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elab2ARC

ย Tool for converting eLabFTW experiment data into ARC (Annotated Research Context) format Here's a concise one-minute tutorial animation explaining how to convert eLabFTW entries into ARCs with just a few clicks using elab2arc:

Please choose your eLabFTW experiments or resources

Type ID        Name Creation Date Author Selection/Preview
No eLabFTW experiments/resources available. Use Token tab to add an eLabFTW API key
Experiment ID can be found in the address bar of the browser.
Experiment ID can be found in the address bar of the browser.

Please select your eLabFTW instance and enter your tokens


  •    Add instance: 
       For example: "https://elabftw.test.hhu.de/api/v2"
Community server is the default and free to use with a waiting queue. Together.AI requires an API key.
Examples: Ollama: http://localhost:11434/v1/chat/completions, LM Studio: http://localhost:1234/v1/chat/completions
Primary model for protocol analysis Hold Ctrl/Cmd to select multiple. These models will be tried if primary hits rate limit.

Enable this to test ISA generation without consuming API credits. Uses predefined E. coli sequencing workflow.

Please choose your ARC

Number Repo Name Link         Select the target ARC directory     
No ARC available. Use home tab to start a conversion
eLabFTW IDs
====>>>
GitLab URL
Please select your ARC
Specify where in the ARC structure your data should be placed. Auto-filled when selecting assay/study.
       

Processing

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Select the folder for conversion

Address:
File Preview
File Preview
Select eLabFTW Instance
Select between the test instance and the Heinrich Heine University Dรผsseldorf (HHU) instance, or add your own eLabFTW instance
Copy the URL of a eLabFTW instance and paste the URL to the input field and add "api/v2/" after the URL.
instance image A detailed user guide can be found here
eLabFTW API keys
eLabFTW API keys can be generated from settings -> API keys
an image of eLabFTW token
The access token will be displayed only once, so make sure to store it securely. Treat it like a password, as anyone with the token can access eLabFTW.
A detailed user guide can be found here
DataHub Personal Access Token
To generate a DataHub personnal access token, go to icon โ†’ preferences โ†’ Access tokens โ†’ check api, read_api, read_user, read_repository, write_repository โ†’ Create. The Scopes are: "check api", "read_api", "read_user", "read_repository" and "write_repository."
An image of DataHub Personal Access Token
The access token will be displayed only once, so make sure to store it securely. Treat it like a password, as anyone with the token can access and modify the DataHUB repo.
A detailed user guide can be found here
eLabFTW Experiment Id
(use comma "," to seperate multiple IDs)
eLabFTW experiment ID can be found in the address bar or in the experiment description.
An image of eLabFTW Experiment Id eLabFTW resources ID can be found in the address bar or in the resource description.
An image of eLabFTW Experiment Id A detailed user guide can be found here
eLabFTW Preview

Empty eLabFTW Preview

No eLabFTW available. Use home tab to start a conversion

Conversion

    Metadata

      Linked Resources and Experiments

          Attachments

          Conversion Status

          Conversion Metadata: Detailed information about LLM prompts, models, timing, and results for troubleshooting.

          No conversion metadata available yet. Metadata is saved after each conversion when LLM is enabled.

          No conversion history yet. Your last 5 conversions will appear here.

          AI Prompt Editor

          Welcome to the AI Prompt Editor! Here you can customize the LLM prompt used for protocol analysis. Changes are saved locally and will be used for future conversions. Click Reset to Default to restore the original prompt.
          System Role: This defines the AI's role and sets the context for analysis. Be clear and specific.
          JSON Schema: This defines the expected structure of the JSON output. Only modify if you understand ISA-Tab format.
          Extraction Rules: These are critical instructions for parameter extraction, sample handling, protocol linking, and data file mapping.
          Examples: Provide concrete examples to guide the AI's output format. Good examples improve extraction accuracy.
          Read-Only Preview: This shows how your sections combine into the final prompt sent to the AI.
          Version History: Your last 50 prompt versions are saved automatically. Compare, restore, export, or import previous versions.
          Version Comparison

          No version history available yet.

          Versions are saved automatically when you click "Save Prompt".