Communities of people are better mappers if they are spatially clustered, as revealed in an interesting new paper by Hristova, Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis inspires further inquiry because it shows a clear correlation between spatial affiliation, the internal community structure and the community’s engagement in terms of coverage", according to the authors. They have studied the similarity patterns among eight hundred contributors to OpenStreetMap, the well-known crowdmapping project and detected the hidden community structure. It is a very promising field of research, coupling a social network analysis of crowdsourced data. Participants to such projects are rarely independent individuals: in most cases, they involve communities more than single participants and it would be crucial to uncover how the underlying social structure reflects on the quantity and the quality of the collected data. It has the greatest relevance for citizen science projects, as data quality is often the key issue determining the success or the failure of the collective effort.

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.

The DBLP Computer Science Bibliography evolved from an early small experimental Web server to a popular service for the computer science community. Many design decisions and details of the public XML-records behind DBLP never were documented. This paper is a review of the evolution of DBLP. The main perspective is data modeling. In DBLP persons play a central role, our discussion of person names may be applicable to many other data bases. All DBLP data are available for your own experiments. You may either download the complete set, or use a simple XML-based API described in an online appendix.

Most aspects of our private and social lives -- our safety, the integrity of the financial system, the functioning of utilities and other services, and national security -- now depend on computing. But how can we know that this computing is trustworthy? In Mechanizing Proof, Donald MacKenzie addresses this key issue by investigating the interrelations of computing, risk, and mathematical proof over the last half century from the perspectives of history and sociology. His discussion draws on the technical literature of computer science and artificial intelligence and on extensive interviews with participants. MacKenzie argues that our culture now contains two ideals of proof: proof as traditionally conducted by human mathematicians, and formal, mechanized proof. He describes the systems constructed by those committed to the latter ideal and the many questions those systems raise about the nature of proof. He looks at the primary social influence on the development of automated proof -- the need to predict the behavior of the computer systems upon which human life and security depend -- and explores the involvement of powerful organizations such as the National Security Agency. He concludes that in mechanizing proof, and in pursuing dependable computer systems, we do not obviate the need for trust in our collective human judgment.