ckanext-csvtocsvw

Extension automatically generating csvw metadata for uploaded textual tabular data. It uploads the data of the first table documented into a datastore for the source csv file. should be used as replacement for datapusher

Requirements

Needs a running instance of the CSVToCSVW Application. Point at it through env variables. Also needed is a Api Token for an account with the right privaledges to make the background job work on private datasets and ressources.

CKAN_CSVTOCSVW_URL=http://${CSVTOCSVW_HOST}:${CSVTOCSVW_APP_PORT}
CSVW_API_TOKEN=${CKAN_API_TOKEN}

You can set the default formats to annotate by setting the env variable CSVTOCSVW_FORMATS for example

CKANINI__CSVTOCSVW__FORMATS="csv txt asc"

else it will react to the following formats: "csv", "txt", "asc", "tsv"

Purpose

Reacts to CSV files uploaded. DEFAULT_FORMATS are "csv; txt" It creates two to sites for each resource.

• /annotate creates CSVW annotation file for a CSV in json-ld format named <csv_filename>-metadata.json, uploades table-1 to ckan datastore o u can explorer it with recline views
• /transform utilizes CSVW metadata to transform the whole content of the csv file to rdf, output is <csv_filename>.ttl The plugins default behavior includes a trigger to csv file uploads, so it runs annotation automatically on upload. The transformation is a bonus feature and outputs standard tabular data as mentioned in the CSVW documentation of the W3C. It must be triggered manually.

TODO: For example, you might want to mention here which versions of CKAN this extension works with.

If your extension works across different versions you can add the following table:

Compatibility with core CKAN versions:

CKAN version	Compatible?
2.8 and arlier	not tested
2.9	yes
2.10	yes

Suggested values:

• "yes"
• "not tested" - I can't think of a reason why it wouldn't work
• "not yet" - there is an intention to get it working
• "no"

Installation

TODO: Add any additional install steps to the list below. For example installing any non-Python dependencies or adding any required config settings.

To install ckanext-csvtocsvw:

1. Activate your CKAN virtual environment, for example:

   . /usr/lib/ckan/default/bin/activate

2. Clone the source and install it on the virtualenv

   git clone https://github.com/Mat-O-Lab/ckanext-csvtocsvw.git cd ckanext-csvtocsvw pip install -e . pip install -r requirements.txt

3. Add csvtocsvw to the ckan.plugins setting in your CKAN config file (by default the config file is located at /etc/ckan/default/ckan.ini).

4. Restart CKAN. For example if you've deployed CKAN with Apache on Ubuntu:

   sudo service apache2 reload

Config settings

None at present

TODO: Document any optional config settings here. For example:

# The minimum number of hours to wait before re-checking a resource
# (optional, default: 24).
ckanext.csvtocsvw.some_setting = some_default_value

Developer installation

To install ckanext-csvtocsvw for development, activate your CKAN virtualenv and do:

git clone https://github.com/Mat-O-Lab/ckanext-csvtocsvw.git
cd ckanext-csvtocsvw
python setup.py develop
pip install -r dev-requirements.txt

Tests

To run the tests, do:

pytest --ckan-ini=test.ini

Releasing a new version of ckanext-csvtocsvw

If ckanext-csvtocsvw should be available on PyPI you can follow these steps to publish a new version:

1. Update the version number in the setup.py file. See PEP 440 for how to choose version numbers.

2. Make sure you have the latest version of necessary packages:

   pip install --upgrade setuptools wheel twine

3. Create a source and binary distributions of the new version:

   python setup.py sdist bdist_wheel && twine check dist/*
   

   Fix any errors you get.

4. Upload the source distribution to PyPI:

   twine upload dist/*
   

5. Commit any outstanding changes:

   git commit -a
   git push
   

6. Tag the new release of the project on GitHub with the version number from the setup.py file. For example if the version number in setup.py is 0.0.1 then do:

   git tag 0.0.1
   git push --tags
   

License

AGPL

Acknowledgments

The authors would like to thank the Federal Government and the Heads of Government of the Länder for their funding and support within the framework of the Platform Material Digital consortium. Funded by the German Federal Ministry of Education and Research (BMBF) through the MaterialDigital Call in Project KupferDigital - project id 13XP5119.