Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affecting more than 200 million people worldwide. With no cure currently available and a rapidly increasing prevalence, emerging approaches such as artificial intelligence (AI) and machine learning (ML) hold promise for advancing the study of AMD. The effective utilization of AI and ML in AMD research is highly dependent on access to high-quality and reusable clinical data. The Findable, Accessible, Interoperable, Reusable (FAIR) principles, published in 2016, provide a framework for sharing data that is easily usable by both humans and machines. However, it is unclear how these principles are implemented with regards to ophthalmic imaging datasets for AMD research. We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. The assessment revealed that none of the datasets were fully compliant with FAIR principles. Specifically, compliance rates were 5% for Findable, 82% for Accessible, 73% for Interoperable, and 0% for Reusable. The low compliance rates can be attributed to the relatively recent emergence of these principles and the lack of established standards for data and metadata formatting in the AMD research community. This article presents our findings and offers guidelines for adopting FAIR practices to enhance data sharing in AMD research.