tooluniverse-dataset-discovery

name

tooluniverse-dataset-discovery

description

Find and evaluate research datasets for any scientific question. Maps research questions to required study designs (longitudinal vs cross-sectional, observational vs experimental, single-cohort vs multi-cohort). Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository. Covers GEO, ArrayExpress, dbGaP, NHANES, UK Biobank, ClinicalTrials.gov, GWAS Catalog, and 30+ scientific repositories.

Dataset Discovery

When to Use

• User asks "find me data about X" or "where can I get data on Y"
• User wants to analyze a relationship between variables
• User needs specific study designs (longitudinal, cross-sectional, experimental)
• User asks about specific surveys or cohorts

Step 1: Understand What the Research Question Requires

Before searching, determine the minimum data requirements:

Study design needed:

• "Does X predict CHANGES in Y over time?" → longitudinal (same people measured repeatedly). Cross-sectional data CANNOT answer this — don't settle for it.
• "Is X associated with Y?" → cross-sectional is sufficient (one-time measurement)
• "Does intervention X cause outcome Y?" → experimental (clinical trial with controls)
• "What genes/proteins are involved in X?" → omics (sequencing, expression, proteomics)

Variables needed:

• List the specific exposure, outcome, and confounder variables
• For each variable, note the measurement type (continuous, categorical, biomarker vs self-report)
• Identify minimum confounders needed (age, sex are almost always required; domain-specific confounders depend on the question)

Population needed:

• Age range, geography, clinical status, sample size requirements
• Power analysis: to detect a small effect (r=0.1), you need ~800 subjects at 80% power