992 resultados para citation impact


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[EN]This essay reviews both the scholarly output and impact factor of Spanish research institutions in the Arts & Humanities Citation Index (A&HCI) database, managed by the Thomson-Reuters Web of Science. Based on a bibliometric analysis of a range of variables it has been possible to identify those institutions with the best performance indicators, the journals publishing the most articles, the most productive areas of research, and other relevant data on publishing patterns in the Humanities. The study reveals that the most productive Spanish institutions in the Humanities are the same as those that lead the performance figures in other areas; it also highlights the outstanding production of the Spanish National Research Council (CSIC).

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[EN] The citation potential is a measure of the probability of being cited. Obviously, it is different among fields of science because of systematic differences in publication and citation behaviour across disciplines. In the past, the citation potential was studied at journal level considering the average number of references in established groups of journals. In this paper, some characterizations of the author’s scientific research through three different research dimensions are proposed: production (journal papers), impact (journal citations), and reference (bibliographical sources). An empirical application, in a set of 120 randomly selected authors in four subject areas, shows that the ratio between production and impact dimensions is a normalized measure of the citation potential at the level of individual authors.

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[EN] The journal impact factor is not comparable among fields of science because of systematic differences in publication and citation behaviour across disciplines. In this work, a source normalization of the journal impact factor is proposed. We use the aggregate impact factor of the citing journals as a measure of the citation potential in the journal topic, and we employ this citation potential in the normalization of the journal impact factor. An empirical application in a set of 224 journals from four different fields shows that our normalization, using the citation potential in the journal topic, reduces the between-group variance with respect to the within-group variance in a higher proportion than the rest of indicators analysed.

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Peer review procedures and citation statistics are important yet often neglected components of the scientific publication process. Here I discuss fundamental consequences of such quality measures for the scientific community and propose three remedial actions: (1) use of a ''Combined Impact Estimate'' as a measure of citation statistics, (2) adoption of an open reviewing policy and (3) acceleration of the publication process in order to raise the reputation of the entire discipline (in our case: behavioural science). Authors, reviewers and editors are invited to contribute to the improvement of publication practice.

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OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.

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La publicación de artículos científicos en revistas con alto factor de impacto es uno de los parámetros que más se valora en la carrera de los investigadores. Aunque existen distintas instituciones que estiman diversos índices de repercusión, el único índice oficial y valorado de forma internacional es el Impact Factor (IF) de Thomson Reuters® que anualmente publica en el Journal Citation Reports (JCR). En este punto, es importante señalar que en estos momentos RENHyD está siendo observada por Thomson Reuters®, al haber sido incluida en un nuevo recurso de información de este productor, llamado ESCI (Emerging Sources Citation Index) y que es un nuevo índice de la colección principal de Web of ScienceT. Creemos que es necesario que todos los implicados en la revista (lectores e investigadores) sepan cómo pueden ayudar a que la revista crezca y mejore su repercusión, la única estrategia es citar los artículos publicados en RENHyD para fundamentar los nuevos trabajos que se publican en otras revistas con factor de impacto. El consumo de nuestras publicaciones es la estrategia que ayude a conseguir el factor de impacto.

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National Highway Traffic Safety Administration, Washington, D.C.

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On February 16, 2005, about 0913 mountain standard time, a Cessna Citation 560, N500AT, operated by Martinair, Inc., for Circuit City Stores, Inc., crashed about 4 nautical miles east of Pueblo Memorial Airport, Pueblo, Colorado, while on an instrument landing system approach to runway 26R. The two pilots and six passengers on board were killed, and the airplane was destroyed by impact forces and postcrash fire. The flight was operating under the provisions of 14 Code of Federal Regulations Part 91 on an instrument flight rules flight plan. Instrument meteorological conditions prevailed at the time of the accident. The National Transportation Safety Board determines that the probable cause of this accident was the flight crew's failure to effectively monitor and maintain airspeed and comply with procedures for deice boot activation on the approach, which caused an aerodynamic stall from which they did not recover. Contributing to the accident was the Federal Aviation Administration's failure to establish adequate certification requirements for flight into icing conditions, which led to the inadequate stall warning margin provided by the airplane's stall warning system.

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The research presented in this paper is part of an ongoing investigation into how best to support meaningful lab-based evaluations of mobile technologies. In our previous work, we developed a hazard avoidance system for use during lab evaluations [1]; in the work reported here, we further assess the impact of this system, specifically in terms of the effect of avoidance cue type on speech-based text entry tasks.

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While openness is well applied to software development and exploitation (open sources), and successfully applied to new business models (open innovation), fundamental and applied research seems to lag behind. Even after decades of advocacy, in 2011 only 50% of the public-funded research was freely available and accessible (Archambault et al., 2013). The current research workflows, stemming from a pre-internet age, result in loss of opportunity not only for the researchers themselves (cf. extensive literature on topic at Open Access citation project, http://opcit.eprints.org/), but also slows down innovation and application of research results (Houghton & Swan, 2011). Recent studies continue to suggest that lack of awareness among researchers, rather than lack of e-infrastructure and methodology, is a key reason for this loss of opportunity (Graziotin 2014). The session will focus on why Open Science is ideally suited to achieving tenure-relevant researcher impact in a “Publish or Perish” reality. Open Science encapsulates tools and approaches for each step along the research cycle: from Open Notebook Science to Open Data, Open Access, all setting up researchers for capitalising on social media in order to promote and discuss, and establish unexpected collaborations. Incorporating these new approaches into a updated personal research workflow is of strategic beneficial for young researchers, and will prepare them for expected long term funder trends towards greater openness and demand for greater return on investment (ROI) for public funds.

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Social media is changing the way we interact, present ideas and information and judge the quality of content and contributions. In recent years there have been hundreds of platforms to freely share all kinds of information and connect across networks. These new tools generate activity statistics and interactions among users such as mentions, retweets, conversations, comments on blogs or Facebook; managers references showing popularity ratings of more references shared by other researchers or repositories that generate statistics of visits or downloads of articles. This paper analyzes that have meaning and implications altmetrics, what are its advantages and critical platforms (Almetric.com, ImpactStory, Plos altmetrics, PlumX), reports progress and benefits for authors, publishers and librarians. It concluded that the value of alternative metrics as a complementary tool citation analysis is evident, although it is suggested that you should dig deeper into this issue to unravel the meaning and the potential value of these indicators to assess their potential.

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Journal impact factors have become an important criterion to judge the quality of scientific publications over the years, influencing the evaluation of institutions and individual researchers worldwide. However, they are also subject to a number of criticisms. Here we point out that the calculation of a journal’s impact factor is mainly based on the date of publication of its articles in print form, despite the fact that most journals now make their articles available online before that date. We analyze 61 neuroscience journals and show that delays between online and print publication of articles increased steadily over the last decade. Importantly, such a practice varies widely among journals, as some of them have no delays, while for others this period is longer than a year. Using a modified impact factor based on online rather than print publication dates, we demonstrate that online-to-print delays can artificially raise a journal’s impact factor, and that this inflation is greater for longer publication lags. We also show that correcting the effect of publication delay on impact factors changes journal rankings based on this metric. We thus suggest that indexing of articles in citation databases and calculation of citation metrics should be based on the date of an article’s online appearance, rather than on that of its publication in print.

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Journal impact factors have become an important criterion to judge the quality of scientific publications over the years, influencing the evaluation of institutions and individual researchers worldwide. However, they are also subject to a number of criticisms. Here we point out that the calculation of a journal’s impact factor is mainly based on the date of publication of its articles in print form, despite the fact that most journals now make their articles available online before that date. We analyze 61 neuroscience journals and show that delays between online and print publication of articles increased steadily over the last decade. Importantly, such a practice varies widely among journals, as some of them have no delays, while for others this period is longer than a year. Using a modified impact factor based on online rather than print publication dates, we demonstrate that online-to-print delays can artificially raise a journal’s impact factor, and that this inflation is greater for longer publication lags. We also show that correcting the effect of publication delay on impact factors changes journal rankings based on this metric. We thus suggest that indexing of articles in citation databases and calculation of citation metrics should be based on the date of an article’s online appearance, rather than on that of its publication in print.

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This paper questions the current concept of quality as used in research assessment rankings and peer review, with special reference to the link often established between impact and the way this impact is measured in the form of citation counting. Taking translation studies as a case study, we will offer a two-level approach to reveal both the macro- and micro-level biases that exist in this regard. We will first review three key aspects related to the idea of the quality of publications, namely peer review, journal indexing, and journal impact factor. We will then pinpoint some of the main macro-level problems regarding current practices and criteria as applied to translation studies, such as Thomson Reuters World of Science's journal coverage, citation patterns, and publication format. Next we will provide a micro-textual and practical perspective, focusing on citation counts and suggesting a series of corrective measures to increase comparability.

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Several technical indicators have been proposed to assess the impact of authors and institutions. Here, we combine the h-index and the PageRank algorithm to do away with some of the individual limitations of these two indices. Most importantly, we aim to take into account value differences between citations-evaluating the citation sources by defining the h-index using the PageRank score rather than with citations. The resulting PR-index is then constructed by evaluating source popularity as well as the source publication authority. Extensive tests on available collections data (i.e., Microsoft Academic Search and benchmarks on the SIGKDD innovation award) show that the PR-index provides a more balanced impact measure than many existing indices. Due to its simplicity and similarity to the popular h-index, the PR-index may thus become a welcome addition to the technical indices already in use. Moreover, growth dynamics prior to the SIGKDD innovation award indicate that the PR-index might have notable predictive power.