Environmental Data Initiative (EDI) - streamlining data curation to accelerate scientific inquiry

环境数据倡议 (EDI) - 简化数据管理以加速科学探究

• 批准号: 1629233
• 负责人: Corinna Gries
• 金额: $ 165.57万
• 依托单位: University of Wisconsin-Madison
• 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: 2016-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629233&HistoricalAwards=false
• 关键词: Environmental; Data; Initiative; EDI; streamlining

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 small 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 project will leverage the collective experience of the LTER community to improve data management across a broad community through communication and collaboration. 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 promote 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. The Provenance Aware Synthesis Architecture repository will ensure long-term availability of data and open data access through federations such as DataONE. This repository will be expanded in several ways to accommodate a broader community of data providers and users. Enhancements include a scalable user identity management system, improved data documentation procedures to simplify data submission for non-technical users, and expanded data-quality assurance tools to accommodate a broader range of community practices. These advances will accelerate scientific inquiry through data curation and publication as well as through data discovery and integration.

全球规模的环境问题，例如粮食安全，疾病的传播以及清洁水的可用性，强调了可以解决特定问题的环境数据的重要性，同时还可以预测未来的状况。大量不同数据的可用性日益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社区提供高效可靠的手段，用于数据管理，存储和共享。开发的设施将使研究人员，政策制定者，经理和其他利益相关者能够带来相关数据，以解决复杂的环境问题。鼓励地理分布的合作的现代方法将用于提高数据策划的效率，而不是可用于单个项目的效率。该项目将提供克服协作技术和社会障碍所需的培训和技能，从而增强基础设施，以解决广泛的空间和时间范围的生态问题。对环境科学的研究通常是由个别调查人员对小型空间和时间范围进行的，对小型空间和时间范围进行了资金，以提供有限的数据策划或共享的能力。存档在稳定，可访问的存储库中，并伴随着适当的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都受益。该项目以长期生态研究（LTER）社区提供的专业知识为基础，以提供这些好处。该项目将利用LTER社区的集体体验来通过沟通和协作来改善广泛社区的数据管理。将开发培训活动，范围从元数据创建的基础知识到采用标准化的最佳实践，以实现特定类型的数据。用于描述数据生命周期的模板的开发将加速合成数据的可用性。该项目将促进共享技术，以开发更常见的数据策划工作流程。培训研讨会的参与者将接受开发工作流程技术，重新使用现有工作流程以及归档和分享其发展的培训。通过这些研讨会，以及社区级的专业知识中心以及基于个人的技能交流，该项目将增加可用的数据量以及数据可发现性和重复使用。 出处意识到的综合体系结构存储库将确保通过诸如DataOne之类的联合会（例如DataOne）的长期数据访问数据和开放数据访问。该存储库将通过多种方式扩展，以适应更广泛的数据提供商和用户社区。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的保证工具，以适应更广泛的社区实践。 这些进步将通过数据策展和发布以及数据发现和集成来加速科学询问。