample 2"]
    B --> S3["Sample 3"]

    B -. executes .-> P1

    P1 -. affects .-> S1
    P1 -. affects .-> S2
    P1 -. affects .-> S3

    P2 -. affects .-> S1
    P2 -. affects .-> S2
    P2 -. affects .-> S3

    P3 -. affects .-> S1
    P3 -. affects .-> S2
    P3 -. affects .-> S3

Reusing Workflows

One workflow may produce thousands of batches over its lifetime.

flowchart TB

    Workflow["Banana Micropropagation Workflow"]

    Workflow --> Jan["January Production"]
    Workflow --> Feb["February Production"]
    Workflow --> Mar["March Production"]
    Workflow --> Trial["Research Trial"]
    Workflow --> Elite["Elite Clone"]
    Workflow --> Customer["Customer Order"]

    Jan --> H1["Independent Batch History"]
    Feb --> H2["Independent Batch History"]
    Mar --> H3["Independent Batch History"]
    Trial --> H4["Independent Batch History"]
    Elite --> H5["Independent Batch History"]
    Customer --> H6["Independent Batch History"]

Every batch follows the same standardized laboratory process while maintaining its own execution history, promoted samples, laboratory data, and audit trail.

This separation between reusable workflows and individual batch executions allows laboratories to continuously improve standardized procedures without sacrificing traceability, reproducibility, or regulatory compliance.