World's Best Scientists 2026 revealed!
Vera Pawlowsky-Glahn

Vera Pawlowsky-Glahn

D-Index & Metrics

Mathematics

D-Index
34
Citations
7721
World Ranking
2854
National Ranking
61

Vera Pawlowsky-Glahn publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Vera Pawlowsky-Glahn sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 82 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 112 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Vera Pawlowsky-Glahn D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Vera Pawlowsky-Glahn sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 137 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 34 D-Index — 21st percentile

21% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Vera Pawlowsky-Glahn is affiliated with the University of Girona in Spain. Their research spans multiple fields, primarily focusing on Computer Science, Environmental Science, and Engineering. Within these domains, their work is concentrated in subfields such as Artificial Intelligence, Geophysics, Environmental Engineering, Mechanics of Materials, and Pollution.

The scientist's main topics of study include:

  • Geochemistry and Geologic Mapping
  • Geological and Geochemical Analysis
  • Soil Geostatistics and Mapping
  • Hydrocarbon Exploration and Reservoir Analysis
  • Heavy Metals in Environment
  • Radioactivity and Radon Measurements
  • Mineral Processing and Grinding

Vera Pawlowsky-Glahn has published extensively in notable scientific venues. Frequent publication outlets include:

  • Journal of Geochemical Exploration
  • Mathematical Geosciences
  • The Science of The Total Environment
  • NAR Genomics and Bioinformatics
  • Frontiers in Microbiology

Several recent papers exemplify the research breadth and depth of this scientist. These include:

  • A compositional approach to the reconstruction of geochemical processes involved in the evolution of Holocene marine flooded coastal karst basins (Mljet Island, Croatia), 2020, Applied Geochemistry
  • Compositional baseline assessments to address soil pollution: An application in Langreo, Spain, 2021, The Science of The Total Environment
  • Chronic kidney disease of unknown origin is associated with environmental urbanisation in Belfast, UK, 2020, Environmental Geochemistry and Health
  • Compositional Data in Geostatistics: A Log-Ratio Based Framework to Analyze Regionalized Compositions, 2020, Mathematical Geosciences
  • Some thoughts on counts in sequencing studies, 2020, NAR Genomics and Bioinformatics

Vera Pawlowsky-Glahn collaborates frequently with several co-authors, contributing to multidisciplinary research efforts. Prominent co-authors include:

  • Juan José Egozcue
  • Behnam Sadeghi
  • Antonella Buccianti
  • Ivan Razum
  • Slobodan Miko

Best Publications

  • Isometric Logratio Transformations for Compositional Data Analysis

    J. J. Egozcue;V. Pawlowsky-Glahn;G. Mateu-Figueras;C. Barceló-Vidal

  • Compositional data analysis : theory and applications

    Vera Pawlowsky-Glahn;Antonella Buccianti

  • Groups of Parts and Their Balances in Compositional Data Analysis

    J. J. Egozcue;V. Pawlowsky-Glahn

  • Modeling and Analysis of Compositional Data

    Vera Pawlowsky-Glahn;Juan J. Egozcue;Raimon Tolosana-Delgado

  • Logratio Analysis and Compositional Distance

    J. Aitchison;C. Barceló-Vidal;J. A. Martín-Fernández;V. Pawlowsky-Glahn

  • Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation

    J. A. Martín-Fernández;C. Barceló-Vidal;V. Pawlowsky-Glahn

  • Geometric approach to statistical analysis on the simplex

    V. Pawlowsky-Glahn;J. J. Egozcue

  • Modelling and Analysis of Compositional Data: Pawlowsky-Glahn/Modelling and Analysis of Compositional Data

    Vera Pawlowsky-Glahn;Juan José Egozcue;Raimon Tolosana-Delgado

  • Proportionality : a valid alternative to correlation for relative data

    David Lovell;Vera Pawlowsky-Glahn;Juan José Egozcue;Samuel Marguerat

  • Simplicial geometry for compositional data

    J. J. Egozcue;V. Pawlowsky-Glahn

  • Geostatistical Analysis of Compositional Data

    Vera Pawlowsky-Glahn;Richardo A. Olea

  • Compositional Data Analysis in the Geosciences: From Theory to Practice

    Antonella Buccianti;G. Mateu-Figueras;Vera Pawlowsky-Glahn

  • Modelling Compositional Change: The Example of Chemical Weathering of Granitoid Rocks

    Hilmar von Eynatten;Carles Barceló-Vidal;Vera Pawlowsky-Glahn

  • BLU estimators and compositional data

    Vera Pawlowsky-Glahn;Juan José Egozcue

  • New Perspectives on Water Chemistry and Compositional Data Analysis

    A. Buccianti;V. Pawlowsky-Glahn

  • The Principle of Working on Coordinates

    Glòria Mateu‐Figueras;Vera Pawlowsky‐Glahn;Juan José Egozcue

  • Composition and Discrimination of Sandstones: A Statistical Evaluation of Different Analytical Methods

    Hilmar von Eynatten;Carles Barceló-Vidal;Vera Pawlowsky-Glahn

  • Exploring Compositional Data with the CoDa-Dendrogram

    Vera Pawlowsky-Glahn;Juan Jose Egozcue

  • Bayes hilbert spaces

    Karl Gerald van den Boogaart;Juan José Egozcue;Vera Pawlowsky-Glahn

  • Relative vs. absolute statistical analysis of compositions: a comparative study of surface waters of a Mediterranean river.

    N. Otero;R. Tolosana-Delgado;A. Soler;V. Pawlowsky-Glahn

  • Understanding perturbation on the simplex: A simple method to better visualize and interpret compositional data in ternary diagrams

    Hilmar von Eynatten;Vera Pawlowsky-Glahn;Juan José Egozcue

  • Zero Replacement in Compositional Data Sets

    J. A. Martín-Fernández;C. Barceló-Vidal;V. Pawlowsky-Glahn

  • Compositional data: the sample space and its structure

    Juan José Egozcue;Vera Pawlowsky-Glahn

  • Lecture Notes on Compositional Data Analysis

    Vera Pawlowsky-Glahn;Juan José Egozcue;Raimon Tolosana Delgado

  • on Compositional Data Analysis

    V. Pawlowsky-Glahn;J. J. Egozcue;R. Tolosana-Delgado

Frequent Co-Authors

Gregory B. Gloor
Gregory B. Gloor University of Western Ontario
Hilmar von Eynatten
Hilmar von Eynatten University of Göttingen
Jürg Bähler
Jürg Bähler University College London
Helmut Lammer
Helmut Lammer Austrian Academy of Sciences
Roger Paredes
Roger Paredes University of Vic - Central University of Catalonia
Albert Soler
Albert Soler University of Barcelona
Rainer Abart
Rainer Abart University of Vienna
Mário Cachão
Mário Cachão University of Lisbon

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