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ABI Sustaining: eBird: Maintaining the Cyberinfrastructure to Support the Collection, Storage, Archive, Analysis, and Access to a Global Biodiversity Data Resource

ABI Sustaining: eBird: Maintaining the Cyberinfrastructure to Support the Collection, Storage, Archive, Analysis, and Access to a Global Biodiversity Data Resource
ABI 维持:eBird:维护网络基础设施以支持全球生物多样性数据资源的收集、存储、存档、分析和访问
批准号:
1356308
负责人:
Steven Kelling
金额:
$63.55万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-07-01 至 2019-07-31

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中文摘要
翻译
康奈尔大学获得了一笔赠款,以支持eBird的持续指数增长,eBird是全球鸟类生物多样性的在线数据资源。随着2002年eBird的推出,eBird开启了一个为观鸟者提供实时在线清单的新时代,并很快成为世界上最大的公民科学项目之一。到2012年,eBird已经积累了1亿次鸟类观测,2014年初这个数字是1.7亿次--有望在短短两年内将前十年的数据量翻一番。EBird在全球拥有20多万名公民科学参与者,它是一个全球规模的实时鸟类监测机制,正在创新保护的新模式。例如,加利福尼亚州的一个首个此类保护项目正在使用eBird模型,在特定地点和准确的时间,针对迁徙过程中水禽和滨鸟的稻田洪水。EBird的公开数据已被6000多名学生、教育工作者、政府工作人员和研究人员下载,产生了110多篇同行评议的科学论文。EBird一如既往地成为观鸟的必备工具,这一点始终如一。每年有超过700万人访问eBird,通过互动探索、可视化和分析工具来探索数据,帮助观鸟者找到更多的鸟。基础和应用生态学的许多研究都建立在对物种分布和丰富度的描述上。对于记录变化、产生原因假说以及最终了解这些变化与整个生态系统健康和功能的关系而言,覆盖广泛空间尺度的长期、组织良好的数据是必要的。虽然收集单一物种发生数据是一个众所周知的过程,但协调收集、整理、获取和存储这些数据并不是一项小任务。经过适当的组织和维持,支持大型物种发生数据集的网络基础设施可以增加数据的价值,而不仅仅是作为汇总观测数据的工具。EBird数据管理基础设施为学生、科学家、土地管理人员、政府和业余爱好者提供了独特的资源。EBird的数据:(1)来自公开可获得和广泛使用的单一、一致收集和管理的来源;(2)代表关于全球所有生物分布的所有可用数据的相当大比例;(3)不仅以最低限度的形式提供这些数据,而且作为一套增值产品提供,从而降低使用这些数据所需的数据管理的门槛。欲了解更多有关eBird的信息,请访问其网站:http://ebird.org.。
英文摘要
Cornell University is awarded a grant to support the continued exponential growth of eBird, an online data resource for global bird biodiversity. With its launch in 2002, eBird opened a new era of live, online checklisting for birders, and it soon became one of the world's largest citizen-science projects. By 2012, eBird had amassed 100 million total bird observations, and early in 2014 that number is 170 million-on track to double the previous decade's worth of data in just 2 years. With more than 200,000 citizen-science participants worldwide, eBird acts as a global-scale, real-time bird monitoring mechanism that is innovating new models of conservation. For example, a first-of-its-kind conservation project in California is using eBird models to target the flooding of rice fields in the specific places, and at precisely the right times, for waterfowl and shorebirds during migration. eBird's openly available data has been downloaded by more than 6,000 students, educators, government staff, and researchers, resulting in more than 110 peer-reviewed scientific papers. True to its beginnings, eBird is still grounded in serving as an essential tool for birding. More than 7 million people access eBird every year to explore data through interactive exploration, visualization and analysis tools that help birders find more birds.Much of the research in basic and applied ecology is founded in descriptions of distribution and abundance of species. Long-term, well-organized data covering broad spatial scales are necessary for documenting change, generating hypotheses for their causes, and ultimately understanding how these changes relate to overall ecosystem health and function. While collecting a single-species occurrence datum is a well-understood process, the coordinated collection, curation, access, and storage of these data is no small task. Appropriately structured and sustained, the cyberinfrastructure that supports large species occurrence datasets can add value to the data, instead of merely acting as a tool for aggregating observations. The eBird data management infrastructure provides a unique resource for students, scientists, land managers, governments and amateurs. eBird's data: (1) come from a single, consistently gathered and curated source that is openly available and widely in use, (2) represents a substantial proportion of all available data on distribution of all organisms globally, and (3) provides these data in not just minimal form but as a set of value-added products that lower the threshold of data management needed to use these data. For more information about eBird, visit its website at http://ebird.org.
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Collaborative Research: ABI Innovation: Dark Ecology: Deep Learning and Massive Gaussian Processes to Uncover Biological Signals in Weather Radar
  • 批准号:
    1661329
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.93万
  • 财政年份:
    2017
  • 负责人:
    Steven Kelling
  • 依托单位:
Collaborative Research: ABI Development: Advancing Map of Life's Impact and Capacity for Sharing, Integrating, and Using Global Spatial Biodiversity Knowledge
  • 批准号:
    1262396
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $1.55万
  • 财政年份:
    2014
  • 负责人:
    Steven Kelling
  • 依托单位:
SoCS: Collaborative Research: A Human Computational Approach for Improving Data Quality in Citizen Science Projects
  • 批准号:
    1209589
  • 项目类别:
    Standard Grant
  • 资助金额:
    $57.54万
  • 财政年份:
    2012
  • 负责人:
    Steven Kelling
  • 依托单位:
Collaborative Research: CDI-Type II: BirdCast: Novel Machine Learning Methods for Understanding Continent-Scale Bird Migration
  • 批准号:
    1125098
  • 项目类别:
    Standard Grant
  • 资助金额:
    $121.79万
  • 财政年份:
    2011
  • 负责人:
    Steven Kelling
  • 依托单位:
海外基金