13,319 research outputs found
How to improve the prediction based on citation impact percentiles for years shortly after the publication date?
The findings of Bornmann, Leydesdorff, and Wang (in press) revealed that the
consideration of journal impact improves the prediction of long-term citation
impact. This paper further explores the possibility of improving citation
impact measurements on the base of a short citation window by the consideration
of journal impact and other variables, such as the number of authors, the
number of cited references, and the number of pages. The dataset contains
475,391 journal papers published in 1980 and indexed in Web of Science (WoS,
Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these
papers. As an indicator of citation impact, we used percentiles of citations
calculated using the approach of Hazen (1914). Our results show that citation
impact measurement can really be improved: If factors generally influencing
citation impact are considered in the statistical analysis, the explained
variance in the long-term citation impact can be much increased. However, this
increase is only visible when using the years shortly after publication but not
when using later years.Comment: Accepted for publication in the Journal of Informetrics. arXiv admin
note: text overlap with arXiv:1306.445
Investigating the interplay between fundamentals of national research systems: performance, investments and international collaborations
We discuss, at the macro-level of nations, the contribution of research
funding and rate of international collaboration to research performance, with
important implications for the science of science policy. In particular, we
cross-correlate suitable measures of these quantities with a
scientometric-based assessment of scientific success, studying both the average
performance of nations and their temporal dynamics in the space defined by
these variables during the last decade. We find significant differences among
nations in terms of efficiency in turning (financial) input into
bibliometrically measurable output, and we confirm that growth of international
collaboration positively correlate with scientific success, with significant
benefits brought by EU integration policies. Various geo-cultural clusters of
nations naturally emerge from our analysis. We critically discuss the possible
factors that potentially determine the observed patterns
Evaluating a Departmentās Research: Testing the Leiden Methodology in Business and Management
The Leiden methodology (LM), also sometimes called the ācrown indicatorā, is a quantitative method for evaluating the research quality of a research group or academic department based on the citations received by the group in comparison to averages for the field. There have been a number of applications but these have mainly been in the hard sciences where the data on citations, provided by the ISI Web of Science (WoS), is more reliable. In the social sciences, including business and management, many journals and books are not included within WoS and so the LM has not been tested here. In this research study the LM has been applied on a dataset of over 3000 research publications from three UK business schools. The results show that the LM does indeed discriminate between the schools, and has a degree of concordance with other forms of evaluation, but that there are significant limitations and problems within this discipline
Cell line name recognition in support of the identification of synthetic lethality in cancer from text
Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus.
Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers
Geodesics in Heat
We introduce the heat method for computing the shortest geodesic distance to
a specified subset (e.g., point or curve) of a given domain. The heat method is
robust, efficient, and simple to implement since it is based on solving a pair
of standard linear elliptic problems. The method represents a significant
breakthrough in the practical computation of distance on a wide variety of
geometric domains, since the resulting linear systems can be prefactored once
and subsequently solved in near-linear time. In practice, distance can be
updated via the heat method an order of magnitude faster than with
state-of-the-art methods while maintaining a comparable level of accuracy. We
provide numerical evidence that the method converges to the exact geodesic
distance in the limit of refinement; we also explore smoothed approximations of
distance suitable for applications where more regularity is required
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