Vera Pawlowsky-Glahn

Artificial Intelligence · Frontiers in Microbiology · 2000–2019

A researcher in the Science corpus: 18 papers; trusts 22 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #2,420 of 35,345 by trust here, #3,978 by citations
• Molecular Biology — 16 citations into it
• Environmental Engineering — 12 citations into it; #307 of 5,479 by trust here, #1,912 by citations
• Sociology and Political Science — 10 citations into it
• Ecology, Evolution, Behavior and Systematics — 9 citations into it
• Mechanics of Materials — 9 citations into it; #160 of 6,291 by trust here, #492 by citations
• Nature and Landscape Conservation — 9 citations into it
• Geochemistry and Petrology — 7 citations into it

Whom Vera Pawlowsky-Glahn trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Juan José Egozcue
2. J. Aitchison
3. James R. Schott
4. Michael J. Greenacre
5. Trevor J. Hastie
6. C. Barceló‐Vidal
7. Josep-Antoni Martín-Fernández
8. Dean Billheimer
9. Robert Tibshirani
10. Hubert W. Lilliefors

Who trusts Vera Pawlowsky-Glahn overall

1. Juan José Egozcue
2. Karel Hron
3. Josep-Antoni Martín-Fernández
4. K. Gerald van den Boogaart
5. Peter Filzmoser
6. Matthias Templ
7. Jonathan Friedman
8. J. Palarea‐Albaladejo
9. Thomas J. Leininger
10. J. Aitchison

Papers from Vera Pawlowsky-Glahn's perspective

Ordered by how strongly the researchers Vera Pawlowsky-Glahn trusts endorse each paper. Same corpus, a different order for every researcher.

1. Biplots of Compositional Data
2. Inferring Correlation Networks from Genomic Survey Data (PLoS Comput. Biol.)
3. Groups of Parts and Their Balances in Compositional Data Analysis
4. Reply to “On the Harker Variation Diagrams; …” by J.A. Cortés
5. Integrating diverse genomic data using gene sets (Genome Biology)
6. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (PLoS Comput. Biol.)
7. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
8. UniFrac: an effective distance metric for microbial community comparison (The ISME Journal)
9. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (Microbiome)
10. Density Estimation for Statistics and Data Analysis

Their papers

• Microbiome Datasets Are Compositional: And This Is Not Optional (2017) — 2,474 citations
• Compositional data: the sample space and its structure (2019) — 113 citations
• Tools for compositional data with a total (2015) — 71 citations
• Modelling and Analysis of Compositional Data: Pawlowsky-Glahn/Modelling and Analysis of Compositional Data (2015) — 272 citations
• Bayes Hilbert Spaces (2014) — 130 citations
• Exploring Compositional Data with the CoDa-Dendrogram (2011) — 109 citations
• Kolmogorov–Smirnov test for spatially correlated data (2009) — 49 citations
• on Compositional Data Analysis (2007) — 276 citations
• Simplicial geometry for compositional data (2006) — 190 citations
• Hilbert Space of Probability Density Functions Based on Aitchison Geometry (2006) — 151 citations
• Groups of Parts and Their Balances in Compositional Data Analysis (2005) — 641 citations
• Geostatistical Analysis of Compositional Data (2004) — 146 citations
• Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation (2003) — 601 citations
• Isometric Logratio Transformations for Compositional Data Analysis (2003) — 1,866 citations
• BLU Estimators and Compositional Data (2002) — 123 citations
• Geometric approach to statistical analysis on the simplex (2001) — 420 citations
• Zero Replacement in Compositional Data Sets (2000) — 72 citations
• Logratio Analysis and Compositional Distance (2000) — 509 citations