Collaborative Research: Digitization TCN: Extending Anthophila research through image and trait digitization (Big-Bee)

合作研究：数字化 TCN：通过图像和性状数字化扩展 Anthophila 研究（Big-Bee）

• 批准号: 2101345
• 负责人: L. Lacey Knowles
• 金额: $ 16.04万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2101345&HistoricalAwards=false
• 关键词: Collaborative; Research; Digitization; TCN; Extending

Declining populations of bees impact plant-pollinator interactions in both natural and agricultural systems. While bees and other insects pollinate most wild plants, and are critical to sustain a large proportion of global food production, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited because we lack sufficient data on the distribution of bee species, and on the behavioral and anatomical traits that may make them either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, wild bees have been collected by researchers and deposited in natural history collections for over 100 years, retaining a wealth of associated attributes that can be extracted from specimen images. This project will digitally capture data and images from these historic specimens, develop tools to measure bee traits from these images, and generate a comprehensive bee trait and image dataset to measure changes through time. This will increase our understanding of specific traits that put bee species at risk of decline - a critical need for both sustaining our agricultural economy and the conservation of our natural resources. In addition, the large image datasets created by this project can be used for new artificial intelligence identification tools that will help improve our future pollinator observation and monitoring efforts.The Big-Bee Thematic Collection Network (Big-Bee TCN) will create over one million high-resolution 2D and 3D images of bee specimens, representing over 5,000 worldwide bee species, including all of the major pollinating species of the United States. The Big-Bee network includes 13 institutions and partnerships with US government agencies. Novel mechanisms for sharing image datasets will be developed and datasets of bee traits will be available through an open data portal, the Bee Library, for research and education. The Big-Bee project will engage the general public in research through community science via crowdsourcing trait measurements and data transcription from images. In addition, training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. All data resulting from this award will be shared with and publicly available through the national digitized biocollections resource, iDigBio.org.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.

蜜蜂数量的减少影响了自然和农业系统中植物-传粉者的相互作用。虽然蜜蜂和其他昆虫为大多数野生植物授粉，并且对维持全球大部分粮食生产至关重要，但它们的数量和多样性都在减少。我们对导致这些下降的因素的理解是有限的，因为我们缺乏足够的关于蜜蜂物种分布的数据，以及关于可能使它们对人类引起的环境变化（如栖息地丧失和气候变化）脆弱或有弹性的行为和解剖特征的数据。幸运的是，野生蜜蜂已被研究人员收集并存放在自然历史收藏馆超过100年，保留了丰富的相关属性，可以从标本图像中提取。该项目将从这些历史标本中数字化捕获数据和图像，开发工具来测量这些图像中的蜜蜂特征，并生成一个全面的蜜蜂特征和图像数据集来测量随着时间的变化。这将增加我们对使蜜蜂物种面临衰退风险的特定特征的理解-这是维持我们的农业经济和保护我们的自然资源的关键需求。此外，该项目创建的大型图像数据集可用于新的人工智能识别工具，这将有助于改善我们未来的传粉者观测和监测工作。（Big-Bee TCN）将创建超过100万张蜜蜂标本的高分辨率2D和3D图像，代表全球5,000多种蜜蜂物种，包括美国所有主要的授粉物种大蜜蜂网络包括13个机构和与美国政府机构的合作伙伴关系。将开发共享图像数据集的新机制，并将通过一个开放的数据门户网站蜜蜂图书馆提供蜜蜂特征数据集，用于研究和教育。大蜜蜂项目将通过众包特征测量和图像数据转录，通过社区科学让公众参与研究。此外，将通过创建和实施以蜜蜂特征和物种识别为重点的研讨会，为自然历史收集工作人员、研究人员和大学生提供数据科学方面的培训和专业发展。该奖项产生的所有数据将通过国家数字化生物资源iDigBio.org共享并公开提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。