This paper introduces mBRSET, the first publicly available diabetic retinopathy retina dataset captured using handheld retinal cameras in real-life, high-burden scenarios, comprising 5,164 images from 1,291 patients of diverse backgrounds. This dataset addresses the lack of ophthalmological data in low- and middle-income countries (LMICs) by providing a cost-effective and accessible solution for ocular screening and management. Portable retinal cameras enable applications outside traditional hospital settings, such as community health screenings and telemedicine consultations, thereby democratizing healthcare. Extensive metadata that are typically unavailable in other datasets, including age, sex, diabetes duration, treatments, and comorbidities, are also recorded. To validate the utility of mBRSET, state-of-the-art deep models, including ConvNeXt V2, Dino V2, and SwinV2, were trained for benchmarking, achieving high accuracy in clinical tasks diagnosing diabetic retinopathy, and macular edema
and in fairness tasks predicting education and insurance status. The mBRSET dataset serves as a resource for developing AI algorithms and investigating real-world applications, enhancing ophthalmological care in resource-constrained environments.