DAT: A Visual Analytics Approach to Science and Innovation Policy

DAT：科学与创新政策的可视化分析方法

• 批准号: 0915528
• 负责人: Martin Ribarsky
• 金额: $ 74.66万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915528&HistoricalAwards=false
• 关键词: DAT; Visual; Analytics; Approach; Science

A fair amount of work, such as map of science visualization methods, has been done in the area of visualization of scientific discovery and of the relationships among scientific disciplines. Typically, both the maps and the portfolio analyses are derived from keyword and/or citation analyses of research papers coupled with categorizations by discipline of journals and conferences. Although useful, this analysis is far from complete because it does not consider the full text of the papers, just the keywords and citations, and does not consider other sources, such as research project abstracts compiled by funding agencies and reports published by agencies or research organizations. A complete analysis upon which to base policy decisions, evaluations of the effectiveness of funding, or assessments of the direction of a field would include an integrated analysis of all these sources.Intellectual Merit: This project develops a visual analytics approach to perform these assessments of multiple sources, including full text of papers, abstracts, and reports that have not been available before. The approach is exploratory, supporting investigations where one does not initially know precisely what one is looking for but rather uses tools that permit the discovery of new relations and the uncovering of insights. Once found these insights can be looked at in more detail, tested with the gathering of new evidence, and then be the basis of further insight discovery. To support this exploratory investigation, analyses must be, at least in initial stages, unstructured and automated. The most significant words and relations must bubble up from the texts themselves. They must be automated because there will be too many text documents to assess in any other way. Yet, the analyses cannot be completely automated; there must be a place to insert understanding, to organize and make sense of what is found and to direct the investigation in a new direction based on what is found. This is exactly where interactive visual analytics makes its contribution, revealing to investigators detailed results in understandable visual displays, providing clues to prompt further exploration, and supporting organization and annotation of collected evidence and pursuit of new hypotheses. This project also looks at changes and trends over time in the paper and other collections. Detailed examination of changes and trends over time brings out behaviors that may be caused by new or newly revealed directions chosen by researchers in a field, by changes in funding, or by new and important applications. The approach that is applied is based on analyses of streaming text organized into stories, reports, or similar narrative structures. The streaming stories are organized on the fly into ?event clusters? of similar stories that begin and end at particular points in time and have detailed time structures. The goal is to identify motivating events such as new funding directions, new directions established by leaders in a field, as well as new interdisciplinary thrusts across fields. Broader Impacts. As indicated by the recent Visualization of Scientific Discovery Workshop (September, 2008) with sponsorship by NSF, the DOE Office of Science, collaboration by several NSF divisions, and broad attendance by program managers, researchers, and business innovators, there is a significant interest and need for visualization. In addition, the just-released Science of Science Policy Roadmap (November, 2008) from the Office of Science and Technology Policy and the National Science and Technology Council prominently mentions the need for visualization, and in particular visual analytics, tools for science analysis. This report also mentions the need for assessment of these tools for real science analysis applications. This project is positioned to meet both these needs by pursuing the development and assessment of a broad, flexible visual analytics approach for real science and innovation policy applications with real data.

在科学发现的可视化和学科间关系的可视化方面，人们做了大量的工作，如科学可视化方法地图等。通常，地图和投资组合分析都来自研究论文的关键字和/或引文分析，以及期刊和会议的学科分类。虽然有用，但这种分析还远远不够完整，因为它没有考虑论文的全文，只是关键词和引文，也没有考虑其他来源，如资助机构汇编的研究项目摘要和机构或研究组织发表的报告。一个完整的分析基础上的政策决定，评估的有效性的资金，或评估的方向，一个领域将包括所有这些来源的综合分析。智力优点：这个项目开发了一个可视化的分析方法来执行这些评估的多个来源，包括全文的论文，摘要，和报告，还没有得到过。该方法是探索性的，支持调查，其中一个最初不知道确切的是什么，一个正在寻找，而是使用的工具，允许发现新的关系和发现的见解。一旦发现这些见解，就可以更详细地查看，通过收集新证据进行测试，然后成为进一步发现见解的基础。为了支持这一探索性研究，分析必须是非结构化和自动化的，至少在初始阶段是这样。最重要的词语和关系必须从文本本身冒出来。它们必须是自动化的，因为有太多的文本文档无法以任何其他方式进行评估。然而，分析不能完全自动化;必须有一个地方来插入理解，组织和理解发现的内容，并根据发现的内容将调查引向新的方向。这正是交互式视觉分析的贡献所在，以可理解的视觉显示向调查人员揭示详细的结果，提供线索以促进进一步的探索，并支持收集证据的组织和注释以及新假设的追求。该项目还着眼于随着时间的推移，在纸和其他集合的变化和趋势。随着时间的推移，对变化和趋势的详细检查会发现可能由某一领域的研究人员选择的新的或新发现的方向，资金的变化或新的重要应用引起的行为。所应用的方法是基于对组织成故事、报告或类似叙事结构的流文本的分析。流媒体的故事是在飞行中组织成？事件集群？类似的故事，开始和结束在特定的时间点，并有详细的时间结构。目标是确定激励事件，如新的资金方向，由领导者在一个领域建立的新方向，以及跨领域的新的跨学科的推力。更广泛的影响。正如最近由NSF赞助的科学发现可视化研讨会（2008年9月）所表明的那样，美国能源部科学办公室，NSF几个部门的合作，以及项目经理，研究人员和商业创新者的广泛参与，对可视化有很大的兴趣和需求。此外，科学技术政策办公室和国家科学技术理事会刚刚发布的科学政策路线图（2008年11月）突出提到了可视化的必要性，特别是可视化分析，科学分析工具。本报告还提到需要评估这些工具在真实的科学分析中的应用。该项目旨在通过开发和评估一种广泛、灵活的可视化分析方法，以满足这两方面的需求，这种分析方法用于使用真实的数据的真实的科学和创新政策应用。