Climate change and viticulture - a quantitative analysis of a highly dynamic research field

In this study, we analyzed the newly emerging research field of climate change in combination with viticulture. Our analyses have two objectives: First, the overall publication output and the growth of research on climate change and viticulture is presented and analyzed. We developed a sophisticated search query to cover the relevant literature as completely as possible and to exclude irrelevant literature. The time evolution of the publications of the research topic as well as the most contributing journals and countries of authors, and the major research areas are presented. Second, most important publications in the historical context of this field are identified. Both analyses are based on a carefully selected publication set of 1039 papers (articles, reviews, and conference proceedings) dealing with the implications of climate change for viticulture. The results reveal that the number of papers published per year dealing with climate change and viticulture shows a strong increase: Since around 2000, the number increased by a factor of ten, whereas in the same time period the overall number of papers increased by a factor of around two. We identified 14 citation classics which include fundamental early works of viticulture with a weak connection to climate change and more recent works with a stronger connection to climate change

The substantive and practical significance of citation impact differences between institutions: Guidelines for the analysis of percentiles using effect sizes and confidence intervals

In our chapter we address the statistical analysis of percentiles: How should the citation impact of institutions be compared? In educational and psychological testing, percentiles are already used widely as a standard to evaluate an individual's test scores - intelligence tests for example - by comparing them with the percentiles of a calibrated sample. Percentiles, or percentile rank classes, are also a very suitable method for bibliometrics to normalize citations of publications in terms of the subject category and the publication year and, unlike the mean-based indicators (the relative citation rates), percentiles are scarcely affected by skewed distributions of citations. The percentile of a certain publication provides information about the citation impact this publication has achieved in comparison to other similar publications in the same subject category and publication year. Analyses of percentiles, however, have not always been presented in the most effective and meaningful way. New APA guidelines (American Psychological Association, 2010) suggest a lesser emphasis on significance tests and a greater emphasis on the substantive and practical significance of findings. Drawing on work by Cumming (2012) we show how examinations of effect sizes (e.g. Cohen's d statistic) and confidence intervals can lead to a clear understanding of citation impact differences

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

Do citations and readership identify seminal publications?

This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline

Does the Committee Peer Review Select the Best Applicants for Funding? An Investigation of the Selection Process for Two European Molecular Biology Organization Programmes

Does peer review fulfill its declared objective of identifying the best science and the best scientists? In order to answer this question we analyzed the Long-Term Fellowship and the Young Investigator programmes of the European Molecular Biology Organization. Both programmes aim to identify and support the best post doctoral fellows and young group leaders in the life sciences. We checked the association between the selection decisions and the scientific performance of the applicants. Our study involved publication and citation data for 668 applicants to the Long-Term Fellowship programme from the year 1998 (130 approved, 538 rejected) and 297 applicants to the Young Investigator programme (39 approved and 258 rejected applicants) from the years 2001 and 2002. If quantity and impact of research publications are used as a criterion for scientific achievement, the results of (zero-truncated) negative binomial models show that the peer review process indeed selects scientists who perform on a higher level than the rejected ones subsequent to application. We determined the extent of errors due to over-estimation (type I errors) and under-estimation (type 2 errors) of future scientific performance. Our statistical analyses point out that between 26% and 48% of the decisions made to award or reject an application show one of both error types. Even though for a part of the applicants, the selection committee did not correctly estimate the applicant's future performance, the results show a statistically significant association between selection decisions and the applicants' scientific achievements, if quantity and impact of research publications are used as a criterion for scientific achievement

Benchmarking scientific performance by decomposing leadership of Cuban and Latin American institutions in Public Health

This is a post-peer-review, pre-copyedit version of an article published in Scientometrics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11192-015-1831-z”.Comparative benchmarking with bibliometric indicators can be an aid in decision-making with regard to research management. This study aims to characterize scientific performance in a domain (Public Health) by the institutions of a country (Cuba), taking as reference world output and regional output (other Latin American centers) during the period 2003–2012. A new approach is used here to assess to what extent the leadership of a specific institution can change its citation impact. Cuba was found to have a high level of specialization and scientific leadership that does not match the low international visibility of Cuban institutions. This leading output appears mainly in non-collaborative papers, in national journals; publication in English is very scarce and the rate of international collaboration is very low. The Instituto de Medicina Tropical Pedro Kouri stands out, alone, as a national reference. Meanwhile, at the regional level, Latin American institutions deserving mention for their high autonomy in normalized citation would include Universidad de Buenos Aires (ARG), Universidade Federal de Pelotas (BRA), Consejo Nacional de Investigaciones Cientı´ficas y Te´cnicas (ARG), Instituto Oswaldo Cruz (BRA) and the Centro de Pesquisas Rene Rachou (BRA). We identify a crucial aspect that can give rise to misinterpretations of data: a high share of leadership cannot be considered positive for institutions when it is mainly associated with a high proportion of non-collaborative papers and a very low level of performance. Because leadership might be questionable in some cases, we propose future studies to ensure a better interpretation of findings.This work was made possible through financing by the scholarship funds for international mobility between Andalusian and IberoAmerican Universities and the SCImago GroupPeer reviewe

Proposals for evaluating the regularity of a scientist'sresearch output

Evaluating the career of individual scientists according to their scientific output is a common bibliometric problem. Two aspects are classically taken into account: overall productivity and overall diffusion/impact, which can be measured by a plethora of indicators that consider publications and/or citations separately or synthesise these two quantities into a single number (e.g. h-index). A secondary aspect, which is sometimes mentioned in the rules of competitive examinations for research position/promotion, is time regularity of one researcher's scientific output. Despite the fact that it is sometimes invoked, a clear definition of regularity is still lacking. We define it as the ability of generating an active and stable research output over time, in terms of both publications/ quantity and citations/diffusion. The goal of this paper is introducing three analysis tools to perform qualitative/quantitative evaluations on the regularity of one scientist's output in a simple and organic way. These tools are respectively (1) the PY/CY diagram, (2) the publication/citation Ferrers diagram and (3) a simplified procedure for comparing the research output of several scientists according to their publication and citation temporal distributions (Borda's ranking). Description of these tools is supported by several examples

Cell type-specific assessment of cholesterol distribution in models of neurodevelopmental disorders

Most nervous system disorders manifest through alterations in neuronal signaling based on abnormalities in neuronal excitability, synaptic transmission, and cell survival. However, such neuronal phenotypes are frequently accompanied – or even caused – by metabolic dysfunctions in neuronal or non-neuronal cells. The tight packing and highly heterogenous properties of neural, glial and vascular cell types pose significant challenges to dissecting metabolic aspects of brain disorders. Perturbed cholesterol homeostasis has recently emerged as key parameter associated with sub-sets of neurodevelopmental disorders. However, approaches for tracking and visualizing endogenous cholesterol distribution in the brain have limited capability of resolving cell type-specific differences. We here develop tools for genetically-encoded sensors that report on cholesterol distribution in the mouse brain with cellular resolution. We apply these probes to examine sub-cellular cholesterol accumulation in two genetic mouse models of neurodevelopmental disorders, Npc1 and Ptchd1 knock-out mice. While both genes encode proteins with sterol-sensing domains that have been implicated in cholesterol transport, we uncover highly selective and cell type-specific phenotypes in cholesterol homeostasis. The tools established in this work should facilitate probing sub-cellular cholesterol distribution in complex tissues like the mammalian brain and enable capturing cell type-specific alterations in cholesterol flow between cells in models of brain disorders