3,802 research outputs found
Eigenfactor : Does the Principle of Repeated Improvement Result in Better Journal Impact Estimates than Raw Citation Counts?
Eigenfactor.org, a journal evaluation tool which uses an iterative algorithm
to weight citations (similar to the PageRank algorithm used for Google) has
been proposed as a more valid method for calculating the impact of journals.
The purpose of this brief communication is to investigate whether the principle
of repeated improvement provides different rankings of journals than does a
simple unweighted citation count (the method used by ISI).Comment: bibliographic information correcte
The assessment of science: the relative merits of post- publication review, the impact factor, and the number of citations
The assessment of scientific publications is an integral part of the scientific process. Here we investigate three methods of assessing the merit of a scientific paper: subjective post-publication peer review, the number of citations gained by a paper, and the impact factor of the journal in which the article was published. We investigate these methods using two datasets in which subjective post-publication assessments of scientific publications have been made by experts. We find that there are moderate, but statistically significant, correlations between assessor scores, when two assessors have rated the same paper, and between assessor score and the number of citations a paper accrues. However, we show that assessor score depends strongly on the journal in which the paper is published, and that assessors tend to over-rate papers published in journals with high impact factors. If we control for this bias, we find that the correlation between assessor scores and between assessor score and the number of citations is weak, suggesting that scientists have little ability to judge either the intrinsic merit of a paper or its likely impact. We also show that the number of citations a paper receives is an extremely error-prone measure of scientific merit. Finally, we argue that the impact factor is likely to be a poor measure of merit, since it depends on subjective assessment. We conclude that the three measures of scientific merit considered here are poor; in particular subjective assessments are an error-prone, biased, and expensive method by which to assess merit. We argue that the impact factor may be the most satisfactory of the methods we have considered, since it is a form of pre-publication review. However, we emphasise that it is likely to be a very error-prone measure of merit that is qualitative, not quantitative
Research groups: How big should they be?
Understanding the relationship between scientific productivity and research group size is important for deciding how science should be funded. We have investigated the relationship between these variables in the life sciences in the United Kingdom using data from 398 principle investigators (PIs). We show that three measures of productivity, the number of publications, the impact factor of the journals in which papers are published and the number of citations, are all positively correlated to group size, although they all show a pattern of diminishing returnsādoubling group size leads to less than a doubling in productivity. The relationships for the impact factor and the number of citations are extremely weak. Our analyses suggest that an increase in productivity will be achieved by funding more PIs with small research groups, unless the cost of employing post-docs and PhD students is less than 20% the cost of a PI. We also provide evidence that post-docs are more productive than PhD students both in terms of the number of papers they produce and where those papers are published
Do altmetrics correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective
An extensive analysis of the presence of different altmetric indicators
provided by Altmetric.com across scientific fields is presented, particularly
focusing on their relationship with citations. Our results confirm that the
presence and density of social media altmetric counts are still very low and
not very frequent among scientific publications, with 15%-24% of the
publications presenting some altmetric activity and concentrating in the most
recent publications, although their presence is increasing over time.
Publications from the social sciences, humanities and the medical and life
sciences show the highest presence of altmetrics, indicating their potential
value and interest for these fields. The analysis of the relationships between
altmetrics and citations confirms previous claims of positive correlations but
relatively weak, thus supporting the idea that altmetrics do not reflect the
same concept of impact as citations. Also, altmetric counts do not always
present a better filtering of highly cited publications than journal citation
scores. Altmetrics scores (particularly mentions in blogs) are able to identify
highly cited publications with higher levels of precision than journal citation
scores (JCS), but they have a lower level of recall. The value of altmetrics as
a complementary tool of citation analysis is highlighted, although more
research is suggested to disentangle the potential meaning and value of
altmetric indicators for research evaluation
A Rejoinder on Energy versus Impact Indicators
Citation distributions are so skewed that using the mean or any other central
tendency measure is ill-advised. Unlike G. Prathap's scalar measures (Energy,
Exergy, and Entropy or EEE), the Integrated Impact Indicator (I3) is based on
non-parametric statistics using the (100) percentiles of the distribution.
Observed values can be tested against expected ones; impact can be qualified at
the article level and then aggregated.Comment: Scientometrics, in pres
Large-scale structure of time evolving citation networks
In this paper we examine a number of methods for probing and understanding
the large-scale structure of networks that evolve over time. We focus in
particular on citation networks, networks of references between documents such
as papers, patents, or court cases. We describe three different methods of
analysis, one based on an expectation-maximization algorithm, one based on
modularity optimization, and one based on eigenvector centrality. Using the
network of citations between opinions of the United States Supreme Court as an
example, we demonstrate how each of these methods can reveal significant
structural divisions in the network, and how, ultimately, the combination of
all three can help us develop a coherent overall picture of the network's
shape.Comment: 10 pages, 6 figures; journal names for 4 references fixe
Marketing data: Has the rise of impact factor led to the fall of objective language in the scientific article?
The language of science should be objective and detached and should place data in the appropriate context. The aim of this commentary was to explore the notion that recent trends in the use of language have led to a loss of objectivity in the presentation of scientific data. The relationship between the value-laden vocabulary and impact factor among fundamental biomedical research and clinical journals has been explored. It appears that fundamental research journals of high impact factors have experienced a rise in value-laden terms in the past 25 years
Differences in Impact Factor Across Fields and Over Time
The bibliometric measure impact factor is a leading indicator of journal
influence, and impact factors are routinely used in making decisions ranging
from selecting journal subscriptions to allocating research funding to deciding
tenure cases. Yet journal impact factors have increased gradually over time,
and moreover impact factors vary widely across academic disciplines. Here we
quantify inflation over time and differences across fields in impact factor
scores and determine the sources of these differences. We find that the average
number of citations in reference lists has increased gradually, and this is the
predominant factor responsible for the inflation of impact factor scores over
time. Field-specific variation in the fraction of citations to literature
indexed by Thomson Scientific's Journal Citation Reports is the single greatest
contributor to differences among the impact factors of journals in different
fields. The growth rate of the scientific literature as a whole, and
cross-field differences in net size and growth rate of individual fields, have
had very little influence on impact factor inflation or on cross-field
differences in impact factor.Comment: 9 pages, 3 figure
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