Biodiversity data should be published, cited, and peer reviewed.

Concerns over data quality impede the use of public biodiversity databases and subsequent benefits to society. Data publication could follow the well-established publication process: with automated quality checks, peer review, and editorial decisions. This would improve data accuracy, reduce the need for users to 'clean' the data, and might increase data use. Authors and editors would get due credit for a peer-reviewed (data) publication through use and citation metrics. Adopting standards related to data citation, accessibility, metadata, and quality control would facilitate integration of data across data sets. Here, we propose a staged publication process involving editorial and technical quality controls, of which the final (and optional) stage includes peer review, the most meritorious publication standard in science.

Ecoinformatics: supporting ecology as a data-intensive science.

Ecology is evolving rapidly and increasingly changing into a more open, accountable, interdisciplinary, collaborative and data-intensive science. Discovering, integrating and analyzing massive amounts of heterogeneous data are central to ecology as researchers address complex questions at scales from the gene to the biosphere. Ecoinformatics offers tools and approaches for managing ecological data and transforming the data into information and knowledge. Here, we review the state-of-the-art and recent advances in ecoinformatics that can benefit ecologists and environmental scientists as they tackle increasingly challenging questions that require voluminous amounts of data across disciplines and scales of space and time. We also highlight the challenges and opportunities that remain.

Defining linkages between the GSC and NSF's LTER program: how the Ecological Metadata Language (EML) relates to GCDML and other outcomes.

The Genomic Standards Consortium (GSC) invited a representative of the Long-Term Ecological Research (LTER) to its fifth workshop to present the Ecological Metadata Language (EML) metadata standard and its relationship to the Minimum Information about a Genome/Metagenome Sequence (MIGS/MIMS) and its implementation, the Genomic Contextual Data Markup Language (GCDML). The LTER is one of the top National Science Foundation (NSF) programs in biology since 1980, representing diverse ecosystems and creating long-term, interdisciplinary research, synthesis of information, and theory. The adoption of EML as the LTER network standard has been key to build network synthesis architectures based on high-quality standardized metadata. EML is the NSF-recognized metadata standard for LTER, and EML is a criteria used to review the LTER program progress. At the workshop, a potential crosswalk between the GCDML and EML was explored. Also, collaboration between the LTER and GSC developers was proposed to join efforts toward a common metadata cataloging designer's tool. The community adoption success of a metadata standard depends, among other factors, on the tools and trainings developed to use the standard. LTER's experience in embracing EML may help GSC to achieve similar success. A possible collaboration between LTER and GSC to provide training opportunities for GCDML and the associated tools is being explored. Finally, LTER is investigating EML enhancements to better accommodate genomics data, possibly integrating the GCDML schema into EML. All these action items have been accepted by the LTER contingent, and further collaboration between the GSC and LTER is expected.

Managing troubled data: coastal data partnerships smooth data integration.

Understanding the ecology, condition, and changes of coastal areas requires data from many sources. Broad-scale and long-term ecological questions, such as global climate change, biodiversity, and cumulative impacts of human activities, must be addressed with databases that integrate data from several different research and monitoring programs. Various barriers, including widely differing data formats, codes, directories, systems, and metadata used by individual programs, make such integration troublesome. Coastal data partnerships, by helping overcome technical, social, and organizational barriers, can lead to a better understanding of environmental issues, and may enable better management decisions. Characteristics of successful data partnerships include a common need for shared data, strong collaborative leadership, committed partners willing to invest in the partnership, and clear agreements on data standards and data policy. Emerging data and metadata standards that become widely accepted are crucial. New information technology is making it easier to exchange and integrate data. Data partnerships allow us to create broader databases than would be possible for any one organization to create by itself.