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.

蜜蜂的种群减少会影响植物 - 托管机构在自然和农业系统中的相互作用。尽管蜜蜂和其他昆虫对大多数野生植物进行了授粉，并且对于维持大部分全球粮食生产至关重要，但它们的数量和多样性都在减少。我们对驱动这些下降的因素的理解是有限的，因为我们缺乏有关蜜蜂物种分布的足够数据，以及可能使它们易受伤害或有弹性的环境变化，例如栖息地丧失和气候变化的行为和解剖特征。幸运的是，研究人员已经收集了野生蜜蜂并将自然史收藏中存放了100多年，保留了可以从样品图像中提取的大量相关属性。该项目将在这些历史标本中以数字方式捕获数据和图像，开发从这些图像中测量蜜蜂特征的工具，并生成一个全面的蜜蜂特征和图像数据集，以测量随着时间的推移的变化。这将提高我们对使蜜蜂物种有衰落风险的特定特征的理解，这是维持我们的农业经济和保护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，这些工具将有助于改善我们未来的授粉媒介观察和监测工作。Big-Bee主题收集网络（Big-Bee TCN）将创建超过一百万个高分辨率2D和BEE标本的高分辨率2D和3D图像，代表了全球5,000多个全球BEE BEE BEE BEE BEE BEE，包括大量的粉丝，包括全联合粉丝属于全球的众所周知，是所有的。 Big Bee网络包括13个机构和与美国政府机构的合作伙伴关系。将开发用于共享图像数据集的新型机制，并将通过开放的数据门户（蜜蜂库）获得蜜蜂特征的数据集，以进行研究和教育。 Big-Bee项目将通过社区科学通过众包性状测量和图像的数据转录来吸引公众研究。此外，将通过创建和实施专注于蜜蜂特征和物种识别的研讨会来为自然历史收集人员，研究人员和数据科学的大学生提供培训和专业发展。该奖项产生的所有数据将与国家数字化生物收视资源共享并公开获得，Idigbio.org。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为是值得通过评估的支持。