NFDI
The Python library NFDI provides:
- basic information about NFDI (also known as Nationale ForschungsDatenInfrastruktur and National Research Data Infrastructure) and all funded NFDI consortia,
- support for Wikidata WikiProject NFDI aimed to create and edit the Wikidata entities and entity schemas relevant for NFDI,
- simple named entity linker on texts for the accepted NFDI consortia.
The NFDI Jupyter Book describes:
- how to use the library,
- how to send SPARQL queries to Wikidata and to get visualisations for NFDI consortia,
- Wikidata WikiProject NFDI and relevant entity schemas,
- how we parsed the data,
- how we edited Wikidata.
Table of contents
Installation
pip install NFDIHow to use
Module info
The module info has classes consortium and consortia:
from nfdi import info
nfdi = info.consortia()
berd = info.consortium('BERD@NFDI')The instance nfdi has the following attributes: 'label', 'homepage', 'wikidata', 'github', 'google', 'linkedin', 'twitter', 'youtube', 'zenodo', 'labels', 'consortia'. For example:
nfdi.twitterprints
"https://twitter.com/NFDI_de"The instances nfdi and berd have the methods print, dict and _wikidata:
json = berd._wikidata()
nfdi.print()The json variable contains JSON representation of the corresponding entity at Wikidata including labels, aliases and descriptions:
print('LABELS', json.get('labels'))
print('DESCRIPTIONS', json.get('descriptions'))
print('ALIASES', json.get('aliases'))It prints:
LABELS {'en': {'language': 'en', 'value': 'BERD@NFDI'}, 'de': {'language': 'de', 'value': 'BERD@NFDI'}, 'fr': {'language': 'fr', 'value': 'BERD@NFDI'}, 'bar': {'language': 'bar', 'value': 'BERD@NFDI'}, 'de-at': {'language': 'de-at', 'value': 'BERD@NFDI'}, 'de-ch': {'language': 'de-ch', 'value': 'BERD@NFDI'}, 'de-formal': {'language': 'de-formal', 'value': 'BERD@NFDI'}, 'en-ca': {'language': 'en-ca', 'value': 'BERD@NFDI'}, 'en-gb': {'language': 'en-gb', 'value': 'BERD@NFDI'}, 'es': {'language': 'es', 'value': 'BERD@NFDI'}, 'nl': {'language': 'nl', 'value': 'BERD@NFDI'}, 'pt': {'language': 'pt', 'value': 'BERD@NFDI'}, 'simple': {'language': 'simple', 'value': 'BERD@NFDI'}}
DESCRIPTIONS {'en': {'language': 'en', 'value': 'NFDI consortium for Business, Economic and Related Data (Social and Behavioural Sciences)'}, 'de': {'language': 'de', 'value': 'NFDI für Wirtschaftsdaten und Verwandtes (Sozial- und Verhaltenswissenschaften)'}}
ALIASES {'en': [{'language': 'en', 'value': 'BERD-NFDI'}], 'de': [{'language': 'de', 'value': 'BERD-NFDI'}], 'fr': [{'language': 'fr', 'value': 'BERD-NFDI'}], 'bar': [{'language': 'bar', 'value': 'BERD-NFDI'}], 'de-at': [{'language': 'de-at', 'value': 'BERD-NFDI'}], 'de-ch': [{'language': 'de-ch', 'value': 'BERD-NFDI'}], 'de-formal': [{'language': 'de-formal', 'value': 'BERD-NFDI'}], 'en-ca': [{'language': 'en-ca', 'value': 'BERD-NFDI'}], 'en-gb': [{'language': 'en-gb', 'value': 'BERD-NFDI'}], 'es': [{'language': 'es', 'value': 'BERD-NFDI'}], 'nl': [{'language': 'nl', 'value': 'BERD-NFDI'}], 'pt': [{'language': 'pt', 'value': 'BERD-NFDI'}], 'simple': [{'language': 'simple', 'value': 'BERD-NFDI'}]}
Module data
The module data has raw data as a dictionary:
from nfdi import data
data.raw()
Module nel
The module nel provides simple rule-based named entity linker for the NFDI consortia. In Jupyter Notebook use
from nfdi.nel import linker, test
t = linker(test)
t.render()where test stores the following sentences:
What are BERD@NFDI, NFDI4Earth, NFDI4DataScience, NFDI-MatWerk, PUNCH4NFDI, FAIRmat and Text+?
How are they related to NFDI4Ing, NFDI4Culture, NFDI4Chem and NFDIGHGA?In Python console use:
from nfdi.nel import linker, test
t = linker(test)
t.serve()The Wikidata QIDs are stored in .ent_id_:
from nfdi.nel import linker, test
t = linker(test)
for span in t.doc.ents:
print((span.text, span.ent_id_, span.label_))It prints:
('BERD@NFDI', 'Q108542181', 'ORG')
('NFDI4Earth', 'Q108542504', 'ORG')
('NFDI4DataScience', 'Q108542422', 'ORG')
('NFDI-MatWerk', 'Q108542607', 'ORG')
('PUNCH4NFDI', 'Q108542637', 'ORG')
('FAIRmat', 'Q108542373', 'ORG')
('Text+', 'Q98271443', 'ORG')
('NFDI4Ing', 'Q98380344', 'ORG')
('NFDI4Culture', 'Q98276929', 'ORG')
('NFDI4Chem', 'Q96678459', 'ORG')
('NFDIGHGA', 'Q98380337', 'ORG')NFDI Jupyter Book
Check out NFDI Jupyter Book.
Deploying the Book locally
First, create and activate conda environment using the provided docs/environment.yml:
conda env create -f docs/environment.yml
conda activate nfdiThen, build the Book:
jb build docsOpen the file docs/_build/html/index.html in a browser.
To remove the build folder, run:
jb clean --all docsDeploying the Book at GitHub
Install ghp-import:
pip install ghp-importOnce the book is built, run:
ghp-import -n -p -f docs/_build/html
