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Dataset results
2,152 results for “outputs”
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/12-degree</p> <p>- REGRID versions are regridded from ORCA0083 grid to a regular 1/12-degree grid</p> <p>- Simulation performed as part of core NOC activities</p> <p>- Forced under version 5.2 of the DRAKKAR observation-based reanalysis dataset (DFS)</p> <p>- Physical simulation described in Kelly, S. J., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2020). They came from the Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean. Earth's Future, 8, e2019EF001394. https://doi.org/10.1029/2019EF001394</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in August 2021</p> <p>- Seafloor fields of model properties for the periods 2006-2015</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- The output has been regridded to a regular 1/12-degree grid</p>
Model output from Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier
<p>This model output dataset accompanies the draft paper 'Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier'.</p>
RMSD and Trp assays for "Structural analysis of a motor with increased mechanical output reveals new transitions in kinesin microtubule motility"
<p>This entry is for our manuscript, "Structural analysis of a motor with increased mechanical output reveals new transitions in kinesin microtubule motility" by Satoki Shibata*, Matthew Y. Wang*, Tsuyoshi Imasaki*, Hideki Shigematsu, Diego Ugarte La Torre, Yuanyuan Wei, Chacko Jobichen, Hajime Hagio, J. Sivaraman, Yuji Sugita, Sharyn A. Endow & Ryo Nitta.</p> <p>*Equal contribution</p> <p>Corresponding Authors: Tsuyoshi Imasaki*, Sharyn A. Endow, Ryo Nitta</p> <p>The deposited datasets are 1) beta-strand residue all-atom RMSD between kinesin-14 NcdY485K ADP and ADP + free Pi nucleotide states and 2) fluorimeter assays of NcdY485K intrinsic Trp fluorescence changes upon addition of free Pi . The data are presented in Fig. 6e, Fig. 7b,c and Supplementary Fig 10. Files are named for the figures in which the data are shown. A computer script for the RMSD analysis and methods for the fluorimeter assays and data analysis are included in the deposited files.</p>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</p>
WoS and Scopus records for the bibliometric analysis in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
Enhanced 3D radiative transfer ACM-RT calculations and output for Cole et al., 2022
<p>For the Earth Cloud, Aerosol, Radiation Explorer (EarthCARE) satellite mission there are a number of algorithms used to process the observations. One of these algorithms, called ACM-RT, is designed to use retrieved geophysical properties to perform forward radiative transfer calculations using 1D and 3D solar and thermal radiative transfer models. The ACM-RT algorithm is documented in an Atmospheric Measurements and Techniques (AMT) article. </p> <p>To illustrate outputs from ACM-RT, including the benefits of 3D radiative transfer, “enhanced” radiative transfer calculations were performed, relative to calculations that would be performed operationally during the EarthCARE mission. In particular, the 3D Monte Carlo radiative transfer calculations used an increased number of samples to reduce the Monte Carlo uncertainty and calculations were performed for more of the input data.</p> <p>The relevant publication for these calculations is:</p> <p>Cole, J. N. S., H. W. Barker, Z. Qu, N. Villefranque and M. W. Shephard : Broadband Radiative Quantities for the EarthCARE Mission: The ACM-COM and ACM-RT Products. Submitted to AMT, November 2022.</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - CellRanger outputs for scRNAseq experiments in Figures 2,5, and 6
<p>Attached are the outputs of the cell ranger pipeline for 10x 3' scRNAseq of HSPCs. For any questions regarding this dataset please contact Leila Perie (leila.perie@curie.fr)</p>
Output files of the 2DV-model for the 79NG fjord
<p>This archive contains the output files of the 2D-vertical GETM runs presented by Reinert <em>et al.</em> (2023).</p> <p>The NetCDF files contain the steady state of the simulation, except for the file “default_initial_state.nc”. See the README file for further details.</p> <p><strong>Reference:</strong></p> <p>Reinert, M., Lorenz, M., Klingbeil, K., Büchmann, B., & Burchard, H. (2023). High-Resolution Simulations of the Plume Dynamics in an Idealized 79°N Glacier Cavity Using Adaptive Vertical Coordinates. <em>Journal of Advances in Modeling Earth Systems,</em> 15(10), e2023MS003721. <a title="Link to the paper describing this dataset" href="https://doi.org/10.1029/2023MS003721" target="_blank" rel="noopener">DOI: 10.1029/2023MS003721</a></p>
Integration of RISE innovations in the fields of OELF, RLA and SHM: input and output datasets (Version 1.0)
<p>Repository of input and output data files associated with Deliverable 6.1 of the <a href="http://rise-eu.org/home/">RISE project</a>.</p> <p>Full deliverable available <a href="http://static.seismo.ethz.ch/rise/deliverables/Deliverable_6.1.pdf">here</a>.</p>
Model outputs for the article "Modelling the evolution of Arctic multiyear sea ice over 2000–2018"
<p><em>icemod_monthly.tar.gz </em>contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.</p> <p>Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).</p> <p><em>transports_monthly_sections.zip </em>contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.</p> <p>In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.</p>
Met Office Unified Model outputs for simulations of Proxima Centauri b and TRAPPIST-1e
<p>Five datasets generated by the Met Office Unified Model configured for exoplanets Proxima Centauri b and TRAPPIST-1e. </p> <p>1. Control Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere using observed planetary data (radius, stellar constant) as model parameters</p> <p>2. Warm Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>3. Control Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere using observed planetary data</p> <p>4. Warm Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>5. Dry Trap: A simulation of TRAPPIST-1e with a dry atmosphere</p> <p>The data is used in Cohen et al. (2023). ""Traveling planetary-scale waves cause cloud variability on tidally locked aquaplanets." Submitted to The Planetary Science Journal.</p> <p>Abstract:</p> <p>"Cloud cover at the planetary limb of water-rich Earth-like planets is likely to weaken chemical<br> signatures in transmission spectra, impeding attempts to characterize these atmospheres. However,<br> based on observations of Earth and solar system worlds, exoplanets with atmospheres should have both<br> short-term weather and long-term climate variability, implying that cloud cover may be less during<br> some observing periods. We identify and describe a mechanism driving periodic clear sky events at<br> the terminators in simulations of tidally locked Earth-like planets. A feedback between dayside cloud<br> radiative effects, incoming stellar radiation and heating, and the dynamical state of the atmosphere,<br> especially the zonal wavenumber-1 Rossby wave identified in past work on tidally locked planets, leads<br> to oscillations in Rossby wave phase speeds and in the position of Rossby gyres and results in advection<br> of clouds to or away from the planet’s eastern terminator. We study this oscillation in simulations of<br> Proxima Centauri b, TRAPPIST 1-e, and rapidly rotating versions of these worlds located at the inner<br> edge of their stars’ habitable zones. We simulate time series of the transit depths of the 1.4 μm water<br> feature and 2.7 μm carbon dioxide feature. The impact of atmospheric variability on the transmission<br> spectra is sensitive to the structure of the dayside cloud cover and the location of the Rossby gyres,<br> but none of our simulations have variability significant enough to be detectable with current methods."</p>
ESSD benchmark output data
<p>This dataset contains the ESSD benchmark output data. Visit <a href="https://github.com/EUPP-benchmark/ESSD-benchmark">https://github.com/EUPP-benchmark/ESSD-benchmark</a> for more information.</p> <p>This dataset is provided as supplementary material with:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p>Please cite this article if you use (a part of) this code for a publication.</p> <p><br> Description of the methods used to get the ESSD benchmark output data<br> -------------------------------------------------------------------------------------------------------</p> <p> - ANET: NN post processing method using ensemble member encoders and dynamic attention<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-ANET">https://github.com/EUPP-benchmark/ESSD-ANET</a><br> <br> - AR-EMOS: EMOS with heteroscedastic autoregressive error adjustments<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-AR-EMOS">https://github.com/EUPP-benchmark/ESSD-AR-EMOS</a><br> <br> - ASRE: Accounting for systematic and representativeness errors<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-ASRE">https://github.com/EUPP-benchmark/ESSD-ASRE</a><br> <br> - DRN: Distributional regression network<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-DRN">https://github.com/EUPP-benchmark/ESSD-DRN</a><br> <br> - DVQR: D-vine copula based postprocessing<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-D-Vine-Copula">https://github.com/EUPP-benchmark/ESSD-D-Vine-Copula</a><br> <br> - EMOS: Ensemble model output statistics<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-EMOS">https://github.com/EUPP-benchmark/ESSD-EMOS</a><br> <br> - RC: Reliability Calibration (IMPROVER)<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-reliability-calibration">https://github.com/EUPP-benchmark/ESSD-reliability-calibration</a><br> <br> - MBM: Member-By-Member postprocessing<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-mbm">https://github.com/EUPP-benchmark/ESSD-mbm</a></p>
Output data for the PyCoxMunk example notebooks
<p>The PyCoxMunk python library allows users to calculate the expected sea surface reflectance under a given set of geometric and meteorological conditions. It is designed for use in satellite remote sensing and supports the majority of common satellite platforms.</p> <p> </p> <p>This archive contains data related to the example notebooks distributed with PyCoxMunk. Here you can find:</p> <p>1) An input dataset for the SLSTR example notebook.</p> <p>2) Output files for the SLSTR, SEVIRI and GOES example notebooks.</p>
FLEXPART 10.4 output for "Occurrence and backtracking of microplastic mass loads including tire wear particles in Northern Atlantic air"
<p>The dataset consists of three (3) files:</p> <p>-- track3h.txt shows the position of the research vessel in each of the seven (7) ship tracks/campaigns in 3-hour resolution in ascii format structured in columns as follows:</p> <p>YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, LONGITUDE, LATITUDE, SHIP TRACK NUMBER</p> <p>-- FLEXPART_720x360_fine.tar.gz shows the footprint emission sensitivities for fine particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.</p> <p>-- FLEXPART_720x360_coarse.tar.gz shows the footprint emission sensitivities for coarse particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>
Simulation output for Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study
<p>This repository contains the (compressed) simulation output from <em>Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study</em>. On a Linux-based system, extract the contents with:</p> <pre><code class="language-bash">tar -xvzf raw_output_compressed.tar.gz </code></pre> <p>Please refer to the published article (https://doi.org/10.3389/fpsyg.2023.1253452) and the associated GitHub (<a href="https://github.com/carldelfin/stability-of-bivariate-correlations">github.com/carldelfin/stability-of-bivariate-correlations</a>) for additional information.</p>
16-year WRF simulation for the Southern Alps of New Zealand (monthly output)
<p>The climatological dataset was produced using the Weather and Research Forecasting (WRF) model configured with two nested domains at 10 km (D1) and 2 km (D2) horizontal grid spacing. It covers the bulk of the South Island of New Zealand and is centered over Brewster Glacier in the Southern Alps. The model was forced every three hours by ERA5 reanalysis data at its outer lateral boundaries. The dataset covers the period of 1 January 2005 to 31 December 2020, providing daily output in the outer domain (D1) and 3-hourly output in the innermost domain (D2). </p> <p>The dataset was generated as part of a DFG-funded project aimed at investigating the atmospheric processes causing impacts of sea surface temperature changes around New Zealand on variations of glacier surface climate and mass balance in the Southern Alps (using Brewster Glacier as a benchmark glacier).</p> <p>The data provided here are a selection of monthly averages from the finest WRF domain (D2; 2-km grid spacing). They are distributed among three different file types containing 4-dimensional, 3-dimensional and invariant output variables, respectively. For the 4-dimensional variables, the data were cropped to below ~200 hPa. In addition, perturbation and base-state atmospheric pressure (WRF variables P and PB) and geopotential (PH and PHB) were combined to produce full model fields, and perturbation potential temperature (T) was converted to total potential temperature.</p>
DCA and GNMDS output for 4640 subplots and 95 vascular plant species in four alpine grasslands
<p>Ordination output from detrended correspondence analysis (DCA) and global non-metric multidimensional scaling (GNMDS).</p> <p>Analyses were performed in R with the <em>vegan</em> package (Oksanen 2022) for the entire data set of 4630 subplots and 95 species' occurrences ('global', indicated by global or missing site name in file names), and for each of four sites: Skjellingahaugen (skj), Gudmedalen (gud), Låvisdalen (lav), and Ulvehaugen (ulv). Access .Rds files with readRDS in R/RStudio.</p> <p>For GNMDS files, k indicates the chosen number of dimensions. See GitHub repository for scripts to produce and perform further analysis with the files in this archive.</p> <p>Analyses performed by EL with scripts based on originals by RH.</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.