cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20407651.v1
title: Hounslow_GAMM_Plots_6.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
DOI: 10.6084/m9.figshare.20407657
title: Hounslow_Model_selection_4 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to perform FSS model selection
DOI: 10.6084/m9.figshare.20407657.v2
title: Hounslow_Model_selection_4 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to perform FSS model selection
DOI: 10.6084/m9.figshare.20407660.v2
title: All_Dives from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Raw data: processed dives used for analyses. Rows are individual dives and columns are described in a separate tab within the csv file.
DOI: 10.6084/m9.figshare.20407660
title: All_Dives from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Raw data: processed dives used for analyses. Rows are individual dives and columns are described in a separate tab within the csv file.
DOI: 10.6084/m9.figshare.20407663.v2
title: Hounslow _GAMMS_5 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code for generalised additive mixed models and generate results for Table 4 in main article.
DOI: 10.6084/m9.figshare.20407666
title: Hounslow_Clustering_2 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to perform k-means and hierarchical cluster analyses and associated figures
DOI: 10.6084/m9.figshare.20407666.v2
title: Hounslow_Clustering_2 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to perform k-means and hierarchical cluster analyses and associated figures
DOI: 10.6084/m9.figshare.20407672
title: Hounslow_PCA_3 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to perform principal component analyses and associated figures
DOI: 10.6084/m9.figshare.20407657.v1
title: Hounslow_Model_selection_4.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
DOI: 10.6084/m9.figshare.20407660.v1
title: All_Dives.xlsx from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
DOI: 10.6084/m9.figshare.20407663
title: Hounslow _GAMMS_5 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code for generalised additive mixed models and generate results for Table 4 in main article.
DOI: 10.6084/m9.figshare.20407663.v1
title: Hounslow_GAMMS_5.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
DOI: 10.6084/m9.figshare.20407666.v1
title: Hounslow_Clustering_2.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
DOI: 10.6084/m9.figshare.20407672.v2
title: Hounslow_PCA_3 from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: R Script: Source code to perform principal component analyses and associated figures
DOI: 10.6084/m9.figshare.20407672.v1
title: Hounslow_PCA_3.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

description: Diving behaviour of ‘surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (<i>Natator depressus</i>) diving behaviour, during its' foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. <i>K</i>-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
DOI: 10.5519/qd.rrpc5q35
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.6982696
title: Supporting data for paper submission to the WRR

description: These are the figures for supporting the data used in the paper "Efficient pipe burst detection in tree-shape water distribution networks using forward-backward transient analysis". The data including the Figs. in the paper, and experimental data from the University of Perugia, and the numerical data for burst and leaky cases.
DOI: 10.5281/zenodo.6982695
title: Supporting data for paper submission to the WRR

description: These are the figures for supporting the data used in the paper "Efficient pipe burst detection in tree-shape water distribution networks using forward-backward transient analysis". The data including the Figs. in the paper, and experimental data from the University of Perugia, and the numerical data for burst and leaky cases.
DOI: 10.5281/zenodo.6983137
title: SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 2d Introduction to nf-core/eager

description: Teaching data for practical session: "2d Introduction to nf-core/eager" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: https://spaam-community.github.io/wss-summer-school/#/2022/ or https://doi.org/10.5281/zenodo.6976711 for slides. Once downloaded, run: <pre><code>tar xvfz &lt;session&gt;.tar.gz</code></pre> to decompress the data directory for the session.
DOI: 10.5281/zenodo.6983136
title: SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 2d Introduction to nf-core/eager

description: Teaching data for practical session: "2d Introduction to nf-core/eager" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: https://spaam-community.github.io/wss-summer-school/#/2022/ or https://doi.org/10.5281/zenodo.6976711 for slides. Once downloaded, run: <pre><code>tar xvfz &lt;session&gt;.tar.gz</code></pre> to decompress the data directory for the session.
DOI: 10.5281/zenodo.5843594
title: Multiple Land-use / Land-cover Dataset (MLULC)

description: This dataset covers the French metropolitan territory (500,000km²). It includes Six open access land-cover maps from various providers (CLC, CGLS-LC100, OSO, OCS-GE cover, OCS-GE use, MOS). A May 2019 Sentinel-2 L3A mosaic ( cloudless image using all maps available during a month) including RVB and NIR. Provided by Theia. A Manually built ground truth of 2300 random points annotated with their labels in each map nomenclature. A consolidated ground truth with the original 2300 and 400 non-random points focusing on rare classes. A suggested train/val/test split (60%,5%,35%). Note that all ground truth points belong to patches of the suggested <strong>test</strong> set. Since this dataset is intended to be used with a deep learning algorithm, the data is split into tiles of 6x6km² following a grid given with the dataset. More information is provided in README. Note that exception made of the ground truth, all the data (Land covers and Sentinel-Images) aren't our property and are only shared as authorized by their respective original license.
DOI: 10.5281/zenodo.6577226
title: Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids

description: Source data and source code for the graphs in "Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids".
DOI: 10.5281/zenodo.5148117
title: Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids

description: Source data and source code for the graphs in "Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids".
DOI: 10.5281/zenodo.6683616
title: In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data

description: The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s. The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters: X-ray wavelength: 0.6888 nm Sample detector distance: 809 mm Incidence angle: 0.5 deg. Detector model: XRD 1621 CN3 EHS Acquisition rate : 10 frames per second (10 Hz) Direct beam position (pixels): 545, 222
description: {"references": ["Starostin et al.; End-to-end deep learning pipeline for real-time processing of surface scattering data at synchrotron facilities"]}
DOI: 10.5523/bris.280mpmf2l7ihj2jl1x2k9qwdw0
title: TDPP PCCP 2022

description: Transient absorption, linear absorption and fluorescence and computational data underpinning published paper.
DOI: 10.34740/kaggle/dsv/4061592
title: AutoXGB
DOI: 10.5281/zenodo.6983175
title: SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 4b Introduction to Genome Mapping

description: Teaching data for practical session: "4b Introduction to Genome Mapping" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: https://spaam-community.github.io/wss-summer-school/#/2022/ or https://doi.org/10.5281/zenodo.6976711 for slides. Once downloaded, run: <pre><code>tar xvfz &lt;session&gt;.tar.gz</code></pre> to decompress the data directory for the session.
DOI: 10.5281/zenodo.6983174
title: SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 4b Introduction to Genome Mapping

description: Teaching data for practical session: "4b Introduction to Genome Mapping" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: https://spaam-community.github.io/wss-summer-school/#/2022/ or https://doi.org/10.5281/zenodo.6976711 for slides. Once downloaded, run: <pre><code>tar xvfz &lt;session&gt;.tar.gz</code></pre> to decompress the data directory for the session.
DOI: 10.5281/zenodo.6983185
title: SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 5b Introduction to Phylogenomics

description: Teaching data for practical session: "5b Introduction to Phylogenomics" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: https://spaam-community.github.io/wss-summer-school/#/2022/ or https://doi.org/10.5281/zenodo.6976711 for slides. Once downloaded, run: <pre><code>tar xvfz &lt;session&gt;.tar.gz</code></pre> to decompress the data directory for the session.
DOI: 10.5281/zenodo.6983184
title: SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 5b Introduction to Phylogenomics

description: Teaching data for practical session: "5b Introduction to Phylogenomics" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: https://spaam-community.github.io/wss-summer-school/#/2022/ or https://doi.org/10.5281/zenodo.6976711 for slides. Once downloaded, run: <pre><code>tar xvfz &lt;session&gt;.tar.gz</code></pre> to decompress the data directory for the session.
DOI: 10.5281/zenodo.6639005
title: Figure data for: Cavity-mediated electron-photon pairs

description: These files contain the figure data and code for the paper "Cavity-mediated electron-photon pairs"
DOI: 10.5281/zenodo.6639004
title: Figure data for: Cavity-mediated electron-photon pairs

description: These files contain the figure data and code for the paper "Cavity-mediated electron-photon pairs"
DOI: 10.5281/zenodo.6683658
title: Crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), GIXD analysis results: diffraction features and crystal structure

description: Analysis result of an <em>in-situ</em> measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. This dataset contains the positions, sizes, and integrated intensities of extracted diffraction peaks with 0.1s time resolution. For crystal structure matching, the provided CIF file (CCDC 1446529) was used.
description: {"references": ["Starostin et al. 2022; End-to-end deep learning pipeline for real-time processing of surface scattering data at synchrotron facilities"]}