User Guides

The Experiments page allows you to manage experiments in Invert. From here, you can review past and ongoing experiments, presented in either list or calendar view. Create a new experiment as necessary or navigate to a given experiment details page for a close-up view on a specific experiment.

Creating a New Experiment

To create a new experiment, simply click the "New Experiment" button. This action will direct you to the 'New Experiment' view, where you can enter the experiment name and edit experiment properties, such as status, start date, end date, and more, as needed. Navigate to the Runs tab to create runs you wish to associate with the new experiment. Once you've filled in the necessary details, the newly created experiment will appear in the experiment list on the Experiments page.

Experiment list

The experiment list on the Experiments page gives you an overview of all your experiments whether past, ongoing or future experiments. Navigate through the list and apply filters to narrow down the view based on experiment status, start date, or end date.

The calendar view provides a visual representation of your experiments, allowing you to see them in a calendar format. Use the arrow buttons for quick navigation.

Clicking on an experiment in the list or calendar view will direct you to the respective experiment summary page.

Experiment Summary Dashboard

For organizations with the Experiment Summary feature enabled, the experiment details page includes a Summary dashboard tab. This tab provides an aggregated overview across all runs in the experiment, including key metric comparisons, run status at a glance, and condition-level groupings. It is designed to give a quick read on how an experiment performed without needing to navigate into individual run pages.

The Import page is your gateway for bringing data into the app. It is organized into two tabs: Files for uploading data from local files, and Integrations for viewing hardware and software connections that push data into Invert automatically.

Files Tab

This is the primary method for uploading bioprocess data from files. Pick a file (or folder), choose a mapping template, and press 'Start Import' to initiate ingestion.

• Step 1: File or Folder Picker

  Select one or more files from your computer using the file picker. Supported file extensions include .csv, .xls, and .xlsx. You can upload multiple files at once, as long as they share the same file structure. Folder upload is also supported — select a folder and all files inside will be uploaded using the same mapping. It is recommended to carefully review files before ingestion to avoid importing faulty data.

  

• Step 2: Mapping Picker

  Select a mapping template that best fits your data structure. Several mappings are available to support the following data types: Run Data, Timeseries Data, or Run Events. Custom mappings can be added to the list upon request. Refer to the mapping example in the top right corner of the screen or consult the Mapping Guide below for more information on a particular mapping.

  When you upload a file, Invert will automatically suggest a mapping if it can infer the format from your file's structure. Accept the suggestion with 'Apply mapping' or dismiss it to choose manually.

  

  

• Step 3: Settings (Timeseries Data Only)

  When importing timeseries data, you have the option to either 'merge' or 'replace' existing datasets. Choose 'merge' to update an existing dataset by either overwriting or appending data. Alternatively, select 'replace' to discard the existing dataset for a given metric and replace it entirely with the imported data.

• Step 4: Start Import

  Click the 'Start Import' button to initiate file ingestion. This will direct you to the 'Importing' page where you can review 'Import details' for more information on ingestion status. Depending on the file size, file ingestion may take up to several minutes to complete.

• Step 5: Ingestion Evaluation

  • Step 5.1: Successful File Uploads

    Upon file ingestion, ensure data imported into Invert meet your expectations. Click 'Runs' or 'Metrics' for a comprehensive overview on the imported data. Consider editing and re-uploading files to update or replace data inside the app as needed.

    

    

  • Step 5.2: Failed ingestion attempts

    Failed file ingestions may occur if the source file contains unexpected or incomplete data, or if the file structure is not supported by the selected mapping. Consult the error log on the Importing page for details and verify the file meets the mapping criteria. Contact Invert support via Help & Support if you need further assistance.

Ingestion Preview

Ingestion Preview lets you see exactly what will be imported before any data is committed. For single-file uploads, Preview is on by default — Invert processes the file and shows a structured summary of what would be created, organized into tabs (Runs, Library, Timeseries, Events, Lineage) so you can catch mapping issues or unexpected data before they land in your workspace. Each tab name shows a count of items, and tabs with zero items are grayed out and non-clickable. The Library tab lists the unique metrics and properties detected in the file so you can verify nomenclature before approval. Click Approve to proceed with the actual import, or go back to adjust the file or mapping. Folder uploads skip the preview step.

Mapping Guide

• Run Data
  • Description: Metadata associated with a run
  • Example: LOT#, Reactor ID, Site, Operator
  • Mapping: 'Run Data'
    • Required Columns:
      • Run
      • Metric A (unit)
    • Recommended Columns:
      • Experiment
• Timeseries Data
  • Description: Time-based metrics
  • Example: Time (h) versus Temperature (**°**C)
  • Mapping:
    • Timeseries Data (absolute time)
      • Required Columns:
        • Run
        • Timestamp
        • Metric A (unit)
    • Timeseries Data (relative time)
      • Required Columns:
        • Run
        • Time (h) or Time (min)
        • Metric A (unit)
• Run Events
  • Description: Notes associated with a run.
  • Example: Reactor Foaming @ 24h EFT
  • Mapping:
    • Run Events (absolute time)
      • Required Columns:
        • Run
        • Timestamp
        • Event Type
    • Run Events (relative time)
      • Required Columns:
        • Run
        • Time (h) or Time (min)
        • Event Type
• Invert Data
  • Description: Import data from an Invert export file
  • Example: Time (h) versus Temperature (**°**C)
  • Mapping:
    • Invert
      • Required Columns:
        • Export file structure generated by Invert

Integrations Tab

The Integrations tab lists all configured data sources for your organization — hardware agents, ELN connections, and other automated data streams. Clicking a data source opens its detail page where you can review connection status and recent ingestion activity.

Import History

Navigate to the Import History page for an overview of all historical file ingestions. Select a specific upload to see details on mappings used, runs created, and metrics imported. You can also download the original source file from this view.

The Runs directory is your hub for managing all your bioprocess runs. Here, you can view, filter, group, and sort your runs in a table format. Whether it's shake flask, bioreactor or any other type of run, this page helps you keep track of your data and serves as a starting point for your analysis.

Get Started

Begin by exploring your runs in the run directory. Use the filtering option to tailor your view to include relevant runs for your analysis. Customize the layout by adding or removing columns. Select runs for bulk editing or timeseries analysis.

Key Features

• Filtering: Narrow down your view by applying filters based on different attributes like experiment, organism type, or any other relevant metadata associated with your runs.

• Sort: Sort your runs alphabetically in ascending or descending order depending on your needs. Click on the arrow down icon inside any of the column headers and select 'Sort A to Z' or 'Sort Z to A'.

• Grouping: Group your runs by specific criteria for a clearer overview. Choose from options like operator, site, strain, or any other run property from the list. Bulk select grouped runs by clicking the checkbox next to the group label.

• Table Layout: Tailor your run directory view by adding or removing columns. Change the column order by dragging-and-dropping. Take advantage of the 'Add Similarities'/'Add differences' feature to quickly compare run data across highlighted runs.

  

• Saved Views: Save your run directory configuration for a more streamlined and reproducible data analysis experience. Apply filter settings and adjust the table layout followed by pressing 'Save'. Select a view from the dropdown to restore a previously built configuration. Views can be shared with teammates via the 'Share' option in the view dropdown, which copies a direct URL to the current view.

  

• Aggregations and Units: Choose between a variety of aggregation settings for timeseries metrics (e.g. Mean, Last, Maximum, Minimum, etc.). Use the built-in unit conversion tool to quickly change between available unit options (e.g. mL/min to L/h).

• Search: Use the search feature to quickly identify relevant runs in the current run directory view. Search for run names or any other metric entry, like NH4OH or Process development.

• Experiment planning: Take advantage of the 'Status' property to organize runs by its status. Use the 'Status' filter and select 'Completed', 'In-progress', 'Scheduled' or any of the other options to further customize your view.

• Quick access to Summary page: Runs and Experiments are clickable entities, allowing you to quickly access the associated Summary page for detailed information related to a particular entry.

• Editing: Select a run and make edits to its associated metadata. Use our bulk editing feature to streamline editing across multiple runs simultaneously. Use shift-click feature for quickly highlighting multiple cells.

• Run merging: To merge two or more runs into a single run, select the relevant runs and click the 'merge' button accessible through the dropdown menu in top right corner. Choose the run to keep and proceed with the run merge.

• Export: Select one or more runs and click 'Export' to open the Full Data Export page. Choose which timeseries metrics and run metadata columns to include, then download a structured Excel file containing all selected data.

• Transfer to Analysis: Choose a specific run or a selection of runs to carry forward to the Analysis page. This allows you to create line, scatter, or bar charts based on the selected data for deeper insights and visualization. You have the option to save the analysis as a sharable report.

The Library page allows you to explore and manage the key components of your bioprocess data analysis: metrics, properties, unit operations, and event types. You can easily add, remove, or modify library entries either through the user interface (UI) or by importing files. The page is organized into tabs: Metrics, Properties, Unit Operations, Event Types, and (when Assist is enabled) Skills.

• Metrics are entities representing time-based data with a distinct x and y value pairs for each data point. Example: Online pH time course signal coming from a bioreactor, or offline product titer concentration time course.
• Properties are single value entities typically considered as meta data providing additional context to a run or experiment. Supported data types are numeric, text, date, among others. Examples: 'Host Organism' with a value 'E. coli' or 'Bioreactor size (L)' and '200'.
• Unit Operations are process steps within a run or experiment that group together related metrics and properties for a specific stage of work.
• Both metrics or properties can be used as formula inputs. Depending on how the formula is configured, formulas may qualify as a metric or property. Examples: A time series aggregation function converts a time series metric into a property (e.g. Maximum(pH) = 7.9) whereas multiplying a metric with a property results in a metric (e.g. Metric[Feed delivery volume (L)] x Property [Feed concentration (g/L)] = Metric[Substrate delivery mass (g)]).
• Parent metrics organize and consolidate data streams into groups. This is recommended because bioprocess hardware and data stream tag names can vary widely. Additionally, this enables Invert based formulas to be grouped with pre-calculated data streams from your hardware. For example, use 'Temperature (°C)' parent metric to bundle 'TP001 (°C)', 'Temp (°C)', and 'T_PV (°C)'.

Navigation

Move between Library sections using the selector in the top left:

Metrics

The Metrics tab provides an exhaustive list of all timeseries metrics currently in Invert. This list includes time series data uploaded by the user or hardware agent - as well as formula-derived time series data. Each entry is a clickable link navigating to individual metric details pages with additional information and editing options.

Properties

On the Properties tab, you'll find a collection of single-value properties. Similar to the Metrics tab, this tab presents entries in a table layout and each linking to the respective details and editing pages.

Unit Operations

On the Unit Operations tab you'll find a collection of process steps. Similar to the Properties tab, this tab presents entries in a table layout and each linking to the respective details and editing pages.

Event Types

The Event Types tab lists all event categories and types defined for your organization. Event types are used when annotating runs on the Analysis page or the Run Summary Events tab. You can view and manage the available event categories (such as Critical Operations, Additions / Removals, and Observations) and the specific types within each category from this tab.

Skills

The Skills tab is available when Assist is enabled for your organization. It lists all Skills created by your team — reusable analysis instructions that Assist can follow when mentioned with @ in a chat. See the Skills article for full details on creating and using Skills.

Key Features

• Data Sources
  • Data Sources column provides insights into the origin of a metric or property. The displayed value is automatically generated and cannot be changed by the user. Example: Time series metric 'Aeration' uploaded via file ingestion - mapping name is used for Data Source value. Similarly, metrics imported via hardware or ELN Integration will automatically derive their label from the respective ingestion agent. Quantities can have multiple Data source labels.

• Adding a new metric
  • Create a new metric by clicking the 'Add' button on the 'Metric' tab. Enter a metric name and adjust metric properties as needed. The newly created metric will show in the metric table view.
• Archiving a Sub or Parent metric
  • To archive a metric or parent metric from the library, click on the metric name to access the details page. Then, open the kebab (⋮) menu in the top right corner and select 'Archive...' to remove the metric. The same kebab-menu pattern is used to archive an individual property, formula, or unit operation type from its detail page.
• Bulk archive
  • Select multiple rows in the Metrics, Properties, or Unit Operations tab using the row checkboxes, then choose 'Archive' from the table's kebab (⋮) menu to archive them all at once. The bulk archive modal lists each item, flags any that are blocked by associated runs or formulas, and lets you proceed with the archivable subset.
• Show Related
  • On the detail page of a metric, property, formula, unit operation type, or event type, the kebab (⋮) menu in the top right corner exposes 'Show related runs' and 'Show related reports' shortcuts. These open the Runs or Reports directory pre-filtered to entries that reference the entity you came from.

Bundling 'Sub metrics' into 'Parent metric'

• Bundle one or multiple sub metrics into a parent metric to streamline metric management across bioreactor platforms. Select relevant sub metrics and click 'Change Parent'. In the modal, select 'Add new parent' from the dropdown and confirm with 'Change Parent'. Specify name and display unit - optionally you can update the sub metric list as needed. Once saved, you may select runs and transfer to analysis page or wait until 'State' updates to 'Ready'. Sub metric<>Parent metric relationships are reversible. Parent metrics can be archived and recreated at any point in time.

Updating Units

• You can update the unit associated with a metric from the metric editing view, either by updating the Default Display Unit or Default Ingestion Unit (see Metric Property Guide). For that access the metric details page by clicking on the metric name in the metric table. Proceed to the metric editing view to modify the unit. Press 'Save' when done.

Adding a Formula

• You can calculate derived-quantities in a streamlined and automated fashion using Invert's formula feature. Formula use cases include KPIs (e.g. Yield, Productivity), mass balance (e.g. reactor volume over time) or signal noise reduction (e.g. moving average). Refer to the 'How to use Formulas?' info box for more information on supported mathematical operations. Formulas accept both properties and metrics as input variables. Depending on the formula configuration, the formula output could either be a time series metric or a single-value property.
  • Example: f(x) = centered_moving_average(DO) = Timeseries metric
  • Example: f(x) = last(Product titer) = Single-value property
• Once a formula is configured, Invert automatically calculate results for runs that meets the formula criteria. Formula calculation triggered upon file ingestion or after changing the formula configuration via formula editing page.

Adding a Constant into an existing Formula

• Enter a formula name and set your dependencies. Press 'Add constant' and pick a constant from the list or create a new constant. Ensure the units of the constants is compatible with the mathematical operation. Proceed with formula creation.

Adding Notes

• You can annotate metrics by adding a note. Notes are accessible from Line charts via tool tip hover. Simply open the metrics editing page and update the 'Notes' section and press 'Save' when done.

Metric & Property Editing Page - User Guide

Name

Description: Name of the metric

Impact: Changing the value will update the metric name.

Example: Oxygen Uptake Rate or Final OD.

Type (Property only)

Description: Indicates the metric data type is timeseries data or run data.

Impact: Changing the data type has implications on the types of analysis the metric can be used for. For instance, only numeric metrics can be used for formulas.

Example: Number, Text, Timeseries, Date, etc.

Default Display Unit

Description: Default unit in which the metric is displayed across the app.

Impact: Changing default display unit converts the metric value into a different unit in accordance with the base unit when displayed in Invert. The value is not altered.

Example: mg/L or g/L

Base Unit

Description: The SI unit in which the metric is stored inside the app.

Impact: Unit conversions and other unit related features require metric units to be unambiguous and defined so that it can be stored in SI unit. E.g. Yield in 'g product/g biomass' should be represented as 'g/g' (kg/kg in SI Unit).

Example: K or kg/m^3

Molar mass

Description: The molar mass of the substance associated with a metric, used to enable molar unit conversions.

Impact: When set, Invert can convert between mass-based and molar units (e.g. g/L ↔ mmol/L) for that metric.

Example: 180.16 g/mol (glucose)

Notes

Description: Text field used for capturing notes

Impact: Text shows when hovering over a metric/formula name in Line charts.

Example: Primary Nitrogen Source | Measured via Thermo Gallery Analyzer

Expresses Timeseries Data

Description: Converts a metric with an 'Unknown' data type into timeseries data. Only applies to metrics that were not classified correctly upon ingestion.

Impact: Once a metric is expressed as timeseries data, this action cannot be reversed.

Uses Log Scale

Description: Enables Log Scale for a specific metric.

Impact: Metric show on a logarithmic Y-axis when feature is turned on.

Disable Interpolation

Description: Disables linear interpolation for a specific metric.

Impact: Interpolation affects the way data sets are shown in line charts. When disabled, data show without connecting lines when feature is turned off.

Resampling Method

Description: Determines how data is aggregated when condensing time series data into manageable intervals. You can select either 'Mean' to smooth data trends by averaging values, or 'Max' to capture the highest value within each interval.

Impact: Choosing 'Mean' provides a clearer view of overall trends by reducing noise, while 'Max' emphasizes peak conditions, making it useful for identifying extreme events or anomalies in the data.

Example: For temperature data, selecting 'Mean' will show the average temperature over each hour, whereas 'Max' will highlight the highest recorded temperature for that period.

Default Ingestion Unit

Description: The unit in which the metric is ingested into the app.

Impact: Changing default ingestion unit alters the metric value. E.g. changing the default ingestion unit to g/L for a metric originally ingested as mg/L will result in a 1000x multiplication of the base values. E.g. 1 mg/L will change to 1 g/L.

Example: mg/L or g/L

Example metric details page

Example metric editing page

The Run Summary page contains all the information associated with a given run. It covers every aspect of your bioprocess including run data, properties, metrics, lineage, and event notes. Switch to the editing view for run editing and archiving or plot your data by clicking the Analysis button.

Navigation

To access the Run Summary page, double-click on a run in the run data table on the Runs page. This will open the Run Details page. Available tabs are: Summary (when enabled), Properties, Metrics, Lineage, Events, Alerts, and Import History.

Summary

The Summary tab provides an AI-generated overview of the run in a structured dashboard format. It is available for organizations with the AI-generated summaries feature enabled. The dashboard is composed of several sections:

• Key Indicators: A curated set of the most relevant metric aggregations (e.g., final titer, peak biomass, mean pH) displayed as cards for at-a-glance review.
• Graphs: Pre-configured line charts showing key timeseries profiles for the run, such as growth curves, feed profiles, or DO/pH trends.
• Objectives & Notes: A text section capturing the run's objectives and any analytical notes.
• Events: A timeline of annotated events associated with the run.
• Lineage: A visual summary of the run's upstream and downstream material stream relationships.

The Summary tab respects run-specific configuration when available, otherwise using organization-level defaults. Click 'Analyze' on any graph in the Summary tab to jump directly to the Analysis page with that chart pre-loaded.

Properties

The Properties tab presents a list of the run meta data, including Run Start, Run End, and any other custom property that was previously associated with the run through file ingestion or manual editing. Navigate to the 'Edit' page to switch to the editing view for run archiving and property editing. Data Sources labels provide insights into the origin of a property. Labels are automatically generated upon ingestion.

Metrics

In the Metrics tab, users can access an overview of the time series data associated with a given run. This tab displays a list of time series metrics and formulas, along with useful aggregations and metric units for easy reference. Metrics are categorized into parent and sub-metrics, allowing users to quickly understand the structure and relationships of the data (See 'Library' Article for more details). From this view, users can navigate directly to the metric details page for more information or archive a specific metric from the run without removing it from the overall metric library. This feature provides a centralized place for exploring and managing time series data efficiently. Data Sources labels provide insights into the origin of a metric. Labels are automatically generated upon ingestion.

Lineage

In the Lineage tab, users can access the process flow diagram that tracks the relationships between individual runs. This functionality is particularly useful for understanding the lineage of a run, such as identifying which seed flask was used to inoculate a certain bioreactor or tracing the bioreactor run used for downstream processing testing. To use this feature, enter a valid run name into the 'Input Run' property and navigate to the Lineage tab. Click 'Add property' to provide additional context to the blocks.

Events

The Events tab facilitates the annotation of timestamped event notes. Users can document important process annotations such as Inoculation, Feed Start, or any other observations or milestones associated with the run. Event notes show in Line Chart enhancing your analysis by providing context to understanding of the bioprocess workflow.

Alerts

The Alerts tab allows users to manage alerts. An alert is a service that is available to users with data streaming into the app in real time via hardware integration. Users can opt into the service and have Invert sent automated notifications emails and create events when condition is reached. Example: Dissolved Oxygen <60 %.

Import History

The Import History tab shows the file upload history for a given run providing insights on the origin of data at a single glance. Each entry in this table links to the relevant page on the Import History tab for additional insights on mappings used, metrics uploaded, etc.

The Analysis page is your go-to destination for visualizing and analyzing your bioprocess data. Choose between line charts, scatter charts, and bar charts to tell the story behind your data. Use line charts for timeseries data, scatter charts for quantitative cross-run comparisons, and bar charts for a clean categorical summary view.

Navigating to the Analysis Page

To access the Analysis page, start by navigating to the Runs page and selecting a set of runs you wish to analyze. Press the 'Analyze' button in the top right corner of this view to transition to the Analysis page. The line chart is the default. Switch chart types using the Chart settings button in the chart header.

Workflow

1. Metric selection

   Select one or multiple metrics from the metric dropdown. Formulas can be added here as well — type the formula name and click 'Add'.

2. Chart settings

   Click the Chart button in the chart header to open the chart settings panel. From here you can change chart type, split by, layout, axis ranges, events, grouping, and time filters.

3. Run selection

   Update run selection as needed by checking/unchecking run checkboxes in the table underneath the graph. Optionally, click 'Run selection' for altering the full list of runs included in the analysis.

4. Full Screen view

   Switch to Full Screen view for a close-up view of the graph.

5. Export

   Export the graph as an image (.PNG) or export the displayed data as an Excel file.

   

   

6. Save Analysis

   Save the analysis as a report by creating a new report or appending it to an existing report.

Chart Settings

All chart configuration is in the Chart button popover, accessible from the chart header. It contains the following sections:

Chart Type

Switch between Line, Bar, and Scatter using the button group at the top of the panel.

Split By

Controls how runs and metrics are distributed across graphs:

• Separate — one graph per run and metric combination.
• Split runs — one graph per run (line charts only).
• Split metrics — one graph per metric; compare a single metric across multiple runs.
• All in one — all runs and metrics in a single graph.

Chart Layout

Controls how many charts appear side-by-side: Auto, Full width, 2 columns, or 3 columns.

X-Axis Range

Set a custom Min, Max, and optional Interval for the x-axis. For time-based line charts, values are entered in the selected time unit (e.g. hours). Overrides compose with drag-zoom so both can be used together. Overridden values are highlighted in the panel. This control is disabled for bar charts and categorical scatter charts.

Y-Axis Settings

Set a custom Min, Max, and optional Interval for each y-axis. Toggle Combined Y-Axis to merge all metrics onto a single axis. When Split by Metrics is active, each metric gets its own independent axis. Values support Python-style exponentiation (e.g. 10**6).

Events (Line Charts)

Toggle Show events on or off. When on, filter by event category — Phases, Additions / Removals, and Observations — using the category buttons.

Time Filters (Line Charts)

Use Data Start and Data End to restrict the x-axis to a specific window (e.g. growth phase only, pre-run data). Use Normalization Basis to control what t=0 means — Run Start, a specific event type (e.g. Feed Start), or a phase start. Click Reset to restore defaults.

Line Charts

Visualization of Timeseries Data

The line chart is the primary tool for visualizing timeseries data. It supports a wide range of layouts for close-up single-run views, per-metric comparisons, and all-in-one overviews across a full experiment.

Drag Zoom (X-Axis)

Click and drag within the graph to zoom into a specific time window. The current zoom bounds appear in the top right corner under "ERT" (Elapsed Run Time). Click the ✕ on the zoom annotation to reset. Drag zoom composes with the X-axis range set in the Chart settings panel — both can be active at the same time.

Custom Axis Ranges

Both the X and Y axes support manually entered Min / Max / Interval values, set via the Chart settings panel (see Chart Settings above). Overridden values are shown highlighted; use the Reset button within each axis section to revert individual axes to their default. Toggling Combined Y-Axis changes which axes are displayed, but stored zoom values for each axis are preserved.

Formulas

Create derived metrics using the built-in formula editor. Type a new metric name into the metric dropdown field and click 'Add'. Enter the formula name, input dependencies, and expression in the editor. Use the formula preview to verify results before saving. For full details on supported operations and formula types, see the Library article. As an example, air flow rate (L/h) and reactor volume (L) can be combined to calculate VVM — a scale-independent aeration metric that enables meaningful comparison across bioreactor sizes ranging from 0.25 L to 100,000 L.

Events & Phases

Utilize the event annotation feature to create time-stamped and interactive event notes, enhancing your analysis. Supported event types are: Inoculation, Induction, Transfection, Sample, Harvest, Drawdown, Feed Start, Foamout, and Observation. Optionally, upload an image to provide additional context to your data. Control which event categories appear on the graph using the Show events toggle and category filters in the Chart settings panel.

Phase markers are visual indicators that delineate different process segments, such as Growth or Production phase. In a formula context, a phase can be selected in the Applied Time Frame dropdown, scoping the formula calculation to that segment only — for example, a Specific Growth Rate formula applied to the Growth phase, or a Productivity formula applied to the Production phase.

Grouping

Use the "Group by" functionality to aggregate and compare related runs based on specific attributes, such as "Experimental Condition", "Strain", or "Alias". When runs are grouped, it enables the analysis of variability (shaded regions representing 16th and 84th percentiles) and central tendencies (median) within those groups. Run IDs in run tables and chart legends are replaced by the attribute name enabling users to assign custom run names.

Metric/Formula Notes

Add notes to metrics or formulas from the Library editing page. On the Analysis page, hover over a metric or formula name to surface the note as a tooltip — useful for documenting assumptions, data sources, or calculation details directly within the chart view.

Run Data Table Customization

Customize the run data table to provide additional context to the timeseries graph. This includes displaying relevant metadata such as strain ID, run ID, bioreactor size, and more, enhancing the interpretability of the visualized data.

Scatter Chart

Aggregation

Use scatter charts when you want to explore relationships between two variables in your data. They are particularly useful when the input variable for X is non-time-based (e.g., Strain ID, Run ID). You have the option to choose between a variety of aggregations for the Y input variable, such as mean, standard deviation, sum, count, minimum, maximum, last value, etc. For example, compare 'Product (Last)' versus 'Run ID' or 'OD (Maximum)' versus 'Strain ID'. Use this tool to identify trends, clusters, outliers, or other patterns in your data, facilitating data-driven decision-making and analysis.

Continuous vs. Categorical X-Axis

When the selected X variable is numeric, you can switch between Continuous and Categorical mode in the Chart settings panel under X-Axis. Continuous mode plots values on a numeric axis and enables statistics. Categorical mode treats each X value as a distinct group, which is useful for comparing conditions that happen to have numeric labels (e.g., passage numbers, concentration levels).

Statistics

Enable the Show statistics toggle in the Chart settings panel to overlay statistical summaries when X values have multiple entries per category. The available statistics are mean, standard deviation, standard error, count, and lower/upper 95% confidence intervals. Statistics are disabled when the X-axis is set to Categorical mode.

Bar Chart

Bar charts offer a clean categorical view of your aggregated run data — ideal for comparing a metric's summary value (e.g., final titer, peak OD, mean pH) across a set of runs or conditions side-by-side. Switch to bar chart view using the Chart settings panel. The X-axis is driven by a categorical run attribute (e.g., Run ID, Strain, Condition) and the Y-axis uses the same aggregation options available in scatter charts (mean, max, last, etc.). Use Color by in the Chart settings panel to color bars by a run attribute for additional visual grouping. Statistics overlays such as mean, standard deviation, and 95% confidence intervals are calculated automatically when multiple runs share the same X category.

Analysis Templates

Analysis Templates let you save and reuse chart configurations — metric selections, split settings, y-axis ranges, and more — so you can apply a consistent view across different sets of runs without re-building it each time.

Creating a Template

Set up your chart view as desired (metrics, split settings, layout, y-axis configuration), then open the template selector in the chart header and choose 'Save as new'. Give the template a name. Templates are shared across your organization.

Applying a Template

Select a saved template from the template dropdown in the chart header. The analysis will update to match the saved configuration. If you modify the view after applying a template, an 'Edited' badge appears to indicate the current state has drifted from the saved template.

Managing Templates

From the template dropdown you can save changes back to the current template ('Save'), rename it, reset to the last saved state ('Reset to saved'), or delete it. Use 'Start over' to clear the template selection and begin with a fresh, unsaved configuration.

Compile your bioprocess data analysis into standardized, reproducible summary reports. A report brings together charts, tables, text, code, and diagrams — all in a single shareable document. Once complete, reports can be shared with collaborators for data review, or duplicated as a template for future experiments.

Adding Content

In report editing mode, click the + button between blocks to open the Insert menu. Every piece of content in a report is a block, and blocks can be reordered, duplicated, or deleted independently.

The available block types are:

• Plot — A chart (line, scatter, or bar) paired with a run data table and a title/description field. This is the primary block type for data visualization. For details on chart configuration options, see the Analysis article.
• Image — Embed an image directly in the report body, useful for annotated screenshots, microscopy images, or process diagrams.
• Attachment — Link a file attachment (e.g. a raw data file, an instrument export, or a protocol document) so it is accessible alongside the analysis.
• Code Block — Execute custom Python analysis inline within the report. Results, plots, and tables appear directly below the code. See Code Blocks below for details.
• Mermaid Diagram — Render flowcharts, process diagrams, or decision trees using Mermaid syntax, without leaving the report editor.
• Table — Insert a structured data table for presenting values, comparisons, or reference data.
• Table of Contents — Automatically generate a linked navigation index from the headings in the report — useful for longer documents.
• Separator — Insert a horizontal rule to create visual breaks between sections.
• Mention — @mention a team member to draw their attention to a specific section of the report.

Code Blocks

Reports support Python Code Blocks, allowing you to execute custom analysis directly within your report. Code blocks enable advanced data transformations, calculations, and visualizations without leaving Invert.

When to Use Code Blocks

Code blocks are useful for:

• Custom calculations: Compute metrics not available as formulas (e.g., specific yield calculations, kinetic parameters)
• Advanced visualizations: Create custom charts beyond standard line and scatter plots
• Data transformations: Reshape or filter data to highlight specific insights
• Batch processing: Analyze multiple runs with custom logic

Creating a Code Block

1. In report editing mode, click the + Insert button and choose Code Block
2. Write your Python code in the editor
3. Click 'Execute' to run the code
4. Results display inline in your report—output, tables, and plots appear immediately below the code block

Available Libraries and Data

Code blocks have access to:

• pandas: DataFrame operations, grouping, aggregations
• numpy: Numerical computations
• matplotlib/seaborn: Plotting and visualizations (recommended for reports)
• scipy: Statistical analysis, curve fitting
• Your report data: Access bioprocess runs, metrics, and properties loaded in the report

Example Code Block

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Example: Calculate specific growth rate
df = pd.DataFrame({
    'time': [0, 2, 4, 6, 8],
    'od': [0.1, 0.2, 0.5, 1.2, 2.8]
})

# Calculate growth rate
df['ln_od'] = np.log(df['od'])
df['specific_growth_rate'] = df['ln_od'].diff() / df['time'].diff()

# Plot results
plt.figure(figsize=(10, 4))
plt.plot(df['time'], df['specific_growth_rate'], 'o-')
plt.xlabel('Time (h)')
plt.ylabel('Specific Growth Rate (1/h)')
plt.title('Growth Rate Analysis')
plt.show()

Tips for Code Blocks

• Keep code simple and focused—complex analysis is harder to review
• Use comments to explain what each section does
• Test calculations with sample data before finalizing
• Use descriptive variable names for clarity
• Avoid external API calls or file operations (sandboxed environment)

Workflow

1. Add New Report:

   Navigate to the Reports page and click 'Add new report' to create a blank report. Type the report title at the top of the document — it sits as a fixed first element above the body content and stays in sync with the report name shown in the page header.

2. Add your first Plot block:

   Click the + Insert button and choose Plot. Enter a title and description, click 'Select Runs', and choose the relevant runs. Then configure your chart — select metrics, chart type, and layout — and press 'Done'. You can return to edit the chart at any time via the 'Edit Plot' button on the block.

3. Build out the report:

   Use the Insert menu to add additional blocks — more plots, code blocks, images, tables, or text separators. Duplicate any existing block to carry forward its run selection and chart settings, which speeds up building multi-chart reports. Blocks can be reordered by dragging.

4. Save and Share:

   Once your report is complete, save it by clicking the 'Save' button. Share the report with collaborators by selecting the 'Copy Share Link' option after pressing the 'Share' button. For organizations with report user access enabled, you can also control which users or groups within your organization can view or edit a report through the 'Share' dialog — useful for managing access to sensitive analyses. Sharing reports with users outside your organization requires assistance from Invert support — contact us via Help & Support to arrange this.

5. Report Archiving:

   To remove a report from the list, press the 'Archive' button accessible in the top right corner of the report.

6. Additional Actions:

   Duplicate a report to create backups or use it as a starting point to streamline the analysis of related experiments. For bulk editing run selection across all plot blocks within a report, press 'Run selection' in the top right corner of the screen and modify run selection.

Alternative Workflow

1. Start Analysis from Runs Page:

   Alternatively, you can begin your analysis from the Runs. Select the runs you're interested in and transfer them to the Analysis page.

2. Visualize Data: Visualize your data using line, scatter, or bar charts. Customize the chart view settings and run data table as needed.

3. Save to Report: Click 'Save' and choose 'Add new report' or 'Add to a report' to incorporate your analysis into a new or existing report.

The Data Quality page gives you a live, organization-wide view of the health of your bioprocess data. It is accessible from the main navigation.

The page is organized into three summary cards at the top and a detailed issue list below.

FAIR Score

The FAIR Score measures how well your data conforms to the FAIR data principles — Findable, Accessible, Interoperable, and Reusable. Each pillar has its own score, and the overall FAIR score is displayed as a circular gauge. Hover over each pillar for a tooltip explaining what it measures and how to improve it. Common factors that affect your FAIR score include whether runs are organized into experiments, whether event annotations are present, and whether metrics are grouped under parent metrics with standard names.

AI Readiness

The AI Readiness card measures how well your data is structured for use with Invert Assist and other AI/ML workflows. It tracks three key signals: whether parent metric names are distinctive (not duplicated or overly generic), whether property values are distinctive across runs (sufficient variation for meaningful comparison), and whether metrics are actively used in reports. A higher AI Readiness score means Assist is more likely to produce accurate, specific analyses against your data.

Data Volume

The Data Volume card gives a snapshot of the total amount of data in your workspace — runs, timeseries metrics, properties, and events — giving you a sense of the overall scale of your dataset.

Data Issues

The Data Issues list surfaces specific, actionable problems found in your data. Each issue includes a title, description, severity (Critical, High, Medium, or Low), the number of affected entities, and a recommended resolution path. Issues are tagged with FAIR pillars and AI readiness categories so you can understand their broader impact.

Issues are organized by priority. Click any issue to open a detail panel with a full explanation of why it matters, the recommended resolution steps, and — where possible — a direct link to the affected data in Invert so you can fix it immediately.

Common issue types include duplicate or similar metric names, properties with malformed numeric values, runs not assigned to experiments, and metrics missing from any reports. Many issues can be resolved without leaving the page using the built-in action panels.

Invert Assist lets you query your bioprocess data in plain language. Instead of navigating manually through plots and exports, you can ask questions conversationally and receive results generated directly from your data. Behind the scenes, Assist writes and executes Python code against your data, so every answer is reproducible and traceable.

What you can do with Invert Assist

• Outlier Detection: Scan all your timeseries data to surface excursions worth investigating further.
• Root Cause Analysis: Identify drivers behind unexpected trends or deviations in your process data.
• Experiment Summarization: Generate clear summaries of multi-run experiments, highlighting key similarities and differences.
• Iterative Exploration: Refine questions and follow up to dig deeper — Assist retains context across the full conversation.
• Report Generation: Ask Assist to create comprehensive analysis reports with charts, tables, and summaries — automatically structured and ready for review.
• Product Q&A: Ask Assist how Invert features work — it can consult the Invert product documentation to answer questions about the platform.

Generating Reports with Assist

Assist can create complete analysis reports from your bioprocess data. Instead of manually building reports from charts and tables, you can ask Assist to generate one in plain language.

How to Generate a Report

1. Select your runs - Choose the runs you want analyzed
2. Open Assist - Click the Assist button at the bottom of the screen
3. Ask for a report - Try prompts like:
   • "Generate a process monitoring report for my most recent experiment"
   • "Create a comparative analysis of these two runs"
   • "Build a summary report highlighting key metrics and trends"
4. Review the result - Assist will produce a report with analysis, charts, and data tables
5. Refine if needed - Ask Assist to modify specific sections or regenerate with a different prompt
6. Save to Reports - Once satisfied, save the report to your Reports library

Tips for Better Reports

• Be specific about what analysis you want (e.g., "Compare yield between these two strains")
• Mention timeframes if relevant (e.g., "Report on the growth phase data")
• Ask follow-ups to refine the analysis (e.g., "Can you add a section on pH impact?")
• Review for accuracy before saving, especially for presentation or publication use

How to use Invert Assist

1. Select your runs. For best performance, starting with a focused set of runs (around 15 or fewer) produces the fastest, most targeted results — you can always load additional runs mid-conversation using Assist's run loading tools.

2. Open the Invert Assist chat panel using the Assist button at the bottom of your screen:

   

   

3. Type your question—for example:

   • “What caused the excursion in Run75?"
   • "Is there any effect of pH on titer based on these runs?"
   • "What is the next experiment that might be interesting to explore?"

4. Review the answer and trace the reasoning chain alongside the code that was executed for the analysis.

5. Use follow-up questions to refine your results or switch context.

   

History and context in Assist

• Your Assist queries and outputs are saved at a user-level. To view a repository of past chats, click the clock icon in the Assist modal.

• In addition to Runs, Notebooks can also be provided as context to Assist. This allows users to leverage existing analysis templates as a reference for Assist to perform calculations. For this, navigate to the relevant notebook page, open Assist modal, and add notebook as context.

• Workspace admins can further add context at the organizational level, which will be used in all Assist queries for your team. Use this space to provide terminology, conventions, or guidelines that should inform the assistant's responses. Organization context can be added under Settings > AI in the left navigation.

Editing Reports with Assist

For organizations with the Assist report editing feature enabled, Assist can also modify existing saved reports — not just generate new ones. Open a report, launch Assist, and ask it to update specific sections, add new plot blocks, or revise text.

When Assist edits a report, it produces a draft — an unsaved, private suggestion you can review before saving. Drafts have these characteristics:

• Unsaved: Drafts are private to you until you explicitly save them
• Editable: You can accept the draft as-is or ask Assist to modify it further before saving
• Stale draft warning: If a draft sits for more than 5 minutes without being reviewed, you'll see a warning (⚠️) indicating the analysis may be outdated
• Accept or reject: You control whether the draft is applied to the saved report or discarded
• Visual diff on review: When you open a draft, modified blocks are marked with a blue stripe and added blocks with a green stripe so you can spot Assist's changes at a glance.

Tips for better results

• Be specific: include timeframes or phases, run names, or metrics in your question.
• Use follow-ups: instead of repeating full queries, build on the last response.

FAQs

Will Invert Assist chat change my data?

No. Invert Assist's chat tooling is read-only. It queries and analyzes data but never edits it.

What data can Invert Assist chat access?

It has access to your structured bioprocess data stored in Invert, including runs, metrics, properties, events, lineage, formulas, and reports. Your data is processed within Amazon Bedrock infrastructure and is never used for model training or fine-tuning.

How does Invert ensure reliability?

Invert maintains a suite of automated benchmarking evaluations that run continuously to characterize Assist's analytical capabilities and detect any regressions caused by model or infrastructure changes.

What if the result is incorrect or Assist misunderstands the question?

Try rephrasing with more specific detail — for example, naming the run, metric, or time range you have in mind. You can also submit feedback using the thumbs-up and thumbs-down icons after a response, which helps the team investigate edge cases and improve accuracy over time.

Still need help? Reach out to our support team via the Help & Support link in the app.

Skills

Your team has established ways of doing RCA, DoE review, regression analysis — methods refined through years of experience. Until now, that knowledge lived in people's heads or buried in SOPs. Now you can capture it directly in Invert as a Skill: a reusable set of instructions that tells Assist how to conduct a specific type of analysis, so it works the way your team works.

How it works

Create a Skill in the Library by giving it a name, description, and a body — written in plain text, code, or both. The body is your method: step-by-step procedures, statistical thresholds, decision criteria, preferred chart types, or Python snippets that define how an analysis should be done.

When you're ready to use it, open Assist, type @ to bring up the mention menu, and select your Skill alongside any runs or reports you want to work with. Assist reads the Skill's instructions and follows your approach — same methodology, every time, regardless of who's running the analysis.

What can you do with Skills?

• Standardize root cause analysis: Define the steps your team follows for RCA — which metrics to check first, what thresholds flag a deviation, how to structure the final summary — and let anyone on the team run the same investigation
• Encode DoE review procedures: Specify how to evaluate experimental results, which statistical tests to apply, and what constitutes a meaningful difference between conditions
• Create reporting templates: Describe the sections, plots, and key metrics that should appear in a campaign summary or tech transfer package, then let Assist generate it
• Capture domain-specific calculations: Include Python code for custom analyses — growth rate calculations, metabolite ratios, yield corrections — so Assist executes them consistently
• Compose multiple Skills: Reference more than one Skill in a single conversation. Combine a "Growth Phase Analysis" skill with a "Metabolite Profile" skill to build a complete picture

Example Skills to get started

Skill name	What it does
Fed-Batch RCA	Walks through a structured root cause analysis: check feeds, DO, pH, temperature, then correlate deviations with titer impact
Campaign Summary	Generates a standardized report with VCD/viability overlay, titer bar chart, and key observations per condition
Scale-Up Comparison	Compares matched parameters between bench and pilot scale, flags any that fall outside defined equivalence bands
Harvest Timing	Evaluates viability trend and titer plateau to recommend optimal harvest window

Available now for all Assist-enabled organizations.