Pieces of our collective human scientific knowledge are constantly defined and modified through our global scientific communication. The most common units of this process are publications, also called articles or papers. These units (i) provide ‘road signs’ for newcomers to a field and (ii) allow the scientific community to steer its work toward consensus-based goals given the available resources. Due to the size of science automated measurements are necessary to achieve these two goals. In particular, the steering aspect involves decisions about manuscript acceptance and science funding, which includes even jobs of scientists. Thus, it seems reasonable to move to the public domain not only scientometric algorithms but also bibliographic data [1]. With more data in the public domain, our current assumptions about the data itself may be challenged.

To measure science, one needs to measure the scientific communication process, which is a network of articles (nodes) connected by citations (directed links) and tagged with article keywords. Most current scientific metrics are built on article-level metrics (ALMs) and the most common ALM is the (total) citation number. The citation number—similarly to other mention-counting ALMs—has the following major properties. First, there are more publications every year (Fig. 1a) and the number of references per publication is growing too (Fig. 1b). Second, papers with an earlier publication date have had until now more time to receive citations. Third, the citation count by itself blanks out citation context [2], which includes citing paper quality. In summary, the citation number tends to favor papers that appeared close (in time and topic) to the origins of large and still active research areas. Improvements