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.

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

   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 an 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
    

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. Reload initial data (optional).

   IMPORTANT NOTE: The initial data package provides some default values for various lookup lists e.g. data sensitivity classes, document or data types. If, while using DAISY, you have customized these default lists, please keep in mind that running the load_initial_data command during update will re-introduce those default values. If this is not desired, then please skip the reloading of initial data step during your update. You manage lookup lists through the application interface.
   
   

   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

   IMPORTANT NOTE: This step can take several minutes to complete.

9. Rebuild the search index.

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

10. 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

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.