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Python implementation of FAIR Data Point

Project description

FAIR Data Point (FDP)

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Python implementation of FAIR Data Point.

FDP is a RESTful web service that enables data owners to describe and to expose their datasets (metadata) as well as data users to discover more information about available datasets according to the FAIR Data Guiding Principles. In particular, FDP addresses the findability or discoverability of data by providing machine-readable descriptions (metadata) at four hierarchical levels:

FDP->catalogs->datasets->distributions

FDP software specification can be found here

FDP has been implemented in:

Installation

To install FDP, do

From pypi

pip install fairdatapoint

Or from this repo

git clone https://github.com/NLeSC/fairdatapoint.git
cd fairdatapoint
pip install .

Running

fdp-run localhost 80

Then visit from your browser: http://localhost/

Unit testing

Run tests (including coverage) with:

pip install .[tests]
pytest

TODO: Include a link to your project's full documentation here.

Deploy with Docker

Download the docker-compose.prod.yml from this repo, change the HOSTNAME in the file to a proper host. The default port is 80, and you can use other port (e.g. 8080) if port 80 is used. Then run the command

docker-compose -f docker-compose.prod.yml up -d

Deploy without Docker

Before deploying FDP, it's necessary to first have a running SPARQL database.

pip install fairdatapoint

# fdp-run <host> <port> --db=<sparql-endpoint>
fdp example.com 80 --db='http://dbpedia.org/sparql'

Web API documentation

FAIR Data Point (FDP) exposes the following endpoints (URL paths):

Endpoint GET POST DELETE
fdp Output metadata triples Remove existing triples for a specific ID, then create new triples with the request data Not Allowed
catalog/ Output all IDs Remove existing triples for a specific ID, then create new triples with the request data Remove all IDs
dataset/ Output all IDs Remove existing triples for a specific ID, then create new triples with the request data Remove all IDs
distribution/ Output all IDs Remove existing triples for a specific ID, then create new triples with the request data Remove all IDs
catalog/<catalogID> Output metadata triples Not Allowed Remove the specific ID
dataset/<datasetID> Output metadata triples Not Allowed Remove the specific ID
distribution/<distributionID> Output metadata triples Not Allowed Remove the specific ID

Access endpoints to request metadata programmatically

FDP: curl -iH 'Accept: text/turtle' [BASE URL]/fdp

Catalog: curl -iH 'Accept: text/turtle' [BASE URL]/catalog/catalog01

Dataset: curl -iH 'Accept: text/turtle' [BASE URL]/dataset/dataset01

Distribution: curl -iH 'Accept: text/turtle' [BASE URL]/distribution/dist01

FDP supports the following RDF serializations (MIME-types):

  • Turtle: text/turtle
  • N-Triples: application/n-triples
  • N3: text/n3
  • RDF/XML: application/rdf+xml
  • JSON-LD: application/ld+json

Contributing

If you want to contribute to the development of FAIR Data Point, have a look at the contribution guidelines.

License

Copyright (c) 2019,

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Credits

This package was created with Cookiecutter and the NLeSC/python-template.

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