Why is Data Sharing in Collaborative Natural Resource Efforts so Hard and What can We Do to Improve it?
Why is Data Sharing in Collaborative Natural Resource Efforts so Hard and What can We Do to Improve it?
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DOI：
10.1007/s00267-014-0258-2
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发表时间：
2014-05-01
影响因子：
3.5
通讯作者：
Barnas, Katie
中科院分区：
文献类型：
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作者：
Volk, Carol J.;Lucero, Yasmin;Barnas, Katie
自然资源科学的研究和管理越来越依赖于从多个来源汇编的非常大的数据集。虽然拥有更多的数据通常是好的，但利用大型、复杂的数据集在数据共享方面带来了挑战，特别是对于分散在不同地点的合作研究人员(“分布式研究团队”)。我们就常见的数据共享问题向自然资源科学家进行了调查。我们的调查答复者(n=118)在提供数据时发现的主要问题是数据请求不明确(包括所请求数据的格式)。在收到数据时，调查答复者报告在描述数据的文件中存在各种不足之处(例如，没有数据收集说明/没有协议、数据汇总或汇总而没有解释)。由于元数据或“关于数据的信息”是有效数据处理的主要障碍，我们建议通过数据词典、协议、自述文件、显式空值文档和处理元数据来记录元数据，这对于任何大规模研究计划都是必不可少的。我们主张所有研究人员，尤其是那些参与分布式团队的研究人员，通过使用几种现成的沟通策略来缓解这些问题，这些策略包括使用组织结构图来定义角色，使用数据流图来概述过程和时间表，以及使用数据更新周期来指导数据处理预期。特别是，我们认为，分布式研究团队放大了数据共享的挑战，使得数据管理培训对自然资源科学家来说更加重要。如果自然资源科学家不能克服通信和元数据记录问题，那么负面的数据共享经验可能会继续破坏许多大型合作项目的成功。
Increasingly, research and management in natural resource science rely on very large datasets compiled from multiple sources. While it is generally good to have more data, utilizing large, complex datasets has introduced challenges in data sharing, especially for collaborating researchers in disparate locations ("distributed research teams"). We surveyed natural resource scientists about common data-sharing problems. The major issues identified by our survey respondents (n = 118) when providing data were lack of clarity in the data request (including format of data requested). When receiving data, survey respondents reported various insufficiencies in documentation describing the data (e.g., no data collection description/no protocol, data aggregated, or summarized without explanation). Since metadata, or "information about the data," is a central obstacle in efficient data handling, we suggest documenting metadata through data dictionaries, protocols, read-me files, explicit null value documentation, and process metadata as essential to any large-scale research program. We advocate for all researchers, but especially those involved in distributed teams to alleviate these problems with the use of several readily available communication strategies including the use of organizational charts to define roles, data flow diagrams to outline procedures and timelines, and data update cycles to guide data-handling expectations. In particular, we argue that distributed research teams magnify data-sharing challenges making data management training even more crucial for natural resource scientists. If natural resource scientists fail to overcome communication and metadata documentation issues, then negative data-sharing experiences will likely continue to undermine the success of many large-scale collaborative projects.