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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

Guide: How to use Schematic for Data Model Development/wiki/spaces/SCHEM/pages/2967568387

Code in Github

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

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

Create Data Model

https://sagebionetworks.jira.com/wiki/spaces/SCHEM/pages/2967568387/Guide+How+to+use+Schematic+for+Data+Model+Development#Create-a-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.

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Convert Data Model from CSV to JSON-LD

https://sagebionetworks.jira.com/wiki/spaces/SCHEM/pages/2967568387/Guide+How+to+use+Schematic+for+Data+Model+Development#Convert-Data-Model

schematic schema convert model.csv

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/wiki/spaces/AKP/pages/1057882353

• Study Description in wiki

• Methods description in each data folder

/wiki/spaces/EPD1/pages/2900819969

AMP-AD

• Second Test
  AD Portal DCA Test Project

• Fileview AD Portal DCA Test Project - Table

• https://github.com/adknowledgeportal/test-data-model

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