DataSchemas
A DataSchema describes the variables needed for a defined research purpose. It gives reviewers a common specification for discussing what they want to measure, how the information should be represented and which source materials can support the choice. A schema can help plan new collection or evaluate the potential of existing cohorts.
Four levels of the schema
| Level | What it groups | Illustrative example |
|---|---|---|
| Module | A mode or broad kind of assessment | Questionnaire data |
| Theme | A broad area of interest | Lifestyle |
| Domain | A more focused factor or outcome | Tobacco use |
| Variable | A concept with a specification for analysis | Current cigarette smoking status |
Renal DataSchema
The historical site described a schema developed with GENECURE for chronic kidney disease research. It appeared alongside the platform’s general schema documentation. Read the archive note on the renal schema for context on how a kidney-research variable would be documented and compared.
Harmonization Platform
The collaborative part of the old platform helped teams review a defined target variable against what each study collected. For example, a team evaluating smoking status might compare exact questionnaire wording, response options and recall periods before accepting two source items as comparable. Review decisions and transformation rules can then be recorded and checked.
See the methods and glossary for the review steps. For current research tools, visit the Maelstrom Catalogue or Maelstrom harmonization guidelines. This page contains reference material and provides no account access.