iBOTS
Data validation
Check the shape, types, units, and assumptions of research data before using it.
Our View
Explicit validation turns data assumptions into checks that can catch missing values, inconsistent metadata, and unexpected inputs before analysis.
Provisional positions are editorial proposals awaiting group review.
Teaching — Covered through data organization and robust software topics; individual offerings are not yet attributed to this practice.
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Related themes: Neural Data AnalysisScientific Computing
Related topics: Programming and Data AcquisitionAI-Assisted Software DevelopmentArrays and Numerical ComputingCalcium Imaging AnalysisDataframes and StatisticsLocal Field Potential (LFP) AnalysisMachine Learning(M)EEG AnalysisNeural Spike AnalysisRobust and Reusable Software
Getting started
Validate data where it enters an analysis. Check required columns, measurement units, identifiers, expected ranges, and the meaning of missing values. Keep these rules close to the code that reads the data so failures are easy to understand.
Begin with a few explicit checks for assumptions that would change your conclusions. As a project grows, reusable schemas can help apply the same rules across files and collaborators.