BD&I: MoveBank: Integrated database for networked organism tracking.

纽约州立大学获得了开发MoveBank的资助，MoveBank是一个综合的动物跟踪研究系统，用于研究社区，包括可靠的数据采集和摄取系统，数据流处理和数据挖掘分析，以及支持各种资源共享模型的可扩展架构。该项目旨在为越来越多的对合作感兴趣的动物追踪研究人员提供服务。它将有接口，以促进最先进的数据分析，挖掘，建模和可视化工具。该博物馆将与普林斯顿大学、加州圣地亚哥大学圣地亚哥超级计算机中心和伊利诺伊大学厄巴纳-香槟分校合作。研究动物运动对应对环境挑战至关重要，包括入侵物种、传染病、气候和土地使用变化。新的跟踪技术使更多的研究人员能够更容易地收集更多的数据，但科学缺乏以可扩展和可靠的方式获取和管理数据的网络基础设施，并且需要工具来有效地分析和整合这些不同的数据集。环境观测站的发展使这种情况更具挑战性，这些环境观测站正在使用自动化方法生成（或承诺生成）非常大的空间明确的数据集，包括GPS，自动无线电遥测系统（ARTS）和运动触发相机网络。将传统跟踪数据库与实时观测数据流相结合，有可能从根本上推进动物跟踪和基于动物的环境预测科学。

The University of the State of New York is awarded a grant to develop MoveBank, an integrated animal tracking research system for the the research community consisting of a reliable data acquisition and ingestion system, data stream processing and data mining analytics, and extensible architecture that supports various resource sharing models. The project is design to serve a growing global community of animal tracking researchers interested in collaboration. It will have interfaces to facilitate state-of-the-art of data analysis, mining, modeling and visualization tools. The museum will collaborate with Princeton University, the San Diego Supercomputer Center at University of California San Diego and the University of Illinois, Urbana-Champaign.Studying animal movement is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. New tracking technologies are allowing more researchers to collect more data more easily, but science lacks the cyberinfrastructure to acquire and manage data in a scalable and reliable fashion and need tools to efficiently analyze and integrate these disparate datasets. This situation is made more challenging by the development of environmental observatories that are generating (or promise to generate) extremely large spatially-explicit datasets with automated methods, including GPS, Automated Radio Telemetry Systems (ARTS), and motion-triggered camera networks. The integration of legacy tracking databases together with real-time observatory data streams has the potential to fundamentally advance the science of animal tracking and animal-based environmental forecasting.