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2,152 results for “outputs”
Selected BATSRUS MHD output data and IE solver data for August 3, 2016
<p>This is a dataset for BATSRUS MHD model output and IE module output used to prepare Figures 2, 3, 4 for a second submission to GRL of a paper by A. M. Keese, N. Buzulukova, C. Mouikis and E. E. Scime "Mesoscale structures in Earth's magnetotail observed using energetic neutral atom imaging". The dataset has one file in .zip format.</p> <p>The file BATSRUS_IE_data_Fig2_3_4.zip contains the data for plotting BATSRUS results and IE module results for the Figures 2, 3, 4.</p> <p>Figure 2: file Fig2_imf_BATSRUS_input.dat has solar wind data used as an input to BATSRUS run.<br> file Fig2_ae_index.dat has model AU and AL indices</p> <p>Figure 3: files Fig3* are standard output files for IE module (ASCII) and could be plotted with spacepy package.</p> <p>Figure 4: file Fig4_BATSRUS_3D_0520UT_nx300_ny150_nz150.csv has 3D output from BATSRUS interpolated to a regular grid (nx=300, ny=150, nz=150) required to plot Figure 4. The Figure 4 could be reproduced with ParaVew free 3D plotting software.</p>
A quantitative method to calibrate the SWAN wave model - pre-processed model outputs
<p>The pre-processed MAT files for the paper 'A quantitative method to calibrate the SWAN wave model'</p>
Selection on vocal output affects laryngeal morphology in rats - data
<p>data used in the publication "Selection on vocal output affects laryngeal morphology in rats"</p>
CESM2 (WACCM6) subseasonal reforecast zonal mean output
<p>This repository archives output from CESM2 (WACCM6) subseasonal reforecasts. Included is zonal mean output of all reforecasts occurring 35 days before and 7 days after any sudden stratospheric warmings from 1999-2019. Each file includes all variables and corresponds to one member's 45-day reforecast.</p> <p>The naming convention is "70Lwaccm6.<month of initialization>.<year-month-day of initialization>.<forecasts member>.cam.h2.processed.nc".</p> <p>Variables included are date, THzm (potential temp), TROP_P (tropopause pressure), UVzm (horizontal eddy mom. flux), UWzm (vertical eddy mom. flux), Uzm (zonal wind), VTHzm (eddy heat flux), Vzm (meridional wind), and Wzm (vertical wind). All values have been interpolated onto pressure. A separate grid file with grid definitions is included.</p>
Output data from: From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions
<p>This repository includes output data from the following article:</p> <p><strong>Montfort, F., Bégué, A., Leroux, L., Blanc, L., Gond, V., Cambule, A.H., Remane, I.A.D., Grinand, C., 2020. From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. <em>Land Degrad Dev</em>.; 32: 49– 65. <a href="https://doi.org/10.1002/ldr.3704">https://doi.org/10.1002/ldr.3704</a></strong></p> <p>This study aimed at characterizing and mapping the underlying factors (human or climatic) in land productivity changes over the 2000-2016 period, in order to assess land degradation in Mozambique.</p> <p>The methodology is based on remote sensing methodology. Land productivity change were first analyzed using MODIS NDVI time-series (2000–2016), and a two-step framework was then used to understand the main factors of these productivity changes, using climate times series (CHIRPS data for rainfall and CRU data for temperature), Land Use and Land Cover Change (LULCC) maps (Laurel project LULCC map), and ground knowledge.</p> <p>This repository includes raster data of annual land productivity change over the 2000-2016 period, annual land productivity climate factors, potential land productivity decrease factors and potential productivity increase factors. Data are available as GeoTIFF raster files at 250 m resolution in the UTM 37S projection (EPSG: 32737).</p>
Data Supplement: Inputs and Outputs for Rahman et al. 2021
<p><strong>Contents:</strong></p> <p><strong>prototype_elec:</strong> Input Data Files (IDFs) generated by tempset for a small office with electric resistance heating. Only heating setpoints are modified.</p> <p><strong>output_elec:</strong> Output csvs corresponding to the IDFs generated in prototype_elec</p> <p><strong>prototype_gas:</strong> IDFs generated by tempset for a small office with gas furnace heating. Only heating setpoints are modified.</p> <p><strong>output_gas:</strong> Output CSVs generated corresponding to the IDFs generated in prototype_gas</p> <p><strong>prototype_cool:</strong> IDFs generated by tempset for a small office with vapor compression cooling. Only cooling setpoints are modified.</p> <p><strong>output_cool:</strong> Output CSVs generated corresponding to the IDFs generated in prototype_cool</p> <p><strong>Summary</strong>:</p> <p>Input and Output data for tradeoffs resulting from specific changes to those schedules in terms of three quantities: total electricity consumption per day; maximum hourly electricity consumption per day; and predicted percentage dissatisfied (occupant thermal comfort). The method for generating schedules requires a single base schedule as the starting point and, using a few clearly defined parameters, transforms it into a set of schedules that can be used for modeling an existing building stock or for performing parametric studies. Temperature setpoint schedules are generated and simulated for a small office building in a cool-dry climate in three different case studies pertaining to changing temperature setpoint schedules. </p> <p><strong>Accompanying software and workflow described here:</strong></p> <p>https://github.com/IMMM-SFA/rahman-etal_2021_tbd</p> <p> </p>
Scanners compared output
We scanned the same model with the Wacom Mobile Studio Pro, Artec EVA and Artec Space Spider, all using Artec Studio and Autopilot. We hope this helps with seeing the different levels of definition. If you start with the Wacom, you can always add an EVA or Space Spider, contact us at[ Patrick Thorn & Co](http://www.patrick-thorn.co.uk) in the UK. Source: Objaverse 1.0 / Sketchfab
Output from carbon forest edge effect analysis: edge effect distance and magnitude
<p>Carbon edge effect distance and magnitude results for edge effects on forest carbon stocks across the tropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <p>method 1: Biomass= θ_1-θ_2⋅exp(-θ_3⋅Distance)</p> <p>method 2: Biomass= β_0+β_1⋅ln(Distance)</p> <p>method 3: Biomass = \eta_0+\eta_1 * Distance</p> <p>Then, for each grid cell, the candidate with the highest $R^2$ is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core () was estimated as the average biomass density at the largest observed distance in the cell.</p> <p>regression_coefficients_as_shapefile - projected spatially as an ESRI Shapefile where the methods are defined as:</p>
SARWIND LG-Mod (v4.01) output data with reference to the Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind)
<p>A zipped folder containing SARWIND LG-Mod (v4.01) output data obtained by processing the Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind).</p> <p>Pre-processed data derived from the Sentinel-1 SAR image have been obtained by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components have been provided by the Numerical Weather Model (NWM) SKYRON.</p> <p>These Earth Observation (EO) data have been used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016) for the sea surface wind field retrieval.</p> <p>The available Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, has been acquired on the 7th Dec 2014, 05:51:54.</p>
Gain and output power of Traveling Wave Tube at W-band
<p>Results of the W-band TWT gain and output power simulated by using MAGIC 3D Particle in Cell Simulators. These results were presented in the paper title "W-band TWTs for New Generation High Capacity Wireless Networks" presented at the 17th International Vacuum Electronics Conference.</p>
SARWIND LG-Mod (v4.01) output data with reference to the Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind)
<p>A zipped folder containing SARWIND LG-Mod (v4.01) output data obtained by processing the Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind).</p> <p>Pre-processed data derived from the Sentinel-1 SAR image have been obtained by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components have been provided by the Numerical Weather Model (NWM) SKYRON.</p> <p>These Earth Observation (EO) data have been used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016) for the sea surface wind field retrieval.</p> <p>The available Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, has been acquired on the 31st Dec 2014, 05:51:53.</p>
Output data computed for the solution of Maxwell's equations on a distributed system using the FDTD method
<p>This is an example output dataset from the distributed computation to solve Maxwell's equations using the FDTD method. This is an example from the report describing the implementation of the computer program - Gillan and Fusco (1998). The example is based on the work reported in the book by Kunz and Leubbers on the FDTD method. It consists of scattering a Gaussian pulse from a sphere of dielectric defined with a 3D cube of size 34x34x34 Yee cells. </p>
FACS output files for neutrophils and monocytes
<p>This dataset contains FACS output files of the studies of the apoptotic rate in neutrophils and monocytes under aflotoxin B1 exposure and treatment with Schiff base derivatives. Following groups of animals were studied:</p> <p>CNTRL – intact animals;</p> <p>PLP, PLT and NLT intact animals received 10-day oral treatment with corresponding Schiff bases at 10mg/kg dosage;</p> <p>AFB1 – rats treated with AFB1 for 21 days at 25μg/kg dosage;</p> <p>AFB1+PLP, AFB1+PLT, AFB1+NLT – AFB1 exposed rats treated with corresponding Schiff bases (mycotoxin and Schiff base dosages were similar in all treated groups). </p> <p>AFB1 - aflatoxin B1, PLP, picolinyl-L-phenylalaninate; PLT, picolinyl-L-tryptophanate; NLT, nicotinyl-L-tryptophanate.</p>
Model output associated with Bucciarelli et al. "Western Indian subantarctic phytoplankton blooms fertilized by iron-enriched Agulhas water"
<p> Annual mean sea surface elevation (zeta), surface chlorophyll (chl), surface DFE (fe0), 500 m DFE (fe500), surface salinity (salt0) and 500 m salinity (salt500) from CROCO PISCES ocean model for two simulations (reference ASAP2 and NAM without Fe sources along African margins).This is associated with publication: Bucciarelli et al. "Iron-enriched Agulhas waters fertilise the Indian Subantarctic Zone"</p>
Example Analysis Output of Xenium Breast Cancer Data
<p>This is an example CartoStore dataset from Xenium Breast Cancer Analysis.</p> <p>Outputs generated from XeniumRanger and FICTURE are combined in this repository. </p> <p>Please refer to https://github.com/seqscope/cartloader for more details. </p>
Global state outputs from GRACE-CLSM data assimilation product from Girotto et al. 2016
<p>Mean and standard deviation of ensemble average state variables and terrestrial water storage (TWS) for GRACE-CLSM data assimilation product from Girotto et al. 2016. The dataset contains both the data assimilation (DA) output and the open-loop (OL), pre-assimilation states used in Scheliga et al. <em>In review.</em></p> <p># GENERAL INFORMATION</p> <p>Title of Dataset: Global state outputs from GRACE-CLSM data assimilation product from Girotto et al. 2016</p> <p><br>## Principal Investigator Information<br>* Name: Manuela Girotto<br>* ORCID: 0000-0003-2795-2063<br>* Institution: University of California, Berkeley<br>* Email: mgirotto@berkeley.edu</p> <p>## Co-Author Information<br>* Name: Ann Scheliga<br>* ORCID: 0000-0002-0217-6451<br>* Institution: University of California, Berkeley<br>* Email: ann_scheliga@berkeley.edu</p> <p>Date of data generation: approximately 2021-09-09</p> <p>Funding sources that supported the collection of the data: NASA Sea Level Change Team (Grant 80NSSC21K0321).</p> <p><br># SHARING/ACCESS INFORMATION</p> <p>* Licenses/restrictions placed on the data: CC-BY-NC<br>* Links to publications that describes the data: Scheliga, A., Girotto, M., Gerlein-Safdi C. Anthropogenic processes and sea-level rise contributions with data assimilation of GRACE and GRACE-FO terrestrial water storage. In review.<br>* Recommended citation for this dataset: Girotto, M. et al. Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model. WRR. https://doi.org/10.1002/2015WR018417</p> <p> </p>
Project Pythia: Output from WRF V3.6 model
Project Pythia is a community resource for learning how to analyze geosciences data using the Scientific Python Ecosystem. This data set is used primarily on the WRF-Python webpage to illustrate how WRF-Python can be used in conjunction with the Python packages Cartopy, Matplotlib, and Basemap. The data set is also used in a vertical interpolation example that is also found on the WRF-Python webpage. The central latitude for the data is 38.5N and the central longitude is 97.5W.
Example input and output files for the virus variant caller pipeline
<p><code>example_config_files.zip</code>: reference annotations for HSV-1 strain 17 and config files describing the input files.</p> <p><code>example_bam_files.zip:</code> alignment files (in BAM format) for two replicates each of the ICP0 and vhs knockout mutants.</p> <p><code>example_fastq_files.zip</code>: read sequences (in FASTQ format) for two replicates each of the ICP0 and vhs knockout mutants.</p> <p><code>example_output.zip</code>: resulting output files</p> <p> </p> <p>After downloading and unpacking the zip-archives, <strong>adjust the constants in the Watchdog workflow</strong> as follows:</p> <p><code><const name="REFERENCE">/path/to/chrHsv1_s17.fa</const></code><br><code><const name="REFERENCE_SNPS">/path/to/reference_snps.txt</const></code><br><code><const name="CONFIG">/path/to/config_strains_HSV1.txt</const></code><br><code><const name="GTF">/path/to/HSV1_Annotation_chrHsv1_s17.gtf</const></code><br><code><const name="samples">/path/to/example_samples.txt</const></code><br><code><const name="replicates">/path/to/example_replicates.txt</const></code></p> <p>where <code>/path/to/</code> is the directory the content of the archive zip-archives is located in.</p> <p>Furthermore, change <code>/path/to/</code> in the <code>example_replicates.txt</code> and <code>example_samples.txt</code> files to the directory the content of the archive zip-archives is located in.</p>
WRF-Chem output- concentrations for the 4 simulations: reference, urbanization, extension of agricultural areas and more urban parks
<p>We use the WRF-Chem air-quality model with an urban canopy scheme to investigate how land use (urban, agriculture, parks) affects the urban climate and chemical processes (dry deposition, biogenic emissions), resulting in changes in air quality for scenarios proposed by the Urban Master Plan of the Metropolitan Area of Barcelona. We selected a heatwave period, 4th-7th July 2015, with maximum temperatures reaching 40 °C in the interior of the AMB and 35 °C on the coast. Here we provide averaged model output for the heatwave period for the different pollutants, surface temperature, wind speed, PBLH, deposition, and biogenic emissions under different urban scenarios: reference (REF), urbanization (URB), extension of agriculture (AGR), and increase in urban parks (PARK).<br><br>The results for a period before the heatwave (1-3 July 2015), with temperatures 3-4 degrees lower than during the heatwave, are also provided. <br><br>Land-use and irrigation is also provided for each scenaio. </p>
Dissecting muscle power output: Evidence of multi-scale power amplification in skeletal muscle
<p class="MsoNormal">Many animals use a combination of skeletal muscle and elastic structures to amplify power output for fast motions. Among vertebrates, tendons in series with skeletal muscle are often implicated as the primary power-amplifying spring, but muscles contain elastic structures at all levels of organization, from the muscle tendon to the extracellular matrix to elastic proteins within sarcomeres. The present study used <em>ex vivo</em> muscle preparations in combination with high-speed video to quantify power output, as the product of force and velocity, at several levels of muscle organization to determine where power amplification occurs. Dynamic ramp shortening contractions in isolated frog flexor digitorum superficialis brevis were compared with isotonic power output to identify power amplification within muscle fibers, the muscle belly, free tendon and elements external to the muscle tendon. Energy accounting revealed that artifacts from compliant structures outside of the muscle–tendon unit contributed significant peak instantaneous power. This compliance included deflection of clamped bone that stored and released energy contributing 195.22±33.19 W kg<sup>−1</sup> (mean±s.e.m.) to the peak power output. In addition, we found that power detected from within the muscle fascicles for dynamic shortening ramps was 338.78 ±16.03 W kg<sup>−1</sup>, or nearly twice the maximum isotonic power output of 195.23±8.82 W kg<sup>−1</sup>. Measurements of muscle belly and muscle–tendon unit also demonstrated significant power amplification. These data suggest that intramuscular tissues, as well as bone, have the capacity to store and release energy to amplify whole-muscle power output.</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.