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Data Model Workflow

Owned by Rich Yaxley
Last updated: Oct 25, 2023
4 min read

This page describes the workflow required to build, edit, and update the data model for MODEL-AD.

Schematic

Summary

Data Modeling at Sage requires using two in-house tools: Schematic and the Data Curator App (DCA). 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.

Documentation

Code in Github

https://github.com/Sage-Bionetworks/schematic

Installation

https://pypi.org/project/schematicpy/

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.,

pyenv install 3.10.10 pyenv virtualenv 3.10.10 py_3_10_10 pyenv activate py_3_10_10 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

Need to run commands from ~/schematic

Data Model Development

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

Create Data Model

The data model is defined in a table, then stored (i.e. serialized) in a JSON-LD schema which specifies attributes as suggested by Schema.org.

Sage Data Models for Reference

Recommendations

  • 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:

  1. Attribute

  2. Description

  3. ValidValues

  4. DependsOn

  5. required

  6. source

  7. parent

  8. properties

  9. dependsOnComponent

Example Model

  • Github:

  • Formatted for readability:

This model does NOT validate as provided.

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 no rule provided, defaults to a string type

    • the attribute datatype is based on the rule

Data Model Validation

Data Model Visualization

Convert Data Model from CSV to JSON-LD

schematic schema convert model.csv

What is JSON-LD?

Data models are formatted in JavaScript Object Notation-LinkedData. JSON-LD in schematic is its support by dataset discoverability in search engines like: ​Dataset Search

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.

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 .

  • @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 vocabulary:

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

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:

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:

  • Dublin Core

The vocabulary should be relevant to the type of data that you are modeling.

Ontology Resources

Metadata Dictionary

AD Knowledge Portal Metadata Dictionary

https://sagebio.shinyapps.io/amp-ad-metadata-dictionary/

Data Curator App

http://dca.app.sagebionetworks.org

https://dca-dev.app.sagebionetworks.org

Projects

 

Folder Structure

Study Content

  • Study Description in wiki

  • Methods description in each data folder

 

AMP-AD

https://github.com/adknowledgeportal/data-models/blob/main/README.md#editing-data-models

AD data model → modular

repo:

branch: test-split-csvs

folders:

modules/

..biosopecimen/

..mouse/

ADM-836 - Getting issue details... STATUS

Term = Attribute in the data model where Parent = DataProperty

test-split0csvs branch

 

 

MODEL-AD

 

ELITE

Annotate study folder with contentType = 'dataset'

Flattened file structure

Create Project

Maintain File permission access easily

Top level: assay folders

All data files of one type in assay folder

 

These assay folder names will be displayed

data_folder/

Schematic Configuration needed config.yml

master_file view ‘synID’

which refers to this:

Fileview - Files and Folders https://www.synapse.org/#!Synapse:syn51753858/tables/

needs to point to this fileview and the data model

fork repo

edit dca-template-config.json

add MODEL-AD folder and edit configuration as needed send a pull request

 

ADKP example

Fileview DCA Asset View that DCA uses

folder contentType = ‘dataset’

One project for all of AD

 

 

Templates


Resources

Glossary

Template

Manifest - metadata table submitted for dataset