Image data are essential in scientific research, from astronomy to microbiology. Advancements in technology have enabled the generation of vast and informative image datasets. To derive valuable insights from these datasets, open distribution is crucial. While centralized efforts exist for hosting open image data, many researchers struggle to prepare and share their data in a Findable, Accessible, Interoperable, and Reproducible (FAIR) manner due to data size, various formats, and complex analysis methods. This project's goal is to provide an open-source training resource that educates researchers on processing and preparing image data with best ORD practices. An online handbook will guide users on image ORD best practices and curate existing image ORD resources. Workshops based on the handbook will be conducted to train researchers and establish community consensus on image ORD best practices. These training materials aim to empower ETH domain researchers to effectively utilize ORD resources.