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.
Documentation
/wiki/spaces/SCHEM/pages/2967568387
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., 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.
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
Documentation
/wiki/spaces/SCHEM/pages/2473623559
Recommendations.
Draw a diagram for data model. Can use templates like ERD example in an app like Lucid
Start small with a basic skeleton and then build the schema
Diagram is a Useful reference when building
Use schematic in dev mode to convert model to JSON-LD regularly to check for errors
Model 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
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
: Thetype
property specifies the type of the resource. In this case, the resource is a book.name
: Thename
property specifies the name of the book.author
: Theauthor
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:
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
Upload Data
https://dca-docs.scrollhelp.site/DCA/Working-version/Project-Agnostic/uploading-data
https://dca-docs.scrollhelp.site/DCA/Working-version/ELITE/validate-and-submit-your-metadata
AD Data Models https://github.com/adknowledgeportal/data-models
DCA app development version
https://dca-dev.app.sagebionetworks.org/
Abby's request for testing
https://sagebionetworks.slack.com/archives/C02A2FBN3G8/p1682116574295509
https://github.com/adknowledgeportal/test-data-model/blob/main/model-ad/model-ad.data.model.jsonld
https://sagebio.shinyapps.io/adknowledgeportal-data-curator/
https://www.synapse.org/#!Synapse:syn33582398/wiki/619343
https://github.com/adknowledgeportal/data_curator
https://github.com/adknowledgeportal/test-data-model
Annotate study folder with contentType = 'dataset'
https://www.synapse.org/#!Synapse:syn36759435/tables/
Add CSV + JSONLD to github – test-data-model
https://github.com/adknowledgeportal/test-data-model
https://github.com/adknowledgeportal/data_curator/blob/18dc00723f2e95a98525ff695401ac67e7785475/schematic_config.yml#L31
Data Model Validation Rules
/wiki/spaces/SCHEM/pages/2645262364
Regular Expression Search of Filenames
extract individual and specimen ID from filenames
Data Model
ELITE
Use schematic in dev mode to convert model to JSON-LD regularly to check for errors
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