Amber Budden

Big data and the future of ecology

The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and inform- ing resource management through data-intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large-scale efforts that have had mixed success in the individual-driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high-value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists?

Participatory design of DataONE—Enabling cyberinfrastructure for the biological and environmental sciences

Abstract The scope and nature of biological and environmental research are evolving in response to environmental challenges such as global climate change, invasive species and emergent diseases. In particular, scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new approaches for managing, preserving, analyzing, and sharing data. Here, we describe the design of DataONE (Data Observation Network for Earth)—a cyberinfrastructure platform developed to support rapid data discovery and access across diverse data centers distributed worldwide and designed to provide scientists with an integrated set of familiar tools that support all elements of the data life cycle (e.g., from planning and acquisition through data integration, analysis and visualization). Ongoing evolution of the DataONE architecture is based on participatory, user-centered design processes including: (1) identification and prioritization of stakeholder communities; (2) developing an understanding of their perceptions, attitudes and user requirements; (3) usability analysis and assessment; and (4) engaging science teams in grand challenge exemplars such as understanding the broad-scale dynamics of bird migration. In combination, the four approaches engage the broad community in providing guidance on infrastructure design and implementation.

Primer on Data Management: What you always wanted to know

www.dataone.org Primer on Data Management: What you always wanted to know* * but were afraid to ask Carly Strasser, Robert Cook, William Michener, Amber Budden Contents Objective of This Primer Why Manage Data? It will benefit you and your collaborators It will benefit the scientific community Journals and sponsors want you to share your data How To Use This Primer The Data Life Cycle: An Overview Data Management Throughout the Data Life Cycle Plan Collect Assure Describe: Data Documentation Preserve Discover, Integrate, and Analyze Conclusion Acknowledgements References Glossary Objective of This Primer The goal of data management is to produce self-describing data sets. If you give your data to a scientist or colleague who has not been involved with your project, will they be able to make sense of it? Will they be able to use it effectively and properly? This primer describes a few fundamental data management practices that will enable you to develop a data management plan, as well as how to effectively create, organize, manage, describe, preserve and share data. Why Manage Data? 2.1. It will benefit you and your collaborators Establishing how you will collect, document, organize, manage, and preserve your data at the beginning of your research project has many benefits. You will spend less time on data management and more time on research by investing the time and energy before the first piece of data is collected. Your data also will be easier for you to find, use, and analyze, and it will be easier for your collaborators to understand and use your data. In the long term, following good data management practices means that scientists not involved with the project can find, understand, and use the data in the future. By documenting your data and recommending appropriate ways to cite your data, you can be sure to get credit for your data products and their use [1]. DataONE Best Practices Primer

Publish openly but responsibly

In their Perspective “Do not publish” (26 May, p. [800][1]), D. Lindenmayer and B. Scheele warn that publishing location data for rare, endangered, or newly described species could promote wildlife poaching, strain landholder relations, and accelerate habitat destruction. To prevent these

Qualitative data sharing and re-use for socio-environmental systems research: A synthesis of opportunities, challenges, resources and approaches

This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1052875.

Communicating and Disseminating Research Findings

This chapter provides guidance on approaches and best practices for communicating and disseminating research findings to technical audiences via scholarly publications such as peer-reviewed journal articles, abstracts, technical reports, books and book chapters. We also discuss approaches for communicating findings to more general audiences via newspaper and magazine articles and highlight best practices for designing effective figures that explain and support the research findings that are presented in scientific and general audience publications. Research findings may also be presented verbally to educate, change perceptions and attitudes, or influence policy and resource management. Key topics include simple steps for giving effective presentations and best practices for designing slide text and graphics, posters and handouts. Websites and social media are increasingly important mechanisms for communicating science. We discuss forms of commonly used social media, identify simple steps for effectively using social media, and highlight ways to track and understand your social media and overall research impact using various metrics and altmetrics.

Management, Archiving, and Sharing for Biologists and the Role of Research Institutions in the Technology-Oriented Age

Data are one of the primary outputs of science. Although certain subdisciplines of biology have pioneered efforts to ensure their long-term preservation and facilitate collaborations, data continue to disappear, owing mostly to technological, regulatory, and ideological hurdles. In this article, we describe the important steps toward proper data management and archiving and provide a critical discussion on the importance of long-term data conservation. We then illustrate the rise in data archiving through the Joint Data Archiving Policy and the Dryad Digital Repository. In particular, we discuss data integration and how the limited availability of large-scale data sets can hinder new discoveries. Finally, we propose solutions to increase the rate of data preservation, for example by generating mechanisms insuring proper data management and archiving, by providing training in data management, and by transforming the traditional role of research institutions and libraries as data generators toward managers and archivers.

Ecology Postdocs in Academia: Primary Concerns and Possible Solutions

The postdoctoral research phase is intended to allow researchers to further develop and strengthen skills needed to succeed in an academic career (NAS et al. 2000). For example, doctoral programs train students to conduct research and publish papers, but often do not include opportunities to develop professional skills, such as project management, balancing multiple research projects, applying for large grants, managing research teams, teaching, and developing the mentorship skills necessary for future careers both within and outside of academia. Many postdoctoral positions, however, do allow early-career researchers to practice these skills sets while deepening their research skills and expanding their collaboration network. Thus, job candidates that have postdoctoral experience are often viewed as more competitive in the academic market (Nerad 1999). For these reasons, and because the ratio of Ph.D.s to available academic positions is increasing (Schillebeeckx et al. 2013), postdoctoral positions have nearly become a prerequisite for modern careers in ecology. The postdoctoral phase is clearly an important, and potentially exciting, time to develop the skills needed to succeed in an academic career. Yet the uncertainty of advancing successfully to the next career stage Ecology Postdocs in Academia: Primary Concerns and Possible Solutions