383 research outputs found
Identifying Research Fields within Business and Management: A Journal Cross-Citation Analysis
A discipline such as business and management (B&M) is very broad and has many fields within it, ranging from fairly scientific ones such as management science or economics to softer ones such as information systems. There are at least three reasons why it is important to identify these sub-fields accurately. Firstly, to give insight into the structure of the subject area and identify perhaps unrecognised commonalities; second for the purpose of normalizing citation data as it is well known that citation rates vary significantly between different disciplines. And thirdly, because journal rankings and lists tend to split their classifications into different subjects – for example, the Association of Business Schools (ABS) list, which is a standard in the UK, has 22 different fields. Unfortunately, at the moment these are created in an ad hoc manner with no underlying rigour. The purpose of this paper is to identify possible sub-fields in B&M rigorously based on actual citation patterns. We have examined 450 journals in B&M which are included in the ISI Web of Science (WoS) and analysed the cross-citation rates between them enabling us to generate sets of coherent and consistent sub-fields that minimise the extent to which journals appear in several categories. Implications and limitations of the analysis are discussed
Assessing the impact of biomedical research in academic institutions of disparate sizes
Abstract Background The evaluation of academic research performance is nowadays a priority issue. Bibliometric indicators such as the number of publications, total citation counts and h-index are an indispensable tool in this task but their inherent association with the size of the research output may result in rewarding high production when evaluating institutions of disparate sizes. The aim of this study is to propose an indicator that may facilitate the comparison of institutions of disparate sizes. Methods The Modified Impact Index (MII) was defined as the ratio of the observed h-index (h) of an institution over the h-index anticipated for that institution on average, given the number of publications (N) it produces i.e. (α and β denote the intercept and the slope, respectively, of the line describing the dependence of the h-index on the number of publications in log10 scale). MII values higher than 1 indicate that an institution performs better than the average, in terms of its h-index. Data on scientific papers published during 2002–2006 and within 36 medical fields for 219 Academic Medical Institutions from 16 European countries were used to estimate α and β and to calculate the MII of their total and field-specific production. Results From our biomedical research data, the slope β governing the dependence of h-index on the number of publications in biomedical research was found to be similar to that estimated in other disciplines (≈0.4). The MII was positively associated with the average number of citations/publication (r = 0.653, p Conclusion The MII should complement the use of h-index when comparing the research output of institutions of disparate sizes. It has a conceptual interpretation and, with the data provided here, can be computed for the total research output as well as for field-specific publication sets of institutions in biomedicine.</p
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
Cardinal temperature differences, determined in vitro, between closely related species and subspecies of pectinolytic bacteria responsible for blackleg and soft rot on potatoes
Potato blackleg and soft rot cause major losses
and are caused by two bacterial genera, Pectobacterium
and Dickeya. Species affecting potatoes are
Pectobacterium atrosepticum (Pba), Pectobacterium
carotovorum subsp. carotovorum (Pcc), Pectobacterium
carotovorum subsp. brasiliense (Pcb), Pectobacterium
wasabiae (Pwa), Dickeya dadantii (Dda) and Dickeya
solani (Dso). Pathogenicity of these species is dependent
on temperature, with each species having its own optimal
temperature and temperature range for growth, leading to
varying degrees of losses. Pectobacterium atrosepticum,
a temperature sensitive species, mainly occurs in temperate
climates, Pcc in temperate to tropical, and Dickeya
spp. in subtropical environments. The aim of this study
was to determine the cardinal growth temperatures for the
species responsible for blackleg and soft rot in vitro.
Bacterial isolates were incubated in a temperature gradient
shaking incubator at 30 different temperatures ranging
from ±5 °C to ±56 °C, and growth measured at two set time intervals. Results were statistically analysed
using the Gaussian function. The optimal temperature
of 31 °C and temperature range of 20 °C to 38 °C for
Pectobacterium carotovorum subsp. brasiliense, was
similar to those recorded for Pcc. Pectobacterium
wasabiae grew at an optimal temperature of 29 °C and
range of 20 °C to 34 °C. Higher optimal temperatures of
32 °Cand 34 °C,with ranges of 21 °C to 38 °Cand 23 °C
to 41 °C were recorded for Dda and Dso, respectively.
The minimal variation in optimal temperatures between
different species might be an indication that temperature
ranges, rather than optimal temperature, play an important
role in disease development. Results for Dso, which
has not yet been reported in South Africa, are especially
important in light of prevailing temperatures in South
African potato production regions.Technology and Human Resources in Industry Programme (THRIP) and Potatoes South Africahttp://link.springer.com/journal/106582017-02-20hb201
Runaway Events Dominate the Heavy Tail of Citation Distributions
Statistical distributions with heavy tails are ubiquitous in natural and
social phenomena. Since the entries in heavy tail have disproportional
significance, the knowledge of its exact shape is very important. Citations of
scientific papers form one of the best-known heavy tail distributions. Even in
this case there is a considerable debate whether citation distribution follows
the log-normal or power-law fit. The goal of our study is to solve this debate
by measuring citation distribution for a very large and homogeneous data. We
measured citation distribution for 418,438 Physics papers published in
1980-1989 and cited by 2008. While the log-normal fit deviates too strong from
the data, the discrete power-law function with the exponent does
better and fits 99.955% of the data. However, the extreme tail of the
distribution deviates upward even from the power-law fit and exhibits a
dramatic "runaway" behavior. The onset of the runaway regime is revealed
macroscopically as the paper garners 1000-1500 citations, however the
microscopic measurements of autocorrelation in citation rates are able to
predict this behavior in advance.Comment: 6 pages, 5 Figure
Towards a new crown indicator: an empirical analysis
We present an empirical comparison between two normalization mechanisms for citation-based indicators of research performance. These mechanisms aim to normalize citation counts for the field and the year in which a publication was published. One mechanism is applied in the current so-called crown indicator of our institute. The other mechanism is applied in the new crown indicator that our institute is currently exploring. We find that at high aggregation levels, such as at the level of large research institutions or at the level of countries, the differences between the two mechanisms are very small. At lower aggregation levels, such as at the level of research groups or at the level of journals, the differences between the two mechanisms are somewhat larger. We pay special attention to the way in which recent publications are handled. These publications typically have very low citation counts and should therefore be handled with special care
Measuring co-authorship and networking-adjusted scientific impact
Appraisal of the scientific impact of researchers, teams and institutions
with productivity and citation metrics has major repercussions. Funding and
promotion of individuals and survival of teams and institutions depend on
publications and citations. In this competitive environment, the number of
authors per paper is increasing and apparently some co-authors don't satisfy
authorship criteria. Listing of individual contributions is still sporadic and
also open to manipulation. Metrics are needed to measure the networking
intensity for a single scientist or group of scientists accounting for patterns
of co-authorship. Here, I define I1 for a single scientist as the number of
authors who appear in at least I1 papers of the specific scientist. For a group
of scientists or institution, In is defined as the number of authors who appear
in at least In papers that bear the affiliation of the group or institution. I1
depends on the number of papers authored Np. The power exponent R of the
relationship between I1 and Np categorizes scientists as solitary (R>2.5),
nuclear (R=2.25-2.5), networked (R=2-2.25), extensively networked (R=1.75-2) or
collaborators (R<1.75). R may be used to adjust for co-authorship networking
the citation impact of a scientist. In similarly provides a simple measure of
the effective networking size to adjust the citation impact of groups or
institutions. Empirical data are provided for single scientists and
institutions for the proposed metrics. Cautious adoption of adjustments for
co-authorship and networking in scientific appraisals may offer incentives for
more accountable co-authorship behaviour in published articles.Comment: 25 pages, 5 figure
The effect of aging on network structure
In network evolution, the effect of aging is universal: in scientific
collaboration network, scientists have a finite time span of being active; in
movie actors network, once popular stars are retiring from stage; devices on
the Internet may become outmoded with techniques developing so rapidly. Here we
find in citation networks that this effect can be represented by an exponential
decay factor, , where is the node age, while other
evolving networks (the Internet for instance) may have different types of
aging, for example, a power-law decay factor, which is also studied and
compared. It has been found that as soon as such a factor is introduced to the
Barabasi-Albert Scale-Free model, the network will be significantly
transformed. The network will be clustered even with infinitely large size, and
the clustering coefficient varies greatly with the intensity of the aging
effect, i.e. it increases linearly with for small values of
and decays exponentially for large values of . At the same time, the
aging effect may also result in a hierarchical structure and a disassortative
degree-degree correlation. Generally the aging effect will increase the average
distance between nodes, but the result depends on the type of the decay factor.
The network appears like a one-dimensional chain when exponential decay is
chosen, but with power-law decay, a transformation process is observed, i.e.,
from a small-world network to a hypercubic lattice, and to a one-dimensional
chain finally. The disparities observed for different choices of the decay
factor, in clustering, average node distance and probably other aspects not yet
identified, are believed to bear significant meaning on empirical data
acquisition.Comment: 8 pages, 9 figures,V2, accepted for publication in Phys. Rev.
Highly clustered scale-free networks
We propose a model for growing networks based on a finite memory of the
nodes. The model shows stylized features of real-world networks: power law
distribution of degree, linear preferential attachment of new links and a
negative correlation between the age of a node and its link attachment rate.
Notably, the degree distribution is conserved even though only the most
recently grown part of the network is considered. This feature is relevant
because real-world networks truncated in the same way exhibit a power-law
distribution in the degree. As the network grows, the clustering reaches an
asymptotic value larger than for regular lattices of the same average
connectivity. These high-clustering scale-free networks indicate that memory
effects could be crucial for a correct description of the dynamics of growing
networks.Comment: 6 pages, 4 figure
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