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

在这个项目中，为了解决研究人员在使用网络分析时遇到的几个问题，构建了网络集成数据交换和分析（IDEANet）平台。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——已经成为一个突出的数据密集型研究主题，具有广泛的应用和重要的联邦研究投资。网络分析在方法中是独一无二的，因为它关注相互依赖，使研究人员能够了解疾病如何在种群中传播，敌对群体组织，或物种的捕食者/猎物模式如何形成生态系统，以及无数其他实质性应用。尽管网络分析被广泛使用，但分析和计算工具都是高度专业化的，并且通常具有特定领域的特性，这严重限制了跨领域的整合，并为想要将这些方法应用于他们的问题的网络分析新手研究者创造了不自然的高门槛。此外，当前的计算工具管理数据的方式使错误无法检测，容易犯，降低了网络分析研究的严谨性和可重复性。IDEANet开发的新的计算工具包和数据存储框架旨在通过集成分析方法和数据归档、发现和分发来解决这些问题。为了使网络数据更容易找到、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统计编程语言集成，具有方法学上最先进的分析例程和指标，与ShinyR应用程序并行开发，允许R新手使用GUI；(2)构建网络数据转换引擎，使用户可以轻松地在应用领域中发现的无数现有格式之间移动数据；(3)提供核心指标计算引擎，可根据多种类型的网络数据自动生成经过审查的最佳实践指标；(4)开发数据存储库和存档系统，利用计算工具以多种格式构建统一的网络数据发布，并预先计算汇总统计和度量，为不同的安全级别量身定制。安全数据存档能力利用了ImPACT （Infrastructure for Privacy-Assured computing）分布式安全数据服务，该服务是在NSF之前的投资下开发的（#1659367）。该奖项由社会、行为和经济科学理事会颁发，由国家科学基金会高级网络基础设施办公室联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.