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2,152 results for “outputs”
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Outputs of the Jupyter Notebook - Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
<p>The dataset contains the outputs of the notebook "Deep learning and variational inversion to quantify and attribute climate change (CIRC23)" published in The Environmental Data Science Book.</p>
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model - model outputs
<p>This dataset contains the model outputs used in the figures in the paper entitled "Implementing the iCORAL (version 1.0) coral reef CaCO<sub>3</sub> production module in the iLOVECLIM climate model" submitted to GMD. For the description of the model and simulations we refer to this article.</p> <p>Provided files:</p> <ul> <li>Surface values of temperature (temp), salinity (salt), phosphate (opo4) and aragonite saturation state (omega) for:</li> </ul> <p>The modern period (mean of 2000-2010): <strong>temp_modern.nc</strong>, <strong>salt_modern.nc</strong>, <strong>opo4_modern.nc</strong>, <strong>omega_modern.nc</strong></p> <p>The pre-industrial (PI, mean of last 100 years of the simulation): <strong>temp_PI.nc</strong>, <strong>salt_PI.nc</strong>, <strong>opo4_PI.nc</strong>, <strong>omega_PI.nc</strong></p> <ul> <li>Coral location for:</li> </ul> <p>Imin=50 μE/m2/s: <strong>coral_location_Imin50.nc</strong></p> <p>Imin=300 μE/m2/s: <strong>coral_location_Imin300.nc</strong></p> <p>The values indicate:</p> <p>4 = presence of corals in the model simulation (coral area less or equal to 5% of the grid cell area) but not in observations</p> <p>3 = presence of corals in both model and observational data</p> <p>2 = presence of corals in observational data but not in the model simulation</p> <p>1 = presence of corals in the model simulation (coral area more than 5% of the grid cell area) but not in observations</p> <ul> <li>Global coral reef area (10<sup>3</sup> km<sup>2</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_area_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>k</sub></em> (the saturating light intensity, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Ik.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>g<sub>max</sub></em> (the maximum production growth): <strong>Total_prod_vs_gmax.txt</strong></li> <li>Global carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and global coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_prod_vs_total_area.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and global production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>): <strong>Total_production_vs_rmse_Perry.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_area_vs_rmse_Perry.txt</strong></li> </ul>
A single neuron in C. elegans orchestrates multiple motor outputs through parallel modes of transmission
<p class="MsoNormal"><span>Animals generate a wide range of highly coordinated motor outputs, which allows them to execute purposeful behaviors. Individual neurons in the circuits that generate behaviors have a remarkable capacity for flexibility, as they exhibit multiple axonal projections, transmitter systems, and modes of neural activity. How these multi-functional properties of neurons enable the generation of adaptive behaviors remains unknown. Here we show that the HSN neuron in <em>C. elegans</em> evokes multiple motor programs over different timescales to enable a suite of behavioral changes during egg-laying. Using HSN activity perturbations and in vivo calcium imaging, we show that HSN acutely increases egg-laying and locomotion while also biasing the animals towards low-speed dwelling behavior over minutes. The acute effects of HSN on egg-laying and high-speed locomotion are mediated by separate sets of HSN transmitters and different HSN axonal projections. The long-lasting effects on dwelling are mediated by HSN release of serotonin that is taken up and re-released by NSM, another serotonergic neuron class that directly evokes dwelling. Our results show how the multi-functional properties of a single neuron allow it to induce a coordinated suite of behaviors and also reveal that neurons can borrow serotonin from one another to control behavior.</span></p>
Aotearoa New Zealand RSQSim model outputs for tsunami modelling
<p>RSQSim model outputs to support the publication: "<strong>A Novel Method to Determine Probabilistic Tsunami Hazard using a Physics-based Synthetic Earthquake Catalog: a New Zealand Case Study</strong>" by Hughes et al. (2023; JGR, submitted). In many respects, these model outputs represent an updated version of the New Zealand RSQSim catalogue published by Shaw et al. (2022; <a href="http://doi.org/10.5281/zenodo.5534462">doi.org/10.5281/zenodo.5534462</a>).</p> <p>This archive contains:</p> <ol> <li>The outputs of an updated RSQSim synthetic earthquake catalogue for Aotearoa-New Zealand.</li> <li>A ~30kyr subset of the main catalogue, trimmed to a minimum M<sub>W</sub> of 7.0.</li> <li>Summary figures comparing the statistical properties of the synthetic catalogue to observations of New Zealand seismicity.</li> <li>Python scripts for visualizing the synthetic earthquake catalogue, and calculating seafloor displacements from synthetic earthquakes that can be used as tsunami model inputs.</li> </ol> <p>For more information, please refer to the publications cited above and/or the more detailed README files within the archive.</p>
Outputs of the Jupyter Notebook - Variational data assimilation with deep prior (CIRC23)
<p>The repository contains the outputs of the notebook "Variational data assimilation with deep prior (CIRC23)" published in The Environmental Data Science Book.</p>
A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum (CESM1.2 output, netcdf files)
<p>Prange, M., Jonkers, L., Merkel, U., Schulz, M., Bakker, P. (2023) A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum, <em>Science Advances</em>.</p> <p> </p> <p><strong>Datasets: CESM1.2 netcdf output, Experiment LGM_ref (1540 years)</strong></p> <p> </p> <p><strong>MOC.nc - </strong>Meridional overturning circulation (Sv) as diagnosed from POP as monthly means</p> <p><strong>SALT.nc</strong> - Salinity (psu) as annual means on POP grid</p> <p><strong>TEMP.nc</strong> - SST (°C) as annual means on POP grid</p> <p><strong>IFRAC.nc</strong> - Sea ice fraction as monthly means on POP grid</p> <p><strong>TREFHT.nc</strong> - Reference height (2 m) temperature (K) as monthly means on CAM grid</p> <p><strong>ZONALSALT.nc</strong> - Zonally averaged Atlantic salinity (psu) as annual means</p> <p> </p> <p><em>POP: Ocean model component of CESM1.2</em></p> <p><em>CAM: Atmosphere model component of CESM1.2</em></p> <p> </p>
Model Output for Eddy Mediated Mixing in the Equatorial Pacific
<p>This repository contains processed model outputs used in the "Eddy Mediated Mixing in the Equatorial Pacific" submitted manuscript. </p>
Terrestrial laser scanning data of urban trees in Milton Keynes, UK: individual trees and Treegraph model outputs
<p><strong>TLS_point_clouds:</strong></p> <p>- Data collection: Terrestrial laser scanning data acquired in leaf-off condition in April 2021.</p> <p>- Scanning instrument: We used a RIEGL VZ-400 with a wavelength of 1550 nm, 0.35 mrad beam divergence and 0.04˚ angular resolution.</p> <p>- Locations: The data were collected from three urban sites in Milton Keynes, UK: Avebury Blvd (Ave), Dansteed Way (Dan), and Overgate (Ove).</p> <p>- File information: Each file is an individual tree point cloud in Polygon File Format (.ply), which can be viewed in software such as CloudCompare.</p> <p> </p> <p><strong>Treegraph_outputs:</strong></p> <p>- Description: Model outputs from <em>Treegraph</em> for individual trees.</p> <p>- File naming convention: [TreeID]-[downsample_voxel_length]-[tip_diameter_if_known].*</p> <ul> <li>*.centres.ply: Skeleton nodes of individual trees.</li> <li>*.mesh.ply: Cylinder model of individual trees.</li> <li>*.json: Geometrical and topological attributes at varying scales, from internode and branch to the whole tree.</li> <li>*.txt: Summary of input parameters, logs of intermediate steps, and a statistical overview of whole-tree structural attributes.</li> </ul>
UKESM1.0-ice simulation output used as test data in Burgard et al., Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
<p>These files contain NEMO ocean model output and domain definitions for the Southern Ocean from UKESM1.0-ice simulations described in section 6.3.2 of Smith et al. "Coupling the U.K. Earth System Model to Dynamic Models of the Greenland and Antarctic Ice Sheets" , Journal of Advances in Modeling Earth Systems, 2021</p> <p>Files labelled "bf663" are the UKESM simulation referred to in that section as "constant 1970 greenhouse gas and other forcings". Files labelled bi646 are the UKESM simulation referred to in that section as "instantaneously quadrupled 1970 CO<sub>2</sub> concentrations".</p> <p>They were used as test data for the performance of neural networks in Burgard et al., "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks", Journal of Advances in Modeling Earth Systems 2023.</p>
GLORY - Input and Output Data
<p>This is the input and output dataset for the study "Representing reservoir water storage in the Global Change Analysis Model (GCAM)".</p>
Pilot output of a hybrid micro-CPV solar panels with integrated micro-tracking and diffuse capture
<p>Dataset of power output of four final generation Insolight/Hiperion modules on the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. These are micro CPV modules using integrated planar tracking and hybrid diffuse collection. The modules have a four terminal output: the "CPV" output corresponds to triple junction micro solar cells under ~100X concentration using integrated planar tracking and the "SI" output corresponds to an array of IBC silicon solar cells which ocupies the rest of hte backplane area and harvests light not captured by the concentrator. See References for more information</p><p><strong>Monitoring campaign:</strong></p><ul><li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li><li>Starting date: 19 Oct 2022</li><li>End date: 1 Dec 2022</li></ul><p><strong>Measurement setup:</strong></p><ul><li>The four modules included in this data set were mounted on an 8 module pilot array.</li><li>The four modules have the following ID numbers: 195, 196, 197, and 198</li><li>The modules were mounted at a fixed mounting angle: Due South, Slope Angle = 30°</li><li>All modules were connected to two Enphase IQ7+ microinverters (one per output) to place them at MPPT.</li><li>CPV and SI voltages and currents wer measured with the Enphase monitoring gateway (Envoy)</li><li><strong>NOTE: Some shading on array in mornings due to time of year.</strong></li></ul><p><strong>Description of data file:</strong></p><ul><li><strong>Data files format:</strong> single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file</li><li><strong>Measurement time</strong>: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously).<ul><li>Date Time (dd/mmm/yyyy HH:MM:SS): time in local civil time (data set begins in CEST / UTC+2 but on 30-Oct-22 transitions to CET / UCT+1 with the end of central european summer time.</li></ul></li><li><strong>Measured meteorological data</strong>: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times.<ul><li>DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5°</li><li>DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º</li><li>DNI_Mid (W/m2): equivalent direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º</li><li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm.</li><li>T_Amb (°C): ambient temperature</li><li>Wind Speed (m/s): wind speed</li><li>Wind Dir. (m/s): wind direction</li></ul></li><li><strong>Processed meteorological data</strong>: these values are calculated from the above meteorological data and provided for convenience<ul><li>DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun's known declination and hour angle from the time.</li><li>GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°).</li><li>SMR_Top_Mid (n.d.): "Spectral Matching Ratio". This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells.</li></ul></li><li><strong>Measured module data:</strong> The MP voltage and current are provided for each output of each module. XXX indicates ID number of module. The power in watts may be found by multiplying these values<ul><li>V_CPV_XXX(V):</li><li>V_SI_XXXi (V)</li><li>I<i>_</i>CPV_XXX(A):</li><li>I<i>_</i>SI_XXXi (A)</li></ul></li></ul>
Recla 2-dimensional scan output
<p>Results for a two-dimensional, two-QTL scan on Chr 8 for two traits: percent time in light and hot plate latency. </p>
A single neuron in C. elegans orchestrates multiple motor outputs through parallel modes of transmission
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A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2017
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Selected large model output files and Buffalo sounding data from: Lake Huron enhances snowfall downwind of Lake Erie: a modeling study of the 2010 near year’s Lake-effect snowfall event
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R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
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Model output tracking smoke from agricultural fires in south Florida from October 2022 - May 2023
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Run and output files from: Holocene population expansion of a tropical bee coincides with early human colonisation of Fiji rather than climate change
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A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2018
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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.