CI-EN: Enhancing and Supporting a Community-Based Data Analysis, Visualization, and Provenance Platform

越来越多的科学突破正在通过先进的计算能力来实现，这些能力使研究人员能够探索不断增长的数字数据。不幸的是，设计和进行计算实验的基础设施没有跟上我们收集和生成数据的能力，导致了前所未有的情况：数据探索现在是发现的瓶颈。 在这个项目中，我们将建立基础设施，以简化数据探索，支持广泛的现代计算资源和工具，以及实现科学结果的透明度和可重复性。这个项目建立在成功的VisTrails开源系统的基础上。 VisTrails为数据密集型研究提供了独特的支持，全面的出处基础设施和以用户为中心的设计。 该系统已应用于广泛的学科，包括环境科学，物理学和生物信息学，无论是研究和教育工作。 为了扩大VisTrails系统对计算机科学界的影响，我们将在三个重要方向上扩展该系统，特别是：增加对多线程和并行执行和编程框架（如Mapreduce）的支持;通过支持Java库和灵活的脚本机制来提高系统的可扩展性;设计新的基础设施，以简化可重复实验的创建和打包。这些改进将通过为计算要求高的实验提供平台，支持大规模数据分析以及来源研究，从而实现新的研究机会。此外，我们希望它将有助于在计算机科学出版物中更广泛地采用经验证的再现性。

Increasingly, scientific breakthroughs are being enabled by advanced computing capabilities that allow researchers to explore growing volumes of digital data. Unfortunately, the infrastructure to design and conduct computational experiments has not kept pace with our ability to gather and generate data, leading to an unprecedented situation: data exploration is now the bottleneck to discovery. In this project, we will build infrastructure to streamline data exploration that supports a wide array of modern computational resources and tools, as well as enable transparency and reproducibility of scientific results.This project builds upon the successful VisTrails open-source system. VisTrails provides unique support for data-intensive research, a comprehensive provenance infrastructure, and a user-centered design. The system has been applied to a broad range of disciplines, including environmental science, physics, and bioinformatics, both for research and educational efforts. With a view towards expanding the impact of the VisTrails system to the computer science community, we will extend it in three significant directions, notably: add support for multi-threaded and parallel execution and programming frameworks such as Mapreduce; improve the extensibility of the system through support for Java libraries as well as flexible mechanisms for scripting; and design new infrastructure to simplify the creation and packaging of reproducible experiments. These improvements will enable new research opportunities by providing a platform for computationally-demanding experiments and supporting for large-scale data analysis, as well as for provenance research. In addition, we hope it will contribute to a broader adoption of verified reproducibility in computer science publications.