Data Information System (DAISY) is a data bookkeeping application designed to help Biomedical Research institutions with their GDPR compliance.

For more information, please refer to the official Daisy documentation.

DAISY was published as an article DAISY: A Data Information System for accountability under the General Data Protection Regulation in GigaScience journal.

You are encouraged to try Daisy for yourself using our DEMO deployment.

• docker: https://docs.docker.com/install/

1. Get the source code

   git clone git@github.com:elixir-luxembourg/daisy.git
   cd daisy

2. Create your settings file

   cp elixir_daisy/settings_local.template.py elixir_daisy/settings_local.py

   Optional: edit the file elixir_daisy/settings_local.py to adapt to your environment.

3. Build daisy docker image

   docker-compose up --build

   Wait for the build to finish and keep the process running

4. Open a new shell and go to daisy folder

5. Build the database

   docker-compose exec web python manage.py migrate

6. Build the solr schema

   docker-compose exec web python manage.py build_solr_schema -c /solr/daisy/conf -r daisy -u default

7. Compile and deploy static files

   cd web/static/vendor
   npm run build
   cd ../../../
   docker-compose exec web python manage.py collectstatic

8. Create initial data in the database

   docker-compose exec web bash -c "cd core/fixtures/ && wget https://git-r3lab.uni.lu/pinar.alper/metadata-tools/raw/master/metadata_tools/resources/edda.json && wget https://git-r3lab.uni.lu/pinar.alper/metadata-tools/raw/master/metadata_tools/resources/hpo.json && wget https://git-r3lab.uni.lu/pinar.alper/metadata-tools/raw/master/metadata_tools/resources/hdo.json && wget https://git-r3lab.uni.lu/pinar.alper/metadata-tools/raw/master/metadata_tools/resources/hgnc.json"
   docker-compose exec web python manage.py load_initial_data

   Initial data includes, for instance, controlled vocabularies terms and initial list of institutions and cohorts.
   This step can take several minutes to complete

9. Load demo data

   docker-compose exec web python manage.py load_demo_data

   This will create mock datasets, projects and create a demo admin account.

10. Optional - import users from an active directory instance

    docker-compose exec web python manage.py import_users

11. Build the search index

    docker-compose exec web python manage.py rebuild_index -u default

12. Browse to https://localhost
    a demo admin account is available:

        username: admin
        password: demo
    

pip install black==23.7.0 pre-commit install black --check . black .

In addition to loading of initial data, DAISY database can be populated by importing Project, Dataset and Partners records from JSON files using commands import_projects, import_datasets and import_partners respectively. The commands for import are accepting one JSON file (flag -f):

docker-compose exec web python manage.py <COMMAND> -f ${PATH_TO_JSON_FILE}

where ${PATH_TO_JSON_FILE} is the path to a json file containing the records definitions. See file daisy/data/demo/projects.json as an example.

Alternatively, you can specify directory containing multiple JSON files to be imported with -d flag:

docker-compose exec web python manage.py <COMMAND> -d ${PATH_TO_DIR}

Information in the DAISY database can be exported to JSON files. The command for export are given below:

docker-compose exec web python manage.py export_partners -f ${JSON_FILE}

where ${JSON_FILE} is the path to a json file that will be produced. In addition to export_partners, you can run export_projects and export_datasets in the same way.

1. Create a database backup.

   docker-compose exec db pg_dump daisy --port=5432 --username=daisy --no-password --clean > backup_`date +%y-%m-%d`.sql

2. Make sure docker containers are stopped.

   docker-compose stop

3. Get last Daisy release.

   git checkout master
   git pull

4. Rebuild and start the docker containers.

   docker-compose up --build

   Open a new terminal window to execute the following commands.

5. Update the database schema.

   docker-compose exec web python manage.py migrate

6. Update the solr schema.

   docker-compose exec web python manage.py build_solr_schema -c /solr/daisy/conf -r daisy -u default

7. Collect static files.

   docker-compose exec web python manage.py collectstatic

8. Rebuild the search index.

   docker-compose exec web python manage.py rebuild_index -u default

9. Reimport the users (optional).

   If LDAP was used during initial setup to import users, they have to be imported again:

   docker-compose exec web python manage.py import_users

See DEPLOYMENT.

To be completed.

./manage.py import_users

Single file mode:

./manage.py import_projects -f path/to/json_file.json

Batch mode:

./manage.py import_projects -d path/to/dir/with/json/files/

Available commands: import_projects, import_datasets, import_partners.

In case of problems, add --verbose flag to the command, and take a look inside ./log/daisy.log.

cd web/static/vendor/
npm ci

cd web/static/vendor
npm run-script build

./manage.py runserver

The following command will install the test dependencies and execute the tests:

python setup.py pytest

run test for a specific file:

python setup.py pytest --addopts web/tests/test_dataset.py

If tests dependencies are already installed, one can also run the tests just by executing:

pytest

To get access to the admin page, you must log in with a superuser account.
On the Users section, you can give any user a staff status and he will be able to access any project/datasets.