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9,882 results for “Scale”
Data for paper: Fine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in Rwanda
<p>Dataset used by code for the paper: Fine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in RwandaFine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in Rwanda</p>
The development of the Awe Experience Scale (AWE-S): A multifactorial measure for a complex emotion
<p>Two dataset of the research "The development of the Awe Experience Scale (AWE-S): A multifactorial measure for a complex emotion"</p>
A soil moisture-dependent model to simulate water table depth and proportions of surface and subsurface runoff and its validation at basin scale
<p>The data is the simulations of the SMD-model, Sy-mdoel, adn Noah-MP in three basins in China and the USA. The vaiables are monthly water table depth, soil moisture, subsurface and total runoff. </p>
Fine-scale species occurrence data
<p>Surveys of individual vascular plant species occurrence and cover in quadrats of 1x1 m on sub-Antarctic Marion Island.</p>
Large-scale three-dimensional hydraulic tomography analyses of long-term municipal wellfield operations
<p>Datasets utilized to accomplish the work that supports the publication of the above manuscript. Basically, the datasets include element and node information that discrete the investigated area for groundwater flow modeling, as well as flow rates (production wells) and head response data (monitoring wells) utilized for inverse modeling.</p>
Dataset for "Impact of downward longwave radiative deficits on Antarctic sea-ice extent predictability for subseasonal time scales"
<p>Dataset for "Impact of downward longwave radiative deficits on Antarctic sea-ice extent predictability for subseasonal time scales"</p> <p> </p> <p> </p>
Validation in Spanish and British English of the Transgender Inclusive Behavior Scale (TIBS).
<p>Data sets of the article Validation in Spanish and British English of the Transgender Inclusive Behavior Scale (TIBS).</p> <p>SPSS data sets were used to perform Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) in Spain and the United Kingdom.</p>
data set related to article Validation of the Proposed Specifiers for Conduct Disorder (PSCD) Scale in a Sample of Italian Students
<p>This record contains raw data related to article Validation of the Proposed Specifiers for Conduct Disorder (PSCD) Scale in a Sample of Italian Students</p>
Archive of the iSALE simulation datasets for Jackson et al. 2022, "Impact generation of holes in the early lunar crust I: scaling relations"
<p>Archive of the iSALE simulation datasets used in Jackson et al. 2022, "Impact generation of holes in the early lunar crust I: scaling relations".</p> <p>This archive contains all necessary information to re-run any of the simulations in the dataset along with plots that can be used to check the outcomes of each simulation. In addition, it also contains a full compilation of all of the derived data used in Jackson et al. 2022 as well as the analysis scripts used to extract that data from the simulations and the scripts that use the derived data to generate the figures in the manuscript.</p> <p>The archive is provided in a standard compressed (.zip) format.</p> <p> </p> <p><strong>Detailed description of the contents</strong></p> <p>Note that this description can also be found in the ReadMe file provided with the archive</p> <p> </p> <p><strong>1) Simulation folders</strong></p> <p>The folder '<em>simsmain</em>' contains files for the iSALE2D simulations that comprise the primary simulation database.</p> <p>For each simulation we provide:</p> <ul> <li>asteroid.inp - the initialisation file for the simulation that would allow it to be re-run</li> <li>[].dat - a text file containing data derived from the final simulation output. These values from all simulations are compiled in alldata.csv. A description of the columns is provided below for alldata.csv</li> <li>[]-Crthickprof.pdf - a figure showing the radial crust thickness profile at the end of the simulation. Light pink shows the cell-by-cell profile, solid red a moving average.</li> <li>[]-Damprof.pdf - a figure showing the radial profile of the mean damage factor (averaged over the depth of the crust at each radial cell) at the end of the simulation. Light grey shows the cell-by-cell profile, solid black a moving average.</li> <li>[]-TmpDam.dpdf - a figure showing the temperature and damage structure of the entire high-resolution zone at the end of the simulation at 1:1 aspect ratio (i.e. square cells are square in the image). The boundary of the crust is outlined in black.</li> <li>[]-TmpDam-zoom.pdf - as above, but zoomed in on the central region of interest in the simulation. This image is stretched to highlight the region of interest (i.e. square cells are not square in the image).</li> <li>[]-temporal.dat - a text file containing data derived from the full simulation time series. These values from all simulations are compiled in alldata_temporal.csv. A description of the columns is provided below for alldata_temporal.csv</li> <li>[]-crdepthtime.png - a figure showing the change in the maximum depression of the upper surface of the simulation over time. This records the excavation and collapse of the initial transient cavity.</li> <li>[]-holeradtime.png - a figure recording the radius of the hole (as measured by the locations at which the crust thickness passes 90 and 95 percent of the initial value) over time. A final plateau is indicative of the simulation having settled.</li> <li>[]-holevoltime.png - a figure recording the volume of the hole within the 95% crust thickness radius over time.</li> <li>[]-trcavtime.png - a figure recording the volume of the surface depression over time. This can be quite sensitive to small fluctuations at the edges of the surface depression and does not always plateau.</li> </ul> <p>Within <em>simsmain</em> each sub-folder is named in the format dW_vX_cY_gZ, where d is the diameter of the impactor (in km), v is the impact velocity (in m/s), c is the crust thickness (in km) and g is the grid resolution of the high-resolution zone (in km). W, X, Y and Z are all in exponential notation with two significant figures (i.e. N.NE+NN).</p> <p>The folder '<em>simsres</em>' contains files for the additional iSALE2D simulations run for resolution testing purposes. These files have the same formats and naming conventions as <em>simsmain</em>.</p> <p>The folder '<em>material</em>' contains the file material.inp, the initialisation file for the material properties which is common to all of the simulations.</p> <p> </p> <p><strong>2) Analysis scripts</strong></p> <p>The folder '<em>analysisscripts</em>' contains the Python scripts that were used to generate the files in <em>simsmain</em> and <em>simsres</em>. There are two scripts, iSALEanalysis_batch.py which generates the end-of-simulation plots and data, and iSALEanalysis_batch_temporal.py which generates the time series plots and data. The temporal script typically takes around 400 times longer to run than the end-of-simulation script since the former must examine all (usually 800) timesteps, whereas the end-of-simulation script examines only the first and last timestep.</p> <p> </p> <p><strong>3) Figures</strong></p> <p>The folder 'figures' contains CSV files summarising all of the data derived from the iSALE2D simulations along with plotting scripts that use this derived data to generate the plots in Jackson et al. 2022.</p> <p>-----------<br> alldata.csv is a compilation of the data in the [].dat files for all 252 simulations in the primary simulation database. There are 14 columns which in order are:</p> <ol> <li>dimp - impactor diameter in km</li> <li>vimp - impact velocity in km/s</li> <li>crd - initial crust thickness in km</li> <li>tdat - simulation time (in seconds) at which the data was taken</li> <li>gres - grid resolution in the high-resolution zone in km</li> <li>rhires - radius of the high-resolution zone in km</li> <li>r_thincr0_95- radial distance (in km) at which the crust thickness exceeds 95% of the initial value</li> <li>r_thincr0_9 - radial distance (in km) at which the crust thickness exceeds 90% of the initial value</li> <li>r_magcr - radius (in km) within which there is direct exposure of magma to the surface (i.e. crust thickness is zero)</li> <li>r_frac09 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.9</li> <li>r_frac08 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.8</li> <li>r_frac05 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.5</li> <li>holevolume - volume (in km^3) of crust excavated from within r_thincr0_95</li> <li>comment2 - impact regime classification, 'partial'= partial penetration, 'complete' = complete penetration, 'cfd' = cratering with full-depth fracturing, 'classical' = classical crater.</li> </ol> <p>-----------<br> alldata_temporal.csv is a compilation of the data in the []-temporal.dat files for all 252 simulations in the primary simulation database. There are 20 columns which in order are:</p> <ol> <li>dimp - impactor diameter in km</li> <li>vimp - impact velocity in m/s (Note the difference from alldata.csv)</li> <li>crd - initial crust thickness in km</li> <li>tdat - simulation time (in seconds) at which the data was taken</li> <li>gres - grid resolution in the high-resolution zone in km</li> <li>rhires - radius of the high-resolution zone in km</li> <li>r_thincr0_95- radial distance (in km) at which the crust thickness exceeds 95% of the initial value</li> <li>r_thincr0_9 - radial distance (in km) at which the crust thickness exceeds 90% of the initial value</li> <li>r_magcr - radius (in km) within which there is direct exposure of magma to the surface (i.e. crust thickness is zero)</li> <li>r_frac09 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.9</li> <li>r_frac08 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.8</li> <li>r_frac05 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.5</li> <li>holevolume - volume (in km^3) of crust excavated from within r_thincr0_95</li> <li>mcrd - maximum depth of the transient cavity in km</li> <li>t_mcrd - simulation time (in seconds) at which transient cavity reaches maximum depth</li> <li>trcav_mcrd - volume (in km^3) of the transient cavity at the time of maximum depth</li> <li>mtrcav - maximum volume of the transient cavity</li> <li>t_mtrcav - simulation time (in seconds) at which transient cavity reaches maximum volume</li> <li>crd_mtrcav - depth of the transient cavity (in km) at the time of maximum volume</li> <li>comment2 - impact regime classification, 'partial'= partial penetration, 'complete' = complete penetration, 'cfd' = cratering with full-depth fracturing, 'classical' = classical crater.</li> </ol> <p>-----------<br> restest-rthin.csv and restest-frac.csv are in the same format as alldata.csv and contain the data for the sets of simulations used to test the resolution dependence of r_thin and r_frac respectively.</p> <p>-----------</p> <p>There are 7 Python plotting scripts:</p> <ol> <li>plotter_main.py - the main plotting script, produces plots that don't fall under another heading</li> <li>plotter_KE.py - produces plots as a function of impact kinetic energy</li> <li>plotter_temporal.py - produces plots using time series data from alldata_temporal</li> <li>plotter_cox.py - produces plots comparing our results to those of Bray et al. 2014, Cox et al. 2008, Cox & Bauer 2015 and Miljkovic et al. 2015. Note that we do not include the CSV files containing the data from those papers since it is not ours to distribute, however they can be easily extracted from the relevant publications.</li> <li>plotter_res.py - produces resolution comparison plots</li> <li>plotter_pi.py - produces Pi-scaling plots</li> <li>kplot-sims.py - produces a plot of the 'k' factor (hole size/impactor mass) for comparison with Perera et al. 2018</li> </ol> <p>All of the plots and fitting summaries produced by the plotting scripts are also included</p>
Data used in paper: Which orographic scales matter most for medium-range forecast skill in the Northern Hemisphere winter?
<p>The data archived here is a set of the data used to generate the figures in the paper 'Which orographic scales matter most for medium-range forecast skill in the Northern Hemisphere winter?', by Takafumi Kanehama, Irina Sandu, Anton Beljaars, Annelize van Niekerk and Francois Lott.<br> <br> The data was generated using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System.<br> <br> For more information, please contact takafumi.kanehama@ecmwf.int.</p> <p> </p>
Greenhouse gases modulate the strength of millennial-scale subtropical rainfall, consistent with future predictions
<p>high-resolution scanning Ca/Ti ratio of Linxia loess records spanning the past 650 kyr</p>
Small scale pilots - Holis GV for HCP & patients
<p>Dataset of feedbacks from Small scale pilots with HCP & patients</p>
Data from: Specialization patterns in symbiotic associations: a community perspective over spatial scales.
<p><strong>Nostoc_rbcLX_alignment: </strong>Alignment of Nostoc rbcLX sequences in FASTA format. </p> <p><strong>Name_equivalences: </strong>Excel cointaining mycociont species names, abbreviations, alignment code for each sample used in the aligment, forest and Nostoc phylogroup.</p> <p><strong>Abstract:</strong> </p> <ol> <li>Specialization, contextualized in a resource axis of an organism niche, is a core concept in ecology. In biotic interactions, specialization can be determined by the range of interacting partners. Evolutionary and ecological factors, in combination with the surveyed scale (spatial, temporal, biological and/or taxonomic) influence the conception of specialization.</li> <li>This study aimed to assess the specialization patterns and drivers in the lichen symbiosis, considering the interaction between the principal fungus (mycobiont) and the associated <em>Nostoc</em> (cyanobiont), from a community perspective considering different spatial scales. Thus, we determined <em>Nostoc</em> phylogroup richness and composition of lichen communities in eleven <em>Nothofagus pumilio</em> forests across a wide latitudinal gradient in Chile. To measure specialization, cyanobiont richness, Simpson’s, and d’ indices were estimated for 37 mycobiont species in these communities. Potential drivers that might shape <em>Nostoc</em> composition and specialization measures along the environmental gradient were analysed. Limitations in lichen distributional ranges due to the availability of their cyanobionts were studied. Turnover patterns of cyanobionts were identified at multiple spatial scales.</li> <li>The results showed that environmental factors shaped the <em>Nostoc</em> composition of these communities, thus limiting cyanobiont availability to establish the symbiotic association. Besides, specialization changed with the spatial scale and with the metric considered. Cyanolichens were more specialized than cephalolichens when considering partner richness and Simpson’s index, whereas the d’ index was mostly explained by mycobiont identity. Little evidence of lichen distributional ranges due to the distribution of their cyanobionts was found. Thus, lichens with broad distributional ranges either associated with several cyanobionts or with widely distributed cyanobionts. Comparisons between local vs. regional scales showed a decreasing degree of specialization at larger scales due to an increase in cyanobiont richness.</li> <li><em>Synthesis</em>. The results support the context dependency of specialization and how its consideration changes with the metric and the spatial scale considered. Subsequently, we suggest considering the entire community, and widening the spatial scale studied as it is crucial to understand factors determining specialization.</li> </ol>
Multi-scale mapping of Australia's terrestrial and blue carbon stocks and their bioregional environmental drivers
<p>Soils in terrestrial and coastal blue carbon ecosystems are an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply a multiscale spatial machine learning model to estimate soil carbon stocks and drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, with ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the top 0–30cm soil layer, terrestrial ecosystems hold 27.6 Gt C (19.6–39.0 Gt C), and blue carbon ecosystems hold 0.35 GtC (0.20–0.62 GtC). Tall open eucalypt and mangrove forests have the largest soil carbon content by area, while eucalypt woodlands and hummock grasslands have the largest total carbon stock due to their large area. Our findings suggest these are important ecosystems for conservation and nature-based mitigation climate change strategies.</p>
Small-scale Fisheries Contributions
<p>This repo makes figures for the "contributions of small-scale fisheries to sustainable development" paper.</p>
Cropland-scale interaction between maize evapotranspiration and groundwater in a well-irrigation district in Mu Us Sandy Land, Northwest China
<p>The data includes the observed meteorological factors, soil water content, evapotranspiration (ETc), irrigation, depth to the water table, maize growth, and so on.</p>
Pay attention: you can fall! The Mini-BESTest scale and the turning duration of the TUG test provide valid balance measures in neurological patients: a prospective study with falls as the balance criterion
<p>The Excel file contains the database used for the LASSO logistic regression, i.e. the study's primary analysis (Figure 3 in the main text).</p>
Dataset: Breakdown of Scaling and Friction Weakening in the Critical Granular Flow
<p>Data sets of granular stick-slip experiment</p>
Numerical modeling on global-scale mantle water cycle and its impact on the sea-level change
<p>This zipped tar file includes scripts and data for submitting manuscript to EPSL.</p>
Living with earthquake-related thoughts: The initial validation and development of the Earthquake Obsession Scale (EOS)
<p>Data set</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.