Online citizen science projects engage volunteers in collecting, analyzing, and curating scientific data. Existing projects have demonstrated the value of using volunteers to collect data, but few projects have reached the full collaborative potential of scientists and volunteers. Understanding the shared and unique motivations of these two groups can help designers establish the technical and social infrastructures needed to promote effective partnerships. We present findings from a study of the motivational factors affecting participation in ecological citizen science projects. We show that volunteers are motivated by a complex framework of factors that dynamically change throughout their cycle of work on scientific projects; this motivational framework is strongly affected by personal interests as well as external factors such as attribution and acknowledgment. Identifying the pivotal points of motivational shift and addressing them in the design of citizen-science systems will facilitate improved collaboration between scientists and volunteers.

International audienceOur goal is to define a list of tasks for graph visualization that has enough detail and specificity to be useful to designers who want to improve their system and to evaluators who want to compare graph visualization systems. In this paper, we suggest a list of tasks we believe are commonly encountered while analyzing graph data. We define graph specific objects and demonstrate how all complex tasks could be seen as a series of low-level tasks performed on those objects. We believe that our taxonomy, associated with benchmark datasets and specific tasks, would help evaluators generalize results collected through a series of controlled experiments

Background: In the mega-diverse insect order Lepidoptera (butterflies and moths; 165,000 described species), deeper relationships are little understood within the clade Ditrysia, to which 98% of the species belong. To begin addressing this problem, we tested the ability of five protein-coding nuclear genes (6.7 kb total), and character subsets therein, to resolve relationships among 123 species representing 27 (of 33) superfamilies and 55 (of 100) families of Ditrysia under maximum likelihood analysis.Results: Our trees show broad concordance with previous morphological hypotheses of ditrysian phylogeny, although most relationships among superfamilies are weakly supported. There are also notable surprises, such as a consistently closer relationship of Pyraloidea than of butterflies to most Macrolepidoptera. Monophyly is significantly rejected by one or more character sets for the putative clades Macrolepidoptera as currently defined (P< 0.05) and Macrolepidoptera excluding Noctuoidea and Bombycoidea sensu lato (P≤ 0.005), and nearly so for the superfamily Drepanoidea as currently defined (P< 0.08). Superfamilies are typically recovered or nearly so, but usually without strong support. Relationships within superfamilies and families, however, are often robustly resolved. We provide some of the first strong molecular evidence on deeper splits within Pyraloidea, Tortricoidea, Geometroidea, Noctuoidea and others.Separate analyses of mostly synonymous versus non-synonymous character sets revealed notable differences (though not strong conflict), including a marked influence of compositional heterogeneity on apparent signal in the third codon position (nt3). As available model partitioning methods cannot correct for this variation, we assessed overall phylogeny resolution through separate examination of trees from each character set. Exploration of "tree space" with GARLI, using grid computing, showed that hundreds of searches are typically needed to find the best-feasible phylogeny estimate for these data.Conclusion: Our results (a) corroborate the broad outlines of the current working phylogenetic hypothesis for Ditrysia, (b) demonstrate that some prominent features of that hypothesis, including the position of the butterflies, need revision, and (c) resolve the majority of family and subfamily relationships within superfamilies as thus far sampled. Much further gene and taxon sampling will be needed, however, to strongly resolve individual deeper nodes.

The concept of semantic tagging and its potential for semantic enhancements to taxonomic papers is outlined and illustrated by four exemplar papers published in

The Encyclopedia of Life (EOL, http://eol.org) aims to provide unprecedented global access to a broad range of information about life on Earth. It currently contains 3.5 million distinct pages for taxa and provides content for 1.3 million of those pages. The content is primarily contributed by EOL content partners (providers) that have a more limited geographic, taxonomic or topical scope. EOL aggregates these data and automatically integrates them based on associated scientific names and other classification information. EOL also provides interfaces for curation and direct content addition. All materials in EOL are either in the public domain or licensed under a Creative Commons license. In addition to the web interface, EOL is also accessible through an Application Programming Interface. In this paper, we review recent developments added for Version 2 of the web site and subsequent releases through Version 2.2, which have made EOL more engaging, personal, accessible and internationalizable. We outline the core features and technical architecture of the system. We summarize milestones achieved so far by EOL to present results of the current system implementation and establish benchmarks upon which to judge future improvements. We have shown that it is possible to successfully integrate large amounts of descriptive biodiversity data from diverse sources into a robust, standards-based, dynamic, and scalable infrastructure. Increasing global participation and the emergence of EOL-powered applications demonstrate that EOL is becoming a significant resource for anyone interested in biological diversity.