SciDataLib

Health	Releases

A Python library writing SciData JSON-LD files.

SciData and JSON-LD

JSON-LD is a convenient (human-readable) encoding of Resource Description Framework (RDF) triples. However, unlike traditional relational databases (e.g., MySQL), the graph has no schema. This is problematic as including data from different sources results in a system with no common way to search across the data. The SciData framework is a structure for users to add data and its metadata that are organized in the graph through the associated SciData ontology.

There are three main sections of the SciData framework:

• the methodology section (describing how the research was done)
• the system section (describing what the research studied and the conditions)
• the dataset section (the experimental data, plus any derived or supplemental data)

The methodology and system sections are generic and users can add any data they need to contextualize the dataset. However, in addition they must provide a JSON-LD context file to semantically describe the data elements included. The dataset section has predefined data structures (dataseries, datagroup, and datapoint) although other strudtures can be included if needed.

Translating the content in JSON-LD. Referencing the JSON-LD below:

• '@context': provides resources that define the context (meaning) of data elements in the document (as a JSON array). It consists of three sections:
  • a list of one or more 'context' files
  • a JSON object containing one or more definitions of namespaces used in the document
  • a JSON object with one entry '@base' that defines the base URL to be prepended to all internal references (i.e. '@id' entries)
• root level '@id': the 'name' of the file and where ingested into a graph database, the graph name
• '@graph': the definition of content that will be represented as triples and identified by the graph name (this is therfore a 'quad')
• '@id' under '@graph': the identifier for the graph. The scidatalib code uses the '@base' to populate this, so they are consistent. As a result, all node identifiers '@id's in the document are globally unique because the '@base' is unique.

{
  "@context": [
    "https://stuchalk.github.io/scidata/contexts/scidata.jsonld",
    {
      "sdo": "https://stuchalk.github.io/scidata/ontology/scidata.owl#"
    },
    {
      "@base": "https://my.research.edu/<uniqueid>/"
    }
  ],
  "@id": "graph name",
  "generatedAt": "<automatically added>",
  "version": "1",
  "@graph": {
    "@id": "https://my.research.edu/<uniqueid>/",
    "@type": "sdo:scidataFramework",
    "uid": "<uniqueid>",
    "scidata": {
      "@type": "sdo:scientificData",
      "methodology": {
        "@id": "methodology/",
        "@type": "sdo:methodology",
        "aspects": []
      },
      "system": {
        "@id": "system/",
        "@type": "sdo:system",
        "facets": []
      },
      "dataset": {
        "@id": "dataset/",
        "@type": "sdo:dataset",
        "dataseries": [],
        "datagroup": [],
        "datapoint": []
      }
    }
  }
}

Installation

Using pip

pip install scidatalib

Manual (from source)

Clone the repository either via:

• HTTP:

git clone https://github.com/ChalkLab/SciDataLib.git

• SSH:

git clone git@github.com:ChalkLab/SciDataLib.git

Create a virtual environment and activate to install the package in the isolated environment:

python -m venv <name of env>
source <env>/bin/activate

To install the package from the local source tree into the environment, run:

python -m pip install .

Or to do so in "Development Mode", you can run:

python -m pip install -e .

To deactivate the virtual environment

deactivate

When finished, remove the virtual environment by deleting the directory:

rm -rf <name of env>

Usage

SciDataLib consists of both a command line interface (CLI) and a library for constructing and modifying SciData JSON-LD files

Command Line Interface

The CLI tool is scidatalib. You can use it to create SciData JSON-LD files via specifying an output JSON-LD filename and additional options to create the content of the file.

Example to create "bare" SciData JSON-LD file:

scidatalib output.jsonld

You can access the additional functionality via the --help option:

scidatalib --help

SciDataLib library

After installation, import the SciData class to start creating SciData JSON-LD:

from scidatalib.scidata import SciData

Example:

from scidatalib.scidata import SciData
import json

uid = 'chalk:example:jsonld'
example = SciData(uid)

# context parameters
base = 'https://scidata.unf.edu/' + uid + '/'
example.base(base)

# print out the SciData JSON-LD for example
print(json.dumps(example.output, indent=2))

Output:

{
  "@context": [
    "https://stuchalk.github.io/scidata/contexts/scidata.jsonld",
    {
      "sci": "https://stuchalk.github.io/scidata/ontology/scidata.owl#",
      "sub": "https://stuchalk.github.io/scidata/ontology/substance.owl#",
      "chm": "https://stuchalk.github.io/scidata/ontology/chemical.owl#",
      "w3i": "https://w3id.org/skgo/modsci#",
      "qudt": "https://qudt.org/vocab/unit/",
      "obo": "http://purl.obolibrary.org/obo/",
      "dc": "https://purl.org/dc/terms/",
      "xsd": "https://www.w3.org/2001/XMLSchema#"
    },
    {
      "@base": "https://scidata.unf.edu/chalk:example:jsonld/"
    }
  ],
  "@id": "",
  "generatedAt": "",
  "version": "",
  "@graph": {
    "@id": "",
    "@type": "sdo:scidataFramework",
    "uid": "chalk:example:jsonld",
    "scidata": {
      "@type": "sdo:scientificData",
      "discipline": "",
      "subdiscipline": "",
      "dataset": {
        "@id": "dataset/",
        "@type": "sdo:dataset"
      }
    }
  }
}

Development

Install using poetry

Install via poetry with dev dependencies:

poetry install

Then, run commands via poetry:

poetry run python -c "import scidatalib"

CLI

Run the CLI in using poetry via:

poetry install
poetry run scidatalib --help

Tests / Linting

Flake8 linting

Run linting over the package with flake8 via:

poetry run flake8 --count

Pytest testing

Run tests using pytest:

poetry run pytest tests/

Code coverage

Get code coverage reporting using the pytest-cov plugin:

poetry run pytest --cov=scidatalib --cov-report=term-missing tests/

Release

For developers, please see Release Workflow.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Links

• SciData Research Paper: https://doi.org/10.1186/s13321-016-0168-9
• SciData Project Website: http://stuchalk.github.io/scidata/
• SciData Project GitHub Repository: https://github.com/stuchalk/scidata

Licensing

MIT