Run Your First Experiment
A complete walkthrough from logging into MINT to running your first analysis plugin — about 5 minutes.
[Screenshot: full MINT window showing the home dashboard, ready to start]
Prerequisites
- A running MINT instance (hosted, direct, or Docker — see Get Started)
- An account with at least the Member role
- At least one analysis plugin installed and visible to your role (your admin can install one from the marketplace if not)
Step 1: Create a project
From the home dashboard, click New project.
[Screenshot: New-project modal with name and description fields]
| Field | What it's for |
|---|---|
| Name | Human-readable label, e.g., "TCA flux pilot" |
| Description | One-line summary shown on the dashboard |
| Members (optional) | Lab colleagues to invite — they get the default project role |
Click Create. You're now inside the project page.
Step 2: Create an experiment
Click New experiment. MINT auto-assigns a unique code from the experiment type, such as LCM-EXP-001 for an lcms_batch type or DR-EXP-001 for dose_response.
| Field | What it's for |
|---|---|
| Title | Human label |
| Type | Pick an experiment type registered by an installed design plugin (e.g., LCMS sequence, drug-response panel). Determines the design fields below. |
| Status | Starts at planned |
| Collaborators (optional) | Single-experiment access grants; the creator is stored as owner |
Fill in the design fields exposed by the experiment type, then Save. The experiment is now in planned status. See Experiments for the status flow.
[Screenshot: experiment-detail page in planned status]
Step 3: Move to ongoing and upload data
Switch the status to ongoing. Most plugins gate result writes on ongoing or completed. Then upload the relevant artifacts (RAW files, sequence sheets, design plates) — the file picker shows the experiment-type's expected attachments.
[Screenshot: experiment file uploader with attachments queued]
Step 4: Run an analysis plugin
Open the Analyze tab on the experiment. Pick an installed analysis plugin from the dropdown, fill in its parameters, and click Run.
The plugin runs in its own isolated subprocess; MINT proxies its UI back into the page. Progress is tracked in the Jobs panel (top-right).
[Screenshot: analysis-plugin sidebar with parameters and Run button]
Approximate runtimes depend on the plugin and dataset size. The Jobs panel shows live status: queued → running → done (or failed).
Step 5: See your results
When the plugin finishes, the experiment's Results tab populates with whatever the plugin wrote (charts, tables, downloadable artifacts). Core MINT stores one result entry per plugin; plugins that need run history keep that history inside their own result payload or plugin-owned tables.
[Screenshot: results tab showing one plugin result entry with charts, tables, and exports]
Step 6: Wrap up
Switch the experiment status to completed when the work is finished. MINT records end_date automatically if it was empty. Plugins may treat completed experiments as read-only, depending on their own workflow rules.
Further steps
- Invite collaborators — see Members & roles
- Install another plugin — see Marketplace
- Use the CLI — see
mintoverview for scripted experiment + project access - Build your own plugin — start with the Plugin Development Guide