The National Archive of Computerized Data on Aging (NACDA) portal: variable-level discovery and custom metadata and...

Population aging has intensified demand for data infrastructure that supports life-course, cohort, and within-person research using longitudinal surveys. However, traditional file-centric dissemination and the growing number and diversity of aging data collections have complicated secondary analysis, limiting researchers' ability to locate comparable measures, evaluate cross-wave continuity, and assemble cross-study analytic datasets. To address these challenges, we introduced the National Archive of Computerized Data on Aging (NACDA) portal, a variable-level metadata discovery and extraction platform developed by NACDA in collaboration with the Inter-university Consortium for Political and Social Research and Colectica.

Established in 2019, the portal integrates 17 longitudinal aging studies encompassing over 210,000 variables. Organized by concept and harmonized metadata, users can identify and extract variables across waves and studies within standardized conceptual frameworks and generate tailored documentation to support transparent, reproducible workflows. We present a use case demonstrating metadata-driven, variable-level access for longitudinal data preparation and cross-study comparison.

The portal provides concept-oriented, variable-level discovery and extraction that streamlines identification of comparable measures, supports cross-wave assessment of measurement continuity, and reduces technical and cognitive burdens associated with assembling longitudinal analytic files. The use case illustrates how variable-level access and autogenerated documentation facilitate efficient dataset construction and reproducible analysis pipelines.

By shifting from file-centric dissemination to harmonized, concept-based metadata and variable-level extraction, the NACDA portal lowers barriers to longitudinal and cross-study aging research. This approach can strengthen transparency, comparability, and reproducibility, supporting cumulative science as aging data resources continue to grow.