UNLABELLED: The association of organisms to their environments is a key issue in exploring biodiversity patterns. This knowledge has traditionally been scattered, but textual descriptions of taxa and their habitats are now being consolidated in centralized resources. However, structured annotations are needed to facilitate large-scale analyses. Therefore, we developed ENVIRONMENTS, a fast dictionary-based tagger capable of identifying Environment Ontology (ENVO) terms in text. We evaluate the accuracy of the tagger on a new manually curated corpus of 600 Encyclopedia of Life (EOL) species pages. We use the tagger to associate taxa with environments by tagging EOL text content monthly, and integrate the results into the EOL to disseminate them to a broad audience of users. AVAILABILITY AND IMPLEMENTATION: The software and the corpus are available under the open-source BSD and the CC-BY-NC-SA 3.0 licenses, respectively, at http://environments.hcmr.gr.

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.

This article explores the opportunities and challenges of creating and sustaining large‐scale “content curation communities” through an in‐depth case study of the Encyclopedia of Life (EOL). Content curation communities are large‐scale crowdsourcing endeavors that aim to curate existing content into a single repository, making these communities different from content creation communities such as Wikipedia. In this article, we define content curation communities and provide examples of this increasingly important genre. We then follow by presenting EOL, a compelling example of a content curation community, and describe a case study of EOL based on analysis of interviews, online discussions, and survey data. Our findings are characterized into two broad categories: information integration and social integration. Information integration challenges at EOL include the need to (a) accommodate and validate multiple sources and (b) integrate traditional peer reviewed sources with user‐generated, nonpeer‐reviewed content. Social integration challenges at EOL include the need to (a) establish the credibility of open‐access resources within the scientific community and (b) facilitate collaboration between experts and novices. After identifying the challenges, we discuss the potential strategies EOL and other content curation communities can use to address them, and provide technical, content, and social design recommendations for overcoming them.

We describe ELVIS (the Ecosystem Location Visualization and Information System), a suite of tools for constructing food webs for a given location. We express both ELVIS input and output data in OWL, thereby enabling its integration with other semantic web resources. In particular, we describe using a Triple Shop application to answer SPARQL queries from a collection of semantic web documents. This is an end-to-end case study of the semantic web's utility for ecological and environmental research.

Despite extensive research, it is still difficult to produce effective interactive layouts for large graphs. Dense layout and occlusion make food webs, ontologies, and social networks difficult to understand and interact with. We propose a new interactive Visual Analytics component called TreePlus that is based on a tree-style layout. TreePlus reveals the missing graph structure with visualization and interaction while maintaining good readability. To support exploration of the local structure of the graph and gathering of information from the extensive reading of labels, we use a guiding metaphor of "Plant a seed and watch it grow." It allows users to start with a node and expand the graph as needed, which complements the classic overview techniques that can be effective at (but often limited to) revealing clusters. We describe our design goals, describe the interface, and report on a controlled user study with 28 participants comparing TreePlus with a traditional graph interface for six tasks. In general, the advantage of TreePlus over the traditional interface increased as the density of the displayed data increased. Participants also reported higher levels of confidence in their answers with TreePlus and most of them preferred TreePlus.

Biodiversity databases have recently become widely available to the public and to other researchers. To retrieve information from these resources, users must understand the underlying data schemas even though they often are not content experts. Many other domains share this problem. We developed an interface, TaxonTree, to visualize the taxonomic hierarchy of animal names. We applied integrated searching and browsing so that users need not have complete knowledge either of appropriate keywords or the organization of the data. Our qualitative user study of TaxonTree in an undergraduate course is the first to describe usage patterns in the biodiversity domain. We found that tree-based interaction and visualization aided users ' understanding of the data. Most users approached biodiversity data by browsing, using common, general knowledge rather than the scientific keyword expertise necessary to search using traditional interfaces. Users with different levels of interest in the domain had different interaction preferences.

Abstract Motivation: Despite substantial efforts to develop and populate the back-ends of biological databases, front-ends to these systems often rely on taxonomic expertise. This research applies techniques from human–computer interaction research to the biodiversity domain. Results: We developed an interactive node-link tool, TaxonTree, illustrating the value of a carefully designed interaction model, animation, and integrated searching and browsing towards retrieval of biological names and other information. Users tested the tool using a new, large integrated dataset of animal names with phylogenetic-based and classification-based tree structures. These techniques also translated well for a tool, DoubleTree, to allow comparison of trees using coupled interaction. Our approaches will be useful not only for biological data but as general portal interfaces. Availability: Working versions of TaxonTree and DoubleTree and video demonstrations of DoubleTree are available at http://www.cs.umd.edu/hcil/biodiversity Supplementary information: All datasets and folk tree illustration are available at http://www.cs.umd.edu/hcil/biodiversity