This paper introduces the initial collection of open-source COVID-19 image data, which currently includes 123 frontal view X-rays. The motivation behind this effort is to streamline the diagnosis of COVID-19 by providing a dataset for computational analysis, as there are no existing large public datasets specifically for this purpose. The dataset is intended to improve the identification of COVID-19 and provide essential data for training and testing Deep Learning-based systems, potentially using transfer learning. The images are collected from public sources to ensure patient confidentiality and are available under a public URL. The expected outcomes include developing tools to predict pneumonia types and patient outcomes, triaging cases in the absence of physical tests, and monitoring the progression of COVID-19 patients. The dataset is compiled from various sources, including medical websites and publications, and includes detailed metadata for each image.This paper introduces the initial collection of open-source COVID-19 image data, which currently includes 123 frontal view X-rays. The motivation behind this effort is to streamline the diagnosis of COVID-19 by providing a dataset for computational analysis, as there are no existing large public datasets specifically for this purpose. The dataset is intended to improve the identification of COVID-19 and provide essential data for training and testing Deep Learning-based systems, potentially using transfer learning. The images are collected from public sources to ensure patient confidentiality and are available under a public URL. The expected outcomes include developing tools to predict pneumonia types and patient outcomes, triaging cases in the absence of physical tests, and monitoring the progression of COVID-19 patients. The dataset is compiled from various sources, including medical websites and publications, and includes detailed metadata for each image.