Chemistry research in Europe: A publication analysis (2006–2016)

2019 ◽  
pp. 016555151986549
Author(s):  
Hakan Kaygusuz
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Polymer Science ◽  
Chemistry Research ◽  
Research Outcome ◽  
Components Analysis ◽  
Number Of Publications

In this article, chemistry research in 51 different European countries between years 2006 and 2016 was studied using statistical methods. This study consists of two parts: In the first part, different economical, institutional and citation parameters were correlated with the number of publications, citations and chemical industry numbers using principal components analysis and hierarchical cluster analysis. The results of the first part indicated that economical and geographical parameters directly affect the chemistry research outcome. In the second part, research in branches of chemistry and related disciplines such as analytical chemistry, polymer science and physical chemistry were analysed using principal components analysis and hierarchical cluster analysis for each country. Publication data were collected as the number of chemistry publications (in Science Citation Index–Expanded (SCI-E)) between years 2006 and 2016 in different chemistry subdisciplines and related scientific areas. Results of the second part of the study produced geographical and economical clusters of countries, interestingly, without addition of any geographical data.

Ranking e valutazione: il caso delle classifiche delle universitŕ

2009 ◽  
pp. 81-114
Author(s):  
Ferruccio Biolcati Rinaldi ◽  
Daniele Checchi ◽  
Chiara Guglielmetti ◽  
Silvia Salini ◽  
Matteo Turri
Keyword(s):  
Cluster Analysis ◽  
Key Words ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Partial Replication ◽  
Validity And Reliability ◽  
University Ranking ◽  
International Ranking ◽  
Components Analysis ◽  
And Cluster Analysis

- Abstract The paper consists of two parts. The first is more general: it introduces to university ranking, shows the leading international ranking, discusses the uses people make of rankings. The second focuses on Italian ranking Censis-la Repubblica developing two different kinds of analyses: after considering indicators validity and reliability, principal components analysis and cluster analysis are applied to a partial replication of Censis-la Repubblica data. A list of points to pay attention comes out of these analyses: it can be useful when defining rankings of complex institutions such as universities.Key words: ranking, university ranking, Censis-la Repubblica, validity and reliability, normalisation and combination of indicators.

Multivariate Analysis of Remains of Molluscan Foods Consumed by Latest Pleistocene and Holocene Humans in Nerja Cave, Málaga, Spain

1997 ◽  
Vol 48 (2) ◽  
pp. 215-227 ◽  
Author(s):  
Francisco Serrano ◽  
Antonio Guerra-Merchán ◽  
Carmen Lozano-Francisco ◽  
José Luis Vera-Peláez
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Late Quaternary ◽  
Western Mediterranean ◽  
Temperature Curve ◽  
Sea Surface ◽  
The Third ◽  
Holocene Sediments ◽  
Components Analysis

AbstractNerja Cave is a karstic cavity used by humans from Late Paleolithic to post-Chalcolithic times. Remains of molluscan foods in the uppermost Pleistocene and Holocene sediments were studied with cluster analysis and principal components analysis, in bothQ and R modes. The results from cluster analysis distinguished interval groups mainly in accordance with chronology and distinguished assemblages of species mainly according to habitat. Significant changes in the shellfish diet through time were revealed. In the Late Magdalenian, most molluscs consumed consisted of pulmonate gastropods and species from sandy sea bottoms. The Epipaleolithic diet was more varied and included species from rocky shorelines. From the Neolithic onward most molluscs consumed were from rocky shorelines. From the principal components analysis inQ mode, the first factor reflected mainly changes in the predominant capture environment, probably because of major paleogeographic changes. The second factor may reflect selective capture along rocky coastlines during certain times. The third factor correlated well with the sea-surface temperature curve in the western Mediterranean (Alboran Sea) during the late Quaternary.

scholarly journals Morphological Differentiation of the Genus Niphargus (Amphipoda) on Corsica — A Numerical Approach

1984 ◽  
Vol 54 (1) ◽  
pp. 147-155
Author(s):  
W. Hovenkamp ◽  
F. Hovenkamp ◽  
J.J. van der Heide
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Morphological Differentiation ◽  
Taxonomic Status ◽  
Numerical Approach ◽  
Short Introduction ◽  
Size Dependent ◽  
Methods Of Analysis ◽  
Components Analysis

A short introduction is provided on the taxonomic status of the genus Niphargus, especially on the species related to N. longicaudatus corsicanus. Previous findings and descriptions are mentioned. An attempt is made to clarify the relationships between Corsican Niphargus populations by means of a cluster analysis and a principal components analysis combined with a cluster analysis. Special attention has been paid to the size-dependent variability of most of the characters. The results of both methods of analysis are compared with each other and evaluated. The morphological differentiation between populations is, on the average, greater than within populations. This, along with the large amount of character variability, makes it very difficult to fit populations into, or to distinguish them from, any of the — often poorly described — taxa of Niphargus.

scholarly journals Basic dimensions of experience of architectural objects’ expressiveness: Effect of expertise

Psihologija ◽  
2015 ◽  
Vol 48 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Slobodan Markovic ◽  
Djordje Alfirevic
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Processing Fluency ◽  
High Regularity ◽  
Components Analysis

The purpose of the present study was to compare the structure of experience of architectural expressiveness of architects and non-architects. Twenty architects and twenty non-architects rated twenty photographs of architectural objects on thirty expressiveness scales. Principal components analysis revealed four factors for both groups of participants: Aggressiveness, Regularity, Color and Aesthetics. In a cluster analysis two clusters of architectural objects were obtained: Choleric (high Aggressiveness and Color) and Phlegmatic (low Aggressiveness and Color, and high Regularity). All objects were highly rated on Aesthetics. Analysis of variance has shown that architects rated both clusters as less aggressive than non-architects. Also, experts rated the Phlegmatic cluster as more aesthetic, while nonexperts rated the Choleric cluster as more aesthetic. These results supported the Processing Fluency model: compared to non-architects, architects processed the expressive information of minimalistic objects (Phlegmatic cluster) with ease, which led towards positive hedonic reactions and higher.

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