Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research

The emerging creative practices of digital collections are increasing in prominence thanks to the access to, and appeal of, data visualizations. Interactive data visualizations, such as a map, timeline, chart, graph, or artist’s rendering of data, enable users to pursue their own research interests within a collection and participate in new areas of research. Use of these data visualizations enhances the scope of the collection and enables a modern and diverse thematic collection to emerge. This article considers the question: Does the addition of data visualizations affect how users participate with a research collection? Impacts on collection development, application of user-centered design, and analysis of users’ exploratory strategies are discussed. Examples of data visualizations used in digital humanities projects and digital collections are presented as case studies.     

档案机构微信推文行业辨识度之省思

档案机构微信推文的行业辨识度是档案机构微信质量建设的重要组成部分。文章用例证法从行政性、文化性和娱乐化三方面分析了档案机构微信推文行业辨识度问题产生原因。基于议程设置原理对档案机构微信推文推送优先度提出建议,从"多推档案事"和"多用档案说事"两个角度提出解决档案机构微信推文内容行业辨识度问题的方法,融入历史主义思想和档案表征解决档案机构微信推文形式创新问题。     

协同搜索过程中用户交流内容与模式研究

[目的/意义]旨在分析协同搜索用户在信息搜索任务过程中的交流内容与模式,从而理解协同搜索用户的关注重点与搜索过程。[研究设计/方法]基于书籍交互检索平台(CLEF-Social Book Search)设计实验,共招募18名被试完成两种搜索任务,通过录音记录对话并对其进行编码和分析,总结交流内容特征和模式。结合任务类型、认知类型组合、服务器记录的搜索交互行为日志以及问卷收集的搜索体验进行了探索分析。[结论/发现]从交流内容上看,协同搜索用户主要理解与评判书目信息、商讨搜索任务计划;比起认知类型不同的用户,相同认知类型的用户在操作交互方面交流更多,在评判决策方面交流较少。交流模式依据讨论内容比重可分为理解评判型、评判主导型、均衡交流型三种,评判主导型用户的任务完成满意度最高。[创新/价值]协同搜索用户的交流反映出搜索过程中需要与同伴商讨协同的焦点,也是需要系统提供协助的重点,给协同搜索系统设计提供一定参考。本研究针对协同搜索的交流内容设计的编码系统对相关的协同交流研究也有借鉴意义。     

Beyond traditional collaborative search: Understanding the effect of awareness on multi-level collaborative information retrieval

Although there has been a great deal of research into Collaborative Information Retrieval (CIR) and Collaborative Information Seeking (CIS), the majority has assumed that team members have the same level of unrestricted access to underlying information. However, observations from different domains (e.g. healthcare, business, etc.) have suggested that collaboration sometimes involves people with differing levels of access to underlying information. This type of scenario has been referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, no studies have been conducted to investigate the effect of awareness, an existing CIR/CIS concept, on MLCIR. To address this gap in current knowledge, we conducted two separate user studies using a total of 5 different collaborative search interfaces and 3 information access scenarios. A number of Information Retrieval (IR), CIS and CIR evaluation metrics, as well as questionnaires were used to compare the interfaces. Design interviews were also conducted after evaluations to obtain qualitative feedback from participants. Results suggested that query properties such as time spent on query, query popularity and query effectiveness could allow users to obtain information about team's search performance and implicitly suggest better queries without disclosing sensitive data. Besides, having access to a history of intersecting viewed, relevant and bookmarked documents could provide similar positive effect as query properties. Also, it was found that being able to easily identify different team members and their actions is important for users in MLCIR. Based on our findings, we provide important design recommendations to help develop new CIR and MLCIR interfaces.     

跳远助跑速度变化对起跳效果的影响——国内外优秀运动员与我院选手的比较分析

笔者采用高速摄影、影片解析、数理统计等方法对西安体育学院男子跳远运动员和国内外优秀选手助跑速度变化而影响起跳效果的因素进行比较研究。结果表明:跳远助跑各段落速度的变化对起跳效果及成绩产生很大影响;西安体育学院男子跳远运动员绝对速度低,起跳前两步速度、速度利用率呈下降趋势,影响起跳初速度;跳远起跳前一步的速度、速度利用率对提高起跳效果要比起跳前9—10m段的速度、速度利用率更具有实际意义。     

关于举办沈阳体育学院“散打王”比赛的构想

借鉴全国“散打王”比赛的成功经验 ,通过对沈阳体育学院民族传统体育系学生和选学民族传统体育专业方向的部分学生及武术系、武术教研室全体教师进行问卷调查 ,对举办沈阳体育学院“散打王”比赛可行性进行分析和论证 ,并根据沈阳体育学院学生现有学习训练的具体情况 ,制定符合沈阳体育学院实际状况的“散打王”竞赛规程 ,同时从安全角度出发 ,选用有利于学生更好地发挥技、战术水平的全国散打锦标赛规则。     

熵的微分统计法

运用累加局城熵的思路,参考经典统计和量子统计原理,建立一种通俗、直观、朴实的统计法,依据它可顺利地求得体系的熵。     

A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean

In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.     

Measuring Search Engine Quality

The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures.