Package that integrates AiiDAlab with Electronic Laboratory Notebooks.
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.
eln_instancerefers to the URL of the ELN API.
eln_typereferst 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_typewill be renamed to this
sample_uuidrefers to the sample unique identifier in the ELN database
file_namerefers to the name of the file attached to the sample and containing information of the specified
file_contentrefers to the content of the file attached to the sample.
noderefers to the AiiDA database node.
tokenrefers to the token that gives access to the ELN database.
export_data()sends the AiiDA node (stored in the
nodeattribute) to the ELN.
import_data()import ELN data into an AiiDA node.
sampleobject that refers to an ELN sample, previously known as
sample.put_data()- put data into the ELN sample.
sample.get_data()- get data from the ELN sample.
To create a new release, clone the repository, install development dependencies with
pip install '.[dev]', and then execute
bumpver update --major/--minor/--patch.
- Create a tagged release with bumped version and push it to the repository.
- Trigger a GitHub actions workflow that creates a GitHub release.
- Use the
--dryoption to preview the release change.
- The release tag (e.g. a/b/rc) is determined from the last release.
--tagoption to switch the release tag.
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).
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