Search (36 results, page 1 of 2)

0.0031774172 = product of:
  0.03177417 = sum of:
    0.022467308 = product of:
      0.044934615 = sum of:
        0.044934615 = weight(_text_:kommunikationswissenschaften in 382) [ClassicSimilarity], result of:
          0.044934615 = score(doc=382,freq=2.0), product of:
            0.15303716 = queryWeight, product of:
              5.315071 = idf(docFreq=590, maxDocs=44218)
              0.02879306 = queryNorm
            0.29361898 = fieldWeight in 382, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.315071 = idf(docFreq=590, maxDocs=44218)
              0.0390625 = fieldNorm(doc=382)
      0.5 = coord(1/2)
    0.009306861 = weight(_text_:in in 382) [ClassicSimilarity], result of:
      0.009306861 = score(doc=382,freq=20.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.2376267 = fieldWeight in 382, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=382)
  0.1 = coord(2/20)

Abstract

Although there is some evidence that online videos are increasingly used by academics for informal scholarly communication and teaching, the extent to which they are used in published academic research is unknown. This article explores the extent to which YouTube videos are cited in academic publications and whether there are significant broad disciplinary differences in this practice. To investigate, we extracted the URL citations to YouTube videos from academic publications indexed by Scopus. A total of 1,808 Scopus publications cited at least one YouTube video, and there was a steady upward growth in citing online videos within scholarly publications from 2006 to 2011, with YouTube citations being most common within arts and humanities (0.3%) and the social sciences (0.2%). A content analysis of 551 YouTube videos cited by research articles indicated that in science (78%) and in medicine and health sciences (77%), over three fourths of the cited videos had either direct scientific (e.g., laboratory experiments) or scientific-related contents (e.g., academic lectures or education) whereas in the arts and humanities, about 80% of the YouTube videos had art, culture, or history themes, and in the social sciences, about 63% of the videos were related to news, politics, advertisements, and documentaries. This shows both the disciplinary differences and the wide variety of innovative research communication uses found for videos within the different subject areas.

Field

Kommunikationswissenschaften

0.0020354062 = product of:
  0.020354062 = sum of:
    0.008650876 = weight(_text_:in in 2856) [ClassicSimilarity], result of:
      0.008650876 = score(doc=2856,freq=12.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.22087781 = fieldWeight in 2856, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2856)
    0.011703186 = product of:
      0.023406371 = sum of:
        0.023406371 = weight(_text_:22 in 2856) [ClassicSimilarity], result of:
          0.023406371 = score(doc=2856,freq=2.0), product of:
            0.10082839 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02879306 = queryNorm
            0.23214069 = fieldWeight in 2856, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2856)
      0.5 = coord(1/2)
  0.1 = coord(2/20)

Abstract

This article introduces a new source of evidence of the value of medical-related research: citations from clinical guidelines. These give evidence that research findings have been used to inform the day-to-day practice of medical staff. To identify whether citations from guidelines can give different information from that of traditional citation counts, this article assesses the extent to which references in clinical guidelines tend to be highly cited in the academic literature and highly read in Mendeley. Using evidence from the United Kingdom, references associated with the UK's National Institute of Health and Clinical Excellence (NICE) guidelines tended to be substantially more cited than comparable articles, unless they had been published in the most recent 3 years. Citation counts also seemed to be stronger indicators than Mendeley readership altmetrics. Hence, although presence in guidelines may be particularly useful to highlight the contributions of recently published articles, for older articles citation counts may already be sufficient to recognize their contributions to health in society.

Date

19. 3.2016 12:22:00

0.0017820279 = product of:
  0.017820278 = sum of:
    0.006117093 = weight(_text_:in in 4291) [ClassicSimilarity], result of:
      0.006117093 = score(doc=4291,freq=6.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.1561842 = fieldWeight in 4291, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=4291)
    0.011703186 = product of:
      0.023406371 = sum of:
        0.023406371 = weight(_text_:22 in 4291) [ClassicSimilarity], result of:
          0.023406371 = score(doc=4291,freq=2.0), product of:
            0.10082839 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02879306 = queryNorm
            0.23214069 = fieldWeight in 4291, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4291)
      0.5 = coord(1/2)
  0.1 = coord(2/20)

Abstract

Counts of tweets and Mendeley user libraries have been proposed as altmetric alternatives to citation counts for the impact assessment of articles. Although both have been investigated to discover whether they correlate with article citations, it is not known whether users tend to tweet or save (in Mendeley) the same kinds of articles that they cite. In response, this article compares pairs of articles that are tweeted, saved to a Mendeley library, or cited by the same user, but possibly a different user for each source. The study analyzes 1,131,318 articles published in 2012, with minimum tweeted (10), saved to Mendeley (100), and cited (10) thresholds. The results show surprisingly minor overall overlaps between the three phenomena. The importance of journals for Twitter and the presence of many bots at different levels of activity suggest that this site has little value for impact altmetrics. The moderate differences between patterns of saving and citation suggest that Mendeley can be used for some types of impact assessments, but sensitivity is needed for underlying differences.

Date

28. 7.2018 10:00:22

0.0016697772 = product of:
  0.016697772 = sum of:
    0.0049945856 = weight(_text_:in in 3211) [ClassicSimilarity], result of:
      0.0049945856 = score(doc=3211,freq=4.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.12752387 = fieldWeight in 3211, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3211)
    0.011703186 = product of:
      0.023406371 = sum of:
        0.023406371 = weight(_text_:22 in 3211) [ClassicSimilarity], result of:
          0.023406371 = score(doc=3211,freq=2.0), product of:
            0.10082839 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02879306 = queryNorm
            0.23214069 = fieldWeight in 3211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3211)
      0.5 = coord(1/2)
  0.1 = coord(2/20)

Abstract

Scientists and managers using citation-based indicators to help evaluate research cannot evaluate recent articles because of the time needed for citations to accrue. Reading occurs before citing, however, and so it makes sense to count readers rather than citations for recent publications. To assess this, Mendeley readers and citations were obtained for articles from 2004 to late 2014 in five broad categories (agriculture, business, decision science, pharmacy, and the social sciences) and 50 subcategories. In these areas, citation counts tended to increase with every extra year since publication, and readership counts tended to increase faster initially but then stabilize after about 5 years. The correlation between citations and readers was also higher for longer time periods, stabilizing after about 5 years. Although there were substantial differences between broad fields and smaller differences between subfields, the results confirm the value of Mendeley reader counts as early scientific impact indicators.

Date

16.11.2016 11:07:22

0.0014850233 = product of:
  0.014850233 = sum of:
    0.005097578 = weight(_text_:in in 3806) [ClassicSimilarity], result of:
      0.005097578 = score(doc=3806,freq=6.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.1301535 = fieldWeight in 3806, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3806)
    0.009752655 = product of:
      0.01950531 = sum of:
        0.01950531 = weight(_text_:22 in 3806) [ClassicSimilarity], result of:
          0.01950531 = score(doc=3806,freq=2.0), product of:
            0.10082839 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02879306 = queryNorm
            0.19345059 = fieldWeight in 3806, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3806)
      0.5 = coord(1/2)
  0.1 = coord(2/20)

Abstract

Purpose Mendeley reader counts have been proposed as early indicators for the impact of academic publications. The purpose of this paper is to assess whether there are enough Mendeley readers for research evaluation purposes during the month when an article is first published. Design/methodology/approach Average Mendeley reader counts were compared to the average Scopus citation counts for 104,520 articles from ten disciplines during the second half of 2016. Findings Articles attracted, on average, between 0.1 and 0.8 Mendeley readers per article in the month in which they first appeared in Scopus. This is about ten times more than the average Scopus citation count. Research limitations/implications Other disciplines may use Mendeley more or less than the ten investigated here. The results are dependent on Scopus's indexing practices, and Mendeley reader counts can be manipulated and have national and seniority biases. Practical implications Mendeley reader counts during the month of publication are more powerful than Scopus citations for comparing the average impacts of groups of documents but are not high enough to differentiate between the impacts of typical individual articles. Originality/value This is the first multi-disciplinary and systematic analysis of Mendeley reader counts from the publication month of an article.

Date

20. 1.2015 18:30:22

0.001391481 = product of:
  0.01391481 = sum of:
    0.004162154 = weight(_text_:in in 57) [ClassicSimilarity], result of:
      0.004162154 = score(doc=57,freq=4.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.10626988 = fieldWeight in 57, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=57)
    0.009752655 = product of:
      0.01950531 = sum of:
        0.01950531 = weight(_text_:22 in 57) [ClassicSimilarity], result of:
          0.01950531 = score(doc=57,freq=2.0), product of:
            0.10082839 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02879306 = queryNorm
            0.19345059 = fieldWeight in 57, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=57)
      0.5 = coord(1/2)
  0.1 = coord(2/20)

Abstract

Webometric network analyses have been used to map the connectivity of groups of websites to identify clusters, important sites or overall structure. Such analyses have mainly been based upon hyperlink counts, the number of hyperlinks between a pair of websites, although some have used title mentions or URL citations instead. The ability to automatically gather hyperlink counts from Yahoo! ceased in April 2011 and the ability to manually gather such counts was due to cease by early 2012, creating a need for alternatives. This article assesses URL citations and title mentions as possible replacements for hyperlinks in both binary and weighted direct link and co-inlink network diagrams. It also assesses three different types of data for the network connections: hit count estimates, counts of matching URLs, and filtered counts of matching URLs. Results from analyses of U.S. library and information science departments and U.K. universities give evidence that metrics based upon URLs or titles can be appropriate replacements for metrics based upon hyperlinks for both binary and weighted networks, although filtered counts of matching URLs are necessary to give the best results for co-title mention and co-URL citation network diagrams.

Date

6. 4.2012 18:16:22

4.8805587E-4 = product of:
  0.009761117 = sum of:
    0.009761117 = weight(_text_:in in 1850) [ClassicSimilarity], result of:
      0.009761117 = score(doc=1850,freq=22.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.24922498 = fieldWeight in 1850, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1850)
  0.05 = coord(1/20)

Abstract

International collaboration tends to result in more highly cited research and, partly as a result of this, many research funding schemes are specifically international in scope. Nevertheless, it is not clear whether this citation advantage is the result of higher quality research or due to other factors, such as a larger audience for the publications. To test whether the apparent advantage of internationally collaborative research may be due to additional interest in articles from the countries of the authors, this article assesses the extent to which the national affiliations of the authors of articles affect the national affiliations of their Mendeley readers. Based on English-language Web of Science articles in 10 fields from science, medicine, social science, and the humanities, the results of statistical models comparing author and reader affiliations suggest that, in most fields, Mendeley users are disproportionately readers of articles authored from within their own country. In addition, there are several cases in which Mendeley users from certain countries tend to ignore articles from specific other countries, although it is not clear whether this reflects national biases or different national specialisms within a field. In conclusion, research funders should not incentivize international collaboration on the basis that it is, in general, higher quality because its higher impact may be primarily due to its larger audience. Moreover, authors should guard against national biases in their reading to select only the best and most relevant publications to inform their research.

4.6534307E-4 = product of:
  0.009306861 = sum of:
    0.009306861 = weight(_text_:in in 2900) [ClassicSimilarity], result of:
      0.009306861 = score(doc=2900,freq=20.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.2376267 = fieldWeight in 2900, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2900)
  0.05 = coord(1/20)

Abstract

Statistics are essential to many areas of research and individual statistical techniques may change the ways in which problems are addressed as well as the types of problems that can be tackled. Hence, specific techniques may tend to generate high-impact findings within science. This article estimates the citation advantage of a technique by calculating the average citation rank of articles using it in the issue of the journal in which they were published. Applied to structural equation modeling (SEM) and four related techniques in 3 broad fields, the results show citation advantages that vary by technique and broad field. For example, SEM seems to be more influential in all broad fields than the 4 simpler methods, with one exception, and hence seems to be particularly worth adding to statistical curricula. In contrast, Pearson correlation apparently has the highest average impact in medicine but the least in psychology. In conclusion, the results suggest that the importance of a statistical technique may vary by discipline and that even simple techniques can help to generate high-impact research in some contexts.

4.3254378E-4 = product of:
  0.008650876 = sum of:
    0.008650876 = weight(_text_:in in 1258) [ClassicSimilarity], result of:
      0.008650876 = score(doc=1258,freq=12.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.22087781 = fieldWeight in 1258, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=1258)
  0.05 = coord(1/20)

Abstract

Journal-based citations are an important source of data for impact indices. However, the impact of journal articles extends beyond formal scholarly discourse. Measuring online scholarly impact calls for new indices, complementary to the older ones. This article examines a possible alternative metric source, blog posts aggregated at ResearchBlogging.org, which discuss peer-reviewed articles and provide full bibliographic references. Articles reviewed in these blogs therefore receive "blog citations." We hypothesized that articles receiving blog citations close to their publication time receive more journal citations later than the articles in the same journal published in the same year that did not receive such blog citations. Statistically significant evidence for articles published in 2009 and 2010 support this hypothesis for seven of 12 journals (58%) in 2009 and 13 of 19 journals (68%) in 2010. We suggest, based on these results, that blog citations can be used as an alternative metric source.

4.1621542E-4 = product of:
  0.008324308 = sum of:
    0.008324308 = weight(_text_:in in 1285) [ClassicSimilarity], result of:
      0.008324308 = score(doc=1285,freq=16.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.21253976 = fieldWeight in 1285, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1285)
  0.05 = coord(1/20)

Abstract

Since its creation in 1991, arXiv has become central to the diffusion of research in a number of fields. Combining data from the entirety of arXiv and the Web of Science (WoS), this article investigates (a) the proportion of papers across all disciplines that are on arXiv and the proportion of arXiv papers that are in the WoS, (b) the elapsed time between arXiv submission and journal publication, and (c) the aging characteristics and scientific impact of arXiv e-prints and their published version. It shows that the proportion of WoS papers found on arXiv varies across the specialties of physics and mathematics, and that only a few specialties make extensive use of the repository. Elapsed time between arXiv submission and journal publication has shortened but remains longer in mathematics than in physics. In physics, mathematics, as well as in astronomy and astrophysics, arXiv versions are cited more promptly and decay faster than WoS papers. The arXiv versions of papers-both published and unpublished-have lower citation rates than published papers, although there is almost no difference in the impact of the arXiv versions of published and unpublished papers.

4.1621542E-4 = product of:
  0.008324308 = sum of:
    0.008324308 = weight(_text_:in in 1658) [ClassicSimilarity], result of:
      0.008324308 = score(doc=1658,freq=16.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.21253976 = fieldWeight in 1658, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1658)
  0.05 = coord(1/20)

Abstract

Recent studies have shown that counting citations from books can help scholarly impact assessment and that Google Books (GB) is a useful source of such citation counts, despite its lack of a public citation index. Searching GB for citations produces approximate matches, however, and so its raw results need time-consuming human filtering. In response, this article introduces a method to automatically remove false and irrelevant matches from GB citation searches in addition to introducing refinements to a previous GB manual citation extraction method. The method was evaluated by manual checking of sampled GB results and comparing citations to about 14,500 monographs in the Thomson Reuters Book Citation Index (BKCI) against automatically extracted citations from GB across 24 subject areas. GB citations were 103% to 137% as numerous as BKCI citations in the humanities, except for tourism (72%) and linguistics (91%), 46% to 85% in social sciences, but only 8% to 53% in the sciences. In all cases, however, GB had substantially more citing books than did BKCI, with BKCI's results coming predominantly from journal articles. Moderate correlations between the GB and BKCI citation counts in social sciences and humanities, with most BKCI results coming from journal articles rather than books, suggests that they could measure the different aspects of impact, however.

4.1621542E-4 = product of:
  0.008324308 = sum of:
    0.008324308 = weight(_text_:in in 2897) [ClassicSimilarity], result of:
      0.008324308 = score(doc=2897,freq=16.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.21253976 = fieldWeight in 2897, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2897)
  0.05 = coord(1/20)

Abstract

Although Mendeley bookmarking counts appear to correlate moderately with conventional citation metrics, it is not known whether academic publications are bookmarked in Mendeley in order to be read or not. Without this information, it is not possible to give a confident interpretation of altmetrics derived from Mendeley. In response, a survey of 860 Mendeley users shows that it is reasonable to use Mendeley bookmarking counts as an indication of readership because most (55%) users with a Mendeley library had read or intended to read at least half of their bookmarked publications. This was true across all broad areas of scholarship except for the arts and humanities (42%). About 85% of the respondents also declared that they bookmarked articles in Mendeley to cite them in their publications, but some also bookmark articles for use in professional (50%), teaching (25%), and educational activities (13%). Of course, it is likely that most readers do not record articles in Mendeley and so these data do not represent all readers. In conclusion, Mendeley bookmark counts seem to be indicators of readership leading to a combination of scholarly impact and wider professional impact.

3.9485664E-4 = product of:
  0.007897133 = sum of:
    0.007897133 = weight(_text_:in in 737) [ClassicSimilarity], result of:
      0.007897133 = score(doc=737,freq=10.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.20163295 = fieldWeight in 737, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=737)
  0.05 = coord(1/20)

Abstract

This study investigates a range of metrics available when a nanoscience and nanotechnology article is published to see which metrics correlate more with the number of citations to the article. It also introduces the degree of internationality of journals and references as new metrics for this purpose. The journal impact factor; the impact of references; the internationality of authors, journals, and references; and the number of authors, institutions, and references were all calculated for papers published in nanoscience and nanotechnology journals in the Web of Science from 2007 to 2009. Using a zero-inflated negative binomial regression model on the data set, the impact factor of the publishing journal and the citation impact of the cited references were found to be the most effective determinants of citation counts in all four time periods. In the entire 2007 to 2009 period, apart from journal internationality and author numbers and internationality, all other predictor variables had significant effects on citation counts.

3.8933393E-4 = product of:
  0.0077866786 = sum of:
    0.0077866786 = weight(_text_:in in 1548) [ClassicSimilarity], result of:
      0.0077866786 = score(doc=1548,freq=14.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.19881277 = fieldWeight in 1548, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1548)
  0.05 = coord(1/20)

Abstract

Citation indicators are increasingly used in book-based disciplines to support peer review in the evaluation of authors and to gauge the prestige of publishers. However, because global citation databases seem to offer weak coverage of books outside the West, it is not clear whether the influence of non-Western books can be assessed with citations. To investigate this, citations were extracted from Google Books and Google Scholar to 1,357 arts, humanities and social sciences (AHSS) books published by 5 university presses during 1961-2012 in 1 non-Western nation, Malaysia. A significant minority of the books (23% in Google Books and 37% in Google Scholar, 45% in total) had been cited, with a higher proportion cited if they were older or in English. The combination of Google Books and Google Scholar is therefore recommended, with some provisos, for non-Western countries seeking to differentiate between books with some impact and books with no impact, to identify the highly-cited works or to develop an indicator of academic publisher prestige.

3.6045312E-4 = product of:
  0.0072090626 = sum of:
    0.0072090626 = weight(_text_:in in 4225) [ClassicSimilarity], result of:
      0.0072090626 = score(doc=4225,freq=12.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.18406484 = fieldWeight in 4225, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4225)
  0.05 = coord(1/20)

Abstract

National governments would like to preside over an expanding and increasingly high-impact science system but are these two goals largely independent or closely linked? This article investigates the relationship between changes in the share of the world's scientific output and changes in relative citation impact for 2.6 million articles from 26 fields in the 25 countries with the most Scopus-indexed journal articles from 1996 to 2015. There is a negative correlation between expansion and relative citation impact, but their relationship varies. China, Spain, Australia, and Poland were successful overall across the 26 fields, expanding both their share of the world's output and its relative citation impact, whereas Japan, France, Sweden, and Israel had decreased shares and relative citation impact. In contrast, the USA, UK, Germany, Italy, Russia, The Netherlands, Switzerland, Finland, and Denmark all enjoyed increased relative citation impact despite a declining share of publications. Finally, India, South Korea, Brazil, Taiwan, and Turkey all experienced sustained expansion but a recent fall in relative citation impact. These results may partly reflect changes in the coverage of Scopus and the selection of fields.

3.531705E-4 = product of:
  0.00706341 = sum of:
    0.00706341 = weight(_text_:in in 3048) [ClassicSimilarity], result of:
      0.00706341 = score(doc=3048,freq=18.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.18034597 = fieldWeight in 3048, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=3048)
  0.05 = coord(1/20)

Abstract

This study aims to identify the way researchers collaborate with other researchers in the course of the scientific research life cycle and provide information to the designers of e-Science and e-Research implementations. On the basis of in-depth interviews with and on-site observations of 24 scientists and a follow-up focus group interview in the field of bioscience/nanoscience and technology in Korea, we examined scientific collaboration using the framework of the scientific research life cycle. We attempt to explain the major motiBiochemistry is a highly funded research area that is typified by large research teams and is important for many areas of the life sciences. This article investigates the citation impact and Mendeley readership impact of biochemistry research from 2011 in the Web of Science according to the type of collaboration involved. Negative binomial regression models are used that incorporate, for the first time, the inclusion of specific countries within a team. The results show that, holding other factors constant, larger teams robustly associate with higher impact research, but including additional departments has no effect and adding extra institutions tends to reduce the impact of research. Although international collaboration is apparently not advantageous in general, collaboration with the United States, and perhaps also with some other countries, seems to increase impact. In contrast, collaborations with some other nations seems to decrease impact, although both findings could be due to factors such as differing national proportions of excellent researchers. As a methodological implication, simpler statistical models would find international collaboration to be generally beneficial and so it is important to take into account specific countries when examining collaboration.t only in the beginning phase of the cycle. For communication and information-sharing practices, scientists continue to favor traditional means of communication for security reasons. Barriers to collaboration throughout the phases included different priorities, competitive tensions, and a hierarchical culture among collaborators, whereas credit sharing was a barrier in the research product phase.

3.531705E-4 = product of:
  0.00706341 = sum of:
    0.00706341 = weight(_text_:in in 3055) [ClassicSimilarity], result of:
      0.00706341 = score(doc=3055,freq=8.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.18034597 = fieldWeight in 3055, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3055)
  0.05 = coord(1/20)

Abstract

Medical research is highly funded and often expensive and so is particularly important to evaluate effectively. Nevertheless, citation counts may accrue too slowly for use in some formal and informal evaluations. It is therefore important to investigate whether alternative metrics could be used as substitutes. This article assesses whether one such altmetric, Mendeley readership counts, correlates strongly with citation counts across all medical fields, whether the relationship is stronger if student readers are excluded, and whether they are distributed similarly to citation counts. Based on a sample of 332,975 articles from 2009 in 45 medical fields in Scopus, citation counts correlated strongly (about 0.7; 78% of articles had at least one reader) with Mendeley readership counts (from the new version 1 applications programming interface [API]) in almost all fields, with one minor exception, and the correlations tended to decrease slightly when student readers were excluded. Readership followed either a lognormal or a hooked power law distribution, whereas citations always followed a hooked power law, showing that the two may have underlying differences.

3.290472E-4 = product of:
  0.006580944 = sum of:
    0.006580944 = weight(_text_:in in 4920) [ClassicSimilarity], result of:
      0.006580944 = score(doc=4920,freq=10.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.16802745 = fieldWeight in 4920, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4920)
  0.05 = coord(1/20)

Abstract

Citation indictors are increasingly used in some subject areas to support peer review in the evaluation of researchers and departments. Nevertheless, traditional journal-based citation indexes may be inadequate for the citation impact assessment of book-based disciplines. This article examines whether online citations from Google Books and Google Scholar can provide alternative sources of citation evidence. To investigate this, we compared the citation counts to 1,000 books submitted to the 2008 U.K. Research Assessment Exercise (RAE) from Google Books and Google Scholar with Scopus citations across seven book-based disciplines (archaeology; law; politics and international studies; philosophy; sociology; history; and communication, cultural, and media studies). Google Books and Google Scholar citations to books were 1.4 and 3.2 times more common than were Scopus citations, and their medians were more than twice and three times as high as were Scopus median citations, respectively. This large number of citations is evidence that in book-oriented disciplines in the social sciences, arts, and humanities, online book citations may be sufficiently numerous to support peer review for research evaluation, at least in the United Kingdom.

3.290472E-4 = product of:
  0.006580944 = sum of:
    0.006580944 = weight(_text_:in in 3440) [ClassicSimilarity], result of:
      0.006580944 = score(doc=3440,freq=10.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.16802745 = fieldWeight in 3440, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3440)
  0.05 = coord(1/20)

Abstract

Individual academics and research evaluators often need to assess the value of published research. Although citation counts are a recognized indicator of scholarly impact, alternative data is needed to provide evidence of other types of impact, including within education and wider society. Wikipedia is a logical choice for both of these because the role of a general encyclopaedia is to be an understandable repository of facts about a diverse array of topics and hence it may cite research to support its claims. To test whether Wikipedia could provide new evidence about the impact of scholarly research, this article counted citations to 302,328 articles and 18,735 monographs in English indexed by Scopus in the period 2005 to 2012. The results show that citations from Wikipedia to articles are too rare for most research evaluation purposes, with only 5% of articles being cited in all fields. In contrast, a third of monographs have at least one citation from Wikipedia, with the most in the arts and humanities. Hence, Wikipedia citations can provide extra impact evidence for academic monographs. Nevertheless, the results may be relatively easily manipulated and so Wikipedia is not recommended for evaluations affecting stakeholder interests.

2.9430876E-4 = product of:
  0.005886175 = sum of:
    0.005886175 = weight(_text_:in in 1229) [ClassicSimilarity], result of:
      0.005886175 = score(doc=1229,freq=8.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.15028831 = fieldWeight in 1229, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1229)
  0.05 = coord(1/20)

Abstract

Data collected by social media platforms have been introduced as new sources for indicators to help measure the impact of scholarly research in ways that are complementary to traditional citation analysis. Data generated from social media activities can be used to reflect broad types of impact. This article aims to provide systematic evidence about how often Twitter is used to disseminate information about journal articles in the biomedical sciences. The analysis is based on 1.4 million documents covered by both PubMed and Web of Science and published between 2010 and 2012. The number of tweets containing links to these documents was analyzed and compared to citations to evaluate the degree to which certain journals, disciplines, and specialties were represented on Twitter and how far tweets correlate with citation impact. With less than 10% of PubMed articles mentioned on Twitter, its uptake is low in general but differs between journals and specialties. Correlations between tweets and citations are low, implying that impact metrics based on tweets are different from those based on citations. A framework using the coverage of articles and the correlation between Twitter mentions and citations is proposed to facilitate the evaluation of novel social-media-based metrics.