Belmont Forum Collaborative Research: World Wide Web of Plankton Image Curation

将计算机科学和技术以及大型和复杂的数据集用于跨学科和跨学科科学的能力正在出现。因此，至关重要的是建立和启用跨国框架，使数据驱动的科学知识能够超越学科和地理边界，最终增加政策和行动的科学基础。全球环境变化研究领域内的国际合作有可能为具有共享服务的联合数据集成和分析系统建立国际基础，汇集公共和私营部门的最佳做法，促进科学界的开放数据和开放科学管理，包括出版、鼓励数据和云提供商及其他机构采用共同标准和实践，造福所有人。该奖项支持美国研究人员参与一个项目，该项目由来自23个国家的29个资助机构通过贝尔蒙特论坛（Belmont Forum）竞争性选出征集关于加强环境变化中跨国、跨学科和跨学科数据使用的科学驱动的电子基础设施创新的提案。 SEI是一项多边倡议，旨在支持研究项目，将环境，社会和经济科学家与数据科学家，计算科学家以及电子基础设施和网络基础设施开发商和提供商聚集在一起，以解决跨学科和跨学科环境变化研究目前面临的一个或多个方法，技术和/或程序挑战，涉及与大型，多样和多来源的跨国数据。SEI呼吁将研究思维和技术创新紧密联系起来，以加速发现驱动的数据使用和开放科学的全过程，并使更广泛的科学界能够从已确定的新的和潜在的破坏性示范者或试点中受益，以找到解决方案。科学研究正在产生越来越多的数字图像，从细胞的显微照片到星系的照片。自动化仪器可以捕获许多图像，然后自动处理以从中提取数据。为了使这些数据对科学界有用并使公众受益，它需要快速生成输出（即使是数百万张图像），一致且易于共享。数字成像可以帮助确定给定环境中存在多少生物体，生物体的多样性以及这些数据是否随时间变化。 这种技术在水下特别有用，因为人类很难直接观察。该项目的主要目标是建立一个万维网的浮游生物图像管理应用程序，收集浮游生物（随洋流漂流的生物）的图像，允许科学家一致地命名它们，存储相关的生态信息（如时间，位置等），并使所有数据都能方便地向社区提供。它将利用数据库设计和机器学习方面的最新进展来处理数十亿张图像，将托管在公共Web服务器上以便于访问，并将促进对科学进步至关重要的协作和共享氛围。这个应用网络将用于解决已被证明具有挑战性的研究，例如快速浮游生物监测以评估生态系统健康，或全球浮游生物多样性分布及其对碳储存或生态系统生产力的贡献的估计。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。

The capability to bring computer science and technology as well as large and complex data sets to bear on interdisciplinary and transdisciplinary science is emerging. It is therefore critically important to establish and enable transnational frameworks so that data-driven scientific knowledge can transcend disciplines and geographical borders, ultimately increasing the scientific underpinnings of policy and action. International collaboration within global environmental change research fields holds the potential to establish international foundations for federated data integration and analysis systems with shared services, bring together best practices from the public and private sectors, foster open data and open science stewardship among the science communities including related areas such as publishing, and encourage data and cloud providers and others to adopt common standards and practices for the benefit of all.This award supports U.S. researchers participating in a project competitively selected by a coalition of 29 funding agencies from 23 countries through the Belmont Forum Call for proposals on Science-driven e-Infrastructure Innovation (SEI) for the Enhancement of Transnational, Interdisciplinary and Transdisciplinary Data Use in Environmental Change. SEI is a multilateral initiative designed to support research projects that bring together environmental, social and economic scientists with data scientists, computational scientists, and e-infrastructure and cyber-infrastructure developers and providers to solve one or more of the methodological, technological and/or procedural challenges currently facing inter-disciplinary and transdisciplinary environmental change research that involves working with large, diverse and multi-source transnational data. The SEI call will intimately link research thinking and technological innovation toward accelerating the full-path of discovery-driven data use and open science and enable a broader scientific community to benefit from the identified new and potentially disruptive demonstrators or pilots toward solutions.Scientific research is generating an increasing number of digital images, from micrographs of cells to pictures of galaxies. Automated instruments can capture many images, which are then processed automatically to extract data from them. For this data to be useful to the scientific community and benefit the general public, it needs quickly generate output (even for millions of images), consistent, and easy to share. Digital imaging can help determine how many organisms are present in a given environment, how diverse the organisms are and whether these data change in time. This technique is particularly useful underwater, where direct observation by humans is difficult. The main goal of this project is to build a World Wide Web of Plankton Image Curation applications that collect images of plankton (the organisms that drift with ocean currents), allow scientists to name them consistently, store associated ecological information (such as time, location, etc.), and make all data easily accessible to the community. It will leverage cutting edge advances in database design and machine learning to process billions of images, will be hosted on public web servers to be easily accessible, and will foster an atmosphere of collaboration and sharing that is essential for the progress of science. This network of applications will be used to tackle studies that have proved challenging, such as fast plankton monitoring to assess ecosystem health, or global estimations of the distribution of planktonic diversity and its contribution to carbon storage or ecosystem productivity.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.