Developers: Anusuriya Devaraju, Robert Huber
F-UJI is a web service to programatically assess FAIRness of research data objects based on metrics developed by the FAIRsFAIR project. The service will be applied to demostrate the evaluation of objects in repositories selected for in-depth collaboration with the project.
The 'F' stands for FAIR (of course) and 'UJI' means 'Test' in Malay. So F-UJI is a FAIR testing tool.
A web demo using F-UJI is available at https://www.f-uji.net
An R client package that was generated from the F-UJI OpenAPI definition is available from https://github.com/NFDI4Chem/rfuji.
The service is in development and its assessment depends on several factors.
- In the FAIR ecosystem, FAIR assessment must go beyond the object itself. FAIR enabling services and repositories are vital to ensure that research data objects remain FAIR over time. Importantly, machine-readable services (e.g., registries) and documents (e.g., policies) are required to enable automated tests.
- In addition to repository and services requirements, automated testing depends on clear machine assessable criteria. Some aspects (rich, plurality, accurate, relevant) specified in FAIR principles still require human mediation and interpretation.
- The tests must focus on generally applicable data/metadata characteristics until domain/community-driven criteria have been agreed (e.g., appropriate schemas and required elements for usage/access control, etc.). For example, for some of the metrics (i.e., on I and R principles), the automated tests we proposed only inspect the ‘surface’ of criteria to be evaluated. Therefore, tests are designed in consideration of generic cross-domain metadata standards such as dublin core, dcat, datacite, schema.org, etc.
- FAIR assessment is performed based on aggregated metadata; this includes metadata embedded in the data (landing) page, metadata retrieved from a PID provider (e.g., Datacite content negotiation) and other services (e.g., re3data).
Python 3.5.2+
In order to deal with 308 redirects, the following patch has to be applied on urrlib: https://github.com/python/cpython/pull/19588/commits
- Download the latest Dataset Search corpus file from: https://www.kaggle.com/googleai/dataset-search-metadata-for-datasets
- Open file fuji_server/helper/create_google_lists.py and set variable 'google_file_location' according to the file location of the corpus file
- Run create_google_lists.py which creates two files in fuji_server/data
The service was generated by the swagger-codegen project. By using the
OpenAPI-Spec from a remote server, you can easily generate a server stub.
The service uses the Connexion library on top of Flask.
Before running the service, please set user details in the config file, see config/server.ini.
To install F-UJI, you may execute the following python-based or docker-based installation commands from the root directory:
pip3 install -r requirements.txt
python3 -m fuji_server -c fuji_server/config/server.inidocker run -d -p 1071:1071 ghcr.io/pangaea-data-publisher/fujiTo access the Swagger user interface, open the url below on the browser:
http://localhost:1071/fuji/api/v1/ui/
Your Swagger definition lives here:
http://localhost:1071/fuji/api/v1/swagger.json
You can provide a different server config file this way:
docker run -d -p 1071:1071 -v server.ini:/usr/src/app/fuji_server/config/server.ini ghcr.io/pangaea-data-publisher/fujiYou can also build the docker image from the source code:
docker build -t <tag_name> .
docker run -d -p 1071:1071 <tag_name>To avoid tika startup warning message, set environment variable TIKA_LOG_PATH. For more information, see https://github.com/chrismattmann/tika-python
If you receive the exception 'urllib2.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] on MacOS, run the install 5A12 command shipped with Python : ./Install\ Certificates.command
This project is licensed under the MIT License; for more details, see the LICENSE file.