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1,646 results for “plot”
Reforested areas of the Castel di Guido estate (Italy): field plots dataset
<p>This dataset contains the measurements (diameter at breast height and notes) carried out in the Castel di Guido estate (Italy) during 1997, 2000, 2003, 2006, 2011, 2013, 2017 in 29 plot areas.</p>
Scatter plots as per Fig. 4, and Spectra-derived TiO2 as per Fig. 7
<p>F. Zhang, J. W. Head, C. Wöhler, R. Bugiolacchi, L. Wilson, A. T. Basilevsky, A. Grumpe, Y. L. Zou.</p> <p>Ring-Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin.</p> <p>Journal of Geophysical Research: Planets, 125, e2019JE005967. https://doi.org/10.1029/2019JE005967</p> <p> </p> <p><strong>Figure 4.</strong> Scatter plots of diameters, heights, and volumes of the 532 RMDSs measured from NAC-derived DTMs: (a) diameter vs. height; (b) diameter vs. volume</p> <p><strong>Figure 7. </strong>M<sup>3</sup>-spectra-derived TiO<sub>2</sub> contents of RMDSs and global maria (latitude range +/- 75 degrees).<strong> </strong>(a) Mean TiO<sub>2</sub> wt% vs. diameter for 2407 RMDSs in seven large mare regions indicated in Figure 2; (b) The TiO<sub>2</sub> content histogram of the measured 2407 RMDSs (green color), compared with the global TiO<sub>2</sub> distribution of all lunar mare regions (red color). Both histograms are normalized to 1, such that the sum of all red or green histogram bins is 1, respectively. The global Ti abundance was taken from the complete lunar mare surface at a latitude range +/- 75 degrees</p> <p> </p>
Long-term data from field erosion plot studies in eastern Austria
<p>Long-term runoff plot experiments were conducted at three sites in eastern Austria to evaluate the impact of conservation tillage. Following soil tillage treatments were investigated: 1) conventional tillage with ploughing in fall (CT), 2) mulch tillage with cover crops during winter period (MT), and 3) no tillage with cover crops during winter period (NT). Soil textures range from silt loam to loam.</p> <p>Datasets include for each site and each year the period of operation of the plots, the planted crop, the annual precipitation, surface runoff, soil loss, surface losses of nitrogen, phopshorus and soil organic carbon as well as absolute and relative crop yields.</p>
Long-term data from field erosion plot studies in eastern Austria
<p>Long-term runoff plot experiments were conducted at three sites in eastern Austria to evaluate the impact of conservation tillage. Following soil tillage treatments were investigated: 1) conventional tillage with ploughing in fall (CT), 2) mulch tillage with cover crops during winter period (MT), and 3) no tillage with cover crops during winter period (NT). Soil textures range from silt loam to loam.</p> <p>Datasets include for each site and each year the period of operation of the plots, the planted crop, the annual precipitation, surface runoff, soil loss, surface losses of nitrogen, phopshorus and soil organic carbon as well as absolute and relative crop yields.</p>
Plots of potential function of ion-acoustic waves
<p>Plots of the potential function of ion-acoustic waves under the KdV equation for nonextensive parameter (a) q = -0.4, (b) q = 0.1, (c) q = 1 and (d) q = 1.2. Here, the local minima of potential curves (a)-(d) show the existence of solitary wave solutions. The positive region corresponds to a compressive solitary solution and negative region corresponds to rarefactive solitary wave solution. </p>
Plots of potential function for the mKdV equation
<p>(a) Potential function for nonextensive parameter q = 3.2 and (b) potential function for Maxwellian limit (q = 1) for the mKdV equation. Here (a) shows the existence of supernonlinear periodic waves, compressive and rarefactive solitary wave solutions of the mKdV equation.</p>
Images and plotting code : Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>These are images to demonstrate the effectiveness of the algorithm and plotting code : Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite. Readme.pdf will provide specific instructions about these folder compression packages.</p>
Plot-Rice v1.0: A global plot-based rice benchmark dataset with spatiotemporal heterogeneity for scientific deep learning
<p>This dataset (Plot-Rice v1.0) offers a global rice benchmark dataset for scientific deep learning at a 10-meter resolution for the year 2023. Plot-Rice v1.0 is constructed based on Sentinel-1 and Sentinel-2 images, encompassing plot-level rice labels and corresponding multi-source feature time series from 20 countries worldwide. It fully considers the spatiotemporal heterogeneity of rice and supports continuous updates, providing a data benchmark for performance comparisons of deep learning models.</p>
Numerical data achieved to plot the graphs
Open the record for dataset details and reuse information.
Plot files and data for 2408.12646
<p>The provided py-plot-files and data reproduce the plot files from the paper 2408.12646. The data also includes the excitation spectra from 2101.02715 ("data/spectral_functions/").</p>
Forest plot: Associations between BMI at baseline and pCR following NACT – stratification according to ER status.
<p>Forest plot: Associations between BMI at baseline and pCR following NACT – stratification according to ER status.</p>
Diversity change in forest plots of Blue Mountains, Jamaica
<p>Beta diversity describes the differences in species composition among communities. Changes in beta diversity over time are thought to be due to selection based on species' niche characteristics. For example, theory predicts that selection that favours habitat specialists will increase beta diversity. In practice, ecologists struggle to predict how beta diversity changes. To remedy this problem, we propose a novel solution that formally measures selection's effects on beta diversity. Using the Price equation, we show how change in beta diversity over time can be partitioned into fundamental mechanisms including selection among species, variable selection among communities, drift, and immigration. A key finding of our approach is that a species' short-term impact on beta diversity cannot be predicted using information on its long-term environmental requirements (i.e. its niche). We illustrate how our approach can be used to partition causes of diversity change in a montane tropical forest before and after an intense hurricane. Previous work in this system highlighted the resistance of habitat specialists and the recruitment of light-demanding species but was unable to quantify the importance of these effects on beta diversity. Using our approach, we show that changes in beta diversity were consistent with ecological drift. We use these results to highlight the opportunities presented by a synthesis of beta diversity and formal models of selection.</p>
FIG. 7. Scatter plot for P4 in New material of Eocene Helaletidae (Perissodactyla, Tapiroidea) from the Irdin Manha Formation of the Erlian Basin, Inner Mongolia, China and comments on Related Localities of the Huheboerhe Area
FIG. 7. Scatter plot for P4 size of Paracolodon and Desmatotherium (in mm).
FIG. 6. Scatter plot for P3 in New material of Eocene Helaletidae (Perissodactyla, Tapiroidea) from the Irdin Manha Formation of the Erlian Basin, Inner Mongolia, China and comments on Related Localities of the Huheboerhe Area
FIG. 6. Scatter plot for P3 size of Paracolodon and Desmatotherium (in mm).
FIG. 8. Scatter plot for M2 in New material of Eocene Helaletidae (Perissodactyla, Tapiroidea) from the Irdin Manha Formation of the Erlian Basin, Inner Mongolia, China and comments on Related Localities of the Huheboerhe Area
FIG. 8. Scatter plot for M2 size of Paracolodon and Desmatotherium (in mm).
Figure 8. Bivariate plots for reputed T in A taxonomic revision of the Tragulus mouse-deer (Artiodactyla)
Figure 8. Bivariate plots for reputed T. napu specimens from the small Sundaland islands.
Distribution of vegetation sampling plots in four ecosystem types
<p>The table contains a number of bear cuscus presence points based on direct and indirect encounters (representative information from the local guide), as well as a number of plots constructed in each bear cuscus presence point in various ecosystem types in Bantimurung Bulusaraung National Park and Hasanuddin University Educational Forest, South Sulawesi.</p> <p>Three plots of 20 x 20 m were constructed in each bear cuscus presence point, with fifteen plots were established in each ecosystem type.</p>
Distribution of vegetation sampling plots in various ecosystem types
<p>The table contains no. of bear cuscus presence points based on direct and indirect encounters (representative information from the local guide), as well as no. of plots constructed in each bear cuscus presence point in various ecosystem types in Bantimurung Bulusaraung National Park and Hasanuddin University Educational Forest, South Sulawesi.</p> <p>Three plots of 20 x 20 m were constructed in each bear cuscus presence point, with fifteen plots were established in each ecosystem type.</p>
Components of the complete budget for SAFE intensive carbon plots
<p><strong>Description: </strong></p> <p>Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.<br> <br> These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.<br> <br> SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/113"><strong>Changing carbon dioxide and water budgets from deforestation and habitat modification</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>Sime Darby Foundation (Grant, SAFE Core data)</li> <li>European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)</li> <li>NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)</li> <li>NERC standard grant: The multi-year impacts of the 2015/2016 El Niño on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1)</li> <li>HSBC Malaysia (Grant)</li> <li>The University of Zurich (Grant)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7307449">here</a></p> <p><strong>Files: </strong>This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx</p> <p><strong>SAFE_CarbonBalanceComponents.xlsx</strong></p> <p>This file contains dataset metadata and 1 data tables:</p> <ol> <li> <p><strong>Carbon balance components data</strong> (described in worksheet Data)</p> <p>Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project</p> <p>Number of fields: 64</p> <p>Number of data rows: 11</p> <p>Fields:</p> <ul> <li><strong>ForestType</strong>: Old-growth or Logged (Field type: categorical)</li> <li><strong>SAFEPlotName</strong>: SAFE plot name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>PlotName</strong>: Plot name (used in field work) (Field type: id)</li> <li><strong>ForestPlotsCode</strong>: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id)</li> <li><strong>WoodyNPP_Stem</strong>: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_CoarseRoot</strong>: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_BranchTurnover</strong>: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_Total</strong>: Total woody net primary producivity (Field type: numeric)</li> <li><strong>CanopyNPP_Leaf</strong>: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Twig</strong>: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Reproductive</strong>: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Miscellaneous</strong>: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Herbivory</strong>: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Total</strong>: Total canopy net primary producivty (Field type: numeric)</li> <li><strong>FineRootNPP</strong>: Fine root productivity (Field type: numeric)</li> <li><strong>TotalNPP_WithoutMycorrhiza</strong>: Total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>TotalNPP_WithMycorrhiza</strong>: Total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithoutMycorrhiza</strong>: Gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithMycorrhiza</strong>: Gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>R_Stem</strong>: Respiration from woody stems (Field type: numeric)</li> <li><strong>R_Leaf</strong>: Leaf Respiration (Field type: numeric)</li> <li><strong>R_FineRoots</strong>: Respiration from fine roots (Field type: numeric)</li> <li><strong>R_CoarseRoots</strong>: Respiration from coarse roots (Field type: numeric)</li> <li><strong>R_SOM</strong>: Respiration from soil organic matter (Field type: numeric)</li> <li><strong>R_Mycorrhiza</strong>: Respiration from mycorrhiza (Field type: numeric)</li> <li><strong>R_Litter</strong>: Respiration from litter layer (Field type: numeric)</li> <li><strong>R_Deadwood</strong>: Deadwood respiration (Field type: numeric)</li> <li><strong>R_auto</strong>: Total autotrophic respiration (Field type: numeric)</li> <li><strong>R_het</strong>: Total heterotrophic respiration (Field type: numeric)</li> <li><strong>R_eco</strong>: Total ecosystem respiration (Field type: numeric)</li> <li><strong>NEP_WithoutMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>NEP_WithMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>AbovegroundBiomassCarbonStock</strong>: Plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>CoarseRootBiomassCarbonStock</strong>: Biomass carbon stock of coarse roots (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Stem</strong>: Standard error of woody stem productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_CoarseRoot</strong>: Standard error of coarse root productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_BranchTurnover</strong>: Standard error of branch turnover productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Total</strong>: Standard error of total woody net primary producivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Leaf</strong>: Standard error of leaf productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Twig</strong>: Standard error of twig productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Reproductive</strong>: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Miscellaneous</strong>: Standard error of unidentified canopy debris (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Herbivory</strong>: Standard error of leaf productivity lost to herbivory (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Total</strong>: Standard error of total canopy net primary producivty (Field type: numeric)</li> <li><strong>SE_FineRootNPP</strong>: Standard error of fine root productivity (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithoutMycorrhiza</strong>: Standard error of total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithMycorrhiza</strong>: Standard error of total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithoutMycorrhiza</strong>: Standard error of gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithMycorrhiza</strong>: Standard error of gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Stem</strong>: Standard error of respiration from woody stems (Field type: numeric)</li> <li><strong>SE_R_Leaf</strong>: Standard error of leaf Respiration (Field type: numeric)</li> <li><strong>SE_R_FineRoots</strong>: Standard error of respiration from fine roots (Field type: numeric)</li> <li><strong>SE_R_CoarseRoots</strong>: Standard error of respiration from coarse roots (Field type: numeric)</li> <li><strong>SE_R_SOM</strong>: Standard error of respiration from soil organic matter (Field type: numeric)</li> <li><strong>SE_R_Mycorrhiza</strong>: Standard error of respiration from mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Litter</strong>: Standard error of litter layer respiration (Field type: numeric)</li> <li><strong>SE_R_Deadwood</strong>: Standard error of deadwood respiration (Field type: numeric)</li> <li><strong>SE_R_auto</strong>: Standard error of total autotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_het</strong>: Standard error of total heterotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_eco</strong>: Standard error of total ecosystem respiration (Field type: numeric)</li> <li><strong>SE_NEP_WithoutMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_NEP_WithMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_AbovegroundBiomassCarbonStock</strong>: Standard error of plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>SE_CoarseRootBiomassCarbonStock</strong>: Standard error of biomass carbon stock of coarse roots (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-08-25 to 2018-07-17</p> <p><strong>Latitudinal extent: </strong>4.1830 to 5.0700</p> <p><strong>Longitudinal extent: </strong>114.0190 to 117.8200</p>
Botanic records from the forest reserves of south west Ghana: Plant species distribution data with checklist and conservation assessments from 114 vegetation plots
<p>South west Ghana is a biodiversity hotspot within the western African lowland tropical rainforest region, supporting many endemic and restricted range plant species. This dataset comprises botanic records from five forest reserves of south west Ghana (Ankasa, Boi Tano, Tano Nimri, Jema Assemkron, Nini Suhein). Vascular plant species distribution data (12,232 records) from 114 vegetation plot samples are presented, surveyed between 1981 and 2015. A plant species checklist including conservation assessments for each species is included. Nomenclature is current as of 2016. The dataset is linked to the publication Marshall et al, 2023, Implications for conservation assessment from flux in the botanical record over 20 years in south west Ghana, Ecology and Evolution <a href="https://doi.org/10.1002/ece3.9775">https://doi.org/10.1002/ece3.9775</a>. The dataset is also used in Marshall et al, 2022, Predictors of plant endemism in two west African forest hotspots, Frontiers in Ecology and Evolution 10:980660 <a href="https://doi.org/10.3389/fevo.2022.980660">https://doi.org/10.3389/fevo.2022.980660</a>.</p>
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.