PyPI version npm version License: Apache


Essential Source of Schemas and Examples (ESSE) contains data formats and associated examples specifically designed for digital materials science (see refs. 1, 2 below).


ESSE can be used as a Node.js or Python package on the server side.


ESSE is compatible with Python 3.6+. It can be installed as a Python package either via PyPI or the repository as below.


pip install esse


virtualenv .venv
source .venv/bin/activate


ESSE can be installed as a Node.js package either via NPM or the repository as below.


npm install @exabyte-io/esse.js


Add "esse-js": "file:PATH_TO_ESSE_REPOSITORY" to package.json.


ESSE contains separate but equivalent interfaces for Python and Javascript. The package provides ESSE class that can be initialized and used as below.


from esse import ESSE

es = ESSE()
schema = es.get_schema_by_id("material")


import {ESSE} from "esse-js";

const es = new ESSE();
const schema = es.getSchemaById("material");


ESSE contains 3 main directories, schema, example and src outlined below.


The schema directory contains the schemas specifying the rules to structure data. A set of core schemas, outlined below, are defined to facilitate the schema modularity.


Primitive directory contains a set of custom primitives that extends default standard primitive types allowed by schema, such as String and Number. Primitives are solely defined by the default primitives and can not be re-constructed from each other.


Abstract directory contains unit-less schemas that are constructed from default and custom primitives.


Reusable directory contains the schemas that are widely used in other schemas to avoid duplication, constructed from the abstract and primitive schemas.


Reference directory contains the schemas defining the rules to structure the references to data sources.


This directory contains the examples formed according to the schemas and implements the same directory structure as the schema directory.

Note: A list of DFT unit functionals (dft_unit_functionals.json) is generated during the Python test from the corresponding prototype file (dft_unit_functionals_proto.json).


This directory contains Python and Javascript interfaces implementing the functionality to access and validate schemas and examples.

A word on functionals

The list of DFT unit functionals (dft_unit_functionals.json) is currently tracked via git LFS. If one wishes to add a new unit functional to that list, please

  • edit the prototype file and
  • generate a new list of unit functional by running python tests, for example (via generate_dft_unit_functionals() from the esse.functionals python module).


Execute the following command from the root directory of this repository to run the tests. The script will run both Javascript and Python tests in which examples are validated against the corresponding schemas.


The script has been tested with node.js v12.16.3 and v8.17.0 as well as Python version 2.7 (up to version 2.3.0) and 3.6+ (for version 2020.10.19 and later).


This repository is an open-source work-in-progress and we welcome contributions. We suggest forking this repository and introducing the adjustments there, the changes in the fork can further be considered for merging into this repository as it is commonly done on Github (see 3 below).

Best Practices

  • Use unique IDs for schemas. One can run sh to automatically set the IDs and reformat examples.

  • Do not use circular references in the schemas, instead leave the type as object and add explanation to description.


1: Data-centric online ecosystem for digital materials science

2: CateCom: A Practical Data-Centric Approach to Categorization of Computational Models

3: GitHub Standard Fork & Pull Request Workflow