Cyberinfrastructure-Enabled Collaboration Networks

网络基础设施使合作研究成为可能，并对科学能力和知识传播产生重大影响。为了应对日益增长的定量评估联邦投资对研究的成果和影响的需求，该项目部署了新的数据，工具，指标和方法，用于评估网络基础设施和建立在其上的数据服务的影响。这项研究有助于研究人员和政策制定者了解网络基础设施如何影响研究人员的协作动态和网络结构。 提供了按纵向、专题、专题、地理、机构和作者维度组织的数据集，研究人员、政策制定者和学生可以访问和使用这些数据集来探索数据密集型的科学和创新政策相关研究。来自GenBank的元数据、来自美国专利商标局的专利数据和来自NIH Export的资助数据使用描述性统计和复杂网络分析的模型进行分析。该项目不仅研究了数据提交和发布网络的拓扑特性，还研究了协作关系的时间顺序以及序列提交和发布网络的重叠。通过切片，绘图和可视化数据，开发适当的采样策略和算法，以更深入地探索协作网络，包括结构和时间。社区检测、机器学习和可视化中使用的算法是本研究的主要计算方法。与研究团体共享的数据产品包括1）包含序列提交、出版物和专利以及它们之间的链接的发现生命周期数据集，以及2）包含美国联邦资助数据和发现生命周期数据集之间的链接的资助因素数据集。

Cyberinfrastructure enables collaborative research and significantly impacts scientific capacity and knowledge diffusion. In response to the growing need for quantitatively evaluating outcomes and impact of federal investment on research, this project deploys new data, tools, metrics, and methods for assessing the impact of cyberinfrastructures and the data services built on them. This research helps researchers and policy makers understand how cyberinfrastructure affects collaboration dynamics and network structures of researchers. Datasets organized by longitudinal, thematic, topical, geographical, institutional, and author dimensions provided, which researchers, policy makers, and students can access and use to explore data-intensive science of science and innovation policy related research.Metadata from GenBank, patent data from U.S. Patent and Trademark Office and funding data from NIH ExPORT are analyzed with descriptive statistics and models from Complex Network Analysis. The project not only examines the topological properties of the data submission and publication networks, but also the temporal ordering of collaborative relationships and the overlap of the sequence submission and publication networks. Through slicing, plotting, and visualizing data, appropriate sampling strategies and algorithms are developed to more deeply explore collaboration networks, both structurally and temporally. Algorithms used in community detection, machine learning, and visualization serve as primary computational methods in this research. Data products to be shared with research communities include 1) discovery lifecycle datasets containing sequence submissions, publications, and patents as well as the links between them and 2) funding factor datasets containing links between U.S. federal funding data and the discovery lifecycle datasets.