Search (9 results, page 1 of 1)

0.03706847 = product of:
  0.07413694 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 3700) [ClassicSimilarity], result of:
          0.028250674 = score(doc=3700,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 3700, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=3700)
      0.25 = coord(1/4)
    0.06707428 = product of:
      0.13414855 = sum of:
        0.13414855 = weight(_text_:assessment in 3700) [ClassicSimilarity], result of:
          0.13414855 = score(doc=3700,freq=4.0), product of:
            0.25917634 = queryWeight, product of:
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.04694356 = queryNorm
            0.51759565 = fieldWeight in 3700, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.046875 = fieldNorm(doc=3700)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The authors set forth a general methodology for conducting bibliometric analyses at the micro level. It combines several indicators grouped into three factors or dimensions, which characterize different aspects of scientific performance. Different profiles or classes of scientists are described according to their research performance in each dimension. A series of results based on the findings from the application of this methodology to the study of Spanish National Research Council scientists in Spain in three thematic areas are presented. Special emphasis is made on the identification and description of top scientists from structural and bibliometric perspectives. The effects of age on the productivity and impact of the different classes of scientists are analyzed. The classificatory approach proposed herein may prove a useful tool in support of research assessment at the individual level and for exploring potential determinants of research success.

0.014115169 = product of:
  0.056460675 = sum of:
    0.056460675 = weight(_text_:term in 3333) [ClassicSimilarity], result of:
      0.056460675 = score(doc=3333,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.25776416 = fieldWeight in 3333, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3333)
  0.25 = coord(1/4)

Abstract

The study of the citation histories and ageing of documents are topics that have been addressed from several perspectives, especially in the analysis of documents with delayed recognition or sleeping beauties. However, there is no general methodology that can be extensively applied for different time periods or research fields. In this article, a new methodology for the general analysis of the ageing and durability of scientific papers is presented. This methodology classifies documents into three general types: delayed documents, which receive the main part of their citations later than normal documents; flashes in the pan, which receive citations immediately after their publication but are not cited in the long term; and normal documents, documents with a typical distribution of citations over time. These three types of durability have been analyzed considering the whole population of documents in the Web of Science with at least 5 external citations (i.e., not considering self-citations). Several patterns related to the three types of durability have been found and the potential for further research of the developed methodology is discussed.

0.003975128 = product of:
  0.015900511 = sum of:
    0.015900511 = product of:
      0.031801023 = sum of:
        0.031801023 = weight(_text_:22 in 2759) [ClassicSimilarity], result of:
          0.031801023 = score(doc=2759,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.19345059 = fieldWeight in 2759, 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=2759)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 3.2009 19:02:48

0.003975128 = product of:
  0.015900511 = sum of:
    0.015900511 = product of:
      0.031801023 = sum of:
        0.031801023 = weight(_text_:22 in 2598) [ClassicSimilarity], result of:
          0.031801023 = score(doc=2598,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.19345059 = fieldWeight in 2598, 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=2598)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

0.003180102 = product of:
  0.012720408 = sum of:
    0.012720408 = product of:
      0.025440816 = sum of:
        0.025440816 = weight(_text_:22 in 4051) [ClassicSimilarity], result of:
          0.025440816 = score(doc=4051,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.15476047 = fieldWeight in 4051, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=4051)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

0.002548521 = product of:
  0.010194084 = sum of:
    0.010194084 = product of:
      0.040776335 = sum of:
        0.040776335 = weight(_text_:based in 3436) [ClassicSimilarity], result of:
          0.040776335 = score(doc=3436,freq=6.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.28829288 = fieldWeight in 3436, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3436)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The article presents three advanced citation-based methods used to detect potential breakthrough articles among very highly cited articles. We approach the detection of such articles from three different perspectives in order to provide different typologies of breakthrough articles. In all three cases we use the hierarchical classification of scientific publications developed at CWTS based on direct citation relationships. We assume that such contextualized articles focus on similar research interests. We utilize the characteristics scores and scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited "followers," articles not considered breakthroughs. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited article in a micro-cluster is considered as a potential breakthrough article (M1); as well as two conceptually different methods, one that detects potential breakthrough articles among the 2% highest cited articles according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS 2% filter, knowledge diffusion is also considered (M2b). The advance citation-based methods are explored and evaluated using validated publication sets linked to different Danish funding instruments including centers of excellence.

0.0024970302 = product of:
  0.009988121 = sum of:
    0.009988121 = product of:
      0.039952483 = sum of:
        0.039952483 = weight(_text_:based in 605) [ClassicSimilarity], result of:
          0.039952483 = score(doc=605,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.28246817 = fieldWeight in 605, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=605)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The data re-collection for tweets from data snapshots is a common methodological step in Twitter-based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID-19-related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. "Revived" tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers.

0.0014713892 = product of:
  0.005885557 = sum of:
    0.005885557 = product of:
      0.023542227 = sum of:
        0.023542227 = weight(_text_:based in 516) [ClassicSimilarity], result of:
          0.023542227 = score(doc=516,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.16644597 = fieldWeight in 516, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=516)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

This study presents an analysis of the use of bibliographic references by individual scientists in three different research areas. The number and type of references that scientists include in their papers are analyzed, the relationship between the number of references and different impact-based indicators is studied from a multivariable perspective, and the referencing patterns of scientists are related to individual factors such as their age and scientific performance. Our results show inter-area differences in the number, type, and age of references. Within each area, the number of references per document increases with journal impact factor and paper length. Top-performance scientists use in their papers a higher number of references, which are more recent and more frequently covered by the Web of Science. Veteran researchers tend to rely more on older literature and non-Web of Science sources. The longer reference lists of top scientists can be explained by their tendency to publish in high impact factor journals, with stricter reference and reviewing requirements. Long reference lists suggest a broader knowledge on the current literature in a field, which is important to become a top scientist. From the perspective of the "handicap principle theory," the sustained use of a high number of references in an author's oeuvre is a costly behavior that may indicate a serious, comprehensive, and solid research capacity, but that only the best researchers can afford. Boosting papers' citations by artificially increasing the number of references does not seem a feasible strategy.

0.0014713892 = product of:
  0.005885557 = sum of:
    0.005885557 = product of:
      0.023542227 = sum of:
        0.023542227 = weight(_text_:based in 265) [ClassicSimilarity], result of:
          0.023542227 = score(doc=265,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.16644597 = fieldWeight in 265, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=265)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.