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DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry

Quantitative shape analysis of juvenile pyroclasts is applied in volcanology to reconstruct the dynamics and styles of eruptions, and to explore the details of tephra transport, dispersal, and emplacement. Morphometric analyses often include comparison of multiple data sets with a set of dimensionle...

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Autores principales: Dürig, T., Schmidt, L. S., White, J. D. L., Bowman, M. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730387/
https://www.ncbi.nlm.nih.gov/pubmed/33303855
http://dx.doi.org/10.1038/s41598-020-78698-0
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author Dürig, T.
Schmidt, L. S.
White, J. D. L.
Bowman, M. H.
author_facet Dürig, T.
Schmidt, L. S.
White, J. D. L.
Bowman, M. H.
author_sort Dürig, T.
collection PubMed
description Quantitative shape analysis of juvenile pyroclasts is applied in volcanology to reconstruct the dynamics and styles of eruptions, and to explore the details of tephra transport, dispersal, and emplacement. Morphometric analyses often include comparison of multiple data sets with a set of dimensionless shape parameters. Here we present “DendroScan”, an open source Matlab program that provides the user with all the multivariate statistical methods needed to produce such morphometric comparisons. Serving as a statistical “toolbox”, DendroScan conducts Levene-, t-, and equivalence tests, presenting the results in ad hoc interpretable graphs. Furthermore, it is designed to conduct dendrogrammatic analyses of particle morphometry, a recently developed approach for the inter-comparison of multiple morphometric data sets. DendroScan produces tree diagrams, in which the analysed samples are sorted according to their morphometric dissimilarity, allowing the user to identify, e.g., samples that are statistically equivalent. To demonstrate DendroScan’s potential, ten experimental samples are compared with volcanic ash samples generated by the Havre 2012 deep-sea eruption in the Kermadec arc (New Zealand). We show how, using DendroScan-based results, information on the eruptive mechanism can be inferred, and how the cooling history of the experimental melt is reflected in the dissimilarity of thermally granulated fragments.
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spelling pubmed-77303872020-12-14 DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry Dürig, T. Schmidt, L. S. White, J. D. L. Bowman, M. H. Sci Rep Article Quantitative shape analysis of juvenile pyroclasts is applied in volcanology to reconstruct the dynamics and styles of eruptions, and to explore the details of tephra transport, dispersal, and emplacement. Morphometric analyses often include comparison of multiple data sets with a set of dimensionless shape parameters. Here we present “DendroScan”, an open source Matlab program that provides the user with all the multivariate statistical methods needed to produce such morphometric comparisons. Serving as a statistical “toolbox”, DendroScan conducts Levene-, t-, and equivalence tests, presenting the results in ad hoc interpretable graphs. Furthermore, it is designed to conduct dendrogrammatic analyses of particle morphometry, a recently developed approach for the inter-comparison of multiple morphometric data sets. DendroScan produces tree diagrams, in which the analysed samples are sorted according to their morphometric dissimilarity, allowing the user to identify, e.g., samples that are statistically equivalent. To demonstrate DendroScan’s potential, ten experimental samples are compared with volcanic ash samples generated by the Havre 2012 deep-sea eruption in the Kermadec arc (New Zealand). We show how, using DendroScan-based results, information on the eruptive mechanism can be inferred, and how the cooling history of the experimental melt is reflected in the dissimilarity of thermally granulated fragments. Nature Publishing Group UK 2020-12-10 /pmc/articles/PMC7730387/ /pubmed/33303855 http://dx.doi.org/10.1038/s41598-020-78698-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Dürig, T.
Schmidt, L. S.
White, J. D. L.
Bowman, M. H.
DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
title DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
title_full DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
title_fullStr DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
title_full_unstemmed DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
title_short DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
title_sort dendroscan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730387/
https://www.ncbi.nlm.nih.gov/pubmed/33303855
http://dx.doi.org/10.1038/s41598-020-78698-0
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