This OSF repository contains some data and code (details below) used in the paper ‘Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases’ by Fittipaldi et al. Abstract: Aging diminishes social cognition, and changes in this capacity can indicate brain diseases. However, the relative contribution of age, diagnosis, and brain reserve to social cognition, especially among older adults and in global settings, remains unclear when considering other factors. Using a computational approach, we combined predictors of social cognition from a diverse sample of 1063 older adults across 9 countries. Emotion recognition, mentalizing, and overall social cognition were predicted via support vector regressions from various factors, including diagnosis (subjective cognitive complaints, mild cognitive impairment, Alzheimer’s disease, behavioral variant frontotemporal dementia), demographics, cognition/executive function, structural brain reserve, and motion artifacts from functional magnetic resonance imaging recordings. Higher cognitive/executive functions and education ranked among the top predictors, outweighing age, diagnosis, and brain reserve. Network connectivity did not show predictive values. Results challenge traditional interpretations of age-related decline, patient-control differences, and brain associations of social cognition, emphasizing the significance of heterogeneous factors. Db: db_full.csv: Full database (N=1063) with demographics and cognitive measures db_minisea.csv: Contains final subjects with valid MiniSEA scores used for all analysis (N=998). db_gm.xlsx: Contains z-scores of grey matter values from VBM analysis (N = 398) db_resting.xlsx: Contains z-scores of functional network and movement parameters from resting state fMRI analysis (N = 388) Neuroimaging data: Preprocessed T1 MRI images can be found in the folder: T1_preprocessed Preprocessed resting state fMRI series can be found in google drive: https://drive.google.com/drive/folders/1Kf7XSLDfZr-ktPW7bjjWA__HnxB1Pa9m?usp=sharing Code: Script_descriptive_reg_lm.R: Performs descriptive statistics of the sample, regression analysis of age effects on MiniSEA, lm models of diagnosis effects on MiniSEA, and model comparisons **Code for support vector regression analysis can be found in: https://github.com/AI-BrainLat-team/Global-Mini-SEA