数据管理能力成熟度模型比较研究与启示

[目的/意义] 对比分析数据管理能力成熟度模型，为图书馆选择与应用数据管理能力成熟度模型提供参考方案。[方法/过程] 通过模型文本的研究，介绍各模型的结构。采用比较分析法从评价维度（功能域）的设置、模型的组织体系、评价标准与规则、可操作性、公开度等5个方面对7个数据管理能力成熟度模型进行评析。[结果/结论] 每个模型具有其特色功能。CMMI、DMM、DCAM和中国DCMM的评价维度较为全面，中国DCMM即中国-数据中心服务能力成熟度模型具有较强的操作性，且其定量与定性相结合的评价方法值得借鉴。研究数据管理的能力成熟度模型（雪城大学秦健教授团队）和研究数据管理能力成熟度模型（澳大利亚ANDS）考虑图书馆数据管理的实际情况，可作为首选模型。基于各模型的对比与评析结果，形成3种适应不同情境的图书馆选择与应用数据管理能力成熟度模型的方案。     

灰色预测法在情报研究中的应用研究综述

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。     

Audience management,online turbulence and lurking in social networking services: A transactional process of stress perspective

In social networking services (SNSs), users’ unclear understanding of the large and invisible audience increases the chances of online turbulence, which is a key source of SNS-induced stress. This growing phenomenon has gained increasing attention in academia and industry due to the undesirable consequences for users and SNS platforms. In this study, we draw from the transactional model of stress to examine how audience management strategies impact online turbulence and lead to neglected unintended audience concern and lurking. We also investigate the role of self-monitoring as a stress inhibitor. We test our model with data collected from 301 SNS users. The results show that the four types of audience management strategies have different effects on online turbulence, which significantly impacts neglected unintended audience concern especially when users have high self-monitoring skills. We believe that this work contributes, both from scientific and practical standpoints, to the understanding of the interventions and stressful responses of online turbulence in SNSs.     

科学数据共享研究现状与展望

[目的/意义] 分析科学数据共享的研究现状及其发展趋势，为促进国内相关理论研究与实践进展提供参考。[方法/过程] 通过对国内外文献进行梳理和归纳，提炼出与科学数据共享研究相关的三大类主题：科学数据共享发展需求、科学数据管理共享模式与机制和科学数据服务共享度与服务效果，并构建科学数据共享管理分析框架，该框架体现上述三类主题之间的因果关联，强调科学数据共享管理与服务的合理面、重要度、关键性。[结果/结论] 国内外现有研究尚存不足，未来研究在加强实证研究、着眼管理机制完善的同时，还需注重共享服务模式的创新。     

Tracking user-role evolution via topic modeling in community question answering

Community question answering (CQA) services that enable users to ask and answer questions are popular on the internet. Each user can simultaneously play the roles of asker and answerer. Some work has aimed to model the roles of users for potential applications in CQA. However, the dynamic characteristics of user roles have not been addressed. User roles vary over time. This paper explores user representation by tracking user-role evolution, which could enable several potential applications in CQA, such as question recommendation. We believe this paper is the first to track user-role evolution and investigate its influence on the performance of question recommendation in CQA. Moreover, we propose a time-aware role model (TRM) to effectively track user-role evolution. With different independence assumptions, two variants of TRM are developed. Finally, we present the TRM-based approach to question recommendation, which provides a mechanism to naturally integrate the user-role evolution with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that (1) the TRM is valid for tracking user-role evolution, and (2) compared with baselines utilizing role based methods, our TRM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach could enable several potential applications in CQA.     

学习团队协作信息搜索的共享心智模型研究

[目的/意义] 对协作信息搜索进行深入研究，为专业化协作信息搜索系统平台建设优化等提供启发和借鉴。[方法/过程] 聚焦于学习团队的信息搜索行为，以高校学生为研究样本，以共享心智模型为切入点，采用扎根理论研究方法，探讨协作信息搜索中团队共享心智模型建构过程。[结果/结论] 识别出四大核心环节——个体感知与探索、团队任务解析、团队信息收集和团队信息整合，并理清各环节的认知活动与共享心智模型内容要素。     

图书流通数据分析的H类指数遴选及应用——以西北农林科技大学图书馆为例

[目的/意义] 优化藏书采访工作和流通管理，实现对藏书采访、藏书总量和结构等状况适时监测、评估、调控等的标准化管理。[方法/过程] 通过对H类指数中的H指数、A指数和G指数相关性、变异性和适应性分析，遴选适用于图书流通数据分析的A指数，并根据帕累托定律采用A指数对西北农林科技大学图书馆2014-2018年的前20%的图书流通数据分别进行统计分析。[结果/结论] 研究结果表明：平均A指数对中图法一级类目与读者性别、读者专业、读者年级以及图书出版社、图书出版年、单位等数据统计分析具有较理想的表现，能够更全面、准确地遴选核心读者群、核心出版者和核心出版年；同时发现，布拉德福定律不仅能够应用于期刊管理，而且能够应用于藏书管理。本文研究能够为探索H类指数分析高校图书馆藏书流通数据提供参考依据。     

政府数据开放中的公众参与模式:理论构建与案例实证

[目的/意义] 探讨政府数据开放中公众参与的理论模式，旨在指导政府部门有效实现公众参与数据开放的政策目标，以及推进政府数据开放中公众参与议题的研究进展。[方法/过程] 采用文献分析法、逻辑分析法和案例分析法，同时以公众参与阶梯理论作为分析工具，构建并实证政府数据开放中的公众参与模式。[结果/结论] 政府数据开放中的公众参与可从理论层面构建为告知型参与、咨询型参与、合作型参与和授权型参与4种模式，其核心涵义分别是政府向公众传达信息、政府咨询公众意见、政府与公众平等合作、政府与公众共同治理。这4种政府数据开放公众参与理论模式一方面在政府意图、主导力量、信息流动、公众信息知晓度和公众自治程度方面存在明显的特征差异，另一方面从国内外政府数据开放公众参与实践案例中获得良好的经验支持。     

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.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.