ADataViewer: exploring semantically harmonized Alzheimer’s disease cohort datasets

Background: Currently, Alzheimer’s disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability,
and the actual content of publicly available datasets often only becomes clear to third-party researchers
once data access has been granted. These aspects severely hinder the advancement of AD research through emerging
data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings
towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation
across multiple datasets is crucial.

Methods: We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level.
Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets.
Finally, we developed a platform that displays vital information about the available datasets.

Results: Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets
with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties
of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements
for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally,
we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts
and paves the way for interoperable AD datasets.

Conclusions: In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying
cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The
platform is available at https://adata.scai.fraunhofer.de/.