6,966 research outputs found
Sometimes the impact factor outshines the H index
Journal impact factor (which reflects a particular journal's quality) and H index (which reflects the number and quality of an author's publications) are two measures of research quality. It has been argued that the H index outperforms the impact factor for evaluation purposes. Using articles first-authored or last-authored by board members of Retrovirology, we show here that the reverse is true when the future success of an article is to be predicted. The H index proved unsuitable for this specific task because, surprisingly, an article's odds of becoming a 'hit' appear independent of the pre-eminence of its author. We discuss implications for the peer-review process
Predicting the long-term citation impact of recent publications
A fundamental problem in citation analysis is the prediction of the long-term
citation impact of recent publications. We propose a model to predict a
probability distribution for the future number of citations of a publication.
Two predictors are used: The impact factor of the journal in which a
publication has appeared and the number of citations a publication has received
one year after its appearance. The proposed model is based on quantile
regression. We employ the model to predict the future number of citations of a
large set of publications in the field of physics. Our analysis shows that both
predictors (i.e., impact factor and early citations) contribute to the accurate
prediction of long-term citation impact. We also analytically study the
behavior of the quantile regression coefficients for high quantiles of the
distribution of citations. This is done by linking the quantile regression
approach to a quantile estimation technique from extreme value theory. Our work
provides insight into the influence of the impact factor and early citations on
the long-term citation impact of a publication, and it takes a step toward a
methodology that can be used to assess research institutions based on their
most recently published work.Comment: 17 pages, 17 figure
Predicting the age of researchers using bibliometric data
The age of researchers is a critical factor necessary to study the bibliometric characteristics of the
scholars that produce new knowledge. In bibliometric studies, the age of scientific authors is
generally missing; however, the year of the first publication is frequently considered as a proxy of the
age of researchers. In this article, we investigate what are the most important bibibliometric factors
that can be used to predict the age of researchers (birth and PhD age). Using a dataset of 3574
researchers from Québec for whom their Web of Science publications, year of birth and year of their
PhD are known, our analysis falls under the linear regression setting and focuses on investigating the
predictive power of various regression models rather than data fitting, considering also a breakdown
by fields. The year of first publication proves to be the best linear predictor for the age of
researchers. When using simple linear regression models, predicting birth and PhD years result in an
error of about 3.7 years and 3.9 years, respectively. Including other bibliometric data marginally
improves the predictive power of the regression models. A validation analysis for the field
breakdown shows that the average length of the prediction intervals vary from 2.5 years for Basic
Medical Sciences (for birth years) up to almost 10 years for Education (for PhD years). The average
models perform significantly better than the models using individual observations. Nonetheless, the
high variability of data and the uncertainty inherited by the models advice to caution when using
linear regression models for predicting the age of researchers
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