ENVIRONMENTS and EOL: identification of Environment Ontology terms in text and the annotation of the Encyclopedia of Life.

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.

Publisher Correction: Open Science principles for accelerating trait-based science across the Tree of Life.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

The Encyclopedia of Life v2: Providing Global Access to Knowledge About Life on Earth.

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.