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The goal of Open Knowledge Maps is to create a visual interface to the world’s scientific knowledge. The base for this visual interface consists of so-called knowledge maps, which enable the exploration of existing knowledge and the discovery of new knowledge. Our open source knowledge mapping software applies a mixture of summarization techniques and similarity measures on article metadata, which are iteratively chained together. After processing, the representation is saved in a database for use in a web visualization. In the future, we want to create a space for collective knowledge mapping that brings together individuals and communities involved in exploration and discovery. We want to enable people to guide each other in their discovery by collaboratively annotating and modifying the automatically created maps.

In a scientific publishing environment that is increasingly moving online,identifiers of scholarly work are gaining in importance. In this paper, weanalysed identifier distribution and coverage of articles from the discipline ofquantitative biology using arXiv, Mendeley and CrossRef as data sources.The results show that when retrieving arXiv articles from Mendeley, we wereable to find more papers using the DOI than the arXiv ID. This indicates thatDOI may be a better identifier with respect to findability. We also find thatcoverage of articles on Mendeley decreases in the most recent years, whereasthe coverage of DOIs does not decrease in the same order of magnitude. Thishints at the fact that there is a certain time lag involved, before articles arecovered in crowd-sourced services on the scholarly web.