Ecological data sharing
Ecological data sharing
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DOI：
10.1016/j.ecoinf.2015.06.010
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发表时间：
2015-09-01
影响因子：
5.1
通讯作者：
Michener, William K.
中科院分区：
文献类型：
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作者：
Michener, William K.
数据共享是使其他人使用的数据的实践。生态学家越来越多地产生和共享大量数据。此类数据可能有助于增加现有的数据收集，并可用于合成工作，例如荟萃分析，参数化模型和验证研究结果（LE。，研究可重复性）。大量生态数据可以通过最全面的机构或数据存储库很容易获得，并且可以作为生态分析的核心。生态数据还用于研究环境之外，并用于决策，自然资源管理，教育和其他目的。数据共享在许多领域（例如海洋学和生物多样性科学（例如分类学数据和博物馆标本））中具有悠久的历史，但最近在生态科学中出现了相对较远的生态学。自1900年代中期以来，已经出现了最初的失败和最新的成功以及从几乎没有有效政策到出现的根本原因社区和数据共享政策，加上数据和元数据标准以及启用工具的开发和采用。在过去的二十年中，社会文化的变革和向更开放科学发展的转变迅速发展，以应对政府组织，出版商和专业社会提出的新要求。随着科学文化的变化，网络基础设施景观也是如此。引入基于社区的数据存储库，数据和元数据标准，软件工具，持续标识符以及联合搜索和发现都帮助颁布了数据共享。然而，有许多挑战和机遇，尤其是当我们朝着更开放的科学发展时。网络基础设施的挑战包括易于使用的元数据管理系统，评估数据质量和出处的巨大困难以及缺乏有助于数据集成和协调的分析和可视化方法。在社会文化领域中，资助者，研究人员和出版商都在澄清政策，角色和责任以及激励数据共享方面都有股份。提出了一系列最佳实践和软件工具的示例，可以通过促进思想生成，研究计划，数据管理以及数据和结果传播，从而实现研究透明度，可重复性和新知识。 （c）2015年作者。由Elsevier B.V.出版
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.