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 rst investigate authorship metrics (authors per entry and edits per author) which are power-law distributed in Wikipedia and we nd they are heavy-tailed in these four systems too. We also nd 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 nd 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.