Harmonization across releases
Connect variables and definitions across survey cycles while documenting differences that affect comparability.
Evidence and infrastructure
Research & Data
From source data to evidence you can use.
Evidence and infrastructure
Make data more useful for research
Useful research begins with clear definitions, documented transformations, and workflows that others can reproduce.
- 01
Data harmonization
Bring variables and records from different releases into a consistent structure.
- 02
Reproducible workflows
Connect data preparation, analytical code, documentation, and provenance.
- 03
Research translation
Turn analytical findings into clear explanations and useful outputs for researchers, practitioners, and institutional decision-makers.
Scientific data infrastructure
Make existing public-health data ready for new questions.
Our Research & Data pillar connects dataset preparation, reproducibility, and applied research through Praxis Data Lab. NHANES provides a practical starting point for work across longitudinal federal data releases.
Provenance and reproducible delivery
Keep source releases, preparation code, variable dictionaries, and validation records connected to analytical outputs.
AI-ready research priorities
Our planned research includes AI-assisted semantic harmonization, metadata generation, and assessment of legacy-dataset readiness. Proposed methods require evaluation before being treated as validated capabilities.
Community use and stewardship
We seek collaborations around dataset governance, transparent contribution processes, sustainable access, and practical training for researchers and public-health education teams.
Beyond the dataset
A result should come with a trail you can follow.
Reusability depends on understanding what the data represents and how it was prepared.
- Variable dictionary
- Definitions, source names, coding, units, and important differences.
- Provenance
- Source releases, retrieval information, transformations, and version history.
- Validation
- Checks for completeness, consistency, and unexpected values.
- Reproducible code
- Documented preparation and analysis steps that can be reviewed and rerun.
Questions worth asking
Design the study before selecting the data.
Is the measure comparable?
Consider changes in definitions, instruments, and collection methods across releases.
Does the design fit the question?
Account for eligibility, missingness, sampling, and the limits of the population represented.
Can someone reproduce the result?
Connect every analytical output with the data, code, and assumptions behind it.
Delivering this pillar through our brands
Research & Data delivered through Praxis Data Lab
We advance our Research & Data pillar through Praxis Data Lab, our data intelligence platform. It brings ingestion, harmonization, validation, analysis, and reproducible delivery together so researchers and institutions can work with dependable evidence.