NetWorkBench: A Large-Scale Network Analysis, Modeling, and Visualization Toolkit for Biomedical, Social Science, and Physics Research

该项目将设计、评估和运行一个独特的分布式、共享资源环境，用于大规模网络分析、建模和可视化，名为NetWorkBench （NWB）。设想的数据-代码-计算资源环境将为对生物医学、社会和行为科学、物理学和其他网络研究感兴趣的研究人员、教育工作者和实践者提供一站式在线门户。NWB将支持跨科学边界的网络科学研究。NWB的用户可以在线访问主要的网络数据集，也可以上传自己的网络。他们将能够使用最有效的算法进行网络分析。此外，他们将能够生成、运行和验证网络模型，以提高他们对特定网络的结构和动态的理解。NWB将提供先进的可视化工具，以交互式地探索和理解特定的网络，以及它们与其他类型网络的交互。计算机科学面临的一个主要挑战是开发一种算法集成框架，该框架支持现有算法和新算法的轻松集成和传播，并且可以处理当今存在的多种网络数据格式。另一个挑战是设计和实现一个易于使用的基于菜单的在线门户界面，用于交互式算法选择、数据操作、用户和会话管理。NWB将在生物、社会和行为科学以及物理研究的不同研究项目和教育环境中进行评估。它将被很好地记录并作为开放源代码提供，以便在其他站点轻松复制和使用。每年的暑期学校和一系列的研讨会和教程计划将该工具介绍给不同的研究团体。NWB将为科学研究界的广大成员（生物学家、物理学家、计算机科学家、社会和行为科学家、工程师等）提供在各自领域开展网络分析、建模和可视化项目的手段。这将导致知识和成果从专家网络研究领域直接转移到更广泛的科学界。研究人员将有机会使用经过验证的算法，而过去这些算法是通过耗时的个人开发的特别计算机程序获得的。预计NWB将加强和鼓励网络的实证分析和模型验证，最终加速网络科学研究的发展。在线教学材料将支持在教育环境中使用NWB。NWB将为许多学科的网络科学研究人员提供一个独特的工具。实际上，NWB可以将在网络理论和实践中积累的知识部署到跨科学领域，只需点击一次网页，任何感兴趣的研究人员、从业者或学生都可以使用。NWB共享资源环境将加快和简化网络科学在生物学、社会和行为科学以及大型基础设施分析方面的应用和教育，从而加快科学发现的速度。

This project will design, evaluate, and operate a unique distributed, shared resources environment for large-scale network analysis, modeling, and visualization, named NetWorkBench (NWB). The envisioned data-code-computing resources environment will provide a one-stop online portal for researchers, educators, and practitioners interested in the study of biomedical, social and behavioral science, physics, and other networks.The NWB will support network science research across scientific boundaries. Users of the NWB will have online access to major network datasets or can upload their own networks. They will be able to perform network analysis with the most effective algorithms available. In addition, they will be able to generate, run, and validate network models to advance their understanding of the structure and dynamics of particular networks. NWB will provide advanced visualization tools to interactively explore and understand specific networks, as well as their interaction with other types of networks. A major computer science challenge is the development of an algorithm integration framework that supports the easy integration and dissemination of existing and new algorithms and can deal with the multitude of network data formats in existence today. Another challenge is the design and implementation of an easy to use menu-based, online portal interface for interactive algorithm selection, data manipulation, user and session management. The NWB will be evaluated in diverse research projects and educational settings in biology, social and behavioral science, and physics research. It will be well documented and available as open source for easy duplication and usage at other sites. An annual summer school and a series of workshops and tutorials are planned to introduce the tool to diverse research communities. The NWB will provide members of the scientific research community at large (biologists, physicists, computer scientists, social and behavioral scientists, engineers, etc.) with the means to carry out network analysis, modeling, and visualization projects in their own fields. This will result in a direct transfer of knowledge and results from the fields of specialist network research to a wider scientific community. Researchers will have access to validated algorithms that in the past have been obtained through time-consuming personal developments of ad hoc computer programs. The NWB is expected to enhance and encourage the empirical analysis and model validation of networks, generating an eventual acceleration in the development of network science research. Online instructional material will support the use of the NWB in educational settings. The NWB will provide a unique tool for network science researchers in many disciplines. In effect, NWB can deploy the knowledge accumulated in network theory and practice across sciences with just one web click to any interested researcher, practitioner, or student. The NWB shared resources environment will speed up and ease network science applications and education in biology, social and behavioral science, and large infrastructure analysis, thereby accelerating the rate of scientific discovery.