Abstract INTRODUCTION Sharing clinical research data is essential for advancing Alzheimer's disease (AD) research, yet challenges in accessibility, standardization, documentation, usability, and reproducibility persist. METHODS We developed R data packages to streamline access to curated datasets from key AD studies. A4LEARN includes data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's (A4) randomized trial and its companion observational study, the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN). ADNIMERGE2 contains curated data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a longitudinal biomarker and imaging study. RESULTS These packages bundle data, documentation, and reproducible analysis vignettes into portable, analysis‐ready formats that can be installed and used within R. We also introduce the alzverse package, which applies a common data standard to integrate study‐specific packages and facilitate meta‐analyses. DISCUSSION By promoting collaboration, transparency, and reproducibility, R data packages provide a scalable framework to accelerate AD clinical research. Highlights R packages enable access to curated Alzheimer's clinical study datasets. A4LEARN and ADNIMERGE2 provide portable, analysis‐ready data resources. R packages integrate data, documentation, and reproducible analysis vignettes. alzverse unifies study packages via common standards to support meta‐analyses. Tools promote transparency, collaboration, and reproducibility in Alzheimer's disease (AD) research
Donohue et al. (Thu,) studied this question.