DataUniversa Indexes
Scalable Index Creation Framework for Any Structured Dataset
Our Index framework demonstrates how heterogeneous internal datasets can be transformed into normalized, explainable scoring systems with transparent weighting and counterfactual sensitivity. We have created these indexes from our internal human-performance datasets as working examples of how the system can construct standardized indexes for any structured dataset—across industries, domains, and signal types.
DataUniversa Indexes
Movement Performance Index (MPI)
A structured index for comparing human movement performance across multiple exercises and contexts.
Human Performance Index (HPI)
A broader framework for evaluating functional physical performance across diverse populations.
Local Records Index (LRI)
A measurement layer for exercise-specific local records collected in real-world environments.
Supra-Additive Load Index (SALI)
Captures cases where combined movements create load or fatigue greater than the sum of individual parts.
Measurement and Normalization Layer
The Indexes layer translates raw observations into structured, comparable measurements. It is the point in the system where heterogeneous real-world data becomes legible across people, populations, environments, and use cases.
While GFF focuses on data collection, the Indexes layer focuses on measurement discipline: defining what is being measured, how it is normalized, what comparisons are valid, and where the limits of interpretation begin.
What This Layer Does
The Indexes layer is designed to make real-world data:
This is where DataUniversa demonstrates how raw observations can be converted into repeatable scoring systems rather than remaining isolated records.
Core Functions
Measurement
Defines the specific variables, benchmarks, and observations being captured.
Normalization
Adjusts for relevant differences so comparisons are more meaningful and less misleading.
Scoring
Transforms multiple inputs into structured indexes that can be tracked, compared, and analyzed.
Explainability
Shows how a score was formed, what components drove it, and how changes in inputs would affect the result.
Boundary Setting
Clarifies what the data supports, what it does not support, and where additional evidence is required.
Why This Layer Matters
Most real-world datasets are difficult to use because they are fragmented, inconsistently defined, and weakly comparable. The Indexes layer addresses that problem by imposing structure between collection and decision use.
It turns raw records into a measurement system.
This is critical for any environment in which organizations need to compare performance, track change over time, identify outliers, or build higher-order models on top of heterogeneous data.