Web of Science Core Collection Journal Evaluation Criteria the Journal Evaluation Process for the Web of Science Core Collection
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Transitive Reduction of Citation Networks Arxiv:1310.8224V2
Transitive reduction of citation networks James R. Clough, Jamie Gollings, Tamar V. Loach, Tim S. Evans Complexity and Networks group, Imperial College London, South Kensington campus, London, SW7 2AZ, United Kingdom March 28, 2014 Abstract In many complex networks the vertices are ordered in time, and edges represent causal connections. We propose methods of analysing such directed acyclic graphs taking into account the constraints of causality and highlighting the causal struc- ture. We illustrate our approach using citation networks formed from academic papers, patents, and US Supreme Court verdicts. We show how transitive reduc- tion reveals fundamental differences in the citation practices of different areas, how it highlights particularly interesting work, and how it can correct for the effect that the age of a document has on its citation count. Finally, we transitively reduce null models of citation networks with similar degree distributions and show the difference in degree distributions after transitive reduction to illustrate the lack of causal structure in such models. Keywords: directed acyclic graph, academic paper citations, patent citations, US Supreme Court citations 1 Introduction Citation networks are complex networks that possess a causal structure. The vertices are documents ordered in time by their date of publication, and the citations from one document to another are represented by directed edges. However unlike other directed networks, the edges of a citation network are also constrained by causality, the edges must always point backwards in time. Academic papers, patent documents, and court judgements all form natural citation networks. Citation networks are examples of directed acyclic graphs, which appear in many other contexts: from scheduling problems [1] to theories of the structure of space-time [2]. -
Citation Analysis for the Modern Instructor: an Integrated Review of Emerging Research
CITATION ANALYSIS FOR THE MODERN INSTRUCTOR: AN INTEGRATED REVIEW OF EMERGING RESEARCH Chris Piotrowski University of West Florida USA Abstract While online instructors may be versed in conducting e-Research (Hung, 2012; Thelwall, 2009), today’s faculty are probably less familiarized with the rapidly advancing fields of bibliometrics and informetrics. One key feature of research in these areas is Citation Analysis, a rather intricate operational feature available in modern indexes such as Web of Science, Scopus, Google Scholar, and PsycINFO. This paper reviews the recent extant research on bibliometrics within the context of citation analysis. Particular focus is on empirical studies, review essays, and critical commentaries on citation-based metrics across interdisciplinary academic areas. Research that relates to the interface between citation analysis and applications in higher education is discussed. Some of the attributes and limitations of citation operations of contemporary databases that offer citation searching or cited reference data are presented. This review concludes that: a) citation-based results can vary largely and contingent on academic discipline or specialty area, b) databases, that offer citation options, rely on idiosyncratic methods, coverage, and transparency of functions, c) despite initial concerns, research from open access journals is being cited in traditional periodicals, and d) the field of bibliometrics is rather perplex with regard to functionality and research is advancing at an exponential pace. Based on these findings, online instructors would be well served to stay abreast of developments in the field. Keywords: Bibliometrics, informetrics, citation analysis, information technology, Open resource and electronic journals INTRODUCTION In an ever increasing manner, the educational field is irreparably linked to advances in information technology (Plomp, 2013). -
Citation Analysis As a Tool in Journal Evaluation Journals Can Be Ranked by Frequency and Impact of Citations for Science Policy Studies
Citation Analysis as a Tool in Journal Evaluation Journals can be ranked by frequency and impact of citations for science policy studies. Eugene Garfield [NOTE: Tbe article reptintedbere was referenced in the eoay vbi.b begins m @g. 409 is Volume I, IIS ia. #dverte#t omission W6Sdiscovered too Me to iwctnde it at it~ proper Iocatioa, immediately follom”ag tbe essay ) As a communications system, the net- quinquennially, but the data base from work of journals that play a paramount which the volumes are compiled is role in the exchange of scientific and maintained on magnetic tape and is up- technical information is little under- dated weekly. At the end of 1971, this stood. Periodically since 1927, when data base contained more than 27 mi[- Gross and Gross published their study tion references to about 10 million dif- (1) of references in 1 year’s issues of ferent published items. These references the Journal of the American Chemical appeared over the past decade in the Socie/y, pieces of the network have footnotes and bibliographies of more been illuminated by the work of Brad- than 2 million journal articles, commu- ford (2), Allen (3), Gross and nications, letters, and so on. The data Woodford (4), Hooker (5), Henkle base is, thus, not only multidisciplinary, (6), Fussier (7), Brown (8), and it covers a substantial period of time others (9). Nevertheless, there is still no and, being in machine-readable form, is map of the journal network as a whok. amenable to extensive manipulation by To date, studies of the network and of computer. -
How Can Citation Impact in Bibliometrics Be Normalized?
RESEARCH ARTICLE How can citation impact in bibliometrics be normalized? A new approach combining citing-side normalization and citation percentiles an open access journal Lutz Bornmann Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Hofgartenstr. 8, 80539 Munich, Germany Downloaded from http://direct.mit.edu/qss/article-pdf/1/4/1553/1871000/qss_a_00089.pdf by guest on 01 October 2021 Keywords: bibliometrics, citation analysis, citation percentiles, citing-side normalization Citation: Bornmann, L. (2020). How can citation impact in bibliometrics be normalized? A new approach ABSTRACT combining citing-side normalization and citation percentiles. Quantitative Since the 1980s, many different methods have been proposed to field-normalize citations. In this Science Studies, 1(4), 1553–1569. https://doi.org/10.1162/qss_a_00089 study, an approach is introduced that combines two previously introduced methods: citing-side DOI: normalization and citation percentiles. The advantage of combining two methods is that their https://doi.org/10.1162/qss_a_00089 advantages can be integrated in one solution. Based on citing-side normalization, each citation Received: 8 May 2020 is field weighted and, therefore, contextualized in its field. The most important advantage of Accepted: 30 July 2020 citing-side normalization is that it is not necessary to work with a specific field categorization scheme for the normalization procedure. The disadvantages of citing-side normalization—the Corresponding Author: Lutz Bornmann calculation is complex and the numbers are elusive—can be compensated for by calculating [email protected] percentiles based on weighted citations that result from citing-side normalization. On the one Handling Editor: hand, percentiles are easy to understand: They are the percentage of papers published in the Ludo Waltman same year with a lower citation impact. -
Google Scholar, Web of Science, and Scopus
Journal of Informetrics, vol. 12, no. 4, pp. 1160-1177, 2018. https://doi.org/10.1016/J.JOI.2018.09.002 Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories Alberto Martín-Martín1 , Enrique Orduna-Malea2 , Mike 3 1 Thelwall , Emilio Delgado López-Cózar Version 1.6 March 12, 2019 Abstract Despite citation counts from Google Scholar (GS), Web of Science (WoS), and Scopus being widely consulted by researchers and sometimes used in research evaluations, there is no recent or systematic evidence about the differences between them. In response, this paper investigates 2,448,055 citations to 2,299 English-language highly-cited documents from 252 GS subject categories published in 2006, comparing GS, the WoS Core Collection, and Scopus. GS consistently found the largest percentage of citations across all areas (93%-96%), far ahead of Scopus (35%-77%) and WoS (27%-73%). GS found nearly all the WoS (95%) and Scopus (92%) citations. Most citations found only by GS were from non-journal sources (48%-65%), including theses, books, conference papers, and unpublished materials. Many were non-English (19%- 38%), and they tended to be much less cited than citing sources that were also in Scopus or WoS. Despite the many unique GS citing sources, Spearman correlations between citation counts in GS and WoS or Scopus are high (0.78-0.99). They are lower in the Humanities, and lower between GS and WoS than between GS and Scopus. The results suggest that in all areas GS citation data is essentially a superset of WoS and Scopus, with substantial extra coverage. -
A New Index for the Citation Curve of Researchers
A New Index for the Citation Curve of Researchers Claes Wohlin School of Engineering Blekinge Institute of Technology Box 520 SE37225 Ronneby Sweden Email: [email protected] Abstract Internet has made it possible to move towards researcher and article impact instead of solely focusing on journal impact. To support citation measurement, several indexes have been proposed, including the h‐index. The h‐index provides a point estimate. To address this, a new index is proposed that takes the citation curve of a researcher into account. This article introduces the index, illustrates its use and compares it to rankings based on the h‐index as well as rankings based on publications. It is concluded that the new index provides an added value, since it balances citations and publications through the citation curve. Keywords: impact factor, citation analysis, h‐index, g‐index, Hirsch, w‐index 1. Introduction The impact of research is essential. In attempts to quantify impact, different measures have been introduced. This includes the impact factor for journals introduced 45 years ago by GARFIELD and SHER [1963] and GARFIELD [2006]. However, it may be argued that the impact of a journal becomes less important as research articles becomes available on‐line and could be easily located even if they are not published in a high impact journal. In general, high impact articles are more important than the journal itself. Historically, highly cited articles have been published in high impact journals, but this pattern may change as for example conference articles are as easily accessible as journal articles. -
Google Scholar: the Democratization of Citation Analysis?
Google Scholar: the democratization of citation analysis? Anne-Wil Harzing Ron van der Wal Version November 2007 Accepted for Ethics in Science and Environmental Politics Copyright © 2007 Anne-Wil Harzing and Ron van der Wal. All rights reserved. Dr. Anne-Wil Harzing Email: [email protected] University of Melbourne Web: www.harzing.com Department of Management Faculty of Economics & Commerce Parkville Campus Melbourne, VIC 3010 Australia Google Scholar: the democratization of citation analysis? Anne-Wil Harzing* 1, Ron van der Wal2 1 Department of Management, University of Melbourne, Parkville Campus, Parkville, Victoria 3010, Australia * Email: [email protected] 2 Tarma Software Research, GPO Box 4063, Melbourne, Victoria 3001 Australia Running head: citation analysis with Google Scholar Key words: Google Scholar, citation analysis, publish or perish, h-index, g-index, journal impact factor Abstract Traditionally, the most commonly used source of bibliometric data is Thomson ISI Web of Knowledge, in particular the (Social) Science Citation Index and the Journal Citation Reports (JCR), which provide the yearly Journal Impact Factors (JIF). This paper presents an alternative source of data (Google Scholar, GS) as well as three alternatives to the JIF to assess journal impact (the h-index, g-index and the number of citations per paper). Because of its broader range of data sources, the use of GS generally results in more comprehensive citation coverage in the area of Management and International Business. The use of GS particularly benefits academics publishing in sources that are not (well) covered in ISI. Among these are: books, conference papers, non-US journals, and in general journals in the field of Strategy and International Business. -
Google Scholar, Sci-Hub and Libgen: Could They Be Our New Partners?
Purdue University Purdue e-Pubs Proceedings of the IATUL Conferences 2017 IATUL Proceedings Google Scholar, Sci-Hub and LibGen: Could they be our New Partners? Louis Houle McGill University, [email protected] Louis Houle, "Google Scholar, Sci-Hub and LibGen: Could they be our New Partners?." Proceedings of the IATUL Conferences. Paper 3. https://docs.lib.purdue.edu/iatul/2017/partnership/3 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. GOOGLE SCHOLAR, SCI-HUB AND LIBGEN: COULD THEY BE OUR NEW PARTNERS? Louis Houle McGill University Canada [email protected] Abstract Since its debut I November 2004, librarians have raised several criticisms at Google Scholar (GS) such as its inconsistency of coverage and its currency and scope of coverage. It may have been true in the early years of Google Scholar but is this still through twelve years after? Is this sufficient to ignore it totally either in our information literacy programs or evaluate its value against the values of subscription-based abstracts and indexes? In this era of severe budget constraints that libraries are facing, can we imagine of substituting most or all of our subject databases with the free access of Google Scholar for discoverability? How much overlap between our databases and Google Scholar? How reliable is Google Scholar? How stable is its content over time? Open Access is getting to be the predominant form of getting access to peer reviewed articles. Many new non-traditional tools (institutional repositories, social media and peer to peer sites) are available out there to retrieve the full-text of peer reviewed articles. -
Exploratory Analysis of Publons Metrics and Their Relationship with Bibliometric and Altmetric Impact
Exploratory analysis of Publons metrics and their relationship with bibliometric and altmetric impact José Luis Ortega Institute for Advanced Social Studies (IESA-CSIC), Córdoba, Spain, [email protected] Abstract Purpose: This study aims to analyse the metrics provided by Publons about the scoring of publications and their relationship with impact measurements (bibliometric and altmetric indicators). Design/methodology/approach: In January 2018, 45,819 research articles were extracted from Publons, including all their metrics (scores, number of pre and post reviews, reviewers, etc.). Using the DOI identifier, other metrics from altmetric providers were gathered to compare the scores of those publications in Publons with their bibliometric and altmetric impact in PlumX, Altmetric.com and Crossref Event Data (CED). Findings: The results show that (1) there are important biases in the coverage of Publons according to disciplines and publishers; (2) metrics from Publons present several problems as research evaluation indicators; and (3) correlations between bibliometric and altmetric counts and the Publons metrics are very weak (r<.2) and not significant. Originality/value: This is the first study about the Publons metrics at article level and their relationship with other quantitative measures such as bibliometric and altmetric indicators. Keywords: Publons, Altmetrics, Bibliometrics, Peer-review 1. Introduction Traditionally, peer-review has been the most appropriate way to validate scientific advances. Since the first beginning of the scientific revolution, scientific theories and discoveries were discussed and agreed by the research community, as a way to confirm and accept new knowledge. This validation process has arrived until our days as a suitable tool for accepting the most relevant manuscripts to academic journals, allocating research funds or selecting and promoting scientific staff. -
Do You Speak Open Science? Resources and Tips to Learn the Language
Do You Speak Open Science? Resources and Tips to Learn the Language. Paola Masuzzo1, 2 - ORCID: 0000-0003-3699-1195, Lennart Martens1,2 - ORCID: 0000- 0003-4277-658X Author Affiliation 1 Medical Biotechnology Center, VIB, Ghent, Belgium 2 Department of Biochemistry, Ghent University, Ghent, Belgium Abstract The internet era, large-scale computing and storage resources, mobile devices, social media, and their high uptake among different groups of people, have all deeply changed the way knowledge is created, communicated, and further deployed. These advances have enabled a radical transformation of the practice of science, which is now more open, more global and collaborative, and closer to society than ever. Open science has therefore become an increasingly important topic. Moreover, as open science is actively pursued by several high-profile funders and institutions, it has fast become a crucial matter to all researchers. However, because this widespread interest in open science has emerged relatively recently, its definition and implementation are constantly shifting and evolving, sometimes leaving researchers in doubt about how to adopt open science, and which are the best practices to follow. This article therefore aims to be a field guide for scientists who want to perform science in the open, offering resources and tips to make open science happen in the four key areas of data, code, publications and peer-review. The Rationale for Open Science: Standing on the Shoulders of Giants One of the most widely used definitions of open science originates from Michael Nielsen [1]: “Open science is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process”. -
How Should Peer-Review Panels Behave? IZA DP No
IZA DP No. 7024 How Should Peer-Review Panels Behave? Daniel Sgroi Andrew J. Oswald November 2012 DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor How Should Peer-Review Panels Behave? Daniel Sgroi University of Warwick Andrew J. Oswald University of Warwick and IZA Discussion Paper No. 7024 November 2012 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected] Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. -
Finding Seminal Scientific Publications with Graph Mining
DEGREE PROJECT, IN COMPUTER SCIENCE , SECOND LEVEL STOCKHOLM, SWEDEN 2015 Finding seminal scientific publications with graph mining MARTIN RUNELÖV KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION Finding seminal scientific publications with graph mining MARTIN RUNELÖV Master’s Thesis at CSC Supervisor at CSC: Olov Engwall Supervisor at SICS: John Ardelius August 16, 2015 Abstract We investigate the applicability of network analysis to the prob- lem of finding seminal publications in scientific publishing. In particular, we focus on the network measures betweenness cen- trality, the so-called backbone graph, and the burstiness of cita- tions. The metrics are evaluated using precision-related scores with respect to gold standards based on fellow programmes and manual annotation. Citation counts, PageRank, and random se- lection are used as baselines. We find that the backbone graph provides us with a way to possibly discover seminal publications with low citation count, and combining betweenness and bursti- ness gives results on par with citation count. Referat Användning av grafanalys för att hitta betydelsefulla vetenskapliga artiklar I detta examensarbete undersöks det huruvida analys av cite- ringsgrafer kan användas för att finna betydelsefulla vetenskapli- ga publikationer. Framför allt studeras ”betweenness”-centralitet, den så kallade ”backbone”-grafen samt ”burstiness” av citering- ar. Dessa mått utvärderas med hjälp av precisionsmått med av- seende på guldstandarder baserade på ’fellow’-program samt via manuell annotering. Antal citeringar, PageRank, och slumpmäs- sigt urval används som jämförelse. Resultaten visar att ”backbone”-grafen kan bidra till att eventuellt upptäcka bety- delsefulla publikationer med ett lågt antal citeringar samt att en kombination av ”betweenness” och ”burstiness” ger resultat i nivå med de man får av att räkna antal citeringar.