Data Modeling involves using two Sage-built tools: Schematic and the Data Curator App (DCA). This document is being written as of 2023-09-14 to describe the workflow required to build, edit, and update the data models for MODEL-AD.

Schematic

Summary

SCHEMATIC is an acronym for Schema Engine for Manifest Ingress and Curation. The Python based tool is a schema-based, metadata ingress ecosystem, intended to streamline of biomedical dataset annotation, metadata validation and submission to a data repository for various data contributors.

Install for data curator app:

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install schematicpy

Setup Python Environment

Schematic will run on Python 3.10. We must control the Python Environment. PyEnv is one option., https://fathomtech.io/blog/python-environments-with-pyenv-and-vitualenv/

pyenv install 3.10.11
pyenv virtualenv 3.10.11 schematic_3_10_11
pyenv activate schematic_3_10_11
pyenv -m pip install schematic_3_10_11
pip install schematicpy

Edit Configuration

The following parameters need to be set in the config.yml

https://github.com/Sage-Bionetworks/schematic/blob/develop/config.yml

Using Schematic

Command Line Reference

https://sage-schematic.readthedocs.io/en/develop/cli_reference.html

Need to run commands from ~/schematic

Data Model

Summary

A data model defines attributes (i.e. data elements) describing metadata associated with any given dataset type. The data model also describes relationships between these attributes.

Documentation

/wiki/spaces/SCHEM/pages/2473623559

Build a Data Model

The data model is defined in a table, then stored (i.e. serialized) in a JSON-LD schema.

The JSON-LD schema follows the specifications from Schema.org for attributes.

https://sagebionetworks.jira.com/wiki/spaces/SCHEM/pages/2473623559/The+Data+Model+Schema#A.-Schema-properties-and-relationships

Schematic DB

https://sagebionetworks.jira.com/wiki/spaces/SCHEM/pages/2473623559/The+Data+Model+Schema#Schemas-and-Schematic-DB

Schematic DB is a package used to ingress the manifests created by Schematic into a database.

Schematic DB will use any of these validation rules:
- str
- float
- num
- int
- date
- If the attribute has none of the above rules it use a string type
- the attribute datatype will be determined based on the rule

Build a Data Model

https://docs.google.com/presentation/d/129pSx58qDm7Y1OQmSSHKDq6tsoD3pW_gDRNXiX2rd0w/edit#slide=id.g13aaf3b8358_0_0

Documentation

/wiki/spaces/SCHEM/pages/2473623559

Recommendations for building Data Model

Draw a diagram. A diagram is a useful reference when developing the model.
Start small with a basic skeleton and then build.
Use schematic in dev mode to convert model to JSON-LD regularly to check for errors

Requirements

The data model requires these columns:

Attribute
Description
ValidValues
DependsOn
required
source
parent
properties
dependsOnComponent

Example Model

https://github.com/Sage-Bionetworks/schematic/blob/develop/tests/data/example.model.csv

Formatted for readability:

Schematic dev mode helps find and deal with erors by iteratively checking JSON-LD

Generate JSON-LD from CSV

schematic schema convert data_model.csv

Data Model Validation

/wiki/spaces/SCHEM/pages/2645262364

Ontology

https://ontofox.hegroup.org/

Data Model Visualization

https://linkml.io/linkml/intro/tutorial.html
https://docs.google.com/spreadsheets/d/1vDdcqt3Lgehyq1iCnlF1H9JZi63pLj-u/edit#gid=1939820452
https://portal.includedcc.org/dashboard
https://linkml.io/schemasheets/#examples
https://docs.google.com/spreadsheets/d/1w6zDfz3_yrCjjrqfpXBGNmd0LZL4B03gr1KfzJtk5Cs/edit#gid=674286209
https://docs.google.com/presentation/d/129pSx58qDm7Y1OQmSSHKDq6tsoD3pW_gDRNXiX2rd0w/edit#slide=id.g4d21a8c2ba_0_11

/wiki/spaces/SCHEM/pages/2453176326

/wiki/spaces/SCHEM/pages/2458419217

Glossary

Manifest - metadata table submitted for datasets

JSON for Linking Data

JSON-LD

Data models are defined in https://www.json.org/json-en.html . Specifically, JSON-LinkedDirectory (JSON-LD) files.
https://cambridgesemantics.com/blog/semantic-university/learn-rdf/rdf-nuts-bolts-2/
One reason we use JSON-LD in schematic is its support by http://schema.org.
And a reason for http://schema.org is dataset discoverability: https://datasetsearch.research.google.com/
JSON-LD useful for search engines

Error Troubleshooting

Create a data model formatted as a CSV

Where is the reference to how data model needs to be formatted?

Convert data model from CSV to JSONLD

schematic schema convert input.csv output.jsonld

Guide to Developing Data Models in JSON-LD

JSON-LD, or JavaScript Object Notation for Linked Data, is a JSON-based format for serializing Linked Data. It extends JSON with additional functionality to represent linked data structures, such as contexts, @id, and @type. JSON-LD is a lightweight and flexible format that can be used to represent a variety of data models.

This guide provides an introduction to developing data models in JSON-LD. It covers the following topics:

JSON-LD syntax
JSON-LD contexts
Modeling entities and relationships
Using vocabularies
Best practices for developing JSON-LD data models

JSON-LD Syntax

JSON-LD documents are valid JSON documents. They consist of key-value pairs, where the keys are strings and the values can be strings, numbers, objects, arrays, or booleans. JSON-LD documents can also contain additional keywords that provide additional information about the data.

The following is an example of a simple JSON-LD document:

JSON{
  "@context": "https://schema.org/",
  "@id": "http://example.com/book1",
  "type": "Book",
  "name": "The Hitchhiker's Guide to the Galaxy",
  "author": "Douglas Adams"
}

This document describes a book with the following properties:

@context: The context URI specifies the vocabulary that is used to interpret the data. In this case, the vocabulary is http://Schema.org .
@id: The @id property uniquely identifies the resource. In this case, the resource is a book.
type: The type property specifies the type of the resource. In this case, the resource is a book.
name: The name property specifies the name of the book.
author: The author property specifies the author of the book.

JSON-LD Contexts

JSON-LD contexts are used to map IRIs (Internationalized Resource Identifiers) to human-readable names. Contexts can also be used to define prefixes for IRIs. This can make JSON-LD documents easier to read and write.

The @context property in a JSON-LD document specifies a context URI. When a JSON-LD processor encounters an IRI in a document, it uses the context to resolve the IRI to a human-readable name.

For example, the following context defines a prefix for the http://Schema.org vocabulary:

JSON{
  "@context": {
    "schema": "https://schema.org/"
  }
}

Using this context, the following JSON-LD document can be interpreted:

JSON{
  "@context": {
    "schema": "https://schema.org/"
  },
  "@id": "http://example.com/book1",
  "type": "schema:Book",
  "name": "The Hitchhiker's Guide to the Galaxy",
  "author": "Douglas Adams"
}

The type property is now prefixed with schema:. This makes the document easier to read and understand.

Modeling Entities and Relationships

Entities in a JSON-LD data model are represented by objects. Relationships between entities are represented by properties. For example, the following JSON-LD document describes a book and a person:

JSON{
  "@context": {
    "schema": "https://schema.org/"
  },
  "@id": "http://example.com/book1",
  "type": "schema:Book",
  "name": "The Hitchhiker's Guide to the Galaxy",
  "author": {
    "@id": "http://example.com/douglas-adams",
    "type": "schema:Person",
    "name": "Douglas Adams"
  }
}

The author property in the book object refers to the person object. This indicates that Douglas Adams is the author of The Hitchhiker's Guide to the Galaxy.

Using Vocabularies

Vocabularies are collections of terms and definitions that are used to describe data. JSON-LD data models can use vocabularies to provide a common understanding of the data.

There are many different vocabularies available. Some popular vocabularies include:

http://Schema.org
Dublin Core
Friend of a Friend (FOAF)
GoodRelations
GeoNames
MusicBrainz

When developing a JSON-LD data model, it is important to choose the appropriate vocabulary. The vocabulary should be relevant to the type of data that you are modeling.

**Best Practices