  Scholarly publications represent at least two benefits for the study of the
scientific community as a social group. First, they attest of some form of
relation between scientists (collaborations, mentoring, heritage,...), useful
to determine and analyze social subgroups. Second, most of them are recorded in
large data bases, easily accessible and including a lot of pertinent
information, easing the quantitative and qualitative study of the scientific
community. Understanding the underlying dynamics driving the creation of
knowledge in general, and of scientific publication in particular can
contribute to maintaining a high level of research, by identifying good and bad
practices in science. In this article, we aim at advancing this understanding
by a statistical analysis of publication within peer-reviewed journals. Namely,
we show that the distribution of the number of papers published by an author in
a given journal is heavy-tailed, but has lighter tail than a power law.
Interestingly, we demonstrate (both analytically and numerically) that such
distributions match the result of an modified preferential attachment process,
where, on top of a Barab\'asi-Albert process, we take finite career span of
scientists into account.
