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For Faculty: Citation Analysis

Tools for Citation Analysis

Scopus    Useful for locating citing references and for performing analyses by author or paper.  Calculates h-index.  For a short tutorial on How article metrics are used in SCOPUS.  More SCOPUS tutorials . . . 

 

Google Scholar    Besides searching citations based on a topic or a person, users can click "cited by" underneath each citation to find publications that have cited that paper. Google Scholar is particularly useful for locating publications that are conference proceedings, pre-prints or technical reports.  See box below on Issues with Google Scholar regarding accuracy of citation counts. To easily track your impact  set up an alert to new cited references on Google Scholar.

 

Web of Science (Science or Social Science Citation Index)    Can be used to demonstrate the impact of particular articles and authors through the "Cited Reference Search" and also allows subject searching through both "quick" and "advanced" searches.  (use at Rutgers DANA library)  How is the JCR Impact Factor calculated?

 

Harzing- Publish or Perish    Instead of using Web of Science, Publish or Perish uses Google Scholar data to calculate its various statistics. Users must download and install the software from http://www.harzing.com/pop.htm#download

 

Scholar H-index Calculator    The h-index is an index that attempts to measure the scientificresearch impact and the productivity of the published work of a scientist or scholar. This Chrome extension will automatically display some of the most known citation indices (h-index, g-index, e-index) for any author (or any arbitrary query regarding journals, keywords, etc.), on top of Google Scholar or CiteSeer.

Altmetrics (Alternative Metrics) is the creation and study of new metrics in order to better quantify the impact and spread of a scholar's work by compiling data on citations, social media mentions, and number of online views of academic articles.  The vision is summarized in:J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), Altmetrics: A manifesto, (v.1.0), 26 October 2010.  See also informative links from one of the new altmetrics vendors, Plum Analytics.

Issues with Google Scholar

Halevi, G., Moed, H., & Bar-Ilan, J. (2017). Review: Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the Literature. Journal of Informetrics, 11823-834. Link to full text @ NJIT

As Google Scholar (GS) gains more ground as free scholarly literature retrieval source it’s becoming important to understand its quality and reliability in terms of scope and content. Studies comparing GS to controlled databases such as Scopus, Web of Science (WOS) and others have been published almost since GS inception. These studies focus on its coverage, quality and ability to replace controlled databases as a source of reliable scientific literature. In addition, GS introduction of citations tracking and journal metrics have spurred a body of literature focusing on its ability to produce reliable metrics. In this article we aimed to review some studies in these areas in an effort to provide insights into GS ability to replace controlled databases in various subject areas. We reviewed 91 comparative articles from 2005 until 2016 which compared GS to various databases and especially Web of Science (WOS) and Scopus in an effort to determine whether GS can be used as a suitable source of scientific information and as a source of data for scientific evaluation. Our results show that GS has significantly expanded its coverage through the years which makes it a powerful database of scholarly literature. However, the quality of resources indexed and overall policy still remains [un]known. Caution should be exercised when relying on GS for citations and metrics mainly because it can be easily manipulated and its indexing quality still remains a challenge.

Moed, H. F., Bar-Ilan, J., & Halevi, G. (2016). A new methodology for comparing Google Scholar and Scopus. Journal of Informetrics, 10533-551.  Link to full text @NJIT

A new methodology is proposed for comparing Google Scholar (GS) with other citation indexes. It focuses on the coverage and citation impact of sources, indexing speed, and data quality, including the effect of duplicate citation counts. The method compares GS with Elsevier’s Scopus, and is applied to a limited set of articles published in 12 journals from six subject fields, so that its findings cannot be generalized to all journals or fields. The study is exploratory, and hypothesis generating rather than hypothesis-testing. It confirms findings on source coverage and citation impact obtained in earlier studies. The ratio of GS over Scopus citation varies across subject fields between 1.0 and 4.0, while Open Access journals in the sample show higher ratios than their non-OA counterparts. The linear correlation between GS and Scopus citation counts at the article level is high: Pearson’s R is in the range of 0.8–0.9. A median Scopus indexing delay of two months compared to GS is largely though not exclusively due to missing cited references in articles in press in Scopus. The effect of double citation counts in GS due to multiple citations with identical or substantially similar meta-data occurs in less than 2% of cases. Pros and cons of article-based and what is termed as concept-based citation indexes are discussed.

Delgado López-Cózar, Emilio; Robinson-García, Nicolás; Torres Salinas, Daniel (2012).  Manipulating Google Scholar Citations and Google Scholar Metrics: simple, easy and tempting.  EC3 Working Papers 6: 29 May, 2012  http://arxiv.org/abs/1212.0638

The launch of Google Scholar Citations and Google Scholar Metrics may provoke a revolution in the research evaluation field as it places within every researchers reach tools that allow bibliometric measuring. In order to alert the research community over how easily one can manipulate the data and bibliometric indicators offered by Google s products we present an experiment in which we manipulate the Google Citations profiles of a research group through the creation of false documents that cite their documents, and consequently, the journals in which they have published modifying their H index. For this purpose we created six documents authored by a faked author and we uploaded them to a researcher s personal website under the University of Granadas domain. The result of the experiment meant an increase of 774 citations in 129 papers (six citations per paper) increasing the authors and journals H index. We analyse the malicious effect this type of practices can cause to Google Scholar Citations and Google Scholar Metrics. Finally, we conclude with several deliberations over the effects these malpractices may have and the lack of control tools these tools offer.

Jacso, P. (2011). "Google Scholar duped and deduped - The aura of "robometrics"." Online Information Review 35(1): 154-160.
       
Researchers showed how easy it was to dupe Google Scholar. In one case, the researchers added invisible words to the first page of one of their conference papers (using the well-known white letter on white screen/paper technique), and modified the content and bibliography of some of their already published papers, then posted them on the web to see if Google Scholar would bite, i.e. would improve their rank position, and increase the number of citations that the targeted papers received, and the number of papers published by the authors. Google Scholar did bite. While the size of Google Scholar kept growing at an impressive rate, the intellectual growth of the Google Scholar software has been stunted.    

Jacso, P. (2009). Google Scholar's Ghost AuthorsLibrary Journal134(18), 26-27.

Bauer, K. and N. Bakkalbasi (2005). "An examination of citation counts in a new scholarly communication environment." DLibMagazine 11(9).  Web. 

Cameron, B. D. (2005). Trends in the usage of ISI bibliometric data: Uses, abuses, and implicationsportal: Libraries and the Academy5(1), 105-125.  

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