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1,646 results for “plot”
CAP22 Tomographic model: Data, Software, Plotting
<p>CAP22 (Boyce et al., 2023), is an absolute P-wavespeed tomographic model with focus on North American, specifically Canadian and Alaskan, mantle structure, provided with and without crustal correction.</p> <p>Please cite the following work when using this data set:</p> <p>Boyce, A., Liddell, M. V., Pugh, S., Brown, J., McMurchie, E., Parsons, A., Estève, C., Burdick, S., Darbyshire, F. A., Cottaar, S. Bastow, I. D., Schaeffer, A. J., Audet, P., Schutt, D. L., Aster, R. C. (2023). A new P-wave tomographic model (CAP22) for North America: Implications for the subduction and cratonic metasomatic modification history of western Canada and Alaska. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025745. <a href="https://doi.org/10.1029/2022JB025745">https://doi.org/10.1029/2022JB025745</a></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-cap22/ or with the following doi: 10.17611/dp/emc.2023.cap22.1. Please find CAP22 and its related files for a detailed description of the distributed model.</p> <p>The CAP22_TOMO_SHARE.tar repository contains:</p> <ul> <li>Raw_data: Hand-picked and processed seismic data (.SAC) from western Canada and Alaskan seismic networks.</li> <li>Original_documentation: Documentation for original distribution of inversion code from MIT global seismology group.</li> <li>Tomography_CAP22: Implementation of MIT inversion algorithm used to produce CAP22 including formatted data.</li> <li>Plotting: Codes and necessary files to reproduce figures in main manuscript.</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> M. V. Liddell: Centre de recherche Geotop, Université du Québec à Montréal, QC, Canada.<br> S. Pugh: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> J. Brown: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> E. McMurchie: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> A. Parsons: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> C. Estève: Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria.<br> S. Burdick: Wayne State University, Geology Department, Detroit, MI, USA.<br> F. A. Darbyshire: Centre de recherche Geotop, Université du Québec à Montréal, QC, Canada.<br> S. Cottaar: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> I. D. Bastow: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK.<br> A. J. Schaeffer: Geological Survey of Canada Pacific Division, Sidney, BC, Canada.<br> P. Audet: University of Ottawa, Department of Earth and Environmental Sciences, Ottawa, ON, Canada.<br> D. L. Schutt: Colorado State University, Department of Geosciences and Warner College of Natural Resources, Fort Collins, CO, USA.<br> R. C. Aster: Colorado State University, Department of Geosciences and Warner College of Natural Resources, Fort Collins, CO, USA.</p> <p> </p> <p> </p>
Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption: Misfit plots in model parameter space
<p>Supporting documents for the correction of the publication <em>Multi‐Event Explosive Seismic Source for the 2022 Mw 6.3 Hunga Tonga Submarine Volcanic Eruption, </em>published in The Seismic Record (<a href="https://doi.org/10.1785/0320220027">https://doi.org/10.1785/0320220027</a>).</p>
Hoehler et al. (2023) Interactive Plot
<p>This interactive figure is published alongside its corresponding dataset in Hoehler et al. (2023) The Metabolic Rate of the Biosphere and its Components. Data has been averaged by species (excluding seedlings and tree saplings), and are presented in a double-logarithmic plot using the python package Bokeh. The plot enables the user to toggle between Metabolic Rate (W) vs. Mass-specific metabolic rate (W g<sup>-1</sup>) on the y-axis and Wet, Dry, or Carbon mass (g) on the x-axis. The types of metabolic rates (i.e. Basal, Field and Maximum) can be displayed using the toggles at the top. The taxonomic group of organisms can be selected/deselected individually using the legend on the right. Log-log-linear correlation lines are shown for taxonomic groups that include 10 or more measurements and have a correlation coefficient >0.25. Metabolic rates can also be normalized to 25<sup>o</sup>C. When "Metabolic rate" is chosen, the plot shows a black line indicating the log-log-linear correlation with mass for all the data. When "Mass-specific metabolic rate" is chosen, the plot shows the geometric mean and standard deviation (as fold-factor) for all data. Additional information is given in the SI Appendix. </p>
B.Sc Datasets used for R Plots
<p>B.Sc Untersuchung der Anhydrobiose bei Nematoden aus extremen Umweltbedingungen, die für die in R erstellten Plots verwendeten Daten </p>
Data and Supplementary Plots for A Shallow Water Model Exploration of Atmospheric Circulation on Sub-Neptunes
<p>This repository contains geopotential maps, zonal wind plots, gifs, and data for the ensemble of possible sub-Neptunes presented in the main manuscript. The data, individual plots, and plot grids are based on the averages of the last 100 simulated days. The data are in the pickle format (see https://docs.python.org/3/library/pickle.html)<br> The gifs are based on the last 1000 simulated hours. These figures and gifs support the analysis presented in the main manuscript.</p>
'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017
<p>Permanent forest plot data from Sabah, Malaysia. Danum Valley Conservation Area. Here we provide a brief description of the permanent plot data collected following the same protocols utilized within the Kuamut forest reserve.</p> <p>Twenty circular field plots with a 30 m radius that were established across the protected area as part of a collaboration between SEARRP and the Carnegie Airborne Observatory (CAO) in 2017. These plots were surveyed following the same protocols as those described by the Kuamut conservation project. The field protocol is provided. Field plot locations where selected using airborne LiDAR to specifically target both high Aboveground Carbon Density (ACD) areas, and also areas with different ACD predictions based on a draft set of carbon mapping models. See Jucker et al. (2018) and Asner et al. (2017) for further details. Note subsequent measurements (in 2020) include more plots. Subsequent measurements allowed further data checking and corrections, so some minor updates to the 2017 dataset have been made since the previous publications. Please see additional Zenodo data entries for the 2020 measure.</p> <p>Use of these data require citation of this dataset and citation of the two original journal articles that delivered and explain the work, and that you inform us of the use of the data. We would also appreciate the opportunity to be involved in work making use of this data. The required citations are as follows:</p> <p>Asner, Gregory P., Philip G. Brodrick, Christopher Philipson, Nicolas R. Vaughn, Roberta E. Martin, David E. Knapp, Joseph Heckler, et al. 2017. “Mapped Aboveground Carbon Stocks to Advance Forest Conservation and Recovery in Malaysian Borneo.” <em>Biological Conservation</em> 217 (June 2017): 289–310. <a href="https://doi.org/10.1016/j.biocon.2017.10.020">https://doi.org/10.1016/j.biocon.2017.10.020</a></p> <p>Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, et al. 2018. “Estimating Aboveground Carbon Density and Its Uncertainty in Borneo’s Structurally Complex Tropical Forests Using Airborne Laser Scanning.” <em>Biogeosciences</em> 15 (12): 3811–30. <a href="https://doi.org/10.5194/bg-15-3811-2018">https://doi.org/10.5194/bg-15-3811-2018</a></p> <p>This work would not be possible without the incredibly hard work and dedication of the entire SEARRP field team, including Philip Ak Ulok; Hii Siew Yee; Remmy Bin Murus; Alexander Karolus; Andy Brian Karolus; Frederica Karolus; Zidey Fulgentius; Welday Bin Girang; Mohamad Taufiq Bin Sumin, Mohd Fadil Bin Abd Karim, Joulu Rasion, and Japin Bin Rasion. We are very grateful to the help of all the field staff not specifically mentioned here.­</p>
Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama
<p>Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50-ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-yr intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m<sup>2</sup> seedling plot in the center of every 5 x 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings) were tagged, mapped, measured, and identified to species in 19,313 1-m<sup>2</sup> seedling plots. Because seedling dynamics are rapid, we censused these seedling plots every 1–2 years. Here we present data from the 14 censuses of these seedling plots conducted between the initial census in 2001 to the most recent census, in 2018. This data set includes nearly 1M observations of ~185,000 individuals of >400 tree, shrub, and liana species. These data will permit spatially-explicit analyses of seedling distributions, recruitment, growth, and survival for hundreds of woody plant species. In addition, the data presented here can be linked to openly-available, long-term data on the dynamics of trees and shrubs ≥1cm dbh in the BCI FDP, as well as existing data sets from the site on climate, canopy structure, phylogenetic relatedness, functional traits, soil nutrients, and topography.</p>
Supplementary information for the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly'
<p>This is the supplementary information of the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly' published as a part of the thesis 'Annotating and making use of the <em>Avena sativa</em> cv. Sang reference genome' by Nikos Tsardakas Renhuldt.</p>
MAgPIE model runs csv for plotting: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>This .zip file contains the data used to create the figures for the paper. It includes .csv files and .nc files for maps. This version includes additional files like the mapping between countries and MAgPIE're economic regions.</p>
RapidEye and Landsat remote sensing measures for Sabah Biodiversity Experiment plots
<p>Experiments under controlled conditions have established that ecosystem functioning is generally positively related to levels of biodiversity but it is unclear how widespread these effects are in real-world settings and whether they can be harnessed for ecosystem restoration. We used a long-term, field-scale tropical restoration experiment to test how the diversity of planted trees affected recovery of a 500-ha area of selectively logged forest measured using multiple sources of satellite data. Replanting using species-rich mixtures of tree seedlings with higher phylogenetic and functional diversity accelerated restoration of remote sensing estimates of aboveground biomass, canopy cover and Leaf Area Index. Our results are consistent with a positive relationship between biodiversity and ecosystem functioning in the lowland dipterocarp rainforests of SE Asia and demonstrate that using diverse mixtures of species can enhance their initial recovery after logging.</p>
Plot attribute table of the Kunbaja online resource
<p>Plot attribute table of the QGIS database of the Kunbaja online resource model as dataset in the .xlsx file format</p>
Adamantanone Plots
<p>Adamantanone plots:</p> <p>i) Equilibrations</p> <p>ii) RCF with Position Restraint</p> <p>iii) RCF without Position Restraint</p>
Soil and census data of Heishiding plot
<p>Intransitive competition has long been acknowledged as a potential mechanism favoring species coexistence. However, its prevalence, variance along environmental gradients, and possible underlying mechanisms (trade-offs) in plant communities (especially in forests) has seldomly been examined. A recently developed "reverse-engineering" approach based on Markov Chain allows us to estimate competitive transition matrices and competitive intransitivity from observational abundance data. Using this approach, we estimated competitive intransitivity of five dominant species in a subtropical forest and then related it to soil fertility (soil organic matter and soil pH) and demographic trade-offs (growth-survival and stature-recruitment trade-offs). In our forest plot, intransitive competition was common among the dominant species and peaked at the intermediate level of soil organic matter. Neither the growth-survival trade-off nor the stature-recruitment trade-off was positively related to competitive intransitivity. Our study for the first time empirically supported the unimodal intransitivity-fertility relationship in forests, which, however, was not mediated by the two demographic trade-offs in our plot.</p>
Dataset for all plots in journal 100Gbps PAM4 photodetector by Hemalatha-Tyndall
<p>The excel file contains all the dataset for the plots included in journal article, '100 Gbps PAM4 ultra-thin photodetectors integrated on SOI platform by micro transfer printing'. </p>
3Dmapper data plot S7
<p>Necessary data to produce plot from figure S7 in pre-print https://www.biorxiv.org/content/10.1101/2023.09.01.555502v1.supplementary-material</p>
Exterior of Plot 22 Sheffield with street art
Plot 22 is a multipurpose creative space at 22 Exchange Street, Sheffield, UK. This is the exterior, with street art by Mila K (door on right, and base of the ramp), Trik 9 (top left of the ramp), Marcus Method and Dala (mid-ramp) and others. 3D reconstruction using Meshroom and Meshlab, from photos taken in March 2021. Source: Objaverse 1.0 / Sketchfab
Plot 62 (Oakland Cemetery)
A 3D rendering of a headstone located in Section 1 / Plot 62 at Oakland Cemetary in Atlanta, GA. Source: Objaverse 1.0 / Sketchfab
Tree species abundance through time in tropical forest census plots, Panama
Open the record for dataset details and reuse information.
Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama
Open the record for dataset details and reuse information.
Forest biomass in subtropical Andes: Plots data
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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.