How to Use Benchling Bioprocess

Benchling Bioprocess is a cloud-native platform built to support high-throughput, structured process development. It enables PD teams to design processes, plan experiments, execute batches, and visualize data — all within a unified environment. By connecting Recipes, Studies, and Worksheets with a structured ISA-88-compatible data model, Bioprocess simplifies tech transfer, improves efficiency, and unlocks downstream data science and ML insights.

This product is especially valuable for process development scientists, bioprocess engineers, and manufacturing science teams working to scale and optimize bioprocesses. Core use cases include:

Benchling Bioprocess supports the entire spectrum of process development activities, including:

• Recipe Design – Users can design and modify their processes using an intuitive, visual recipe designer that allows them to sequence the order of unit operations, connect material inputs and outputs, and define process parameters.
• Run Planning – Users can apply the Recipes they’ve created to flexibly design experiments with variations across parameters and materials.
• Batch Execution – Users can execute their Recipe Runs with guided operator instructions that are dynamically populated from the Recipe and Recipe Run plan, along with fields for structured and automated data capture.
• Data Visualization – Users can visualize data with full experimental context and traceability within the platform, or move their structured data directly into 3rd party analysis tools or AI/ML pipelines.
   

Benchling Terminology and Relationships

• Study: A structured container for planning and executing Recipe Runs. Bioprocess uses Process Development (PD) Studies specifically. PD Studies link Recipes to structured execution and data capture.
• Recipe: A visual process template built inside a Template Collection. Recipes define unit operations, process parameters, and material/equipment flows.
• Unit Operation: A modular component of a Recipe containing operational steps, definitions (parameters, materials, equipment), and optional Steps.
• Definition: Configurable aspects of a Unit Operation — including parameters (e.g., temperature), material inputs/outputs, and equipment used.
• Material: Input/output components used in a Unit Operation (e.g., media, buffers, intermediates).
• Recipe Run: A single instance of running a Recipe template with one or more Conditions. This often corresponds to a single experiment.
• Recipe Run Plan: A draft version of a Recipe Run that has not been finalized yet. You can edit this as many times as you like before creating a Recipe Run from it.
• Condition: A specific variation in process parameters across unit operations. Conditions define the variables tested in the Recipe Run.
• Replicate: One or more repeated executions of a condition.
• Unit Operation Instance: An instance of a Unit Operation within a Recipe Run. If you take a Recipe with three Unit Operations in it and design a Recipe Run with 3 conditions and 1 replicate each, you end up with (1) Recipe Run and (9) Unit Operation Instances
• Worksheet: The “mini” notebook entries that are created when a Recipe is executed. Each worksheet corresponds to a given unit operation and includes all Steps.

In Benchling Bioprocess, a Study allows users to plan and execute Recipe Runs. Each Recipe Run correlates to a single Recipe, which is made up of one or more Unit Operations. Within each Unit Operation, users can configure Definitions and Step Groups, which further define operator actions and data capture.

When creating a Study from a Recipe, users define one or more Conditions and one or more Replicates:

• Conditions represent sets of distinct parameter deviations (e.g., different pH levels or feed rates).
• Replicates are repeated runs for each condition to ensure experimental reliability.

The execution-side hierarchy in Benchling Bioprocess is as follows: Study > Multiple Recipe Runs > Multiple Unit Operation Instances > Multiple Steps
 

This feature is available for customers with the Bioprocess package enabled in their Benchling tenant. If you're unsure whether your tenant is licensed, please contact your Customer Success representative or reach out to support@benchling.com 

Benchling Bioprocess is built around four major functions: Process Design, Experiment Design, Experiment Execution, and Data Analysis. These functions are deeply integrated to support end-to-end process development and ensure that data flows seamlessly from planning to insight.

The Process Design phase relies on a core Benchling element called a Recipe. Recipes represent the scientific blueprint of your process—they define the sequence of steps (unit operations), the flow of materials, and the critical parameters and equipment used at each stage. A well-designed Recipe captures the intent and structure of a bioprocess and forms the foundation for all downstream activities, including experiment planning, execution, and analysis.

In the sections below, you’ll learn how Recipes function in Benchling and how they support each stage of the Bioprocess workflow.

Process Design | Recipes

What Are Recipes in Benchling Bioprocess?

In Benchling Bioprocess, a Recipe is a digital blueprint of your scientific process. It defines the sequence of unit operations—like inoculation, transfection, or purification—and captures all key process details, including materials, equipment, parameters, and operator instructions.

Recipes are the foundation for planning and executing experiments in a structured, repeatable way. Once created, a Recipe can be reused across multiple studies, helping teams streamline execution, reduce manual errors, and ensure consistency across experiments.

Where Recipes Live: Template Collections

All Recipes are created and managed within Template Collections. These collections act like folders for related Recipes and help organize templates by team, process, or product. They also control who can view, use, or modify Recipes.

Who Can Configure Recipes?

To design or edit a Recipe, users need at least WRITE access to the Template Collection that contains it. This permission level ensures that only trained users—typically Bioprocess Admins, scientific leads, or implementation specialists—can make changes to process templates. This protects data integrity and helps maintain consistent execution across teams.

Example: A Simple Upstream Recipe

You can see here an example of an upstream Recipe with four unit operations: Seed Train, Inoculation, Bioreactor Culture and Harvest. Each unit operation includes materials, equipment, parameters, and operator instructions that guide execution.

Unit Operation are self-contained module within a Recipe that represents a distinct stage of the process. Each Unit Operation defines the transformation of material inputs into outputs by specifying its parameters, equipment, and materials. It can also include structured operator Steps (pre-run, run, post-run) that guide execution and data capture

Material outputs from one unit operation can be routed to the inputs of a subsequent unit operation. However, Recipes in Bioprocess are designed to be linear and sequential—unit operations themselves cannot branch within a single Recipe. If your process includes different paths or variations in steps, each sequence of unit operations should be configured as a separate Recipe.

🛠️ Ready to learn how to build and configure Recipes? See our How to Configure Benchling Bioprocess article.

Experiment Design | Process Development Study

Study Creation

A Process Development (PD) Study is a structured container for planning and executing experiments (as Recipe Runs) with Recipes. Recipe Runs define the experimental scope — including conditions and replicates — and automatically generate Worksheets for execution. Your admin must configure at least one Process Development Study schema to create a new study and add a Recipe to the Study. To learn how to configure a Study schema see our How to Configure Benchling Bioprocess article.

Only users with Write access to a Project or Folder can create a Study inside that folder.

To create a study, follow the steps below: 

1. Use the Studies icon, navigate to + option to Create, and select from the PD Study Schemas available.
2. Fill in metadata:
   • Study name
   • Project folder
   • Any required custom fields from the schema
   • Description (appears on the Overview tab)
   • Click Create to proceed to experiment planning via Recipe Run creation.

Create a Recipe Run Plan

To begin experiment planning, add a Recipe Run Plan to your PD study. 

1. Click the + Plan recipe run option from the center or top right corner of your PD Study page.

2. Specify Metadata: Select the Recipe from dropdown, give the run plan a Description (optional), and data entry mode (i.e. executing all conditions in the same worksheet, or each condition in a separate worksheet).

   

3. Click Plan
4. Click on the newly-created Recipe Run Plan in the list of Recipe Runs to open up the plan and start filling out your experiment design.

Naivgating a Recipe Run Plan

• Click Edit to start adding/removing/updating conditions.
• Click to edit parameter/material/equipment setpoints and to add Conditions to the Recipe Run Plan. (More advanced Condition modifications live in the next section)
• Click to change: Condition names, Condition descriptions, the Recipe used, the data entry mode, the number of conditions or replicates.
• Click on either of the Design tabs to add one or more Conditions.
• Click Metadata to see metadata or to modify the Recipe Run Plan's description. (This description will become the description of the resultant Recipe Run).
• Click Link unit operations to pre-configure the links between Unit Operation Instances in the Recipe Run Plan and Unit Operation Instances in Recipe Runs in the Study. You can not link Unit Operations between one Recipe Run Plan and another.

Design Experiment: Conditions & Replicates 

Set Unit Operation Values

For each Condition, use the Materials, Parameters, and Equipment tables to override default Recipe values:

• Values left blank will default to the Recipe’s original setpoints.
• Click into each Unit Operation to adjust values specific to each Condition.

• These will appear as Planned values for user reference in Recipe Run Execution

  
   

• Click Save to save the new values entered, or Cancel to revert back to the last saved version of this Recipe Run Plan.

Turning the Recipe Run Plan into a Recipe Run

⚠️ After this point, Conditions, Replicates, and configured setpoints cannot be changed – however new conditions can be added to an existing Recipe Run. Read how below.

Click on the Create button (it will read Save and create if you have unsaved changes to the Recipe Run Plan).

Data entry mode

When executing a study, there are two ways to enter data into Worksheets depending on your team’s workflow:

1. All Conditions Together (Parallel Execution)

• All condition-replicates are combined into a single Worksheet view.

  

• Structured tables (Material Input/Output, Equipment, Parameter Setpoints/Measurements, Registry, Results, Inventory, Lookup) all include a Condition-Replicate column as the first column.

  

• Operators record data for every condition-replicate in the same table and same worksheet, choosing or importing the correct condition-replicate for each row.

• This mode is ideal when conditions are run in parallel, reducing clicks and reflecting how work is performed in the lab.

Tip: Data in every structured table must be associated with a Condition-Replicate to be available for downstream analysis. You can populate the column directly in Benchling, or map it automatically when importing data via CSV or lab automation.

2. One Condition at a Time

• Each condition-replicate opens in its own Worksheet.

  

• Operators move between Worksheets using the dropdown menu to record data separately for each condition-replicate.

  

• This mode can be helpful when conditions are run sequentially or by different team members handling specific replicates.

Adding New Conditions to a Recipe Run

1. Open your active Recipe Run and navigate to the Design tab.
2. Click Edit and select Add Conditions (note; condition name is editable via ⚙️ icon)
3. Enter the new condition names/values..
4. Press Save — new worksheets are automatically created for these conditions.
5. Run worksheets as normal; all data remains linked to the same Recipe Run, preserving provenance while allowing flexible study design and analysis.

Adding Multiple Recipe Runs

You can add multiple Recipe Runs to a single PD Study. 

These Recipe Runs can then be linked at the Unit Operation Instance level — an output from one unit operation can be split across multiple downstream operations, or outputs from many operations can be joined into a single downstream operation. 

This linking makes it possible to trace inputs and outputs across workflows, and perform study-wide analyses.

Linking Unit Operation Instances 

1. Open the Recipe Run where you want to link a previous Run.

2. In the new Recipe Run, click Link to existing.

   
   

3. Select one or more Unit Operation instances (use Shift to select multiple).
   • A sidebar will open on the right, showing the available Unit Operations within the same Study.

4. Choose the Unit Operations you want to link.

• Once selected, a line will display the connection between the source and target Unit Operations.

5. To add additional links, select another Unit Operation instance and repeat.
6. Note when linking;

• Source = the Unit Operation instance you are linking from
• Target = the Unit Operation instance you are linking to

This distinction is important for downstream analysis and when querying links in the warehouse.

7. Once you’ve added all necessary links, click Confirm to save.
8. After confirming, you’ll return to the Recipe Run page. Links will now appear in the Unit Operation Instances table under the column Previous unit operations

Viewing and Editing Unit Operation Links

There are two ways to view existing links between Unit Operation instances in a Recipe Run:

• Check the Previous unit operations column.
• Click Link to existing to open a visual graph of all links for that Recipe Run.

Editing Unit Operation Links

1. From the Recipe Run page, select Link to existing.
   • A visual graph of all links for the Recipe Run will appear.
2. Click any Unit Operation instance in the Recipe Run to open the right-hand panel.
3. Deselect the link(s) you want to remove, then click Confirm.
   • This will remove the connection between the source and target Unit Operation instances.
   • Updates are applied everywhere the link is surfaced, including the Previous unit operations column, datasets, and the warehouse.

Experiment Execution | Completion of Worksheets

In the next section, we will cover the execution process, where operators will follow these instructions to perform tasks and record experimental data.  Operators will execute the Study by completing the Recipe Runs Worksheets.  Worksheets will guide the operator to capture parameters, materials, equipment, and step-level data like results and entities.

Once a Recipe Run has been created, it will appear in the PD Study Overview tab.

• Navigate to the Recipe Run view by clicking on Recipe Run Name to see all Unit Operation Instances of the Run.

• Selecting the Unit Operation Instance Worksheet chip will open the Worksheet for execution.

  

Navigating Worksheets

• Header (1): Shows @mentionable Worksheet ID (e.g., WKS28), unit operation, run name, and number of conditions (click to view).
• Header (2): Options to Analyse, Export Audit Logs, and update Run Status
• Worksheet (3): Shows auto-generated Pre-Run, Run, and Post-Run steps.
• Worksheet (4): Access step history (clock icon) and update Unit Operation Step Status (Complete Step, Skip, Fail).

Recipe Steps

Recipes contain Unit Operations which consist of sequential Steps that define tasks for operators. These tasks can be assigned via the study item tabs.

Let’s see now how to add new and complete steps, initiate and complete a Run, then cover how to update specialized BioProcess definition tables (Material Inputs, Outputs, Equipment, Parameters).

Adding New Recipe Steps

If a Recipe Step was not included in the Recipe but needs to be tracked:

1. Click the + icon next to the Step Group (Pre-Run, Run, Post-Run).
2. Enter the Step name (note: the name cannot be changed later).
3. Choose the Step position within the group.
4. Optionally, start from a template or sub-template.
5. Click Add Step.

Completing Recipe Steps

• Users must update the status of Recipe Steps (to Completed, Failed, or Skipped) in order to lock Worksheet steps from further edits once the Run is completed.

1. Click a Step to open it.
2. Review instructions and complete the required tables.
3. Click Complete Step (or use the dropdown to Skip / Mark as Failed).

Pre-Run Recipe Steps

• Pre-Run Steps are preparatory actions that need to take place before the main experiment can begin.
• Pre-Run Steps are optional and can remain open when the Run is started.
• Parameter confirmation and measurement Steps typically remain open during execution so data can be added throughout the Run

Run Recipe Steps

• Run steps are the core actions of the experiment. Runs must be explicitly started/ended and includes start/end time tracking which affects process time calculations in Analysis datasets. 

Starting a Run

1. In the Worksheet header, select Start Run.

2. Once started:
   • The Worksheet locks to the defined conditions.
   • Any open Pre-Run Steps remain available for completion.
   • Operators can begin filling out Run Steps in real time.
3. Continue completing Steps as tasks are performed.

Ending a Run

1. Confirm all required Steps are either Completed, Skipped, or Failed.
2. Confirm all Material Outputs have been Submitted.
3. In the Worksheet header, select Complete run.

4. After ending the Run:
   • Worksheets with status of Completed, Skipped, Failed, are locked to prevent further edits.
   • Run status updates to Completed.
   • All captured data is preserved for audit and analysis.

Post-Run Steps

• Post-Run Steps are actions required to properly close down the experiment once it has been completed. These may include activities such as deviation tracking.
• While Post-Run Steps are optional, each should have its status updated to Completed, Failed, or Skipped. Updating the status locks the step from further edits and ensures the experiment record is finalized.

Definitions Tables (Parameters, Material Inputs, Outputs, Equipment)

Remember, Definitions are configurable aspects of a Unit Operation — including parameters (e.g., temperature), material inputs/outputs, and equipment used.

• Confirmation tables pull in planned values from the Recipe Run design.
• Use confirmation tables to confirm actual equipment, material inputs/amounts, and parameter setpoints used.
• Record any deviations from planned values in Comments.

• Parameter Measurement tables capture actual parameter values measured during execution.
  • To record multiple values for the same parameter:
    • Click + → Add parameter measurement → Select parameter → New row added
    • Drag rows by number to reorder/group.

Data Analysis 

Data in the study can be analyzed using the Analysis tool in Benchling. The Analysis tool allows users to transform your data, explore model fits and trends, and insert results and datasets back into the originating Worksheet for full traceability. 

Creating an Analysis

1. Analysis tool can be opened from the Study listing page (to analyze multiple studies), Study, Recipe Run, or Worksheet overview using the Analyze💡 button in the top-right corner of the page.
2. From there a Create Analysis modal will appear giving the option to Use Template or Create new Analysis.
3. If creating a new Analysis, assign a name and select which structured tables you wish to export (Material Inputs, Outputs, Equipment, Parameters, Results, Registrations)
4. Click Create to generate Analysis
5. From your new Analysis you can transform your data by creating new views, and save new or apply existing analysis templates. 

For more details, see the Analysis article. With Benchling Bioprocess, you have access to both Standard and Advanced Analysis features.

Analyzing multiple Studies

1. Click the Studies icon in the main app navigation (the one that looks like a book)

2. Expand the list to full screen by clicking the > button here

   

3. Select 1 or more studies to analyze. The studies you select must all be Process Development Studies.

   

4. Click Analyze

5. Give the analysis a name in this modal, then click Create

   

The resulting Analysis will include all datasets from all selected Studies. This will be as if you'd clicked the Analyze button on each of those studies and then merged the datasets from the three Analyses into one Analysis.

Analyzing a subset of Recipe Runs in a single Study

If you want to analyze a subset of Recipe Runs in a Study, select the runs of interest in the Recipe Run overview before hitting the Analyze button on the Study overview. (selecting no Recipe Runs before clicking Analyze is as if you'd selected all runs).

Difference between hitting Analyze with no Recipe Runs selected and clicking it with ALL Recipe Runs selected:

The resulting analyses will look the same at first. However the analysis created after explicitly selecting all Recipe Run in the Study will always be constrained to those Recipe Runs when you click Refresh Data on the Analysis. If new Recipe Runs are added to the study and you hit Refresh Data on the analysis, those new runs will NOT show up in the analysis after refresh. The analysis created when you select no Recipe Runs before hitting Analyze WILL include new Recipe Runs that are added to the Study after the Analysis was created.

What data is included in the generated analyses

The scope of data included in each of these tables depends on how the analysis was created (from a Worksheet, Recipe Run, multiple Recipe Runs, a Study, multiple Studies). The tables will always be the same.

When creating an analysis from a Worksheet, you do have the option of selecting which of these tables you want included in the Analysis. Creating the Analysis from all other sources always includes all of these tables.

Where the data in an Analysis comes from

The best way to think about this is to understand that the Analysis we create includes data from and about one or more Unit Operation Instances. The different entry points just dictate how wide of a net we cast for Unit Operation Instances to include.

In parallel execution the Condition - Replicate column that you see in all structured data entry tables is how we decide which Unit Operation Instance to associate a given row of data (parameter/material/equipment, entity, result) with a Unit Operation Instance. Leaving Condition - Replicate blank on any row means that data will not be included in any analysis because it is not associated with a Unit Operation Instance. Don't leave Condition - Replicate blank.

Within a Unit Operation Instance how the data in each analysis table is queried.

Automatic PD Charting

When you create an analysis from a Process Development Study or Recipe Run, Benchling automatically generates charts and summary tables based on your results data—saving significant time in data visualization.

What Gets Created Automatically

Charts:

• Line charts for time-series data (when your results contain time-based columns like "Day," "Hour," "Timepoint," or "Process Time Recorded")
• Bar charts for non-time-series data (when your results contain entity/sample references or numerical cross-sections)
• One chart is created per numeric column in each result schema

Summary Tables:

• Parameter Summary Table: Parameters organized by condition (one row per condition, one column per parameter)
• Material Input Summary Table: Material inputs organized by condition
• Joined Summary Table: Combined view of parameters and materials

All automatically generated charts and tables are fully editable—you can modify, delete, or add to them as needed.

How Automatic Charting Works

The system analyzes all Results recorded in Worksheets within your study and determines the appropriate chart type:

1. Time-Series Detection: The system looks for columns with time-related keywords (day, hour, timepoint, process time recorded, etc.)
   • If a time column is detected → Creates line charts
   • The time column becomes the X-axis
   • Each numeric column gets its own line chart with the time column as X-axis
2. Non-Time-Series Data: If no time column is detected
   • Creates bar charts instead
   • Uses entity/sample names as the X-axis (if entity links exist)
   • Uses result IDs as X-axis (if no entity links exist)
3. Series Grouping: All charts use "Condition-Replicate-Unit-Operation" as the series to enable easy comparison across experimental conditions

Understanding Chart Types

Time-Series Line Charts:

• X-axis: Time column (Hours, Days, Timepoint)
• Y-axis: Each numeric measurement (pH, temperature, VCD, etc.)
• Series: Separate lines for each Condition-Replicate-Unit Operation combination
• Example: Bioreactor results tracking pH, temperature, and VCD over hours will generate three line charts

Bar Charts:

• X-axis: Sample/entity names or result IDs
• Y-axis: Each numeric measurement (purity, yield, concentration, etc.)
• Series: Separate bars for each Condition-Replicate-Unit Operation combination
• Example: Purification results measuring purity and yield across fractions will generate two bar charts

Summary Tables: Summary tables transform the "tall" format of raw parameter data (one row per value) into an easy-to-read format with one row per condition and one column per value, making it simple to understand your experimental setup at a glance.

Customizing Automatic Charts

All automatically generated charts can be modified:

Delete charts: Click the "..." button → Select "Delete view"

Modify charts: Click the chart step → Use the chart editor to:

• Change series grouping (e.g., change to "Condition" only for cross-unit-operation comparisons)
• Apply filters (e.g., filter specific chromatography fractions)
• Adjust axes, colors, or other visual properties

Add custom charts: Click Add Step → Choose Chart → Configure manually

Best Practices

Use descriptive column names:

• For time data: Include words like "Day," "Hour," "Time," "Timepoint"
• For relative time measurements: Use "Hours since inoculation" rather than absolute timestamps
• Descriptive names help the system identify the correct chart type

Understand the design philosophy:

• Automatic charts aim to be useful "more often than not," not 100% perfect
• They provide a great starting point that can be refined for specific needs
• All generated steps are standard steps you can modify or delete

Work with worksheet data:

• Automatic charting works with Results recorded in worksheet tables
• Datasets uploaded via lab automation or Connect Runs don't trigger automatic charts but are available in the analysis

Troubleshooting

No automatic charts appear:

• Verify this is a Process Development Study (feature only works for PD Studies)
• Confirm you have Results recorded in Worksheets (not just uploaded datasets)
• Check that result schemas contain numeric columns
• Ensure the analysis was created after December 2025

Wrong column used as X-axis:

• Manually edit the chart to change the X-axis
• If this occurs frequently, contact your admin to adjust keyword detection settings

Charts are empty:

• This is normal if result data hasn't been entered yet
• Empty charts don't indicate an error

Charts need refinement:

• This is expected—automatic charts provide a starting point
• Delete unhelpful charts, modify others, and add custom charts as needed

Key Points to Remember:

1. Automatic PD Charting saves time by instantly creating visualizations
2. Charts can be customized, deleted, or supplemented with custom charts
3. Use descriptive column names to help the system identify data patterns
4. The feature provides a starting point—refinement for specific use cases is expected and normal
5. All functionality is available through standard Analysis features

For questions about Automatic PD Charting, contact your Benchling administrator or support@benchling.com.

Exploring Process Development Study UI

Navigating PD Study

The PD Study serves as a central hub for creating Recipe Runs and Analyses, as well as aggregating the objects and data generated through those activities. A PD Study has 5 tabs detailed below. 

Study Level - Overview Tab

• Allows users to initiate new Recipe Runs and generate Study-wide Analyses.
• Consolidates all existing Recipe Runs and associated Analyses in a single view.
• Provides clickable access to each Recipe Run for viewing design and worksheet level detail.

Study Items Tab

• Displays all objects related to a PD Study in a searchable, filterable view.
• All operational tasks (Recipe Unit Operations, Recipe Steps) are actionable from this view.
• These tasks can be filtered by status (e.g., Pending, In Progress, Completed) and assigned to coordinate work.
• Includes Group By filters to organize tasks by Unit Operation, making it easy to view related Steps together.

Results Tab

• Displays all Results recorded against Registered Entities associated with the PD Study.

Metadata Tab

• Displays Study-level metadata, including creation date, location, and schema-specific fields.
• Useful for tracking where the Study lives and who created it.
• Allows you to set notification schedules to remind task assignees about pending work.
• Helps ensure timely execution by keeping stakeholders informed.

Recipe Run Navigation

Users can navigate from the Study Overview tab a Recipe Run view by clicking on Recipe Run Name. The recipe run view has two tabs, Overview and Design.

Overview

• Unit Operations Instances (Unit Operation × Condition × Replicate) are listed with status and worksheets.
• Status Boxes show the count of instances per status (e.g., Planned, In Progress).
• Selecting one or more Status Boxes filters the view accordingly.
• Selecting a Unit Operation Instance row opens its worksheet.
   

Design 

• Shows a read-only table of the experiment setup created during Study design.
• Displays the full Condition × Unit Operation matrix for easy reference.
• Allows you to verify which parameters, materials, and equipment were configured for each Condition.
• Useful for reviewing experimental intent before or during execution.

Status logic and task completion

Statuses are tightly controlled and follow a clear hierarchy. Terminal statuses cannot be amended.

Recipe Experiment Run Statuses

• Active: When execution begins
• Completed: Auto-updated when all Unit Ops are terminal
• Failed / Cancelled: Manual from dashboard, propagates to Unit Ops

Recipe Unit Operation Statuses

• Pending → In Progress: When user starts the Worksheet
• In Progress → Completed / Skipped / Failed: Manual by user
• Cancelled / Invalid: Triggered by failed or cancelled Experiment Run

Recipe Step Statuses

• Not Ready / Ready / In Progress: Initial states
• Completed / Skipped / Failed: Manual actions
• Cancelled / Invalid: Not exposed in UI yet (post-M1 behavior)

Steps are not automatically synced with Unit Op statuses. When a Unit Op is marked terminal, users can optionally transition all Steps to Skipped.

For more questions, please contact support@benchling.com or your Benchling representative.