Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Here we propose a simple formula for describing the characteristic properties of memes in the scientific literature, which is based on their frequency of occurrence and the degree to which they propagate along the citation graph. The product of the frequency and the propagation degree is the meme score, which accurately identifies important and interesting memes within a scientific field. We use data from close to 50 million publication records from the Web of Science, PubMed Central and the American Physical Society to demonstrate the effectiveness of our approach. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative metrics confirm that the meme score is highly effective, while requiring no external resources or arbitrary thresholds and filters.