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

目前有成千上万的科学家创建了数百万的数据集，这些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长意味着数据潜力的相应增加，从而为重要的新合作研究计划提供支持。然而，数据的绝对数量和多样性为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。要充分利用由“科学长尾”管理的独特数据，需要专门创建新的工具来帮助科学家搜索相关数据集。DataBridge是一个电子科学协作环境工具，专为探索一套丰富的社会计量工具和相应的相关算法空间而设计，并自适应定义将大量不同数据集连接到社会计量网络中的语义桥。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通过人际关系来探索相关性空间（数据相互关联的不同方式）。通过将数据、人类互动以及使用方法和实践联系起来，可以创建丰富的社会网络模型，将大量长尾科学数据相互连接起来，从而增强科学协作和发现。DataBridge通过直接实现和改进在大型、分布式和多样化的集合中发现相关科学数据，支持科学和工程的进步。该系统还将提供一种向DataBridge发布数据的简便方法，并通过协作和引用来激励数据生产者这样做。该设计将是领域不可知的，高度可扩展和自适应，支持包含新的相关算法和索引技术。DataBridge将在开源许可下发布，从而使该技术得到更广泛的使用和众包改进。该项目提出的概念——通过社会计量学网络分析在语义上连接数据——将对非科学数据收集产生影响，并将有效地改善通过网络获取和发现信息的方式。

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.