New software aims to make scoring data from a major child study clearer

The open-source package provides customizable scoring procedures for data from a large United States study of brain development and child health.

Editorial illustration — not from the study.

The ABCD Study follows brain development and child health across the United States and produces a large, varied dataset. Researchers introduce ABCDscores, an open-source R package used to calculate most non-proprietary summary scores in the study’s tabulated data resource.

The package replaces less transparent scoring workflows based on REDCap or external scripts with customizable, documented procedures in R. Its creators say it is designed to apply standard scoring methods consistently across assessments and large datasets.

How the package scores data

The researchers introduced ABCDscores, an R software package used by the ABCD Study to compute most non-proprietary summary scores in its tabulated data resource. It provides standard scoring methods for different assessments and allows researchers to customize scoring for specific needs. The package is intended to make the calculations transparent and reproducible, replacing workflows that previously relied on the REDCap electronic data capture system or external scripts.

Why transparent scoring matters

Summary scores turn detailed assessment results into variables that researchers can use in analyses. By putting scoring procedures into an open-source, customizable package, ABCDscores could make it easier for researchers to see how scores were calculated and apply the same procedures across large datasets. The authors also present it as a possible model for similar tools and standardized scoring frameworks in other research studies.

Evidence and caveats

This is a software article describing ABCDscores, an open-source R package used to compute most non-proprietary summary scores in the tabulated ABCD Study data resource. The abstract does not report an independent evaluation, error rates, time savings, or comparisons with previous scoring methods. The package does not cover proprietary summary scores, and its usefulness may depend on researchers’ data and scoring needs.

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