CDI-Type I: Understanding complex, Dynamic, Multi-Relational Networks In a Large-Brained Social Mammal Through Visual Graph Inquiry

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。对哺乳动物的长期研究是科学家和公众的宝贵资源，但很少有这样的数据集得到充分利用。这在很大程度上是因为，大量数据以多种格式（例如电子表格、文本、图像文件）存储，生物学调查往往限于手工方法和传统的统计分析。为了提高分析能力，我们开发了一个数据仓库，用于收集迄今为止最全面的长期海豚数据集。该数据库包含25年以上的详细观察数据，包括1200只海豚的14，000次目击记录，重点关注214只海豚（详细的个体行为数据），遗传，生态（栖息地，猎物，捕食者）和广泛的人口统计数据。 处理这些数据的生物学家使用传统的统计和线性（因果）方法进行分析。 即使有大量的纵向数据集，科学家也必须选择几个变量，并且可以很容易地选择不太重要甚至是“错误”的特征。 为了促进更多的交互式数据探索，我们建议开发一个全面的可视化图形查询平台，其中包含动态图形的查询语言，图形挖掘算法，以及社区和个体动物的直观视觉映射，这将使生物学家在进行特定的分析之前能够探索更广泛的数据模式。虽然在图数据库、图挖掘和图可视化方面的新研究正在浮出水面，但用于查询、探索和可视化动态图的集成的整体方法还不存在。这些计算工具为生物学家提供了更动态的多维方法，以帮助回答与以下相关的生物学问题：（1）网络结构的时空和动态维度;（2）行为的社会文化传播;（3）影响女性生殖的社会，生态和人口因素。 通过利用生物学和计算方法，我们的新工具将有助于揭示大型，动态，异构数据集的属性及其潜在的社会复杂性。 该项目将作为数据收集、数据整合、数据管理、可视化数据探索和数据分析领域的大规模动物观察研究的模板。 很少有关于哺乳动物的纵向数据集可以广泛获得。我们开发的一些工具将使科学家能够直观地探索模式，而不是以表格形式下载一组变量。这将使科学家能够探索数据属性，并有望产生比传统数据分析更多的见解。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Long-term studies of mammals are precious resources for scientists and the public, but rarely are such data sets fully exploited. This is largely because, with volumes of data stored in many formats (e.g. spreadsheets, text, image files), biological inquiry is often limited to manual approaches and traditional statistical analyses. To improve analytical capability, we have developed a data warehouse for the most comprehensive long-term dolphin dataset collected to date. This warehouse, containing 25+ years of detailed observational data,, including 14,000 sighting records on 1200 dolphins, focal follows on 214 individuals (detailed individual behavioral data), genetic, ecological (habitat, prey, predators), and extensive demographic data. Biologists working with this data have used traditional statistical and linear (cause-effect) approaches for analysis. Even with large longitudinal data sets, scientists must choose just a few variables, and can easily select less-important or even the "wrong" feature. To promote more interactive data exploration, we propose developing a comprehensive visual graph inquiry platform that contains a query language for dynamic graphs, graph mining algorithms, and an intuitive visual mapping for community and individual animals that will allow biologists to explore broader data patterns before they commit to a particular set of analyses. While new research is surfacing in graph databases, graph mining, and graph visualization, an integrated, holistic approach for querying, exploring, and visualizing dynamic graphs does not exist. Such computational tools offer biologists access to more dynamic multi-dimensional approaches to help answer biological questions related to (1) spatio-temporal and dynamic dimensions of network structure; (2) socio-cultural transmission of behavior; and (3) social, ecological, and demographic factors influencing female reproduction. By exploiting both biological and computational approaches, our new tools will help unveil the properties of large, dynamic, heterogeneous data sets and their underlying social complexity. This project will serve as a template for observational large scale animal studies in the areas of data collection, data integration, data management, visual data exploration, and data analysis. Very few longitudinal datasets on mammals are widely available. Some of the tools we develop will enable scientists to visually explore patterns rather than download a set of variables in table form. This will enable scientists to explore data properties and is expected to yield more insights than traditional data analysis.