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187 results for “multivariate analysis”

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zenodo40/100

Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

<p><strong>Data Description</strong>:</p> <p>To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using&nbsp;multivariate geographic clustering (MGC)&nbsp;approach coupled with machine learning models. First, we used the&nbsp;MGC approach&nbsp;to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological&nbsp;variables, and physiographic variables) to&nbsp;20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN)&nbsp;and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6&nbsp;+&nbsp;3.2 and 108.3&nbsp;+&nbsp;8.2 Pg C, respectively.</p> <p>Files in collection (32):</p> <p>Collection contains 22 soil properties geospatial rasters,&nbsp;4 soil SOC geospatial rasters,&nbsp;2 ISCN site&nbsp;SOC observations&nbsp;csv files, and 4 R scripts</p> <p>gNATSGO&nbsp;TIF files:</p> <p>├── available_water_storage_30arc_30cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[30 cm depth soil&nbsp;available&nbsp;water storage]<br> ├── available_water_storage_30arc_100cm_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [100 cm depth soil&nbsp;available&nbsp;water storage]<br> ├── caco3_30arc_30cm_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;[30 cm depth soil CaCO3 content]<br> ├── caco3_30arc_100cm_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [100 cm depth soil CaCO3 content]<br> ├── cec_30arc_30cm_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [30 cm depth soil cation exchange capacity]<br> ├── cec_30arc_100cm_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [100 cm depth soil cation exchange capacity]<br> ├── clay_30arc_30cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[30 cm depth soil clay content]<br> ├── clay_30arc_100cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[100 cm depth soil clay content]<br> ├── depthWT_30arc_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [depth to water table]<br> ├── kfactor_30arc_30cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[30 cm depth soil erosion factor]<br> ├── kfactor_30arc_100cm_us.tif&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [100 cm depth soil erosion factor]<br> ├── ph_30arc_100cm_us.tif &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [100 cm depth soil pH]<br> ├── ph_30arc_100cm_us.tif &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [30 cm depth soil pH]<br> ├── pondingFre_30arc_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [ponding frequency]<br> ├── sand_30arc_30cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [30 cm depth soil sand content]<br> ├── sand_30arc_100cm_us.tif&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[100 cm depth soil sand content]<br> ├── silt_30arc_30cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [30 cm depth soil silt content]<br> ├── silt_30arc_100cm_us.tif &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; [100 cm depth soil silt content]<br> ├── water_content_30arc_30cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;[30 cm depth soil water content]<br> └── water_content_30arc_100cm_us.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[100 cm depth soil water content]</p> <p>SOC TIF&nbsp;files:</p> <p>├──30cm SOC mean.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[30 cm depth soil SOC]<br> ├──100cm SOC mean.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[100 cm depth soil SOC]<br> ├──30cm SOC CV.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[30 cm depth soil SOC coefficient of variation]<br> └──100cm SOC CV.tif&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;[100 cm depth soil SOC&nbsp;coefficient of variation]</p> <p>site&nbsp;observations csv files:</p> <p>ISCN_rmNRCS_addNCSS_30cm.csv&nbsp; &nbsp; &nbsp; &nbsp;30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p>ISCN_rmNRCS_addNCSS_100cm.csv&nbsp; &nbsp; &nbsp; &nbsp;100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p><br> <strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.</p> <p><strong>Geospatial projection</strong>:&nbsp;</p> <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p>&nbsp;</p>

opencc-by-4.0Jan 2023View details →
zenodo36/100

A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.

<p>The&nbsp; spectra were provided as unprocessed raw data in the manufacturers data format (Bruker), as labelled two zip archives with SARS CoV 2 positives and negative, according to the reviewer&#39;s recommendation.</p> <p>This information belongs to the publication (in review in&nbsp; &nbsp;<em>Journal of Virological Methods</em>)<br> &quot;A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs&quot;<br> All the information belongs to the National Reference Institute, INEI-ANLIS DR CARLOS G MALBRAN, BUENOS AIRES, ARGENTINA.</p>

opencc-by-4.0Aug 2020View details →
dryad36/100

Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales

1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.

opencc-zeroDec 2016View details →
dryad36/100

Multivariate trait analysis reveals diatom plasticity constrained to a reduced set of biological axes

<p>Trait-based approaches to phytoplankton ecology have gained traction in recent decades as phenotypic traits are incorporated into ecological and biogeochemical models. Here, we use high-throughput phenotyping to explore both intra- and interspecific constraints on trait combinations that are expressed in the cosmopolitan marine diatom genus Thalassiosira. We demonstrate that within Thalassiosira, phenotypic diversity cannot be predicted from genotypic diversity, and moreover, plasticity can create highly divergent phenotypes that are incongruent with taxonomic grouping. Significantly, multivariate phenotypes can be represented in reduced dimensional space using principal component analysis with 77.7% of the variance captured by two orthogonal axes, here termed a 'trait-scape'. Furthermore, this trait-scape can be recovered with a reduced set of traits. Plastic responses to the new environments expanded phenotypic trait values and the trait-scape, however, the overall pattern of response to the new environments was similar between strains and many trait correlations remained constant. These findings demonstrate that trait-scapes can be used to reveal common constraints on multi-trait plasticity in phytoplankton with divergent underlying phenotypes. Understanding how to integrate trait correlational constraints and trade-offs into theoretical frameworks like biogeochemical models will be critical to predict how microbial responses to environmental change will impact elemental cycling now and into the future.</p>

opencc-zeroJun 2022View details →
zenodo36/100

Time-generalized multivariate analysis of EEG responses reveals a cascading architecture of semantic mismatch processing

<p>This entry includes the required data to conduct the analysis from&nbsp;our paper. It includes recordings, a look-up csv for target word onsets and montage file. Github page for analysis code:&nbsp;https://github.com/heikele/GAT_n4-p6</p> <p>&nbsp;</p> <p>Abstract from submitted paper:</p> <p>Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis &ndash; a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time &ndash; to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding&nbsp;indicatesthat early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time,&nbsp;inconsistent withpredictions of strictly serial models. These results&nbsp;are in accordance withan incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.</p> <p>&nbsp;</p> <p>Keywords: EEG; ERP; semantic mismatch; N400; P600; multivariate pattern analysis; generalization across time decoding</p>

opencc-by-4.0Feb 2018View details →
dryad36/100

Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data

The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.

opencc-zeroApr 2020View details →
dryad36/100

Data from: Fossil abalone of Europe and their relationships with modern Haliotis (Haliotidae, Gastropoda): A multivariate analysis

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publicAug 2024View details →
dryad36/100

Multivariate trait analysis reveals diatom plasticity constrained to a reduced set of biological axes

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publicJun 2022View details →
dryad36/100

Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data

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publicMay 2020View details →
dryad36/100

Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales

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publicAug 2018View details →
dryad36/100

Data from: Comparative analysis of the multivariate genetic architecture of morphological traits in three species of Gomphocerine grasshoppers

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publicOct 2019View details →
dryad36/100

Replicate analysis from: tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models

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publicApr 2025View details →
dryad32/100

Data from: Multivariate ratio analysis reveals Trigonoderus pedicellaris Thomson (Hymenoptera, Chalcidoidea, Pteromalidae) as a valid species

We demonstrate by multivariate ratio analysis (MRA) the validity of two female colour morphs as separate species in what was previously regarded as a single species, Trigonoderus cyanescens (Förster, 1841) (Hymenoptera: Pteromalidae). As a result, T. pedicellaris Thomson, 1878 stat.r., is resurrected from synonymy under T. cyanescens and T. filatus binubilatus Erdős, 1960 syn.n. is synonymized with T. cyanescens. More than 20 characters were measured as part of two MRA dataset analyses. The first analysis excluded all measurements related to the gaster, whereas the second included gaster length, gaster breadth and seventh gaster tergite breadth. The first analysis revealed that the best separating morphometric ratios for the two species are head breadth:metatibia length and OOL:parastigma length, whereas the second analysis revealed OOL:gaster length as the second best separating ratio. The measurement error of all characters was below the admissible level of 30%. Gaster length proved to be a good character for separating the two groups, showed the lowest measurement error, and its percentage coefficient of variation was not greater than for other characters. This indicates that gaster length should not be discarded out-of-hand as a morphometric character in Pteromalidae. The variables that gave the best separating ratios included different body parts; therefore we suggest that the body of a specimen should be taken as a whole for use in MRA analyses, where each distance measurement can interact freely with any other. A key, figures and re-descriptions of T. cyanescens and T. pedicellaris are provided.

opencc-zeroDec 2012View details →
dryad32/100

Data from: Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama

There is genetic evidence that the two species of Brazilian gray Mazama, Mazama gouazoubira and Mazama nemorivaga, belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M. gouazoubira and reddish for M. nemorivaga, and sperm head dimensions. Multivariate analysis of sperm head dimension and format data accurately discriminated three groups for species with total percentage of misclassified of 0.71. The individual analysis, by animal, and the multivariate analysis have also discriminated correctly all five animals (total percentage of misclassified of 13.95%), and the canonical plot has shown three different clusters: Cluster 1, including individuals of M. nemorivaga; Cluster 2, including two individuals of M. gouazoubira; and Cluster 3, including a single individual of M. gouazoubira. The results obtained in this work corroborate the hypothesis of the formation of new genera and species for gray Mazama. Moreover, the easily applied method described herein can be used as an auxiliary tool to identify sibling species of other taxonomic groups.

opencc-zeroDec 2015View details →
zenodo32/100

FIGURE 6 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)

FIGURE 6. Canonical Discriminant Analysis (CDA) plots for males and females together, including Georgian specimens. Specimens, sample centroids and group perimeters are represented. For details, percentage of variance explained, and other data, see text.

opennotspecifiedJun 2021View details →
zenodo32/100

FIGURE 3 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)

FIGURE 3. UPGMA trees (males) derived from Mahalanobis' distances among sample centroids. See text for interpretation.

opennotspecifiedJun 2021View details →
zenodo32/100

FIGURE 8 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)

FIGURE 8. Box Plots of dorsalia values in different populations of the D, clarkorum/dryada studied (numbers of samples as in Material and Methods): 1) Sümela, 2) Çataldere, 3) Hemşin, 4) Ayder, 5) Hatila Plateau, 6) Ortacalar 7) Subaşı &amp; Yoldere (Turkish "dryada"), 8) Cankurtaran Pass, 9) Karagöl, 10) Charnaly River Gorge (Georgian D. dryada). The center is the median, the length of the rectangle is the interquartile range (from the 25th to the 75th percentile) and represents the central 50% of data. Line is the spread from minimum to maximum values. Green point is a severe outlier.

opennotspecifiedJun 2021View details →
zenodo32/100

FIGURE 2 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)

FIGURE 2. Canonical Discriminant Analysis (CDA) plots for males. Specimens, sample centroids and group perimeters are represented. For details, percentage of variance explained, and other data, see text.

opennotspecifiedJun 2021View details →
zenodo32/100

FIGURE 5 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)

FIGURE 5. UPGMA trees (females) derived from Mahalanobis' distances among sample centroids. See text for interpretation.

opennotspecifiedJun 2021View details →
zenodo32/100

FIGURE 1. Map showing the localities from where specimens have been studied. 1 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)

FIGURE 1. Map showing the localities from where specimens have been studied. 1. Sümela, Maçka, Trabzon, northeastern Anatolia, Turkey, 2. Çataldere, Kaptanpaşa, Rize, northeastern Anatolia, Turkey, 3. 11 km southwest of Hemşin, Rize, northeastern Anatolia, Turkey, 4. Ayder Plateau, Çamlıhemşin, Rize, northeastern Anatolia, Turkey, 5. Hatila Plateau, Artvin, northeastern Anatolia, Turkey, 6. 16 km northeast of Ortacalar, Arhavi, Artvin, northeastern Anatolia, Turkey, 7. Subaşı Village, Hopa, Artvin, northeastern Anatolia, Turkey, 8. Cankurtaran, Hopa, Artvin, northeastern Anatolia, Turkey, 9. Karagöl, Borçka, Artvin, northeastern Anatolia, Turkey, 10. Charnaly, Georgia

opennotspecifiedJun 2021View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record