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A metadata annotation service to support standardised metadata annotation developed by T3.2.

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Metadata Annotation Workbench

The Metadata Annotation Workbench supports standardised metadata annotation. The prototypical service will support researchers and data holders with the FAIRification of already existing and new data collections.

Table of contents

Requirements

  • Docker
  • Docker Compose
  • Python 3.8
  • Yarn
  • Node

General info

The web application allows users to upload data collection instruments in the convenient and common Microsoft Excel-Spreadsheet format. The metadata annotation is performed in the web browser. The user is provided with search results of the instrument's data items from the terminology service and can perform a manual text search. The initial search suggestion for each data item is generated by string matching and simple natural language processing like tokenization and stemming. The user can select an ontology for annotation from all ontologies included in the terminology service. Detailed information about a concept is displayed in the semantic information widget that is provided by the terminology service. The annotated instrument can then be downloaded comprising the data items and corresponding annotations as international resource identifier (IRI), a unique and machine-readable identifier that leads to the metadata of the data item.

Setup

Development

Start the backend/install virtual environment:

$ cd backend
$ python3 -m venv venv 
$ source ./venv/bin/activate
$ pip3 install -r ./requirements.txt 
$ export FLASK_APP=restapi 
$ export FLASK_ENV=development
$ flask run

Start the frontend:

$ cd frontend
$ yarn install
$ yarn start

Production

Create production build for all services:
Uncomment build: ./frontend and build: ./backend in docker-compose.yaml

$ docker-compose --env-file dev.env build
$ docker-compose --env-file dev.env up

To stop a production build of all services: docker-compose --env-file dev.env down

License

The project is MIT licensed.

Funding

This work was done as part of the NFDI4Health Consortium and is published on behalf of this Consortium (www.nfdi4health.de). It is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 442326535.