Data from: "Size" and "shape" in the measurement of multivariate proximity ...
1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), le...
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ftdatacite:10.5061/dryad.6r5j8 2024-02-04T09:59:11+01:00 Data from: "Size" and "shape" in the measurement of multivariate proximity ... Greenacre, Michael 2018 https://dx.doi.org/10.5061/dryad.6r5j8 https://datadryad.org/stash/dataset/doi:10.5061/dryad.6r5j8 en eng Dryad https://dx.doi.org/10.1111/2041-210x.12776 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 visualization correspondence analysis Euclidean distance Ordination Bray-Curtis dissimilarity Cluster analysis logarithmic transformation Multivariate analysis chi-square distance Dataset dataset 2018 ftdatacite https://doi.org/10.5061/dryad.6r5j810.1111/2041-210x.12776 2024-01-05T04:39:59Z 1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as: (i) ordinations of the units, where interpoint distances optimally display the measured differences; (ii) clustering the units into homogeneous clusters; or (iii) assessing differences between pre-specified groups of units (e.g., regions, periods, treatment-control groups). 2. These methods all conceal a fundamental question: To what extent are the differences between the sampling units, computed according to the chosen proximity function, capturing the "size" in the multivariate observations, or their "shape"? "Size" means the overall level of the measurements: for example, some samples contain higher total abundances or more biomass, others less. "Shape" means the relative levels of the ... : Barents Sea fish abundancesAbundance counts of 41 fish species at 158 stations in the Barents Sea. This is a subset of a larger data set that spans more stations and years.fish.txtExperimental dataThree samples of size n=10 each, one from a control and two from two treatments. Each sample unit consists of abundances of 45 marine species.experiment.txt ... Dataset Barents Sea DataCite Metadata Store (German National Library of Science and Technology) Barents Sea Bray ENVELOPE(-114.067,-114.067,-74.833,-74.833) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
visualization correspondence analysis Euclidean distance Ordination Bray-Curtis dissimilarity Cluster analysis logarithmic transformation Multivariate analysis chi-square distance |
spellingShingle |
visualization correspondence analysis Euclidean distance Ordination Bray-Curtis dissimilarity Cluster analysis logarithmic transformation Multivariate analysis chi-square distance Greenacre, Michael Data from: "Size" and "shape" in the measurement of multivariate proximity ... |
topic_facet |
visualization correspondence analysis Euclidean distance Ordination Bray-Curtis dissimilarity Cluster analysis logarithmic transformation Multivariate analysis chi-square distance |
description |
1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as: (i) ordinations of the units, where interpoint distances optimally display the measured differences; (ii) clustering the units into homogeneous clusters; or (iii) assessing differences between pre-specified groups of units (e.g., regions, periods, treatment-control groups). 2. These methods all conceal a fundamental question: To what extent are the differences between the sampling units, computed according to the chosen proximity function, capturing the "size" in the multivariate observations, or their "shape"? "Size" means the overall level of the measurements: for example, some samples contain higher total abundances or more biomass, others less. "Shape" means the relative levels of the ... : Barents Sea fish abundancesAbundance counts of 41 fish species at 158 stations in the Barents Sea. This is a subset of a larger data set that spans more stations and years.fish.txtExperimental dataThree samples of size n=10 each, one from a control and two from two treatments. Each sample unit consists of abundances of 45 marine species.experiment.txt ... |
format |
Dataset |
author |
Greenacre, Michael |
author_facet |
Greenacre, Michael |
author_sort |
Greenacre, Michael |
title |
Data from: "Size" and "shape" in the measurement of multivariate proximity ... |
title_short |
Data from: "Size" and "shape" in the measurement of multivariate proximity ... |
title_full |
Data from: "Size" and "shape" in the measurement of multivariate proximity ... |
title_fullStr |
Data from: "Size" and "shape" in the measurement of multivariate proximity ... |
title_full_unstemmed |
Data from: "Size" and "shape" in the measurement of multivariate proximity ... |
title_sort |
data from: "size" and "shape" in the measurement of multivariate proximity ... |
publisher |
Dryad |
publishDate |
2018 |
url |
https://dx.doi.org/10.5061/dryad.6r5j8 https://datadryad.org/stash/dataset/doi:10.5061/dryad.6r5j8 |
long_lat |
ENVELOPE(-114.067,-114.067,-74.833,-74.833) |
geographic |
Barents Sea Bray |
geographic_facet |
Barents Sea Bray |
genre |
Barents Sea |
genre_facet |
Barents Sea |
op_relation |
https://dx.doi.org/10.1111/2041-210x.12776 |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.6r5j810.1111/2041-210x.12776 |
_version_ |
1789963855776972800 |