BIGDATA: Mid-Scale: ESCE: DCM: Collaborative Research: DataBridge - A Sociometric System for Long-Tail Science Data Collections

BIGDATA：中型：ESCE：DCM：协作研究：DataBridge - 长尾科学数据收集的社会计量系统

• 批准号: 1247602
• 负责人: Gary King
• 金额: $ 46.33万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-11-01 至 2016-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247602&HistoricalAwards=false
• 关键词: BIGDATA; Mid; Scale; ESCE; DCM

DataBridgeThere are currently thousands of scientists creating millions of data sets describing an increasingly diverse matrix of social and physical phenomena. This rapid increase in both amount and diversity of data implies a corresponding increase in the potential of data to empower important new collaborative research initiatives. However, the sheer volume and diversity of data presents a new set of challenges in locating all of the data relevant to a particular line of research. Taking full advantage of the unique data managed by the "long-tail of science" requires new tools specifically created to assist scientists in their search for relevant data sets. DataBridge is an e-science collaboration environment tool designed specifically for the exploration of a rich set of sociometric tools and the corresponding space of relevance algorithms, and their adaptation to define semantic bridges that link large numbers of diverse datasets into a sociometric network. Data from several large NSF funded projects will be analyzed to develop relevance-based data discovery methods. Sociometric network analysis (SNA) algorithms will be used to explore the space of relevancy (different ways data can be related to each other) by metadata and ontology, by pattern analysis and feature extraction, and via human connections. By linking data, human interactions, and usage methods and practices, rich models of social networks inter-connecting massive long tail science data can be created that enhance scientific collaboration and discovery. DataBridge supports advances in Science and Engineering by directly enabling and improving discovery of relevant scientific data across large, distributed and diverse collections. The system will also provide an easy means of publishing data to the DataBridge and incentivize data producers to do so by enabling collaboration and citation. The design will be domain-agnostic and highly extensible and adaptive, supporting inclusion of new relevance algorithms and indexing techniques. DataBridge will be distributed under an open source license enabling wider use and crowd-sourced improvements of the technology. The concepts developed in the project - semantically linking data through sociometric network analysis - will have an impact on non-scientific data collections and will effectively improve access and discovery of information over the web.

数据桥目前有数千名科学家创建了数百万个数据集，描述了日益多样化的社会和物理现象矩阵。数据数量和多样性的快速增加意味着数据支持重要的新合作研究计划的潜力也相应增加。然而，数据的庞大数量和多样性给定位与特定研究领域相关的所有数据带来了一系列新的挑战。充分利用“科学长尾”管理的独特数据需要专门创建的新工具来帮助科学家搜索相关数据集。 DataBridge 是一种电子科学协作环境工具，专门设计用于探索丰富的社会计量工具和相关算法的相应空间，以及它们的适应定义语义桥梁，将大量不同的数据集链接到社会计量网络中。将分析来自几个大型 NSF 资助项目的数据，以开发基于相关性的数据发现方法。 社会计量网络分析 (SNA) 算法将用于通过元数据和本体、模式分析和特征提取以及人际关系来探索相关性空间（数据可以以不同方式相互关联）。通过链接数据、人类互动以及使用方法和实践，可以创建丰富的社交网络模型，将大量长尾科学数据互连起来，从而增强科学协作和发现。 DataBridge 通过直接支持和改进大型、分布式和多样化集合中相关科学数据的发现来支持科学和工程的进步。该系统还将提供一种将数据发布到 DataBridge 的简单方法，并通过支持协作和引用来激励数据生产者这样做。该设计将与领域无关，具有高度可扩展性和适应性，支持包含新的相关算法和索引技术。 DataBridge 将在开源许可下分发，从而实现该技术的更广泛使用和众包改进。该项目中开发的概念——通过社会计量网络分析在语义上链接数据——将对非科学数据收集产生影响，并将有效改善网络信息的访问和发现。