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1,646 results for “plot”
The raw data and datasets generated to plot the Figure 2 to Figure 8
<p>(1) The Terrestrial Hybrid Repeated Gravity Observation data is applied from Data Sharing Infrastructure of National Earthquake Data Center(http://data.earthquake.cn).Only Chinese language link(https://data.earthquake.cn/datashare/report.shtml?PAGEID=datasourcelist&dt=40280d0453e5add30153e5ee3dc1001f; https://data.earthquake.cn/datashare/report.shtml?PAGEID=datasourcelist&dt=40280d0453e5add30153e5f03dd10022). Data can be requested through the email application form or the offline application form. <strong>We provide the raw data (a constant value is removed for reading).</strong> </p> <p>(2) Earthquake catalog data comes from the end of China Seismic Experimental Site webpage (http://www.cses.ac.cn/sjcp/ggmx/2021/132.shtml). Click on "cata2019".<br> (3) <sup>3</sup>He/<sup>4</sup>He release data is downloaded from the supplementary data of published article. (https://github.com/mzhangrocks/Plateau-Growth) We have cited this article in this paper.<br> (4) The 3-D P- and S-wave community velocity model of the crust and uppermost mantle in southwest China is downloaded from the supplementary data of published article. (https://github.com/liuyingustc/SWChinaCVM),(SWChinaCVM-1.0, DOI:10.12093/02md.02.2019.01.v1). We have cited this article in this paper.</p>
Mode Splitting Spectrogram and Analysis Plots, 11 May 2022
<p>Spectrogram collected in Spectrum Lab by Steve WA5FRF. Digitized manually using MATLAB. Digitization and computation code at <a href="https://github.com/KCollins/wa5frf-plots">https://github.com/KCollins/wa5frf-plots</a>.</p>
Lightcurves, Colour-Colour Ratio and CuSum plots for HD 179949 Chandra Observations - Acharya et al. (2022)
<p>Plots made for the paper "X-ray Activity on SPI Candidate HD 179949" Anshuman Acharya et al. (2022). This includes:<br> 1. All Light curves for the 7 passbands considered (4 physically motivated + 3 default Chandra passbands) + Broad band (0.5 - 7.0 keV) for the 5 datasets. The filename format is: "dataset ObsID"_"passband name"_lightcurve.pdf.<br> 2. Colour-colour ratios for the passbands (9 combinations) for the 5 datasets. The filename format is: "dataset ObsID"+ColourRatio_"soft passband name"+"hard passband name".pdf.<br> 3. Cumulative Sum of residuals plot for Model 1 (referred to as model 2m in the paper: chosen model for 6121, not chosen for 5427 and 6119) and Model 2 (referred to as model 2v in the paper: chosen model for 5427, 6119, 6121 and 6122). Note that Model 1 plots for ObsIDs 6120 and 6122 are not made as <span class="math-tex">\(\Delta\)</span>Cstat > 2 <span class="math-tex">\(\sigma\)</span> for them. The filename format is: Model1/Model2_"dataset ObsID"_residuals_for_CuSum.pdf.<br> 4. p-value calculated for the Cumulative Sums for all the above cases. The filename format is: Model1/Model2_"dataset ObsID"_p-value_for_CuSum.pdf.</p> <p>Dataset ObsIDs are: 5427, 6119, 6120, 6121 and 6122.<br> Passbands used:<br> Physically motivated: Oxy (0.5-0.7 keV), Fe (0.7-0.9 keV), Ne (0.9-1.2 keV), Mg (1.2-3.0 keV).<br> Default Chandra passbands: ultrasoft (0.2-0.5 keV), soft (0.5-1.2 keV), medium + hard (1.2-8.0 keV), broad (0.5-7.0 keV).</p>
KC0P 40m SEQP Scatter Plot of Reception by AC0C
<p>Scatter Plot of signal strength of KC0P 40m cw transmissions as received by AC0C RBN at a distance of 391 miles before, during,</p> <p>and after eclipse day. Shows an enhancement six minutes after totality crossed a line between transmitter and receiver.</p>
2401.10320 Plots & Data
<p>Plots and raw data for <a href="https://arxiv.org/abs/2401.10320">https://arxiv.org/abs/2401.10320</a></p> <p>The data files are npz (compressed numpy arrays) that can be read via<code> numpy.load()</code>.</p> <p>Example scripts for reading, parsing and plotting the data are included.</p> <p>The accompanying raw energy-momentum tensors for the RHIC dataset are provided in:<br>DOI: <a href="https://doi.org/10.5281/zenodo.10820531">https://doi.org/10.5281/zenodo.10820531</a></p> <p>The accompanying raw energy-momentum tensors for the LHC dataset are provided in:<br>DOI: <a href="https://doi.org/10.5281/zenodo.10820458">https://doi.org/10.5281/zenodo.10820458</a></p>
Nitrogen fertilization plot level data - a Finnish case study
<p>The data is based on the fertilization study where we evaluated the spatial evenness of nitrogen (N) fertilization (measured fertilization dose on circular plots with radius of 7,98 m) in fertilized treatments and effects of fertilization treatments (target of 0, 150 and 200 kg N /ha) on short-term volume growth responses in two ground-fertilized (2018) Scots pine and in two airborne-fertilized (2019) Norway spruce study sites on medium fertile (Myrtillus-type, MT) upland forest sites in Eastern Finland. It was also studied the relationships between measured fertilization dose, N concentrations in needles and soil organic (humus) layer, and volume growth of trees (calculated based on diameter at breast height and height of sample trees) based on block level averages (based on data measured for three circular plots in each block). In each study area, we established three one hectare replicate blocks (with three circular study plots of 200 m² in each) for fertilizer treatments with target of 0, 150 and 200 kg N /ha. Spatial evenness of the fertilization was measured with textile funnels. Height, breast height diameter and vitality (living/dead) of trees were measured annually in each circular plot. Nutrient concentrations in needles (analysed in Yara laboratory in United Kingdom) and soil organic (humus) layer (analysed at UEF laboratory) were measured once. </p>
Dataset for "Is the linear relationship between the slope and intercept observed in field emission S-K plots an artifact?"
<p>The dataset are in text format, the files to analyze and generate the figures are in jupyter format for python with a copy in pdf format.</p> <p>Description of files and folders :</p> <p><strong>I) Analyzed</strong></p> <p>This folder contains Fowler-Nordheim analysis of the I-V data in the folder Data, that where obtained in the publication "All field emission experiments are noisy, ... are any meaningful ?"</p> <p><strong>II) Data</strong></p> <p>This folder contains the different experimental I-V data</p> <p><strong>III) Numerical data<br></strong></p> <p>This folder contains the different noisy I-V data generated by the file "Simul"</p> <p><strong>IV) Fig1</strong></p> <p>This file generates the figure "MySK4.svg" based on the data in the folders <strong>I) Analyzed</strong> and <strong>III) Numerical data</strong></p> <p><strong>V) Fig2</strong></p> <p>This file analyses the data in the folder <strong>II) Data</strong> and then extract the parameters of their SK plot.</p> <p>It gathers the SK plot parameters of several work in the litterature.</p> <p>It plots "Slopm4.svg" and "Intm4.svg".</p> <p><strong>VI) Simul</strong></p> <p>This file generates the different noisy I-V data necessary fir Fig1</p> <p><strong>VII) FigSuppl</strong></p> <p>This file shows the different experimental Fowler-Nordheim plots obtained from the files in the folder <strong>II) Data</strong></p>
Ground surface temperature measurements at grazed and ungrazed plots in Central Mongolia
<p>Ground surface temperature measurements from two sites with different topographic aspect in Central Mongolia. The dataset includes both grazed and ungrazed plots, and covers ca. 14 months from May 2022 to August 2023.</p>
Data+Analysis+Plotting scripts for "Constraint on the dissipative tidal deformability of neutron stars"
<p>The .zip file contains three directories. </p> <p>1. GW170817-Strain: the raw data (glitch free). Downloaded from https://gwosc.org/events/GW170817/.<br>2. Bilby-Output: the output from running our Bilby sampling scripts. These can be found at https://github.com/JLRipley314/NRTidal-D/tree/main<br>3. Plotting-Scripts: the plotting scripts we used in our paper https://arxiv.org/abs/2312.11659.</p> <p>NOTE: If you want to make sure the plotting scripts work properly, you should download bilby and related dependencies as described in https://github.com/JLRipley314/NRTidal-D/tree/main (or at https://doi.org/10.5281/zenodo.11589416)</p>
Full inventory of ten permanent plots installed in pockets of different tree functional types along the Moni River transects (Yangambi, Democratic Republic of Congo)
<p>Most of the tropical forests of Central Africa are characterised by a remarkable abundance of light-demanding canopy species. A popular hypothesis is that these forests are still recovering from the intense slash-and-burn farming activities that ended abruptly in the 19th century with the arrival of the colonists. Today, it is assumed that the zones occupied by crop fields until the 19th century are covered by forests dominated by light-demanding species. However, this hypothesis of human disturbance has not yet been sufficiently tested using spatial distribution. So, using the 'Kernel Density Estimation' (KDE) tool in the SAGA GIS software, we mapped the density distribution of light-demanding species, subdivided into 3 tree functional types, along transects in the Moni river catchment. We also produced a similar map for a particular shade-tolerant species, 'Gilbertiodendron dewevrei'. The species were then divided into the following groups, known as 'functional tree types': LLP=Long-Lived Pioneer, NPLD=Non-Pioneer Light Demanding, SLP=Short-Lived Pioneer, and STS=Shade-Tolerant Species. At the end of this analysis, a density distribution map of the species of each tree functional type was produced. This map highlights the pockets (zones with a high density relative to the study site average) of tree functional types. For each type of pocket, we selected the pockets with a high density of trees of the group concerned and which were not on the edge between the forest and village crops or fallow land. This is how the location of the permanent plots was determined. Next, we installed a total of ten full forest inventory plots (1 ha each) inside and outside the pockets located by the KDE analysis along the Moni River transects. More specifically, we installed one plot in a pocket of short-lived pioneers (SLP-01), three plots in pockets of long-lived pioneers (LLP-01 to -03), two plots in pockets of NPLD (NPLD-01 and -02), two plots in pockets of the shade-tolerant species Gilbertiodendron dewevrei (GIL-01 and -02) and finally two plots were located in a mixed old-growth forest outside the pockets (MIX-01 and -02). These plots were established (1) for long-term monitoring of biodiversity and forest dynamics; and (2) to see if there is a difference in terms of species composition and abundance of light demanders between the forest inside the pockets and that outside the pockets.</p>
Clausal Congruence Closure Paper Logs, Plots and Tables
<p>These are the log files and other experimental data of our paper on clausal<br>congruence closure together with generated plots and tables.</p>
Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression
<p><strong>Questions.</strong> Plant cover values in vegetation-plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is if we can detect species trends in vegetation-plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.</p> <p><strong>Location</strong>. The Netherlands, Western Europe.</p> <p><strong>Methods. </strong>We used vegetation-plot data collected from 10.000 fixed plots which were surveyed once every four years during 1999-2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.</p> <p><strong>Results.</strong> We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.</p> <p><strong>Conclusions. </strong>Our model enables researchers to detect trends in cover using longitudinal vegetation-plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring datasets.</p>
Plotting code and data for figures in ''Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers''
<h2>2024_Hu_SpaceWeather_Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers</h2> <p>This package includes the scripts and files for reproducing the plots (or subplots) in the paper, J. Hu, S. McDonald, A. Chartier, A. L. Rubio, S. Datta-Barua, Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers, Space Weather.</p> <p> </p> <p> </p> <p>plotting_code_fig6.m : MATLAB code plotting the figure 6, SAMI3/IDA4D TEC global map</p> <p>---> calc_noon.m : MATLAB function calculating noon time location for the specific UT</p> <p>---> plot_tec_map.m : MATLAB function inside the plotting_code_fig.m</p> <p>plotting_code_fig8.m : MATLAB code plotting the figure 8, validation results compared to Millstone Hill Incoherent Scatter Radar measurements</p> <p>plotting_code_fig9.m : MATLAB code plotting the figure 9, validation results compared to SuperDARN measurements</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Fig. 2. Plot covering 500m in D E A D W O O D A C C U M U L A T I O N I N D E C I D U O U S Dominated Forests
Fig. 2. Plot covering 500m² and trees and stumps that belong to it.
Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
Animated plots of the spectrum and polarisation of Cassiopeia A based on a 24 hour observation with LOFAR Station IE613 (LBA)
<p>This collection of animated plots over time of the variation in channel flux for the linear polarisation channels XX, XY and YY and Stokes Parameters U, V, I and Q against frequency for Cassiopeia A as observed at LOFAR station SE607 between 2018-04-06 and 2018-04-07 using the LBA</p>
Animated plots of the spectrum and polarisation of Cassiopeia A based on a 24 hour observation with LOFAR Station IE613 (LBA)
<p>This collection of animated plots over time of the variation in channel flux for the linear polarisation channels XX, XY and YY and Stokes Parameters U, V, I and Q against frequency for Cassiopeia A as observed at LOFAR station IE613 between 2018-04-06 and 2018-04-07 using the LBA</p>
Animated Plots of the variance of observed data from LOFAR Station IE613
<p>This series of animated plots illustrates the variation of the linear polarisation channels and Stokes parameters for an observation of CasA from LOFAR station IE613 against altitude and azimuth throughout the frequency range of LOFAR HBA.</p>
FIg. 1. Map of the central western coastline of Western Australia with known distribution of Urodacus butleri, n. sp. (solid circles). Inset A illustrates the continent of Australia and the area containing the known distribu- tion. Inset B illustrates an enlargement of Barrow Island with the distribution plotted.
FIg. 1. Map of the central western coastline of Western Australia with known distribution of Urodacus butleri, n. sp. (solid circles). Inset A illustrates the continent of Australia and the area containing the known distribu- tion. Inset B illustrates an enlargement of Barrow Island with the distribution plotted.
Sample output of Alt-Azimuth plots from beamModelTester
<p>Sample Output from BeamModelTester operating in various alt-azimuth plotting modes.</p>
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.