New scientific discoveries and knowledge emerge when the existing corpus of data, information and knowledge is archived, discoverable and interpretable. Data sharing perceptions and practices have evolved over the last several decades in response to the emergence of large research programs and networks, as well as requirements from research sponsors, institutions and publishers. Five practices that facilitate effective data sharing are described herein: (1) creating and following a data management plan; (2) adhering to a reasonable data sharing and attribution license; (3) comprehensively documenting the data following community standards and best practices; (4) protecting and making available data, metadata, and algorithms and workflows via a trusted community data repository; and (5) disseminating and advertising the existence of the data. Preparing a preservation-ready data product for submission to a repository requires that the data contributor logically, consistently and clearly name and describe the data package, including the variables, files and algorithms and workflows. Furthermore, the data should be quality assured, completely and comprehensively documented, and protected throughout the research. Data repositories often have specific data organization and submission guidelines and play an important role in preserving and disseminating data that may represent a valuable scientific resource for decades to come.

Essential Biodiversity Variables (EBV) are fundamental variables that can be used for assessing biodiversity change over time, for determining adherence to biodiversity policy, for monitoring progress towards sustainable development goals, and for tracking biodiversity responses to disturbances and management interventions. Data from observations or models that provide measured or estimated EBV values, which we refer to as EBV data products, can help to capture the above processes and trends and can serve as a coherent framework for documenting trends in biodiversity. Using primary biodiversity records and other raw data as sources to produce EBV data products depends on cooperation and interoperability among multiple stakeholders, including those collecting and mobilising data for EBVs and those producing, publishing and preserving EBV data products. Here, we encapsulate ten principles for the current best practice in EBV-focused biodiversity informatics as 'The Bari Manifesto', serving as implementation guidelines for data and research infrastructure providers to support the emerging EBV operational framework based on trans-national and cross-infrastructure scientific workflows. The principles provide guidance on how to contribute towards the production of EBV data products that are globally oriented, while remaining appropriate to the producer's own mission, vision and goals. These ten principles cover: data management planning; data structure; metadata; services; data quality; workflows; provenance; ontologies/vocabularies; data preservation; and accessibility. For each principle, desired outcomes and goals have been formulated. Some specific actions related to fulfilling the Bari Manifesto principles are highlighted in the context of each of four groups of organizations contributing to enabling data interoperability - data standards bodies, research data infrastructures, the pertinent research communities, and funders. The Bari Manifesto provides a roadmap enabling support for routine generation of EBV data products, and increases the likelihood of success for a global EBV framework.

1 Data Archiving and Networked Services, Royal Netherlands Academy of Arts and Sciences, The Hague, Netherlands, 2 National Snow and Ice Data Centre, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, United States of America, 3 College of University Libraries & Learning Sciences, The University of New Mexico, Albuquerque, New Mexico, United States of America, 4 Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece, 5 Research Data Management Solutions, Elsevier, Jericho, Vermont, United States of America, 6 Australia National Data Service, Melbourne, Australia

Landcover data and bobwhite hunting records were used to assess both hunter habitat preferences and the frequency of northern bobwhite encounters by hunting parties in relation to habitat composition during the 1994-1995 and 1995-1996 hunting seasons at the Joseph W. Jones Ecological Research Center in southern Georgia. Patterns of habitat use by hunters, and the frequency of bobwhite encounters varied within and between years, depending on habitat quality, food availability, and other factors. Landscape-scale analyses of standardized bobwhite covey densities (based on coveys pointed in the field) and habitat composition and configuration for the 1994-1995 hunting season revealed that bobwhite densities were: (1) positively associated with the overall percentage agriculture and food plot habitat (reaching a maximum at 30-35% agriculture); and (2) positively associated with edge complexity, and positively associated with agricultural mean patch size [reaching a maximum at 2-3 hectares (5-6 acres)]. Consequently, larger food plots may be more important for increasing bobwhite encounter rates than numerous very small food plots [ < 0.1 hectares (0.25 acres)]. Results of this, and related ongoing studies, have important implications for both landscape design and multiple use resource management. activities in the context of northern bobwhite habitat management in southern upland pine forest ecosystems.

While the effects of climate change on top predators are well documented, the role of predation on ecosystem level carbon production is poorly developed, despite it being a logical consequence of trophic dynamics. Trophic cascade effects have shown predator mediated changes in primary production, but we predict that predators should lower the overall biomass capacity of any system with top down control. Through a simple Bayesian belief network model of a typical marine foodweb, we show that predator removal, as is common through activities such as fishing and shark finning, results in higher biomasses of lower trophic level fish and zooplankton, resulting in higher net carbon production by the system. In situations common throughout much of the ocean, where activities such as shark finning and over fishing reduce the highest tropic levels, the probability of net carbon production increasing in the model was ~ 60%, and unlike previous studies on simple food chains, trophic cascade effects were not present. While the results are preliminary, and sources of uncertainty in data and models are acknowledged, such results provide even more strength to the argument to protect open sea fish stocks, and particularly large predators such as sharks, cetaceans and game fish.

Data sharing is the practice of making data available for use by others. Ecologists are increasingly generating and sharing an immense volume of data. Such data may serve to augment existing data collections and can be used for synthesis efforts such as meta-analysis, for parameterizing models, and for verifying research results (Le., study reproducibility). Large volumes of ecological data may be readily available through institutions or data repositories that are the most comprehensive available and can serve as the core of ecological analysis. Ecological data are also employed outside the research context and are used for decision-making, natural resource management, education, and other purposes. Data sharing has a long history in many domains such as oceanography and the biodiversity sciences (e.g., taxonomic data and museum specimens), but has emerged relatively recently in the ecological sciences.A review of several of the large international and national ecological research programs that have emerged since the mid-1900s highlights the initial failures and more recent successes as well as the underlying causes from a near absence of effective policies to the emergence of community and data sharing policies coupled with the development and adoption of data and metadata standards and enabling tools. Sociocultural change and the move towards more open science have evolved more rapidly over the past two decades in response to new requirements set forth by governmental organizations, publishers and professional societies. As the scientific culture has changed so has the cyberinfrastructure landscape. The introduction of community-based data repositories, data and metadata standards, software tools, persistent identifiers, and federated search and discovery have all helped promulgate data sharing. Nevertheless, there are many challenges and opportunities especially as we move towards more open science. Cyberinfrastructure challenges include a paucity of easy-to-use metadata management systems, significant difficulties in assessing data quality and provenance, and an absence of analytical and visualization approaches that facilitate data integration and harmonization. Challenges and opportunities abound in the sociocultural arena where funders, researchers, and publishers all have a stake in clarifying policies, roles and responsibilities, as well as in incentivizing data sharing. A set of best practices and examples of software tools are presented that can enable research transparency, reproducibility and new knowledge by facilitating idea generation, research planning, data management and the dissemination of data and results. (C) 2015 The Author. Published by Elsevier B.V.

The field of ecology is poised to take advantage of emerging technologies that facilitate the gathering, analyzing, and sharing of data, methods, and results. The concept of transparency at all stages of the research process, coupled with free and open access to data, code, and papers, constitutes "open science." Despite the many benefits of an open approach to science, a number of barriers to entry exist that may prevent researchers from embracing openness in their own work. Here we describe several key shifts in mindset that underpin the transition to more open science. These shifts in mindset include thinking about data stewardship rather than data ownership, embracing transparency throughout the data life-cycle and project duration, and accepting critique in public. Though foreign and perhaps frightening at first, these changes in thinking stand to benefit the field of ecology by fostering collegiality and broadening access to data and findings. We present an overview of tools and best practices that can enable these shifts in mindset at each stage of the research process, including tools to support data management planning and reproducible analyses, strategies for soliciting constructive feedback throughout the research process, and methods of broadening access to final research products.