A model of social collaboration in Molecular Biology knowledge bases

Manual annotation of biological data cannot keep up with data production.
Open annotation models using wikis have been proposed to address
this problem. In this empirical study we analyse 36 years of knowledge
collection by 738 authors in two Molecular Biology wikis (EcoliWiki
and WikiPathways) and two knowledge bases (OMIM and Reactome). We
first investigate authorship metrics (authors per entry and edits
per author) which are power-law distributed in Wikipedia and we find
they are heavy-tailed in these four systems too. We also find surprising
similarities between the open (editing open to everyone) and the
closed systems (expert curators only). Secondly, to discriminate
between driving forces in the measured distributions, we simulate
the curation process and find that knowledge overlap among authors
can drive the number of authors per entry, while the time the users
spend on the knowledge base can drive the number of contributions
per author.