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Python API for the Human Brain Project Knowledge Graph

Project description

fairgraph: a Python API for the Human Brain Project Knowledge Graph.

Authors: Andrew P. Davison, Onur Ates, Yann Zerlaut, Glynis Mattheisen, CNRS

Copyright CNRS 2019

fairgraph is an experimental Python library for working with metadata in the HBP/EBRAINS Knowledge Graph, with a particular focus on data reuse, although it is also useful in metadata registration/curation. The API is not stable, and is subject to change.


To get the latest release:

pip install fairgraph

To get the development version:

git clone
pip install -r ./fairgraph/requirements.txt
pip install -U ./fairgraph

Basic setup

The basic idea of the library is to represent metadata nodes from the Knowledge Graph as Python objects. Communication with the Knowledge Graph service is through a client object, for which an access token associated with an HBP Identity account is needed.

If you are working in a Collaboratory Jupyter notebook:

from jupyter_collab_storage import oauth_token_handler
token = oauth_token_handler.get_token()

If working outside the Collaboratory, we recommend you obtain a token from and save it as an environment variable, e.g. at a shell prompt:

export HBP_AUTH_TOKEN=eyJhbGci...nPq

and then in Python

token = os.environ['HBP_AUTH_TOKEN']

Once you have a token:

from fairgraph import KGClient

client = KGClient(token)

Retrieving metadata from the Knowledge Graph

The different metadata/data types available in the Knowledge Graph are grouped into modules, currently commons, core, brainsimulation, electrophysiology, software, minds and uniminds. For example:

from fairgraph.commons import BrainRegion
from fairgraph.electrophysiology import PatchedCell

Using these classes, it is possible to list all metadata matching a particular criterion, e.g.

cells_in_ca1 = PatchedCell.list(client, brain_region=BrainRegion("hippocampus CA1"))

If you know the unique identifier of an object, you can retrieve it directly:

cell_of_interest = PatchedCell.from_uuid("153ec151-b1ae-417b-96b5-4ce9950a3c56", client)

Links between metadata in the Knowledge Graph are not followed automatically, to avoid unnecessary network traffic, but can be followed with the resolve() method:

example_cell = cells_in_ca1[3]
experiment = example_cell.experiments.resolve(client)
trace = experiment.traces.resolve(client)

The associated metadata is accessible as attributes of the Python objects, e.g.:


You can also access any associated data:

import requests
import numpy as np
from io import BytesIO

download_url = trace.data_location['downloadURL']
data = np.genfromtxt(BytesIO(requests.get(download_url).content))

Advanced queries

While certain filters and queries are built in (such as the filter by brain region, above), more complex queries are possible using the Nexus query API

from fairgraph.base import KGQuery
from fairgraph.minds import Dataset

query = {
    "path": "minds:specimen_group / minds:subjects / minds:samples / minds:methods / schema:name",
    "op": "in",
    "value": ["Electrophysiology recording",
              "Voltage clamp recording",
              "Single electrode recording",
              "functional magnetic resonance imaging"]
context = {
            "schema": "",
            "minds": ""

activity_datasets = KGQuery(Dataset, query, context).resolve(client)
for dataset in activity_datasets:
    print("* " +

Storing and editing metadata

For those users who have the necessary permissions to store and edit metadata in the Knowledge Graph, fairgraph objects can be created or edited in Python, and then saved back to the Knowledge Graph, e.g.:

from fairgraph.core import Person, Organization
from fairgraph.commons import Address

mgm = Organization("Metro-Goldwyn-Mayer")
author = Person("Laurel", "Stan", "", affiliation=mgm)
mgm.address = Address(locality='Hollywood', country='United States')

Getting help

In case of questions about fairgraph, please e-mail If you find a bug or would like to suggest an enhancement or new feature, please open a ticket in the issue tracker.


EU Logo

This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2).

Project details

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