superstar54 / aiidalab-eln

AiiDAlab-ELN

Integrate AiiDAlab with Electronic Laboratory Notebooks (ELN). This repository implements a general API for interfacing AiiDAlab with some ELN and a concrete implementation for the integration with the cheminfo ELN.

AiiDAlab-Cheminfo ELN implementation

As a first prototype we implemented an integration with the open-source cheminfo ELN. The ELN and integration can be tested via the public deployment of the ELN. Documentation on how to use the frontend can be found here.

API

• eln_instance refers to the URL of the ELN API.
• eln_type referst to the type of ELN, e.g. "cheminfo", "openbis".
• data_type "subfolder" in the cheminfo data schema of characterization techniques, e.g., "xray", "isotherm" spectrum_type will be renamed to this
• sample_uuid refers to the sample unique identifier in the ELN database
• file_name refers to the name of the file attached to the sample and containing information of the specified data_type.
• file_content refers to the content of the file attached to the sample.
• node refers to the AiiDA database node.
• token refers to the token that gives access to the ELN database.
• export_data() sends the AiiDA node (stored in the node attribute) to the ELN.
• import_data() import ELN data into an AiiDA node.
• sample object that refers to an ELN sample, previously known as sample_manager.
• sample.put_data() - put data into the ELN sample.
• sample.get_data() - get data from the ELN sample.

For maintainers

To create a new release, clone the repository, install development dependencies with pip install '.[dev]', and then execute bumpver update --major/--minor/--patch. This will:

1. Create a tagged release with bumped version and push it to the repository.
2. Trigger a GitHub actions workflow that creates a GitHub release.

Additional notes:

• Use the --dry option to preview the release change.
• The release tag (e.g. a/b/rc) is determined from the last release. Use the --tag option to switch the release tag.

Acknowledgements

This work is supported by the MARVEL National Centre for Competency in Research funded by the Swiss National Science Foundation, as well as by the MaX European Centre of Excellence funded by the Horizon 2020 EINFRA-5 program, Grant No. 676598 and an European Research Council (ERC) Advanced Grant (Grant Agreement No. 666983, MaGic).