Preserving the integrity of the scientific record: data citation and linking

The production of research data is increasing rapidly, meaning that it is now rarely possible to publish the data that underpins a piece of research within the article describing that research. Yet, if the conclusions drawn from the research are to stand up to scrutiny, then the data must be made available, and a permanent link made between the data and the publication. Data citation is promoted as an easily understood way of making this link, while at the same time providing the dataset creators with the attribution and credit they deserve for making their data available for verification checks and reproducibility tests. This paper discusses data citation principles and gives examples of data citation and other forms of data‐publication linking in practice.     

交叉科学成果影响力研究:使用数据与引用数据视角

随着交叉科学研究在促进社会发展重大综合性问题解决方面的优势逐渐凸显,越来越多的国家对交叉科学研究给予高度的重视与支持,如何对交叉科学的研究成果进行有效的鉴定与评估也成为科技管理部门亟待解决的重要问题。本文在传统引文指标的基础上,引入PLoS官方平台的使用数据(html浏览、xml下载及pdf下载)作为补充,综合评价交叉科学研究成果的影响力情况。以2009-2013年发表在开源期刊PLoS Computational Biology的研究论文为例,研究结果表明:(1)学科交叉水平与论文影响力之间存在一定的正向关系,学科交叉水平高的论文,对应的使用数据与引用数据要明显高于学科交叉水平较低的论文;(2)论文的使用数据与引用数据相互促进,在引用数据达到峰值时,对应的使用数据也会随之出现一定的回升;(3)学科交叉程度对使用数据与引用数据之间的相关关系也有较为显著的影响。本文从使用数据和引用数据两个维度探索交叉科学研究成果的影响力,为当前交叉科学研究成果影响力的评价提供了新的借鉴与参考。     

科研数据出版模式、流程及引用策略研究

[目的/意义]在总结国内外科研数据出版及数据引用规范发布现状的基础上,一方面梳理现有数据出版模式及优劣势,分析数据出版的主要流程;另一方面结合经典案例,提出不同出版模式下相应的推荐引用策略.[方法/过程]通过分析数据出版领域的相关研究报告及论文,对比各数据出版模式,对各出版模式的优劣势、数据出版流程及引用策略进行综合分析.[结果/结论]现有出版模式主要有4种,分别是数据独立出版、数据论文出版、合作出版以及期刊自行出版,每种出版模式下引用策略不同.数据出版的一般流程需要经历5个阶段,未来的研究可以从克服各数据出版模式的局限以及数据出版体系的构建入手.     

国外科学数据引用研究进展

从引用意识、引用对象以及长期保存3个方面总结目前数据引用所面临的关键问题。从数据引用规范的发展、文献管理软件的调研、数据引用利益相关者的责任与义务3个方面概括目前国外数据引用的研究进展,发现数据引用规范格式不兼容,文献管理软件缺乏对数据引用的支持,并且数据引用利益相关者责任与义务不明确。最后,针对关键问题提出对策建议以及今后研究的方向。     

Research software citation in the Data Citation Index: Current practices and implications for research software sharing and reuse

The aim of this study is to explore the phenomenon of research software citation and, in particular, to draw attention to the increasing importance of this form of citation in scholarly communication. This research sheds light on the current status of formal software citation that is captured by citation databases. Data for the study were gathered from more than 67,000 research software records available in public repositories indexed by Clarivate Analytics’ Data Citation Index (DCI). The metadata characteristics of the indexed records and citation data were then analyzed. Research software was rarely cited in the DCI, suggesting that the documented reuse of research software rarely occurs or is not well documented. Institutional repositories attracted few citations and had low rate of citation. It proved impossible, however, using the available data to isolate specific identifiers that can promote formal software citation. The findings presented here offer insights into research software citation that will be of interest to funding agencies, publishers, researchers, and research organizations.     

一个基于XML的引文索引模型设计及其实现

本文概述了引文索引在科学研究、信息检索和技术开发等方面的独特利用价值 ,结合当今Web上数据表示和数据交换的新标准XML的优势 ,提出一个基于XML标记语言的引文索引模型 ,在此模型的基础上提出引文索引构建、检索和引文分析的实现方法     

Dimensions: A competitor to Scopus and the Web of Science?

Dimensions is a partly free scholarly database launched by Digital Science in January 2018. Dimensions includes journal articles and citation counts, making it a potential new source of impact data. This article explores the value of Dimensions from an impact assessment perspective with an examination of Food Science research 2008–2018 and a random sample of 10,000 Scopus articles from 2012. The results include high correlations between citation counts from Scopus and Dimensions (0.96 by narrow field in 2012) as well as similar average counts. Almost all Scopus articles with DOIs were found in Dimensions (97% in 2012). Thus, the scholarly database component of Dimensions seems to be a plausible alternative to Scopus and the Web of Science for general citation analyses and for citation data in support of some types of research evaluations.     

Data credit distribution: A new method to estimate databases impact

It is widely accepted that data is fundamental for research and should therefore be cited as textual scientific publications. However, issues like data citation, handling and counting the credit generated by such citations, remain open research questions.Data credit is a new measure of value built on top of data citation, which enables us to annotate data with a value, representing its importance. Data credit can be considered as a new tool that, together with traditional citations, helps to recognize the value of data and its creators in a world that is ever more depending on data.In this paper we define data credit distribution (DCD) as a process by which credit generated by citations is given to the single elements of a database. We focus on a scenario where a paper cites data from a database obtained by issuing a query. The citation generates credit which is then divided among the database entities responsible for generating the query output. One key aspect of our work is to credit not only the explicitly cited entities, but even those that contribute to their existence, but which are not accounted in the query output.We propose a data credit distribution strategy (CDS) based on data provenance and implement a system that uses the information provided by data citations to distribute the credit in a relational database accordingly.As use case and for evaluation purposes, we adopt the IUPHAR/BPS Guide to Pharmacology (GtoPdb), a curated relational database. We show how credit can be used to highlight areas of the database that are frequently used. Moreover, we also underline how credit rewards data and authors based on their research impact, and not merely on the number of citations. This can lead to designing new bibliometrics for data citations.     

基于K-means聚类的期刊操控引用行为特征指标研究

认为部分期刊为了追求被引排名,开始操纵期刊的引用。这样的行为影响了期刊引文数据的质量以及利用引文开展的科学研究的可靠性。提出引文年代集中度、自引率、被引密度和引用密度4个引文指标用于识别期刊是否存在操纵引用的行为,并采用数据挖掘中的K-means聚类方法检验指标的有效性,实验结果显示这些指标都达到了较好的识别效果。     

A sensitivity analysis of researchers’ productivity rankings to the time of citation observation

In this work we investigate the sensitivity of individual researchers’ productivity rankings to the time of citation observation. The analysis is based on observation of research products for the 2001–2003 triennium for all research staff of Italian universities in the hard sciences, with the year of citation observation varying from 2004 to 2008. The 2008 rankings list is assumed the most accurate, as citations have had the longest time to accumulate and thus represent the best possible proxy of impact. By comparing the rankings lists from each year against the 2008 benchmark we provide policy-makers and research organization managers a measure of trade-off between timeliness of evaluation execution and accuracy of performance rankings. The results show that with variation in the evaluation citation window there are variable rates of inaccuracy across the disciplines of researchers. The inaccuracy results negligible for Physics, Biology and Medicine.     

Using the Web for research evaluation: The Integrated Online Impact indicator

Previous research has shown that citation data from different types of Web sources can potentially be used for research evaluation. Here we introduce a new combined Integrated Online Impact (IOI) indicator. For a case study, we selected research articles published in the Journal of the American Society for Information Science & Technology (JASIST) and Scientometrics in 2003. We compared the citation counts from Web of Science (WoS) and Scopus with five online sources of citation data including Google Scholar, Google Books, Google Blogs, PowerPoint presentations and course reading lists. The mean and median IOI was nearly twice as high as both WoS and Scopus, confirming that online citations are sufficiently numerous to be useful for the impact assessment of research. We also found significant correlations between conventional and online impact indicators, confirming that both assess something similar in scholarly communication. Further analysis showed that the overall percentage for unique Google Scholar citations outside the WoS were 73% and 60% for the articles published in JASIST and Scientometrics, respectively. An important conclusion is that in subject areas where wider types of intellectual impact indicators outside the WoS and Scopus databases are needed for research evaluation, IOI can be used to help monitor research performance.     

我国图书情报领域期刊论文的科学数据引用特征研究

[目的/意义] 探索期刊论文科学数据引用特征与规律不仅有助于描述学科领域对科学数据的利用情况，还能够揭示学术成果表达中的数据引用模式。[方法/过程] 以我国图书情报领域6种期刊2017年与2018年第一期刊载论文为样本，结合国家标准《信息技术科学数据引用》的引用元素，采用内容分析法从9个维度对样本论文的科学数据引用行为进行数据编码，应用统计学方法描述图书情报领域期刊论文科学数据引用特征并探索不同维度特征间的关联关系。[结果/结论] 图书情报领域期刊论文广泛引用来自国内外的统计整理类科学数据，对期刊论文中个人研究科学数据的引用量较大；科学数据引用标注方式与科学数据类型存在一定对应关系，但多样化的标注方式缺乏统一性；二手引用现象较为突出，二手引用程度与科学数据创建者类型相关。     

Quantitative evaluation of alternative field normalization procedures

Wide differences in publication and citation practices make impossible the direct comparison of raw citation counts across scientific disciplines. Recent research has studied new and traditional normalization procedures aimed at suppressing as much as possible these disproportions in citation numbers among scientific domains. Using the recently introduced IDCP (Inequality due to Differences in Citation Practices) method, this paper rigorously tests the performance of six cited-side normalization procedures based on the Thomson Reuters classification system consisting of 172 sub-fields. We use six yearly datasets from 1980 to 2004, with widely varying citation windows from the publication year to May 2011. The main findings are the following three. Firstly, as observed in previous research, within each year the shapes of sub-field citation distributions are strikingly similar. This paves the way for several normalization procedures to perform reasonably well in reducing the effect on citation inequality of differences in citation practices. Secondly, independently of the year of publication and the length of the citation window, the effect of such differences represents about 13% of total citation inequality. Thirdly, a recently introduced two-parameter normalization scheme outperforms the other normalization procedures over the entire period, reducing citation disproportions to a level very close to the minimum achievable given the data and the classification system. However, the traditional procedure of using sub-field mean citations as normalization factors yields also good results.     

专利引用网络主路径方法研究述评与展望

[目的/意义] 对现有专利引用网络主路径方法研究内容进行总结梳理,为今后应用该方法解决技术演化进程中的关键性专利技术识别和主流线索提取提供理论支撑。[方法/过程] 系统梳理相关研究成果,从算法研究、应用研究、方法优化扩展研究三个方面总结现有研究内容与特点,分析当前研究的局限性,探讨未来研究发展方向。[结果/结论] 当前研究的主要局限性在于：对路径发展驱动力的多元性与系统性揭示不够、忽视不同引证关系对路径演化的影响差异性、对演化的动态性关注不足、多主路径方法本质上仍属单目标搜索。未来研究将关注以下几个方向：对算法思想进行实质性与创新性拓展、更强调动态性与未来预测性、优化计算效率以增强适用性与实用性、发挥专利引用主路径在产业化扩散研究中的独特优势。     

科学数据规范引用关键问题探析

[目的/意义]明确实现规范引用需要解决的基本问题、重点和难点问题,并分析相关利益群体在这一过程中需要承担的责任和未来努力的方向,以引导其更好地厘清思路,推动实践。[方法/过程]借鉴国内外相关研究及实践经验,从科学数据规范引用价值认同入手,从如何引用、引用什么和何时引用三方面阐述科学数据规范引用的基本问题与难点。以此为基础,分析数据规范引用给相关利益群体带来的机遇和提出的要求。[结果/结论]理想的科学数据引用规范应该明确回答如何引用、引用什么和何时引用3个基本问题。如何引用,就是需要制定面向科学数据的元数据规范、建立能够承认作者贡献的评价体系并提供支持科学数据引用的参考文献管理工具;引用什么,就是需要明确引用数据的版本、粒度、数据验证方式以及引用对象等问题;何时引用,就是需要厘清使用数据的情景。相关利益群体应该看到科学数据规范引用给各自带来的机遇以及提出的要求,不断调整以推动数据引用的实践。     

Boost Movie Ticket Sales by Location-Based Advertising: A Bayesian VAR Approach

Compared to previous research studying movie advertising focusing on traditional advertising, this study introduce location-based ads (LBA), an important new media advertising, to raise limited coverage of traditional movie advertising. This study explores the role of LBA in generating ticket sales dynamically and compares it with that of pop-up window ads (PWA). Using daily aggregate cinema sales data on about three million consumers, developed time-series models reveal that (a) LBA can affect ticket sales in short and long terms, and its sales impact can last for 9 days across audience segments; (b) compared to low involvement audience segments, sales impact of LBA is 15 times larger for high involvement audience segment in long term; (c) ticket sales impact of LBA are cumulatively 3 times and 47 times stronger than those of PWA in low involvement audience segment and high involvement audience segment, respectively.     

拨开引文分析迷雾，破解科研评价困惑——评亨克·莫德的《科研评价中的引文分析》

简要介绍荷兰著名科学计量学家亨克·莫德的专著《科研评价中的引文分析》。该书主要分为8个主要单元,探讨基础科学研究部门和科技期刊评价,ISI引文索引,社会科学与人文科学评价,准确性,引文分析与同行评议等问题。书中的结论为我们有效利用引文分析方法提供理论指导和实践借鉴。     

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.     

中文文献引文情感语料库构建

基于内容的引文情感分析克服了传统基于引用频次的引用同一化问题,是引文内容分析领域一个重要的研究热点。然而引文情感分析依赖于带标注的数据集,目前大规模高质量的引文情感语料资源匮乏,严重制约了该领域的研究。因此,本文在分析引文情感表达方式的基础上提出了一套适用于引文情感表示的标注体系,并详细阐述了语料库建设的技术和方法。采用人机结合的标注策略,借助完善的引文标注系统,构建了规模较大的中文文献的引文情感语料库。统计结果显示,在中文信息处理和科技管理领域情感褒义和贬义总的引用的占比分别为22%和6%,引文情感标注kappa值达到0.852,表明该语料库能够客观地反映作者的情感倾向性,可为论文评价、引文网络分析和情感分析等相关领域的研究提供数据支撑。     

An Introduction to Altmetrics

Altmetrics, or alternative metrics, are forging a new way to capture the impact of not only articles, but also scholarly or research “products” by tracking them when they are mentioned online, such as in blogs or social media platforms. While altmetrics have a lot of potential, there are also some limitations preventing their full acceptance alongside traditional citation metrics. This column will explain the basics of altmetrics and altmetric tools, discuss some of the ways they can be used in libraries, and explore some possible concerns with this new metric. A list of resources for additional information is also included.