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Researchers affiliated with multiple institutions are increasingly seen in current scientific environment. In this paper we systematically analyze the multi-affiliated authorship and its effect on citation impact, with focus on the scientific output of research collaboration. By considering the nationality of each institutions, we further differentiate the national multi-affiliated authorship and international multi-affiliated authorship and reveal their different patterns across disciplines and countries. We observe a large share of publications with multi-affiliated authorship (45.6%) in research collaboration, with a larger share of publications containing national multi-affiliated authorship in medicine related and biology related disciplines, and a larger share of publications containing international type in Space Science, Physics and Geosciences. To a country-based view, we distinguish between domestic and foreign multi-affiliated authorship to a specific country. Taking G7 and BRICS countries as samples from different S&T level, we find that the domestic national multi-affiliated authorship relate to more on citation impact for most disciplines of G7 countries, while domestic international multi-affiliated authorships are more positively influential for most BRICS countries.
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This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.
In over five years, Bornmann, Stefaner, de Moya Anegon, and Mutz (2014) and Bornmann, Stefaner, de Moya Anegon, and Mutz (2014, 2015) have published several releases of the www.excellencemapping.net tool revealing (clusters of) excellent institutions worldwide based on citation data. With the new release, a completely revised tool has been published. It is not only based on citation data (bibliometrics), but also Mendeley data (altmetrics). Thus, the institutional impact measurement of the tool has been expanded by focusing on additional status groups besides researchers such as students and librarians. Furthermore, the visualization of the data has been completely updated by improving the operability for the user and including new features such as institutional profile pages. In this paper, we describe the datasets for the current excellencemapping.net tool and the indicators applied. Furthermore, the underlying statistics for the tool and the use of the web application are explained.
This study aims to analyze 343 retraction notices indexed in the Scopus database, published in 2001-2019, related to scientific articles (co-)written by at least one author affiliated with an Iranian institution. In order to determine reasons for retractions, we merged this database with the database from Retraction Watch. The data were analyzed using Excel 2016 and IBM-SPSS version 24.0, and visualized using VOSviewer software. Most of the retractions were due to fake peer review (95 retractions) and plagiarism (90). The average time between a publication and its retraction was 591 days. The maximum time-lag (about 3,000 days) occurred for papers retracted due to duplicate publications; the minimum time-lag (fewer than 100 days) was for papers retracted due to unspecified cause (most of these were conference papers). As many as 48 (14%) of the retracted papers were published in two medical journals: Tumor Biology (25 papers) and Diagnostic Pathology (23 papers). From the institutional point of view, Islamic Azad University was the inglorious leader, contributing to over one-half (53.1%) of retracted papers. Among the 343 retraction notices, 64 papers pertained to international collaborations with researchers from mainly Asian and European countries; Malaysia having the most retractions (22 papers). Since most retractions were due to fake peer review and plagiarism, the peer review system appears to be a weak point of the submission/publication process; if improved, the number of retractions would likely drop because of increased editorial control.
Computing such correlation coefficient would be straightforward had we had available the rankings given by the prize committee to all scientists in the pool. In reality we only have citation rankings for all scientists. This means, however, that we have the ordinal rankings of the prize winners with regard to citation metrics. I use maximum likelihood method to infer the most probable correlation coefficient to produce the observed pattern of ordinal ranks of the prize winners. I get the correlation coefficients of 0.47 and 0.59 between the composite citation indicator and getting Abel Prize and Fields Medal, respectively. The correlation coefficient between getting a Nobel Prize and the Q-factor is 0.65. These coefficients are of the same magnitude as the correlation coefficient between Elo ratings of the chess players and their popularity measured as numbers of webpages mentioning the players.
By combining data from the text, citation, and reference databases with data from the ADS readership logs we have been able to create Second Order Bibliometric Operators, a customizable class of collaborative filters which permits substantially improved accuracy in literature queries. Using the ADS usage logs along with membership statistics from the International Astronomical Union and data on the population and gross domestic product (GDP) we develop an accurate model for world-wide basic research where the number of scientists in a country is proportional to the GDP of that country, and the amount of basic research done by a country is proportional to the number of scientists in that country times that countrys per capita GDP. We introduce the concept of utility time to measure the impact of the ADS/URANIA and the electronic astronomical library on astronomical research. We find that in 2002 it amounted to the equivalent of 736 FTE researchers, or $250 Million, or the astronomical research done in France. Subject headings: digital libraries; bibliometrics; sociology of science; information retrieval
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