Introduction: Harmonization Recommendations from the Gateway to Global Aging Data

The Gateway to Global Aging Data (Gateway) is a research infrastructure developed to make data from high-value cohort studies widely accessible and (re)usable and, therefore, enhance reproducibility and accelerate scientific progress (Lee, Phillips, & Wilkens, 2021). For the past 20 years, the Gateway has become a core resource supporting both data users and producers of the Health and Retirement Study and its international network of studies (HRS-INS). HRS-INS collects data from over 351,000 older adults from 47 countries: the United States, Mexico, Costa Rica, Brazil, Chile, South Korea, Japan, China, India, Indonesia, Malaysia, Thailand, Russia, Israel, Ghana, South Africa, England, Ireland, Scotland, Northern Ireland, Austria, Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and Switzerland. The growth of HRS-INS has opened exciting research opportunities, especially through the purposeful collection of cross-national data on the same set of key domains: health and health behaviors, cognition, work and retirement, economic status, family, and social networks. As noted by King and Chatterji (2026), the National Institute on Aging (NIA) has supported the HRS-INS and fostered cross-national comparative research to learn how different institutional, policy, social, cultural, and environmental contexts influence aging and life-course outcomes. The promise of cross-country analyses, while it generates knowledge and insights to inform policy and interventions to improve the health of older adults in the United States, has not yet been fully exploited. This is partly due to the high toll required for cross-country analyses. As local contexts, cultures, languages, and policies, as well as survey administration environments, vary widely, each individual HRS-INS makes modifications to survey questions and modes, necessitating ex post harmonization, and learning about multiple surveys and key modifications made in various local contexts multiplies the knowledge and efforts required for undertaking well cross-national studies. The Gateway is one of the first metadata-driven systems, making key information about cohort studies readily available, facilitating cross-country and cross-wave comparisons (Lee, Phillips, & Wilkens, 2021). We have built harmonized data files (i.e., data files with identically defined, research-ready variables), and in the process, we have learned many idiosyncratic differences and various adaptations made by each data-producing team. We have documented and made these related details available together with the harmonized data and programming codes, but such discussions are often buried in lengthy codebooks and do not draw proper attention. To that end, this supplement issue is intended to highlight crucial features of survey elements that warrant further attention from researchers interested in conducting cross-country analyses. This issue is also aimed at supporting researchers interested in developing new HRS-like studies by offering key content and recommendations. Lee et al. (2026) provide recommendations for core-content domains to enable the study of aging processes, especially the dynamic associations among health, economic status, and social engagement, and highlight the importance of sampling strategy to draw population-level inferences. Cross-country analyses are particularly valuable in aging research, where life-course trajectories are the key topic of investigation, and between- and within-country differences can be leveraged to better understand the determinants of health (Angrisani & Lee, 2020; Banks et al., 2020), including the risk of Alzheimer’s Disease and Alzheimer’s Disease Related Dementias (AD/ADRD). When studying early life risk factors’ influence on late-life outcomes, the longitudinal study design employed by these surveys is very important, as biological, psychological, and socioeconomic trajectories, which are integrally linked and dynamically evolving, can be concurrently quantified over much of the life course. With the development and implementation of the Harmonized Cognitive Assessment Protocol (HCAP) as a sub-study of HRS-INS, the HCAPs offer unprecedented opportunities to examine how early life exposures and experiences across different environments shape AD/ADRD risk. Nichols et al. (2026) discuss key lessons learned from developing and administering cognitive assessments cross-nationally using both the HRS-INS cognitive batteries and the HCAP. Guidance from past data-collection efforts can improve ongoing data collection and inform the design of new studies. Furthermore, insights into study design and measurement challenges help data users understand the limitations and pitfalls of the data and how they might impact analyses. In the past decade, the NIA has expanded investments in AD/ADRD research, supporting the development of over 20 HCAP studies in both high-income countries (HICs) and low-and middle-income countries (LMICs) (King & Chatterji, 2026). To assess cognitive capacity and dementia status, a range of adaptations is required, particularly in LMICs, where literacy and numeracy are low and cultural and social contexts are significantly different from those in HICs. Gross et al. (2026) propose a cross-culturally adaptive framework with both a fixed set of cognitive tests and flexible cognitive tests unique to each specific country, emphasizing core domains important for both cross-country comparisons as well as relevant to cognitive aging in local contexts. Briceño et al. (2026) provide guidance on cross-cultural test adaptation, laying out steps to follow from planning to implementation, pretesting, and field preparation. Meijer and Liu (2026) address the methodological issues in analyzing the HCAP data, with particular attention to missing data, and present concrete recommendations on best practices for study design, data collection, and post-hoc treatments to mitigate challenges posed by missing data and for use and reporting of imputed HCAP data. In this supplement, the included papers discuss not only harmonized survey data from high-value HRS-INS and HCAP datasets but also harmonized exposome data. The Gateway to Global Aging Data project has constructed harmonized environmental exposome measures of air pollution, natural spaces (green space and blue space), late-night artificial light, temperature, and precipitation. Clements et al. (2026) demonstrate the value of these exposome data by evaluating spatial and temporal patterns of five environmental measures (i.e., PM2.5, NO2, O3, green space,and blue space) linked to older adults in eight countries (Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, and the United States) and presenting the observed variations in exposure levels and variability both within and between countries. Another important exposome initiative is the Gateway to Global Aging Policy Explorer. While policies have profound implications for health and related behaviors, the lack of standardized and harmonized policy data hampers the cross-country evaluation of policy impact. The Gateway addresses these barriers by collecting, harmonizing, and documenting policies on education, retirement, and long-term care systems over time, and linking these policy measures to the HRS-INS data based on individual characteristics. Using these linked data, Avendano et al. (2026) demonstrate that geographical and historical variations in compulsory-schooling laws, pension-eligibility criteria, and long-term-care provision frameworks constitute credible and policy-relevant determinants of individuals’ educational attainment, timing of retirement, and the use of formal care. They also demonstrate how Gateway’s harmonized datasets and policy measures can be employed to construct valid instruments for causal inference and suggest that these procedures are adaptable to a broader range of surveys and data beyond the HRS-INS. The data and resources the Gateway has been building and sharing offer unprecedented depth and breadth and foster open science. The open-science approach not only provides broad access to high-value datasets but also promotes data reuse for secondary analyses, enhancing research rigor and reproducibility, and thus accelerates scientific progress. Through continued commitment to open science and accompanied user support, the Gateway will continue to contribute to aging research in the United States and globally. The studies in this supplement highlight the strengths of the Gateway in this endeavor, provide useful illustrations of the applicability of the Gateway, and identify challenges and potential for further development in this important effort. This work was supported by the National Institutes of Health/National Institute on Aging (R01AG030153 to J.L.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. J.L. is a co-author on Clements et al. (2026), Gross et al. (2026), Lee et al. (2026), and Nichols et al. (2026), for which J.R.B. served as the decision editor. Following the completion of the editorial process of all articles to be included in the supplement (“Harmonization Recommendations from the Gateway to Global Aging Data”), J.L. invited J.R.B. to contribute to this introductory editorial given his familiarity with the supplement’s content through the editorial process. J.L. had no involvement in the editorial decisions for the articles published in the supplement. This article appears as part of the supplement titled “Harmonization Recommendations from the Gateway to Global Aging Data,” sponsored by the National Institute on Aging (R01AG030153).