EAGER: Towards the Web of Biodiversity Knowledge: Understanding Data Connectedness to Improve Identifier Practices

生物多样性研究调查地球上生命的多样性和变异性。这一科学领域涉及许多研究学科，如遗传学、生物、植物和动物、栖息地和生态系统及其相互作用的研究。生物多样性研究人员面临的一个长期挑战是发现、获取、“挖掘”和整合来自这些学科的复杂而多样的信息。随着“大数据”技术和基础设施的日益普及，新的方法现已成为可能。该项目将探索和采用这种先进技术来检索和挖掘各种现有的开放生物多样性数据源，目的是生成一幅改进的地球生物多样性的整体图景或“知识图谱”。该项目还将确定完成这项绘图任务所需的数据做法和发现的关系，目的是为今后数据系统的发展提供信息，并就这些技术进行培训。为了将生物多样性知识与数据标准和分类相结合，人们进行了许多尝试，但这种“精确匹配”方法一直难以取得令人满意的结果。该项目旨在开发跨数据集关联记录的新方法，这些方法不依赖于匹配的识别符，而是使用数据记录之间的推断关系而不是显式关系。这是一种实验性的方法，还没有大规模尝试过。将探索公开可获得的生物多样性、遗传、文献和其他数据之间的联系；并将开发软件基础设施，以合并和连接多个生物多样性数据集。另一个目标是量化识别实践与构建现有生物多样性、遗传、文献和其他数据之间联系的能力之间的关系。该项目将利用和补充其他正在进行的大型合作努力，这些努力有助于生物多样性知识、数据科学和基础设施的广泛整合，如生命百科全书(EOL)和国家科学基金会支持的iDigBio项目。最终目的是了解哪些数据实践为生物多样性社区提供了最大的价值，从而为有关识别符的政策、标准和培训提供信息。这反过来又可以探索新的基础和跨学科研究问题，并有可能改进美国和国际上广泛的数据聚合器和数据生产者的实践。该项目由国家科学基金会的公共接入倡议支持，该倡议由NSF高级网络基础设施办公室代表基金会管理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Biodiversity research investigates the variety and variability of life on Earth. This field of science crosses many research disciplines such as genetics, studies of organisms, plants and animals, habitats and ecosystems, and their interactions. A long-standing challenge for biodiversity researchers is to find, access, "mine", and integrate complex and diverse information from those disciplines. New approaches have now become possible with the increasing availability of "big data" techniques and infrastructure. This project will explore and employ such advanced techniques for retrieval and mining of a wide range of available open biodiversity data sources, with the aim of generating an improved holistic picture or "knowledge graph" of Earth's biodiversity. The project will also identify the data practices and discovered relationships that were needed to accomplish this graph-building task, with the aim of informing the development of future data systems and training on these techniques. Many attempts have been made to link together biodiversity knowledge using linked identifiers coupled with data standards and taxonomies, but satisfactory results with such "exact matching" approaches have been elusive. This project aims to develop new methods of relating records across datasets that do not rely on matching identifiers but instead employ inferred rather than explicit relationships between data records. This is an experimental approach that has not yet been attempted at scale. Linkages between publicly available biodiversity, genetic, literature, and other data will be explored; and software infrastructure will be developed to combine and link multiple biodiversity datasets. Another goal is to quantify the relationship between identifier practices and the ability to construct links between available biodiversity, genetic, literature, and other data. This project will draw on and complement other large ongoing collaborative efforts that contribute to broad integration of biodiversity knowledge, data science, and infrastructure such as the Encyclopedia of Life (EOL) and the NSF-supported iDigBio project. The ultimate aim is to understand which data practices provide the most value to the biodiversity community and thereby inform policy, standards, and training on identifiers. This, in turn, can enable the exploration of new fundamental and cross-disciplinary research questions, and potentially improve practices of a wide range of US and international data aggregators and data producers. This project is supported by the National Science Foundation's Public Access Initiative which is managed by the NSF Office of Advanced Cyberinfrastructure on behalf of the Foundation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.