Despite the widespread use of charts as a medium for communicating data in science domains, we lack a systematic understanding of the goals and principles of effective visual communication. Existing research mostly focuses on the means, i.e. the encoding principles, and not the end, i.e. the key takeaway of a chart. To address this gap, we start from the first principles and aim to answer the fundamental question: how can we describe the message of a scientific chart? We contribute a fact-evidence reasoning framework (FaEvR) by augmenting the conventional visualization pipeline with the stages of gathering and associating evidence for decoding the facts presented in a chart. We apply the resulting classification scheme of fact and evidence on a collection of 500 charts collected from publications in multiple science domains. We demonstrate the practical applications of FaEvR in calibrating task complexity and detecting barriers towards chart interpretability.