科技资源管理研究热点领域的定量分析

利用信息可视化方法对Web of Science(SCI, SSCI,A&HCI) 中1986—2009 年收录的科技资源管理论文的引文数据进行文献共被引分析,以图谱的方式展示科技资源管理研究关键节点所代表的科技资源管理研究专家及其著作对科技资源管理发展所起的重要作用,并探讨了国际科技资源管理研究的热点和前沿领域。     

学术评价的新思路：h 指数的改进、应用与发展

在参考国内外大量论文的基础上,文章系统梳理了h 指数的主要研究路径并介绍了相关进展,认为h 指数 对学术评价的意义更可能在于为全面反思学术评价体系的根本问题提供了契机。     

化工专业中文图书的零借阅信息分析

本文利用文献计量学方法,针对天津大学图书馆化工专业中文图书的零借阅状况进行分析,从图书分类、书库管理和文献自身特性等方面总结了导致文献零借阅的原因,并针对图书的零借阅情况提出了具体的应对措施,为学科化的资源评价提供依据。     

The ARL Microform Project for the Bibliographic Control of Microforms: Report on a Survey of Microform Sets in U.S. and Canadian Libraries

No abstract available for this article.     

Preparing for New Degree Plans: Finding the Essential Journal Titles in an Interdisciplinary World

The creation of new degrees at an academic institution often requires additional library resources to provide adequate support. Identifying the needed library materials can be especially difficult for interdisciplinary programs. The process of serials analysis undertaken for new interdisciplinary degrees at the University of Texas at Dallas was explained in this session. The presenter also discussed results and the need for ongoing analysis of serials collections used for academic programs.     

医学期刊中常见的几种统计学分析方法错用误用案例辨析

针对医学论文中常用统计学分析方法使用不规范的问题,查阅了医学统计学专业书籍和相关医学期刊的典型文章,从医学专业角度举例分析医学论文中常用统计学分析方法使用不规范的问题,包括误用logistic回归分析处理生存分析和未进行统计学分析直接得出结论,并针对该问题提出正确的统计学处理方法建议。     

Research characteristics on health law in China: Social network analysis

To determine the nature and extent of the emerging field of “health law” scholarship in China, we retrieved 2956 publications about various aspects of health law published up through 2014 in 268 journals from the “Chinese Social Science Citation Index” and from the list of Chinese “Core Journals” maintained by the Peking University Library. By bibliometric analysis, we observed some interesting results which were different from our expectations. The collaboration network among Chinese health law (CHL) authors was relatively small, loose, unstable and early in its development. Kan Tian and Lixin Yang were leading authors, Renmin University of China was the primary institution, and Beijing was the predominant region studying Chinese health law (CHL). Kan Tian ranked 1st in the collaboration network and Lixin Yang published the most number of articles on CHL. Comprehensive universities made up the majority of institutions studying CHL, while medical colleges did not focus on health law research, possibly due to medical schools focusing mainly on clinical education and not attaching as much importance to social and humanistic aspects of medicine. Food safety, health administration, and the general principles of civil law were the main areas of focus in CHL research, which is basically in line with what we expected.     

Scientific community detection via bipartite scholar/journal graph co-clustering

This paper stems from the observation that researchers in different fields tend to publish in different journals. Such a relationship between researchers and journals is quantitatively exploited to identify scientific community clusters, by casting the community detection problem into a co-clustering problem on bipartite graphs. Such an approach has the potential of leading not only to the fine- grained detection of scholar communities based on the similarity of their research activity, but also to the clustering of scientific journals based on which are the most representative of each community. The proposed methodology is purely data-driven and completely unsupervised, and does not rely on any semantics (e.g. keywords or a-priori subjective categories). Moreover, unlike “flat” data structures (e.g. collaboration graphs or citation graphs) our bipartite graph approach blends in a joint structure both the researcher's attitude and interests (i.e., freedom to select the venue where to publish) as well as the community's recognition (i.e., acceptance of the publication on a target journal); as such may perhaps inspire further scientometric evaluation strategies. Our proposed approach is applied to the Italian research system, for two broad areas (ICT and Microbiology&Genetics), and reveals some questionable aspects and community overlaps in the current Italian scientific sectors classification.     

Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations?

Dissertations can be the single most important scholarly outputs of junior researchers. Whilst sets of journal articles are often evaluated with the help of citation counts from the Web of Science or Scopus, these do not index dissertations and so their impact is hard to assess. In response, this article introduces a new multistage method to extract Google Scholar citation counts for large collections of dissertations from repositories indexed by Google. The method was used to extract Google Scholar citation counts for 77,884 American doctoral dissertations from 2013 to 2017 via ProQuest, with a precision of over 95%. Some ProQuest dissertations that were dual indexed with other repositories could not be retrieved with ProQuest-specific searches but could be found with Google Scholar searches of the other repositories. The Google Scholar citation counts were then compared with Mendeley reader counts, a known source of scholarly-like impact data. A fifth of the dissertations had at least one citation recorded in Google Scholar and slightly fewer had at least one Mendeley reader. Based on numerical comparisons, the Mendeley reader counts seem to be more useful for impact assessment purposes for dissertations that are less than two years old, whilst Google Scholar citations are more useful for older dissertations, especially in social sciences, arts and humanities. Google Scholar citation counts may reflect a more scholarly type of impact than that of Mendeley reader counts because dissertations attract a substantial minority of their citations from other dissertations. In summary, the new method now makes it possible for research funders, institutions and others to systematically evaluate the impact of dissertations, although additional Google Scholar queries for other online repositories are needed to ensure comprehensive coverage.     

Ranking scientific articles based on bibliometric networks with a weighting scheme

As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.