Collaborative Research: HNDS-I: IDEANet: Integrating Data Exchange and Analysis of Networks

在本项目中，构建了网络综合数据交换和分析(IDEANet)平台，目的是解决研究人员在网络分析中面临的几个问题。在过去的30年里，网络分析-一种测量和建模对象(人、地点、蛋白质等)之间联系模式的方法-已经成为一个具有广泛应用和巨额联邦研究投资的突出的数据密集型研究主题。网络分析在方法中是独一无二的，因为它侧重于相互依赖，使研究人员能够了解，例如，疾病如何通过种群传播、敌对群体组织或物种的捕食者/猎物模式塑造生态系统，以及无数其他实质性应用。尽管网络分析被广泛使用，但分析和计算工具是高度专业化的，通常具有特定于领域的特性，这严重限制了跨领域的集成，并为想要将这些方法应用于他们的问题的网络分析新手创造了异常高的进入门槛。此外，目前的计算工具管理数据的方式使错误无法检测和容易出错，降低了网络分析研究的严密性和重复性。IDEANet开发的新的计算工具包和数据存储框架旨在通过整合分析方法和数据归档、发现和分发来解决这些问题。为了使网络数据更易于查找、使用和共享，IDEANet追求四个综合目标：(1)提供一个易于使用的软件工具，通过R统计编程语言与方法学上最先进的分析例程和指标相结合，与允许R新手使用图形用户界面的ShinyR应用程序并行开发；(2)建立一个网络数据转换引擎，允许用户在现有的各种应用领域之间轻松地移动数据；(3)提供一个核心指标计算引擎，自动为多种类型的网络数据生成经过审查的最佳实践指标；(4)开发一个数据储存库和档案系统，利用计算工具建立多种格式的统一网络数据发布，并根据不同的安全级别预先计算汇总统计数据和衡量标准。安全数据归档能力利用了在之前的国家科学基金会投资(#1659367)下开发的分布式安全数据服务Impact(保证隐私的计算基础设施)。这一奖项由社会、行为和经济科学局颁发，由NSF高级网络基础设施办公室联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In this project, the Integrated Data Exchange and Analysis for Networks (IDEANet) platform is constructed with the aim of solving several problems that have confronted researchers using network analysis. Over the last 30 years, network analysis — a method for measuring and modeling the patterns of connections between objects (people, places, proteins, etc.) — has emerged as a prominent data-intensive research theme with broad applications and with significant Federal research investment. Network analysis is unique amongst methodologies in its focus on interdependence, allowing investigators to understand how, for example, diseases spread through populations, hostile groups organize, or predator/prey patterns of species shape an ecosystem, as well as myriad other substantive applications. Despite the wide use of network analysis, the analytical and computational tools are highly specialized and often have domain-specific idiosyncrasies that severely limit integration across fields and creates unnaturally high barriers to entry for investigators new to network analysis who want to apply these methods to their problems. Moreover, current computational tools manage data in ways that make errors undetectable and easy to make, lowering rigor and reproducibility in network analysis studies. The new computational toolkit and data storage framework developed in IDEANet aims to solve these problems through its integration of analytic methods and data archiving, discovery, and distribution. To make network data easier to find, use and share, four integrated objectives are pursued in IDEANet: (1) Provision of an easy-to-use software tool, integrated via the R statistical programming language, with methodologically state-of-the-art analysis routines and metrics, developed in parallel with a ShinyR app that allows GUI use for R novices; (2) Construction of a network data translation engine that allows users to easily move data between the myriad extant formats found across application fields; (3) Provision of a core metrics compute engine that automatically generates vetted best practice metrics on network data of multiple types; (4) Development of a data repository and archival system that leverages the compute tools to build harmonized network data releases in multiple formats with pre-computed summary statistics and metrics, tailored to different levels of security. The secure-data archive capacity makes use of ImPACT (Infrastructure for Privacy-Assured CompuTations) distributed secure data services developed under prior NSF investment (#1659367). This award by the Directorate for Social, Behavioral, and Economic Sciences is jointly supported by the NSF Office of Advanced Cyberinfrastructure.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.