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Accounting Academy

MODULE 1: Fundamentals

MODULE 2: Calculations

MODULE 3: Batch Partitioning & LCA

MODULE 4: Mass Balance with Ledgers

MODULE 5: Integration Testing

MODULE 6: Advanced Patterns & Production

Documentation Index

Fetch the complete documentation index at: https://docs.mangrovesystems.com/llms.txt

Use this file to discover all available pages before exploring further.

What you’ll learn in this lesson:
  • Configure event types with appropriate datapoints
  • Distinguish required vs. optional fields when creating events
  • Create events manually and via bulk import
  • Attach evidence to events for audit traceability
Events and datapoints are how operational activity enters Mangrove. Each event is a single occurrence of something that happened (e.g., a delivery, a meter reading). Each event holds one or more datapoints — the measured or recorded values that your models use.

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Defining event types and their datapoints

Before you can collect data, you must configure the event types your project uses. An event type is a template — it defines which datapoints each instance of that event will have.
Example: A “Biochar Delivery” event type might require: delivery date, biochar mass, carbon content, and delivery ticket ID. Every event of that type will have the same structure.

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Where to configure event types

Event types are configured in Data Inputs > Input Settings within each project. Only after an event type is configured can you create events — whether you add them directly or bulk import them.

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What an event type defines

An event type ties together:
  • A name/slug — identifies the type (e.g., “Biochar Delivery”, “CO2 Injection Measurement”)
  • A set of datapoint types — each with a slug and value type (number, string, date) so Mangrove knows how to store and validate values
In data inputs , events are high-frequency inputs whose measurements change from batch to batch — unlike static inputs, which stay constant. Examples of event types and their datapoints:
Event TypeExample Datapoints
Biochar DeliveryWet Mass Transported, Loaded Distance, Deadhead Distance, Vehicle Type
CO2 InjectionHourly Mass Injected, Flow Rate, Temperature, Density, Meter Status

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Linking event types to models

For event data to feed calculations, your production models must reference the datapoints from these event types.
When designing an event type, think ahead: which datapoints will feed into which model nodes? Defining event types in line with your model’s expected inputs keeps the dataflow coherent.

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Required vs. optional fields

When you create an event, some fields are required and others are optional.
For each event you add directlyin the Data Inputs section:
FieldRequired?Notes
Event typeRequiredSelect a configured input type
Date rangeRequiredStart and end date/time
Datapoint valuesPer configRequired/optional set per datapoint in the event type
Tracking IDOptionalExternal reference ID (e.g., invoice number)
LocationsOptionalAssociate with one or more locations
EvidenceOptionalFiles or URLs supporting the event

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Creating events

You can create events two ways: one-by-one in the UI, or in bulk via a file upload.

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Creating events manually

To add events directly :
1

Select event type

Open the project’s Data Inputs section and add a new event. Select the event type.
2

Set date range

Set the start and end date/time of the event.
3

Enter datapoint values

Fill in the values for each datapoint defined by the event type.
4

Add optional fields

Optionally set Tracking ID, locations, and attach evidence (files or URLs).

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Creating events via bulk import

To bulk import events:
1

Prepare your data

Use a CSV, XLS, or XLSX file. Populate required columns with correct date formats and units.
2

Upload the file

Upload to the corresponding Data Source for the project.
3

Review loaded events

Events appear in the Events feed and Analytics. Check Data Inputs > Bulk Jobs for status — failed imports show as errored with a message. You can Reverse or Retry as needed.
Best practice: paste values only to avoid bringing in formulae or links from spreadsheets.
Admin users can define transformations on each data source — Python code that turns each incoming file into a results array of event objects. Each object specifies event_type, start_time, end_time, data_points (array of slug and value), and optionally tracking_id, locations, feedstock, evidences, and notes.Transformations can also generate evidence from the file (e.g., attaching parsed content as JSON or CSV evidence).

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Attaching evidence to events

Evidence is documentation that supports the data in an event. Verifiers use it to validate reporting accuracy.

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Why attach evidence

Batches and reports tie back to source data and evidence. Attaching clear, relevant evidence streamlines verification and keeps a strong audit trail from credit to source.

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How to add evidence

  • When adding events directly — click Add Evidence and attach files or URLs
  • After a bulk import — open each event and use Upload Evidence
  • In a transformation — include an evidences array with name, type (json, geojson, text, csv), and content

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Supported file types

CategorySupported formats
DocumentsPDF, Excel (.xls, .xlsx), Word (.doc, .docx), .txt, CSV
ImagesJPG/JPEG, PNG, GIF
VideosMP4, MOV, AVI
ArchivesZIP
GeospatialGeoJSON, Shapefiles
CodeJSON

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Check your understanding

In Data Inputs > Input Settings within the project. You must configure an event type before you can create events of that type.
Event type, date range (start and end), and datapoint values for the required datapoints defined by that event type. Tracking ID, locations, feedstock, and evidence are optional.
You’re now ready to design your first event type and build it in Mangrove in the Module 1 Exercise.