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Figure 5. Head capsule EES plots. A, axis 2 in Termite soldier defence strategies: a reassessment of Prestwich's classification and an examination of the evolution of defence morphology using extended eigenshape analyses of head morphology
Figure 5. Head capsule EES plots. A, axis 2 vs. axis 1. B, axis 2 vs. axis 3. C, modelled mode of dorsal head capsule shape change along axes 1, 2 and 3.
Arc fault detection and appliances classification in AC home electrical networks using Recurrence Quantification Plots and Image Analysis
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p> <p> </p>
Experimental investigation of composite materials for sliding friction dampers: data, plots, photos and videos of the tests
<p><strong>Folder DATA</strong></p> <p>This folder contains the data acquired by testing the friction pads M1, M2, M3, M4 and M5 under the following loading protocols:</p> <ul> <li>Linear static loading (M);</li> <li>Cyclic loading with constant amplitude (CA);</li> <li>Cyclic loading with decreasing amplitude at low rate (DA);</li> <li>Cyclic loading with increasing amplitude at low rate (IA);</li> <li>Cyclic loading with increasing amplitude at moderate rate (IA-H);</li> <li>Cyclic loading with increasing amplitude at high rate (IA-HH);</li> <li>Pulse-like loading protocol (PL);</li> <li>Mainshock-aftershock protocol (MS-AS): mainshock (MS), first aftershock (AS1) and second aftershock (AS2).</li> </ul> <p>The data include:</p> <ul> <li><em>Time</em>: time (unit: second);</li> <li><em>F</em>: axial force experienced by the sliding friction damper (unit: kN);</li> <li><em>N_bolt</em>: bolt preload (unit: kN);</li> <li><em>mu</em>: friction coefficient of the considered pad (unit: dimensionless);</li> <li><em>delta</em>: axial displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. delta</em>: total cumulative displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. E</em>: total cumulative energy dissipated by the sliding friction damper (unit: kJ);</li> <li><em>max Tin</em>: maximum temperature tracked close to the sliding interface (unit: Celsius);</li> <li><em>Tout</em>: temperature tracked at the surface of the inner slotted plate (unit: Celsius).</li> </ul> <p> The data are organized as follows:</p> <ul> <li>Folder <strong>T100</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 100 Nm. Each EXCEL file <strong>T100_M_Y</strong> saved in the folder <strong>T100</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 200 Nm. Each EXCEL file <strong>T200_M_Y</strong> saved in the folder <strong>T200</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>Fs150</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 150 kN. Each subfolder <strong>Fs150_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H). Each EXCEL file <strong>Fs150_X_Y</strong> saved in the subfolder <strong>Fs150_X</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <ul> <li>Folder <strong>Fs300</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 300 kN. Each subfolder <strong>Fs300_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2). Each EXCEL file <strong>Fs300_X_Y</strong> saved in the subfolder <strong>Fs300_X </strong>contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <p><strong>Folder PHOTOS</strong></p> <p>This folder contains the following photos:</p> <ul> <li>Folder <strong>01_FrictionDamper</strong>: photos of the sliding friction damper and its components.</li> <li>Folder <strong>02_Instrumentation</strong>: photos of the instrumentation used for the data acquisition during the experimental campaign.</li> <li>Folder <strong>03_FrictionPads</strong>: <ul> <li>Subfolder <strong>BeforeTesting</strong>: photos of the friction pads before the experimental campaign.</li> <li>Subfolder <strong>AfterTesting</strong>: photos of the friction pads at the end of each loading protocol. The photo <strong>Fs150vs300_X_Y</strong> shows the condition of the pad Y (Y = M1, M2, M3, M4, M5) at the end of the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2) performed for an expected slip load of 150 kN and 300 kN (the pads shown at the top of each photo are those tested for an expected slip load of 150 kN). Similarly, the photo <strong>Fs300_X_Y</strong> shows the condition of the pad Y at the end of the loading protocol X performed for an expected slip load of 300 kN.</li> </ul> </li> <li>Folder <strong>04_Tests</strong>: photos taken from the east and north side of the sliding friction damper during the loading protocols that caused the fracture of the pads <ul> <li>Subfolder <strong>Fs150</strong>: photos taken during the tests conducted for an expected slip load of 150 kN. Each folder <strong>Fs150_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, DA, IA, IA-H).</li> <li>Subfolder <strong>Fs300</strong>: photos taken during the tests conducted for an expected slip load of 300 kN. Each folder <strong>Fs300_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, IA, IA-H). A video was recorded live during the loading protocols IA-HH, PL, MS, AS1 and AS2 (see folder <strong>VIDEOS</strong>).</li> </ul> </li> </ul> <p><strong>Folder PLOTS</strong></p> <p>This folder contains the following MATLAB plots:</p> <ul> <li><em>Force-Disp</em>: axial force – axial displacement response of the sliding friction damper;</li> <li><em>Preload-CumDisp</em>: bolt preload as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>FrictionCoeff-CumDisp</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>Temp-CumDisp</em>: rise in temperature as a function of the total cumulative displacement experienced by the sliding friction damper (the temperature values reported for the expected slip load of 150 kN correspond to “max Tin”, whereas those reported for the expected slip load of 300 kN correspond to “Tout”);</li> <li><em>FrictionCoeff-LoadingHistoryEffect</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-RateEffect</em>: friction coefficient of the considered pad as a function of sliding velocity experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-TempEffect</em>: friction coefficient of the considered pad as a function of the rise in temperature tracked during different loading protocols;</li> <li><em>FrictionCoeff-PressureDependency</em>: mean and standard deviation of the friction coefficient of the considered pad obtained for different expected slip loads and loading protocols;</li> <li><em>FrictionCoeffStaticDynamic-PressureDependency</em>: mean of the static and dynamic friction coefficient of the considered pad obtained for different expected slip loads and loading protocols.</li> </ul> <p>The MATLAB plots are organized as follows:</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the linear static loading protocol (M) for a tightening torque of 200 Nm.</p> <ul> <li>Folder <strong>Fs150 and Fs300</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the considered loading protocol (M, CA, DA, IA, IA-H, IA-HH, PL, MS-AS) for an expected slip load of 150 kN and 300 kN.</p> <p><strong>Folder VIDEOS</strong></p> <p>This folder contains the following videos:</p> <ul> <li>Folder <strong>T100</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 100 Nm under the linear static loading protocol (M). The videos <strong>T100_M_Y_East</strong> and <strong>T100_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>T200</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 200 Nm under the linear static loading protocol (M). The videos <strong>T200_M_Y_East</strong> and <strong>T200_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs150</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 150 kN. The videos <strong>Fs150_X_Y_East</strong> and <strong>Fs150_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs300</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 300 kN. The videos <strong>Fs300_X_Y_East</strong> and <strong>Fs300_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively. The videos obtained for the loading protocols IA-HH, PL, MS, AS1 and AS2 were recorded live during each test.</li> </ul>
Regional plot x species data for alpine vegetation
<p>Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe (SACEU) are compared to those of the western US and Canada (WUSAC) because they differ in their geographies and histories. Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America. In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation. The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western US and Canada, less isolation and more mixing of species from refugia has caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria.</p>
AFRP22 Tomographic Model & MTZ Receiver Functions: Data, Software, Plotting
<p>AFRP22 (Boyce et al., 2023), is an adaptively parameterized, absolute P-wave tomographic model for Africa provided with and without crustal correction. Also provided is mantle transition zone Receiver Functions for new stations in the Turkana Depression (data embargo lifted 2024-05-24).</p> <p>Citation:</p> <p>Boyce, A., Kounoudis, R., Bastow, I. D., Cottaar, S., Ebinger, C. J., Ogden, C. S. Mantle Wavespeed and Discontinuity Structure below East Africa: Implications for Cenozoic Hotspot Tectonism and the Development of the Turkana Depression (under revision) Geochemistry, Geophysics, Geosystems.</p> <p>This repository accompanies the publication of the tomographic model on the IRIS-EMC that can be found here: https://ds.iris.edu/ds/products/emc-afrp22/ or with the following doi: <a href="https://doi.org/10.17611/dp/emc.2023.afrp22.1">https://doi.org/10.17611/dp/emc.2023.afrp22.1</a>. Please find AFRP22 and its related files for a detailed description of the distributed model.</p> <p>The AFRP22_RFs_SHARE.tar repository contains:</p> <ul> <li>Plotting: Codes and necessary files to reproduce figures in main manuscript.</li> <li>tomography <ul> <li>Tomography_AFRP22: Implementation of MIT inversion algorithm used to produce AFRP22 including formatted data.</li> <li>Raw_data: Hand-picked and processed seismic data (.SAC) from new African seismic networks.</li> <li>Original_documentation: Documentation for original distribution of inversion code from MIT global seismology group </li> </ul> </li> <li>receiver_functions <ul> <li>Processing codes (SMURFPy) already available at doi: <a href="https://doi.org/10.5281/zenodo.4337257">10.5281/zenodo.4337257</a></li> <li>Raw_data - Data files for P, PP and PKP RFs, CCP stacked volumes produced by SMURFPy code and picked discontinuity files</li> </ul> </li> </ul> <p>The following authors contributed to this work:</p> <p>A. Boyce: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> R. Kounoudis: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK.<br> I. D. Bastow: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK.<br> S. Cottaar: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> C. J. Ebinger: Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118, USA.<br> C. S. Ogden: School of Geography and Geology, University of Leicester, Leicester, LE1 7RH, UK.<br> </p>
Data and R scripts for KCC plots in Douville (2023)
<p>Data and R scripts for plotting Fig. 3 and Fig.4 in Douville (2023)</p> <p>Douville H. (2023) Robust and perfectible constraints on human-induced Arctic climate change. Communications Earth & Environment (revised)</p>
GC Skew and origin of replication-related plots for all Archaea genomes.
<p>We generated data and plots exhaustively for all Archaea complete genomes in the <a href="https://www.ncbi.nlm.nih.gov/datasets/genomes/?taxon=2157">NCBI dataset </a>in April 2022.</p> <p>Plots and data available:</p> <p><strong>SkewI and GC Skew:</strong></p> <ul> <li> <p> <strong>SkewI</strong> and <strong>GC content</strong> of every archaeon complete genome.</p> </li> <li> <p><strong>Cumulative GC Skew plot</strong> of every complete genome, with <strong>an estimation of the origin and termination</strong> point in the sequence, <strong>obtained by analyzing the plot</strong></p> </li> <li> <p>The same results are available sorted by taxonomy (Euryarchaeota, Crenarchaeota, Thaumarchaeota) and SkewI values.</p> </li> </ul> <p> </p> <p><strong>Dot Plots:</strong></p> <ul> <li> <p><strong>Dot plots</strong> comparing two by two each of the ten genomes having the highest SkewI value for any given taxonomy. An <strong>estimation of the origin of replication</strong> based on the cumulative GC Skew is represented.</p> </li> <li> <p>Dot plots comparing two by two a genome of <strong>high SkewI </strong>with a genome of <strong>low SkewI</strong>.</p> </li> </ul> <p> </p> <p><strong>Clinker</strong>:</p> <ul> <li> <p><strong>Comparison of genes around origin/terminus of replication within a taxonomy</strong>. Results obtained by running <strong>Clinker</strong> on a portion of 20 000 nucleotides before and after the origin/terminus of replication of all genomes belonging to the taxonomy.</p> </li> </ul> <p> </p> <p><strong>Violin plots</strong>:</p> <ul> <li> <p>Visual representation of the repartition of SkewI and GC Skew in different taxonomies of archaea and selected bacteria.</p> </li> <li> <p>Similar plots for <strong>TA skew</strong>, <strong>cumulative skew per codon position</strong> and <strong>cumulative skew for non-coding regions.</strong></p> </li> </ul> <p> </p> <p>Please refer to the readme file for further information about the results and references.</p>
Plots dataset of the Kunbaja online resource
<p>Information layer "plots" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Plot layer screenshot of the Kunbaja online resource
<p>Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Magyar plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
PLO(SC)²: Plots and Scripts for scRNA-seq analysis
<p><strong>Availability</strong></p> <p>The PLOSC-project is available from <a href="https://github.com/mjoppich/PLOSC">https://github.com/mjoppich/PLOSC</a> .</p> <p>The sequencing data (h5-files) were taken from:</p> <p>Pekayvaz K, Leunig A, Kaiser R, Joppich M, Brambs S, Janjic A, et al. Protective immune trajectories in early<br> viral containment of non-pneumonic SARS-CoV-2 infection. Nature communications. 2022 Feb;13(1):1018.<br> Available from: <a href="https://www.nature.com/articles/s41467-022-28508-0">https://www.nature.com/articles/s41467-022-28508-0</a>.</p> <p><strong>Background</strong><br> scRNA-seq analysis has become a standard technique to study biological systems.<br> With decreasing costs for scRNA-seq experiments, these also become increasingly complex.<br> While the typical scRNA-seq analysis frameworks provide functionalities for the analysis of even such data sets, the required steps to follow for such experiments become complicated.<br> Moreover, default plots are not suitable to provide specific insight into such complex data sets, and should be enhanced, such that camera-ready fully-interpretable plots are provided.</p> <p><strong>Results</strong><br> We thus describe here a collection of plotting and analysis scripts for use in Seurat-based scRNA-seq data analyses.<br> We first provide a collection of script blocks which allows for an easy basic analysis of scRNA-seq from Seurat object creation, filtering, and over data set integration in less than 10 steps.<br> Subsequently, we provide code blocks for the easy differential analysis of the obtained data sets, including visualizations.<br> Finally, several visualizations enhancing the functionalities of scRNA-seq analysis frameworks are presented, such as the enhanced Heatmap and DotPlot.<br> These, particularly, allow the user to specify how the shown values should be scaled, allowing the creation of condition-wise plots.</p> <p><strong>Conclusions</strong><br> With the PLO(SC)² framework the data analysis of scRNA-seq experiments becomes more stream-lined, and visualizations for interpreting complex datasets are provided.<br> The PLO(SC)² scripts are available from GitHub, including a notebook showing how PLO(SC)² is applied on the use-case data presented here. This way, fellow researchers can directly apply the methods on their data.</p>
Raw data for the plot in the article entitled "Electrophoretic assembly and electronic transport properties of rapidly synthesized Sb2Te3 nanoparticles"
<p>data plotted in the article in csv format</p>
Met Office ocean glider investigation plots
<p>These plots are the results of a Met Office investigation into the impact of using ocean glider observations on the AMM15 model. These plots are a supplement to a report currently in development.</p>
Supplemental files: Q-Q plots and Shapiro-Wilk tests of normality
<p><em>Symphyotrichum</em> (Asteraceae) is a well-circumscribed genus on the basis of morphological and molecular data, but species boundaries remain poorly understood. Here, the species delimitation of the contentious <em>Symphyotrichum</em> <em>subulatum</em> group (<em>Symphyotrichum</em> subg. <em>Astropolium</em>) is illuminated using morphometric, phylogenomic, and geographical analyses. <em>Symphyotrichum mexicanum</em> sp. nov., a new species endemic to central Mexico, is described and distinguished from <em>Symphyotrichum</em> <em>expansum</em> based on its morphometric attributes, phylogenetic placement, geographic range, and ecological specialization.</p>
Gene tree discord, simplex plots, and statistical tests under the coalescent
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Data, analytical, and plotting codes needed to reproduce meta-analyses on within-season divorce in birds
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funspace: an R package to build, analyze and plot functional trait spaces
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Biweekly soil-moisture in the 18 CARBONO Project plots, La Selva Biological Station, March 1998-October 2018
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Data from: Winter game crop plots for gamebirds retain hedgerow breeding songbirds in an improved grassland landscape
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Biweekly fine litterfall in the 18 CARBONO Project plots, La Selva Biological Station, October 1997-October 2018
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
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.
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.
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.
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.