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1,646 results for “plot”
Dataset supporting plots and findings in Kirsten et al. 2024, DOI: 10.1038/s41550-023-02153-z
<p>These are the data that support the plots and findings in "A link between repeating and non-repeating fast radio bursts through their energy distributions", appeared in Nature Astronomy. DOI: 10.1038/s41550-023-02153-z</p><p>The scripts used for the analysis of the data and for creating the plots from the article are available at <a href="https://github.com/astroflash-frb/frb20201124A-kirsten-2023">this github repo</a>. Please cite Kirsten et al. 2024 when using these data for any publications.</p>
Fig. 23. Principal Components Analysis plot for 23 and 25 in Two new species of Vaejovis (Scorpiones: Vaejovidae) belonging to the mexicanus group from Aguascalientes, Mexico, with comments on the homology and function of the hemispermatophore
Fig. 23. Principal Components Analysis plot for 23 and 25 measurements taken from males and females, respectively. First and second PCA for males were body size and metasoma length (97% of variance explained), and for females it was patella and carapace lengths (92% of variance explained) [upper plate]. The variable vector dimensions that separate the principal components of males and females are shown in the lower plate.
Supplementary Figure 1 QQ plot of rate of decline from baseline in ODI score (ODI1: 7D, ODI2: 14D, ODI3: 21D, ODI4: 30D).
<p><span>QQ plot of rate of decline from baseline in ODI score (ODI1: 7D, ODI2: 14D, ODI3: 21D, ODI4: 30D).</span></p>
Data and Plotting Tools to Reproduce Figures in Manuscript: A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments
<p>This repository contains two gzipped tarballs: plottingTools.tar.gz and plottingData.tar.gz. Within plottingData.tar.gz are the data files necessary to reproduce all of the figures in the manuscript: A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments. Within plottingTools.tar.gz are the scripts necessary to reproduce the figures.</p> <p>Contents of plottingTools.tar.gz:</p> <ul> <li>globalVariables.py: this contains some user-defined variables such as where the data is stored and what extension to use for saved figures (pdf, png, etc.)</li> <li>plotFigure*.py: these scripts generate the various figures in the manuscript</li> <li>publication.sty: this sets the formatting for the figures</li> <li>requirements.txt: this contains the python packages necessary to execute the scripts</li> </ul>
Bar plots for the variables under study
<p>A compilation of six figures representing bar plots with the values for null subject rate, verb score and balancing vs. deranking strategy in the languages of UD and CIEP.</p>
A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Plots
<p>A collection of all plots generated for deriving the conclusions seen in "<span><span>A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems</span></span>".</p>
Fig. 7. Bayesian Skyline Plot analysis showing population size over time. The x in Echinoderes galadrielae Grzelak & Sørensen 2022, sp. nov.
Fig. 7. Bayesian Skyline Plot analysis showing population size over time. The x-axis is the time to the present in years, while the y-axis is the product between the effective population size (Ne) and the generation length (t) in a log scale. The mean estimate (black solid line) and 95% highest probability density limits (grey area) are shown.
Fig. 12. STRUCTURE bar plots for Xerochrysum bicolor, X in There's gold in them thar hills! Morphology and molecules delimit species in Xerochrysum (Asteraceae; Gnaphalieae) and reveal many new taxa
Fig. 12. STRUCTURE bar plots for Xerochrysum bicolor, X. halmaturorum, and X. sp. Lofty Ranges major clustering modes K = 3. Bar plots show each individual as a horizontal bar divided into segments on the basis of the proportion of ancestry suggested for 1–10 subpopulations across all of the 10 runs; n = 51. Fine black lines delineate sampling locations.
Key results and plot files for the paper "Diversity of biomass usage pathways to achieve emissions targets in the European energy system"
<p>Key results and plot files for the paper:</p> <p>Millinger, M., Hedenus, F., Zeyen, E. <em>et al.</em> Diversity of biomass usage pathways to achieve emissions targets in the European energy system. <em>Nat Energy</em> <strong>10</strong>, 226–242 (2025). https://doi.org/10.1038/s41560-024-01693-6</p>
Phase plots for the sample of super-slowly rotating Ap (ssrAp) stars from the Zwicky Transient Facility survey
<p>This figure (B.1) is supplementary material to the paper "A new sample of super-slowly rotating Ap (ssrAp) stars from the Zwicky Transient Facility survey" by S. Huemmerich, K. Bernhard and E. Paunzen that has been accepted at Astronomy & Astrophysics (A&A). It shows the light curves of all sample stars, phased with the periods derived in our study (cf. Table A.1) and indicated in the plots. Red and blue symbols correspond to Zwicky Transient Facility (ZTF) <em>r</em> and <em>g</em> band data, respectively. For clarity, <em>g</em> band data have been shifted by varying amounts in the phase plots.</p>
GPS data and plotting codes for Remote Sensing paper titled Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity
<p>Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper Gajek et al., Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity</p>
Data and plotting code for 'Many-body-localized discrete time crystal with a programmable spin-based quantum simulator'
<p>Underlying data and plotting for the publication: "Many-body-localized discrete time crystal with a programmable spin-based quantum simulator", J. Randall et al., 2021.</p> <p>This directory contains:</p> <p>- “Data_analysis.ipynb”: The master Jupyter notebook from which all plots produced in the main text and supplementary materials can be reproduced.<br> - “Data.zip”: A zipped folder containing the json files as loaded within “Data_analysis.ipynb”. These files contain the underlying x, y, and, where appropriate, y error values for each corresponding plot.</p> <p>The notebook should be executed using python3.</p>
Data from: Pulled diversification rates, lineages-through-time plots and modern macroevolutionary modelling
<p>Estimating time-dependent rates of speciation and extinction from dated phylogenetic trees of extant species (timetrees), and determining how and why they vary, is key to understanding how ecological and evolutionary processes shape biodiversity. Due to an increasing availability of phylogenetic trees, a growing number of process-based methods relying on the birth-death model have been developed in the last decade to address a variety of questions in macroevolution. However, this methodological progress has regularly been criticised such that one may wonder how reliable the estimations of speciation and extinction rates are. In particular, using lineages-through-time (LTT) plots, a recent study (Louca & Pennell, 2020) has shown that there are an infinite number of equally likely diversification scenarios that can generate any timetree. This has led to questioning whether or not diversification rates should be estimated at all. Here we summarize, clarify, and highlight technical considerations on recent findings regarding the capacity of models to disentangle diversification histories. Using simulations we illustrate the characteristics of newly-proposed "pulled rates" and their utility. We recognize that the recent findings are a step forward in understanding the behavior of macroevolutionary modelling, but they in no way suggest we should abandon diversification modelling altogether. On the contrary, the study of macroevolution using phylogenetic trees has never been more exciting and promising than today. We still face important limitations in regard to data availability and methodological shortcomings, but by acknowledging them we can better target our joint efforts as a scientific community.</p>
Figure 6. Branch length comparisons for 147 taxa. All common bipartition branch lengths plotted against the 147t 3p in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation
Figure 6. Branch length comparisons for 147 taxa. All common bipartition branch lengths plotted against the 147t 3p analysis bipartition branch lengths. Confidence intervals (CI) as for Figure 9.
Updated SpMM Plots
<p>Updated SpMM Plots for an SpMM submission.</p>
Raw data for "Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring"
<p>This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).</p>
Video Games Sales plot
Plot of global sales vs. release year
Data, plotting scripts, and figures for "Accelerating Reactive-Flow Simulations using Vectorized Chemistry Integration"
<p>Data, plotting scripts, and figures for the article "Accelerating Reactive-Flow Simulations using Vectorized Chemistry Integration".</p>
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter. in Termite coprolites (Blattodea: Isoptera) from the Early Cretaceous of eastern Inner Mongolia, Northeast China
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter.
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
ScienceDex guides
Understand access before you commit
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.