ABI Development: Enabling broad-scale ecological analysis and synthesis through PASTA Plus, a component of the Environmental Data Initiative

ABI 开发：通过环境数据计划的组成部分 PASTA Plus 实现大规模生态分析和综合

• 批准号: 1565103
• 负责人: Mark Servilla
• 金额: $ 153.02万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565103&HistoricalAwards=false
• 关键词: ABI; Development; Enabling; broad; scale

Global-scale environmental issues such as food security, the spread of disease, and the availability of clean water emphasize the importance of environmental data that can address specific problems while also providing predictions of future conditions. The increasing availability of large volumes of different kinds of data offers new opportunities to address these issues. This project will provide the environmental research community with efficient and reliable means for data management, storage, and sharing. The facilities developed will allow researchers, policy makers, managers, and other stakeholders to bring relevant data to bear on complex environmental questions. Modern approaches that encourage geographically distributed collaboration will be used to increase efficiency of data curation beyond those available for single projects. The project will provide the training and skills needed to overcome technical and social barriers to collaboration, thereby enhancing infrastructure to address ecological questions over broad spatial and temporal scales. Research in environmental sciences is often conducted by individual investigators over limited spatial and temporal scales under funding models that provide limited capacity for data curation or sharing. Data that are archived in a stable, accessible repository and that are accompanied by appropriate metadata benefit both data producers and consumers through improved discoverability and reliability. This project builds on expertise available in the Long Term Ecological Research (LTER) community to provide these benefits. The Provenance Aware Synthesis Architecture repository will ensure long-term availability of data and open data access through federations such as DataONE. This system will be expanded in several ways to accommodate a broader community of data providers and users. These enhancements include a scalable user identity management system, improved data documentation procedures to simplify data submission for non-technical users, and expand the data-quality assurance tools to accommodate a broader range of community practices. Training activities will be developed that range from the basics of metadata creation to the adoption of standardized best practices for specific types of data. The development of templates for describing a data lifecycle will accelerate the availability of data for synthesis. The project will leverage the collective experience of the LTER community to improve data management across a broad community through communication and collaboration. It will facilitate shared technology to develop more commonly usable and more efficient approaches to data curation workflows. Participants in training workshops will be trained in developing workflow technology, re-using existing workflows, and archiving and sharing their developments. Through these workshops, together with community-level centers of expertise, and individual-based skill exchanges, the project will increase the volume of data available along with data discoverability and reuse. These advances will accelerate scientific inquiry through data curation and publication as well as through data discovery and integration.

粮食安全、疾病传播和清洁水的供应等全球范围的环境问题强调了环境数据的重要性，环境数据可以解决特定问题，同时还可以提供对未来状况的预测。大量不同类型数据的可用性不断增加，为解决这些问题提供了新的机会。该项目将为环境研究界提供高效可靠的数据管理、存储和共享手段。开发的设施将使研究人员、政策制定者、管理者和其他利益相关者能够利用相关数据来解决复杂的环境问题。将使用鼓励地理分布式协作的现代方法来提高数据管理的效率，超越单个项目的效率。该项目将提供克服合作的技术和社会障碍所需的培训和技能，从而增强基础设施，解决广泛的空间和时间范围内的生态问题。环境科学研究通常由个体研究者在有限的空间和时间范围内进行，资助模式的数据管理或共享能力有限。存档在稳定、可访问的存储库中并附有适当元数据的数据通过提高可发现性和可靠性使数据生产者和消费者受益。该项目以长期生态研究 (LTER) 社区的专业知识为基础，以提供这些好处。 Provenance Aware Synthesis Architecture 存储库将通过 DataONE 等联盟确保数据的长期可用性和开放数据访问。该系统将以多种方式进行扩展，以适应更广泛的数据提供者和用户社区。这些增强功能包括可扩展的用户身份管理系统、改进的数据文档程序以简化非技术用户的数据提交，以及扩展数据质量保证工具以适应更广泛的社区实践。将开展培训活动，范围从元数据创建的基础知识到针对特定类型数据采用标准化最佳实践。用于描述数据生命周期的模板的开发将加速数据合成的可用性。该项目将利用 LTER 社区的集体经验，通过沟通和协作来改进广泛社区的数据管理。它将促进共享技术开发更常用、更有效的数据管理工作流程方法。培训研讨会的参与者将接受开发工作流程技术、重用现有工作流程以及归档和分享其开发成果的培训。通过这些研讨会，以及社区级专业知识中心和基于个人的技能交流，该项目将增加可用数据量以及数据的可发现性和重用性。这些进步将通过数据管理和发布以及数据发现和集成加速科学探究。