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2,152 results for “outputs”
Outputs of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series
<p>The dataset contains the outputs of the notebook "Concatenating a gridded rainfall reanalysis dataset into a time series" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Timothy Lam (author), University of Exeter, <a href="https://github.com/timo0thy">@timo0thy</a></p> </li> <li> <p>Marlene Kretschmer (author), University of Reading, <a href="https://github.com/MarleneKretschmer">@MarleneKretschmer</a></p> </li> <li> <p>Samantha Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Rachel Prudden (author), Met Office Informatics Lab, <a href="https://github.com/RPrudden">@RPrudden</a></p> </li> <li> <p>Elena Saggioro (author), University of Reading, <a href="https://github.com/ESaggioro">@ESaggioro</a></p> </li> <li> <p>Nick Homer (reviewer), University of Edinburgh, <a href="https://github.com/NHomer">@NHomer</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>NOAA National Center for Environmental Prediction (creator)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Eugenia Kalnay, Director, NCEP Environmental Modeling Center</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, Roy Jenne, and Dennis Joseph. The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3):437 – 472, 1996. URL: <a href="https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml">https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml</a>, <a href="https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2">doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2</a>.</p> </li> </ul> <p><em>Pipeline documentation</em></p> <ul> <li> <p>Marlene Kretschmer, Samantha V. Adams, Alberto Arribas, Rachel Prudden, Niall Robinson, Elena Saggioro, and Theodore G. Shepherd. Quantifying causal pathways of teleconnections. Bulletin of the American Meteorological Society, 102(12):E2247 – E2263, 2021. URL: <a href="https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml">https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml</a>, <a href="https://doi.org/10.1175/BAMS-D-20-0117.1">doi:10.1175/BAMS-D-20-0117.1</a>.</p> </li> </ul>
Model outputs of Wei et al. (2022): "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophysical research Letters.
<p>The .mat file includes all model data used in the study "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophyscial Research Letters, 2022. The .txt file contains description of all physical variables contained in the .mat file.</p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
Outputs of the paper "Blockchain-enabled Server-less Federated Learning"
<p>This repository contains the different outputs generated for the paper "Blockchain-enabled Server-less Federated Learning", submitted to Computer Networks (COMNET) journal. Two types of outputs are provided:</p> <ol> <li><strong>Blockchain queue simulator (output_queue_simulator):</strong> results of the simulations done in the batch-service queue simulator (https://github.com/fwilhelmi/batch_service_queue_simulator) to characterize the queue latency of blockchain applications.</li> <li><strong>Tensorflow (output_tensorflow):</strong> results of the simulations done in Tensorflow Federated (TFF), resulting from the application of different models to the federated EMNIST dataset.</li> </ol> <p>Each folder also includes the scripts used to execute the corresponding simulations. For more details, see the repository in https://github.com/fwilhelmi/blockchain_enabled_federated_learning</p>
Outputs of molecular dynamics simulations of two NS1 ZIKV variants in the membrane presence
<p>Files corresponding to outputs obtained through Molecular Dynamics (MD) simulations of two Non-structural (NS) proteins 1 of the Zika virus from Uganda (ZIKV-UG) and Brazil (ZIKV-BR). Simulations were performed using GROMACS 5.1.5 or later versions. Systems were built based on atomistic models (https://zenodo.org/record/5608521#.YvDNZTlBzJw) and converted to a coarse-grained representation employing MARTINI 2.2p ElNeDyn. It was assumed to be NS1 systems in <em>apo</em> and <em>holo</em> forms (<em>i.e.</em>, in the absence and presence of a lipid bilayer, respectively). The membrane model tries to reproduce a lipid concentration of an endoplasmic reticulum lipid bilayer. <em>Holo</em> and <em>apo</em> systems were simulated until they reached 20 and 10 µs, respectively. Trajectories do not include water molecules. For the specific case of <em>holo</em> systems, frames were skipped every 5 frames, which means that processed trajectories are equivalent to simulations when it is recorded every 1000 ps. More details can be found at <a href="https://doi.org/10.1021/acs.jcim.2c01461">https://doi.org/10.1021/acs.jcim.2c01461</a></p> <p>Note: Some topology and index files important for MD analysis are also present.</p>
Outputs from Isca perturbed parameter ensemble (PPE) simulations (Part II)
<p>Outputs from Isca perturbed paramter ensemble (PPE) simulations under 1xCO2 and 4xCO2, in which the simulations are prescribed with Q-flux. The output is the extracted data for the first 20-year simulations for the surface temperature and radiative fluxes at the top of the atmosphere (TOA).</p> <p>This is Part II, and Part I can be found at: <a href="https://doi.org/10.5281/zenodo.5150241">10.5281/zenodo.5150241</a></p>
CICE6 standalone sea ice surface temperature output
<p>CICE6 standalone sea ice concentration and sea ice surface temperature data for July-August and October-November 2019. JRA55-do 1.4.0 (2010-2018) and JRA55-do 1.5.0 (2019) was used for atmospheric forcing, ocean forcing was taken from an ACCESS-OM2 (1deg_jra55_iaf_omip2_cycle6) output. A wave propagation and attenuation model (Meylan et al., 2014) was implemented into CICE6 to enable wave forcing from WaveWatch III (CAWCR). </p>
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
<p>The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).</p> <p>The data can be used and shared freely.</p>
INLAND DVGM output - SSP1-RCP2.6 and SSP3-RCP8.5
<p>We used the INLAND DGVM model to simulate the water and carbon cycle in the present climate and also under two different CC and LUC scenarios. To stabilize the carbon and water stocks, we used a 500-years spin up run, with dynamic vegetation, performed with historical data from CRU-JRA (Climatic Research Unit and Japanese reanalysis - detailed by Harris, 2019). After that, we started the two different scenario and recorded the model outputs from 2016 to 2095 (80 years), in a 0.5 X 0.5 degrees grid (latitude AND longitude).</p> <p>The meteorological forcing and CO<sub>2</sub> concentration are taken from HADGEM2-ES (Collins <em>et al.</em>, 2011), from ISIMIP<a href="#_ftn1"><sup><sup>[1]</sup></sup></a> Project (Inter-Sectorial Impact Model Intercomparison Project), from 2b Repository (<a href="https://data.isimip.org/">https://data.isimip.org/</a>), described at Frieler et al., 2017. We choose to use the Bias corrected data. As input of land use and land cover maps, we used the refined regional scenarios provided by (Bezerra et al., 2022), data available at <a href="https://zenodo.org/record/5123560">https://zenodo.org/record/5123560#.YVJFzzlv80M</a> (Bezerra et al., 2021),</p> <p><a href="#_ftnref1">[1]</a> https://www.isimip.org/documents/564/ISIMIP2b_protocol_210131_noEnergy_IV_JS-1_IV_pmp-1.pdf</p>
Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"
<p>Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript. </p>
Subpolar ocean variability analysis using DePreSys4 hindcast output
<p>The dataset contains python scripts and pre-processed data, which was generated using DePreSys4 Met Office ensemble hindcast output. These scripts were used to create a subset of ensemble members based on seasonal NAO indices. The approach could be used to perform an ensemble-composite analysis to examine the impacts of NAO forcing on the ocean circulation in the subpolar North Atlantic ocean. </p>
MHD model output for Ganymede's magnetosphere during Juno's flyby
<p>This dataset contains the complete simulation output from our MHD model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021 (Duling et al. 2022).</p> <p>The data was obtained by our application of the PLUTO simulation code v4.4 (Mignone et al. 2007) (http://plutocode.ph.unito.it/) described in Duling et al. 2022.</p> <p>The dataset includes the simulation variables on the simulation grid for a single timestep after steady state was reached. The grid has spherical geometry (r, theta, phi) with phi=0° longitude pointing towards Jupiter (positive y axis of the GPhiO system), phi=90° longitude pointing in the upstream direction (negative x axis of GPhiO) and theta=0° latitude at Ganymede's north pole (positive z axis of GPhiO). The following model variables are included:</p> <p>rho: plasma mass density<br> prs: thermal plasma pressure<br> vx1: plasma velocity radial component<br> vx2: plasma velocity theta component<br> vx3: plasma velocity phi component<br> Bx1: magnetic field radial component<br> Bx2: magnetic field theta component<br> Bx3: magnetic field phi component</p> <p>Additionally the following derived variables are included:</p> <p>Jx1: electric current density radial component<br> Jx2: electric current density theta component<br> Jx3: electric current density phi component<br> Bpx1: plasma magnetic field radial component<br> Bpx2: plasma magnetic field theta component<br> Bpx3: plasma magnetic field phi component</p> <p>Plasma magnetic field means that part of the total magnetic field that results from the plasma interaction. It equals the total magnetic field subtracted by the homogeneous upstream field and Ganymede's intrinsic and induced field.</p> <p>All values are in normalized units with these normalization factors:</p> <p>NORMR = 2.631e8 cm<br> NORMV = 1.4e7 cm/s<br> NORMRHO = 1.661e-22 g/cm^3<br> NORMPRS = 3.255e-08 dyne/cm^2<br> NORMB = 6.395e-04 Gauss<br> NORMJ = 5.801e-03 statA/cm^2</p> <p>In Duling et al. 2022 we present results of a model sensitivity study. This dataset includes model output from our best guess setup (default setup) only.</p> <p>Since the data is in PLUTO's binary format "flt" we provide a Python code snippet that reads the data to data arrays.</p> <p><strong>grid.out</strong><br> This ASCII file contains the grid dimensions and coordinates of the cell boundaries.</p> <p><strong>data.0020.flt</strong><br> This binary file contains the simulation variables on the cell centers of the grid.</p> <p><strong>pluto.0.log</strong><br> This ASCII file contains the header of the PLUTO logfile.</p> <p><strong>read_data.py</strong><br> This Python code snippet helps with reading the data.</p>
Metabolite identification dataset: "Magnitude-Preserving Ranking for Structured Outputs" Brouard et al. (2017)
<p>Metabolite identification dataset used to for the IOKR and MP-IOKR experiments in the publication:</p> <p><a href="https://proceedings.mlr.press/v77/brouard17a.html">"Magnitude-Preserving Ranking for Structured Outputs" by Brouard et al. (2017)</a></p> <p>The code to run the experiments can be found on Github: <a href="https://github.com/aalto-ics-kepaco/iokr_for_metabolite_identification">https://github.com/aalto-ics-kepaco/iokr_for_metabolite_identification</a> (mp-iokr/application/ACML17).</p> <p>The tar-files need to be unpacked.</p> <p> </p>
Output from model simulations of a turbulent ice shelf-ocean boundary current
<p><em>This dataset contains output from 2 LES and 19 MITgcm simulations of an idealised configuration of the ice shelf-ocean boundary current. Core fields are provided such as velocity and density and these are given as ice-plane averaged. The output was generated to make an inter-model comparison of the representation of dynamical processes at the ice shelf-ocean boundary. The two LES configurations differ in their sub-grid-scale parameterisation. The MITgcm simulations investigate the sensitivity to various parameter changes including: resolution, diffusivity coefficients, advection scheme and melt-parameterisation. All configurations are outlined in Patmore et al. (2022).</em></p>
Tanioka and Matsumoto (2017), MESMO output files
<p>These are output files used in Tanioka and Matsumoto (2017), Global Biogeochemical Cycles</p> <p>For list of experiments please refer to Table 3 of Tanioka and Matsumoto (2017). File names correspond to the simulation ID numbers listed.</p>
WRF-Chem output supporting manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates"
<p>Selected output from WRF-Chem v3.6.1 on black carbon deposition, rainfall, and snowfall over Southeast Asia. These files were used to prepare the figures and tables in the manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates" by these authors. Files are in NetCDF format and contain metadata describing their contents. The naming convention is:</p> <p>YYYY_MM_EXT_daily_12km_dustfix.nc</p> <p>where YYYY_MM is the simulated year and month and the extensions are:</p> <p>NFC - No Further Control emission scenario</p> <p>MIT - Mitigation emission scenario</p> <p>EN - Simulated El Nino year</p> <p>LN - Simulated La Nina year</p> <p> </p>
Figure 2: The intput and output forms of the transformation engine
<p>The goal of the project was to develop a translation system between two<br> knowledge representation forms. One of the representation forms is a new<br> kind of abstract predicate oriented conceptual graphs called ECG. The second<br> representation form is a set of sentences of a given language. While the ECG<br> is a general, language independent tool, the sentences belong to a language<br> and a speci¯c grammar.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
Output data from: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes output data from the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard</strong>. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>This repository includes Madagascar forest cover (forXXXX.tif), forest density (fordensXXXX.tif), distance to forest edge (dist_edge_XXXX.tif) and forest fragmentation index (fragXXXX.tif) for the years 1953, 1973, 1990, 2000, 2005, 2010 and 2014. Data are available as GeoTIFF raster files at 30m resolution in the UTM 38S projection (EPSG:32738).</p>
Output from the SCOFF clustering of myExperiment Workflow Fragments
<p>Using a semantic-similarity approach, we clustered the annotated, abstracted workflow fragments (see https://doi.org/10.5281/zenodo.1147545 and https://doi.org/10.5281/zenodo.1147485). This zip file contains the results of that clustering.</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.