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2,152 results for “outputs”
Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration - model outputs
<p>These tar file are associated with an article submitted to Geoscientific Model Development under identification number gmd-2021-413 (https://www.geoscientific-model-development.net): Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration<br> By A. Voldoire, R. Roehrig, H. Giordani, R. Waldman, Y. Zhang, S. Xie, MN Bouin</p> <p>3 files correspond to code components that can be distributed freely</p> <p>- surfex.tgz for the surfex v8.0 distributed under a Cecill-C License</p> <p>- oasis-mct-3.0.tgz for oasis-mct3.0 distributed under a GNU General Public License</p> <p>- nemo_v3.6.tgz for the nemo, distibuted under a Cecill-C License</p> <p>These three components are mainly fortran codes.</p> <p>The last file "<a href="https://zenodo.org/api/files/e94922e7-22eb-445b-acc3-03e11ca1af6b/CNRM-CM6-1D_published_experiments.tgz?versionId=2460ec63-89f5-415f-9613-1954f048b238">CNRM-CM6-1D_published_experiments.tgz </a>" contains all model outputs that have been used in this article. These model outputs are in netcdf format and organized by experiment.</p>
Models and output datasets for OSeMOSYS Bolivia electricity system paper
<p>These are files with the outputs for each scenario in the paper "Analyzing Carbon Emissions Policies for the Bolivian Electric Sector" submitted to the journal Renewable and Sustainable Energy Transition.</p>
WEATHER AND CLIMATE PILOT: RISICO OUTPUT EXAMPLE
<p>RISICO model prediction for 14 august 2021</p>
WEATHER AND CLIMATE PILOT: WRF OUTPUT EXAMPLE
<p>WRF OUTPUT EXAMPLE (14 AUGUST 2021 00UTC RUN)</p>
Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output
<p>This repository contains large-eddy simulation output from the CFD model <em>Diablo</em>. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript <em>Regimes and transitions in the basal melting of Antarctic ice shelves</em> submitted to the <em>Journal of Physical Oceanography</em> (December 2021).</p>
TIGAR Tonga Run Output
<p>A barotropic version of the TIGAR (Transient Inertia Gravity and Rossby wave dynamics) model has been run at T170 horizontal resolution for the 2022 Tonga eruption. TIGAR solves primitive equations on the sphere using the Hough harmonics [Vasylkevych and Žagar, 2021] thereby providing the time evolution of Rossby and inertia-gravity waves.</p>
Raw output data for "Covalency in actinide(IV) hexachlorides in relation to the chlorine K-edge X-ray absorption structure", DOI: 10.1039/D1SC06454A
<p>This repository contains raw output files related to the following article:</p> <p>Authors: Dumitru-Claudiu Sergentu and Jochen Autschbach<br> Title: Covalency in Actinide(IV) Hexachlorides in Relation to Chlorine K-Edge X-ray Absorption Structure<br> DOI: https://doi.org/10.1039/D1SC06454A</p> <p>Each archive: 1) ThCl6.rar 2) UCl6.rar 3) NpCl6.rar and 4) PuCl6.rar<br> contains output data produced by ADF (raw DFT outputs associated with the article, the ADF program can be found at: https://www.scm.com/product/adf/) and OpenMolcas (raw RASSCF and RASSI outputs associated with the article, the OpenMolcas program can be found at: https://gitlab.com/Molcas/OpenMolcas). Each folder in the archives contains 00README.txt files describing its content(s).</p> <p> </p> <p> </p>
Model output for analyses conducted in de la Vega & Buchanan et al. (2022) Global Change Biology
<p>Model data for:</p> <p>De La Vega & Buchanan et al. (2022) Multi-decadal environmental change in the<br> Barents Sea recorded by seal teeth. Global Change Biology.</p> <p>Each netcdf file (.nc) contains model output from simulations performed with the<br> NEMO-SI3-PISCES global ocean-seaice-biogeochemistry model. These simulations were<br> forced by the Japanese Atmospheric Reanalysis 55 (Tsujino et al. 2018), which<br> provides "best-guess" atmospheric conditions since 1958 to present day. Simulations<br> for this study were performed from Jan 1958 - Dec 2019.</p> <p>Variables included here are:<br> - zonal velocity (u) m s-1<br> - meridional velocity (v) m s-1<br> - potential temperature (thetao) degrees C<br> - salinity (so) psu<br> - sea ice concentraton (siconc) 0-1<br> - nitrate (NO3) mmol m-3<br> - nitrate 15 (NO3_15) mmol m-3<br> - small particulate organics (POC) mmol m-3<br> - small particulate organics 15 (POC_15) mmol m-3<br> - large particulate organics (GOC) mmol m-3<br> - large particulate organics 15 (GOC_15) mmol m-3<br> - nanophytoplankton NPP (PPPHYN) mol C m-3 s-1<br> - diatoms NPP (PPPHYD) mol C m-3 s-1<br> - nanophytoplankton chlorophyll (NCHL) mmol m-3<br> - diatoms chlorophyll (DCHL) mmol m-3<br> - atmospheric N deposition (NDEP) kg N m-2 s-1</p> <p><br> Files:<br> ETOPO_JRA55_pic_1y_u_1958-2019.nc<br> ETOPO_JRA55_pic_1y_v_1958-2019.nc<br> ETOPO_JRA55_pic_1y_TS_1958-2019.nc<br> ETOPO_JRA55_pic_1y_ice_1958-2019.nc<br> ETOPO_JRA55_pic_1y_isos_1958-2019.nc<br> ETOPO_JRA55_pic_1y_npp_1958-2019.nc<br> ETOPO_JRA55_pic_1y_chl_1958-2019.nc<br> ETOPO_JRA55_ndep_1y_isos_1958-2019.nc<br> ETOPO_JRA55_ndep_1y_npp_1958-2019.nc<br> ETOPO_JRA55_ndep_1y_chl_1958-2019.nc<br> ETOPO_ndep_files_1801-2100.nc --> Atmospheric deposition</p> <p>Naming convention:<br> "ETOPO" refers to being on a regular 1x1 degree horizontal grid<br> "JRA55" refers to forcing by the Japanese atmospheric reanalysis<br> "pic" refers to an experiment WITHOUT the historical increase in<br> atmospheric nitrogne deposition </p> <p> "ndep" refers to an experiment WITH the historical increase in<br> atmospheric nitrogne deposition<br> "1y" refers to the timestep resolution, here all 1 year. Thus, all<br> data presented here is annually averaged values.<br> "u" refers to zonal velocity<br> "v" refers to meridional velocity<br> "TS" refers to temperature and salinity<br> "ice" refers to sea ice<br> "isos" refers to NO3, NO3_15, POC, POC_15, GOC and GOC_15<br> "npp" refers to PPPHYN and PPPHYD<br> "chl" refers to NCHL and DCHL</p> <p><br> Atmospheric deposition files are created by linear interpolation using the fields<br> produced by Hauglustaine et al. (2014).</p> <p><br> Contact:</p> <p> Camille.De-La-Vega@liverpool.ac.uk<br> Pearse.Buchanan@liverpool.ac.uk</p> <p> </p> <p>Refs:</p> <p> Tsujino et al. (2018) JRA-55 based surface dataset for driving ocean–sea-ice<br> models (JRA55-do). Ocean Modelling. doi:10.1016/j.ocemod.2018.07.002</p> <p> Hauglustaine et al. (2014) A global model simulation of present and future<br> nitrate aerosols and their direct radiative forcing of climate. Atmospheric<br> Chemistry and Physics. doi:10.5194/acp-14-11031-2014.</p> <p> </p> <p> </p>
Processed model output and observational products used in `Observed winds crucial for September Arctic sea ice loss'
<p>Processed model output from wind-nudging experiments used to investigate Arctic sea ice loss. Also includes processed observational data shown in the manuscript.</p> <p> </p> <p>For further details, see </p> <p>Roach, L. A and Blanchard-Wrigglesworth E. (2022). Observed winds crucial for September Arctic sea ice loss. Accepted at Geophysical Research Letters</p>
Neodymium isotopes as a paleo-water mass tracer: A model-data reassessment: Model Output Data
<p>This dataset contains model output for the simulations presented in <em>"Neodymium isotopes as a paleo-water mass tracer: A model-data reassessment, Quaternary Science Reviews 279 (2022), 107404".</em></p> <p>The NetCDF4 files contain the following variables:</p> <p>3D fields:</p> <ul> <li>Potential Temperature</li> <li>Salinity</li> <li>North Atlantic dye tracer</li> <li>Epsilon Nd</li> <li>Nd concentration</li> </ul> <p>2D field:</p> <ul> <li>AMOC stream function</li> </ul>
COMPAS v02.27.05 output at representative metallicities
<p>This dataset contains the output data of rapid binary population synthesis produced using COMPAS v02.27.05. The details of the initial conditions and setup are described in the "COMPAS_settings_table.pdf" file. We simulate 5 million binaries, at representative metallicities, and store them in HDF5 files, the default format for COMPAS simulations. Each HDF5 file contains the following groups:</p> <ul> <li> ‘/BSE_Common_Envelopes’</li> <li> ‘/BSE_Double_Compact_Objects’</li> <li> ‘/BSE_Supernovae’</li> <li> ‘/BSE_System_Parameters’</li> <li> ‘/Run_Details’</li> </ul> <p>For details about the data, such as variable names and attributes, visit the <a href="https://compas.readthedocs.io/en/latest/">compas.readthedocs.io</a>.<br> The metallicities chosen for the simulations are:</p> <ol> <li>Z=0.0001: the lowest metallicity accounted for in our stellar evolution tracks;</li> <li>Z=0.0002: a proxy metallicity for I Zwicky 18;</li> <li>Z=0.001;</li> <li>Z=0.0021: a proxy metallicity for the Small Magellanic Cloud;</li> <li>Z=0.0047: a proxy metallicity for the Large Magellanic Cloud;</li> <li>Z=0.01;</li> <li>Z=0.0142: a metallicity representative of the Sun;</li> <li>Z=0.02: a super-solar metallicity, aka "old Solar";</li> <li>Z=0.03: the highest metallicity accounted for in our stellar evolution tracks;</li> <li>U[log<sub>10</sub>(0.0001), log<sub>10</sub>(0.03)]: log-uniformly distributed metallicities from the COMPAS metallicities.</li> </ol> <p>This results in 50 million binaries in total.</p> <p>In addition to the Table in the "COMPAS_settings_table.pdf" file, we include a "Run Details" text file, which contains the setup as specified via COMPAS, for a particular batch of the Z=0.02 simulation. This file contains a representative description of the global properties of all the 10 simulations, but some details, e.g. Clock-Time, vary from batch to batch.</p> <p>For questions, comments, or suggestions, contact compas-user@googlegroups.com or visit <a href="https://compas.science/">compas.science/</a>.<br> Visit <a href="https://github.com/TeamCOMPAS/COMPAS">Team COMPAS Github page</a> to get access to the source code.<br> Finally, if you use any of these data, we kindly request that, in recognition of this team effort, the paper is cited as:</p> <p>Team COMPAS: J. Riley et al., “Rapid Stellar and Binary Population Synthesis with COMPAS”, <em>The Astrophysical Journal Supplement Series</em>, vol. 258, no. 2, 2022. <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ac416c/meta">doi:10.3847/1538-4365/ac416c</a>.</p>
Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information (artifact)
<p>This is the artifact associated with the OOPSLA 2022 paper titled "Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information".</p> <p>It includes:</p> <ul> <li>A README file with in-depth instructions for accessing and using all artifact components.</li> <li>The raw and anonymized data for each participant in the quantitative studies.</li> <li>The raw and anonymized audio transcripts for each participant in the qualitative studies.</li> <li>Working versions of every user interface used in the experiments.</li> <li>An experimental model-finder, which is a modified version of the <a href="http://alloytools.org/">Alloy</a> model finder, which demonstrates the "2+1-" visualization mode from the paper.</li> </ul>
GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"
<p>Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°) </p>
Central California ROMS Output and Grid
<p>A portion of the ROMS output and grid for a sub-region of the Central California (100 m horizontal resolution) simulations. The output files (roms_sample_out.nc, roms_sample_out_2.nc) contains ROMS output vertically interpolated to z-levels (as opposed to sigma levels) for easier visualization and processing. The output file shows a period of perpendicular interaction between internal tidal bores and submesoscale fronts and filaments corresponding to results described in the publication. The grid file (sample_grd.nc) for this sub-domain is also included.</p> <p> </p>
Current consumption for different LoRaModules and output powers
<p>This dataset contains the measured input power for a given transmission power at 1.8V and 3.3V for different LoRaModules, including: SX1276 (RFM95, PA Boost Configuration), SX1276 (inAir9), SX1262 (DevBoard), SX1261 (custom Board).</p>
Territory-level temperature influences breeding phenology and reproductive output in three forest passerine birds
<p>Temperature plays an important role in determining the breeding phenology of birds in temperate climates, with higher spring temperatures associated with earlier breeding. However, the effect of localised territory-scale temperature variations is poorly understood, with relationships between temperature and breeding phenology mostly studied using coarse-grained climatic indices. Here, we interpolate spring temperatures recorded at 150 m2 grid intersections encompassing 417 ha of forest to examine the influence of territory-scale temperature, and its interaction with mean annual temperature, on territory selection, breeding phenology, clutch size and fledging success for three co-occurring single-brooded passerine birds using data from 672 nests over four years. All species exhibited significant trends in reproductive traits associated with territory-scale temperature. Pied flycatchers Ficedula hypoleuca settled in cooler territories first, where they raised more fledglings. Blue tits Cyanistes caeruleus laid larger clutches in warmer territories in warm years and always laid earlier at warmer territories irrespective of annual temperature variation. Contrastingly, pied flycatcher and wood warbler Phylloscopus sibilatrix breeding phenology was earlier at warmer territories in cool years and cooler territories in warm years, with wood warbler clutch size responding similarly to this interaction. Greater previous breeding experience and increased higher rates of historical territory occupancy (territory quality) also predicted earlier breeding phenology and higher fledging success for pied flycatchers. We suggest that the migratory pied flycatcher and wood warbler are best synchronised with their prey availability in cooler years at a local population level. However resident blue tits match local phenology across all years, which is potentially advantageous under warmer predicted climate change scenarios. We conclude that temperature at the territory scale can be an important driver of settlement and breeding phenology and influence reproductive traits.</p>
FOCI model output used in the study by Ivanciu et al. - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change
<p>This dataset contains the model output used in the analysis presented in the study by Ivanciu et al., 2022 - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change. Four ensembles of three simulations each were performed with the global coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020). Details about the ensembles can be found in the above-mentioned publication. The files containing "past" in their name belong to the ensemble "PAST", the files containing "future" in their name belong to the ensemble "FUTURE", the files containing "future_GHG" in their name belong to the ensemble "GHG" and the files containing "future_Ozone" in their name belong to the ensemble "OZONE" from the publication.</p>
Monthly-Mean Model Output for Paper Titled "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?"
<p>These tarballs contains monthly-mean model output, which was primarily what was presented in the AGU JAMES paper titled "Do nudging tendencies depend on the nudging timescale chosen in atmospheric models?". Also included are the scripts used to set up these simulations, allowing reproducibility of the portion of the paper that used 3-hourly output. The 3-hourly output was not included, as it totaled ~7TB.</p>
Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
<p><span>Ecologists often study biodiversity by evaluating species occupancy and the relationship between occupancy and other covariates. Occupancy models are now widely used to account for false absences in field surveys and to reduce bias in estimates of covariate relationships. Existing occupancy models take as inputs binary detection/non-detection observations of species at each visit to each site. However, autonomous sensing devices and machine learning models are increasingly used to survey biodiversity, generating a new type of observation record (i.e., continuous-score data) that reflects the model's confidence a species is present in each autonomously sensed file, instead of binary detection/non-detection data. These data are not directly compatible with traditional binary occupancy modeling methods.</span></p> <p><span>Here, we develop a new occupancy model that models continuous scores on a visit level as a Gaussian mixture, combining a distribution of scores for files that do contain the species of interest and a distribution of scores for files that do not. The model takes as input continuous scores for each autonomously sensed and classified file, along with an optional small number of binary, manually verified detection and non-detection annotations.</span></p> <p><span>We present a simulation study that shows that over a range of empirically realistic parameters, our model outperforms traditional occupancy models that are based on binary annotation alone. We also apply this new model to an empirical case study using data generated from five machine learning classifiers applied to autonomous acoustic recordings gathered in the eastern United States.</span></p> <p><span>Because our occupancy model generalizes allowable input data beyond binary observations, it is particularly well-suited to the increasing volume of machine learning classified data in ecology and conservation.</span></p>
MITgcm model setup and output for "Modeling ocean circulation in the Bellingshausen Sea"
<p>MITgcm model setup and output for "Modeling ocean circulation in the Bellingshausen Sea".</p> <p>Here, it contains the results of the Amundsen and the Bellingshausen Sea from 1992 to 2020. </p> <p><Changes from run260 to this model><br> This is improved version of run260 with 70 vertical layers. <br> To adjust melt rate of the George VI ices shelf, we change the values of heat transfer coefficient γT similar to run260.</p> <p>This experiment was conducted in two separate sessions due to changes in timestep (150 -> 120).<br> We can refer to input/data.diagnostics for the details of the model output name.<br> Outputs are the monthly mean.</p> <p><br> <strong>(Contents)</strong></p> <p>ABSmodel_1992_2009_code.tar.gz (code to run this simulation between 1992 and 2009)</p> <p>ABSmodel_2010_2020_code.tar.gz (code to run this simulation between 2010 and 2020)</p> <p>ABSmodel_1992_2009_input.tar.gz (input file required for this simulation between 1992 and 2009)</p> <p>ABSmodel_2010_2020_input.tar.gz (input file required for this simulation between 1992 and 2009)</p> <p>ABSmodel_1992_2009_results.tar.gz</p> <p>ABSmodel_2010_2020_results.tar.gz</p> <p>(due to size limit of 50GB, please check https://ecco.jpl.nasa.gov/drive/files/ECCO2/LLC1080_REG_AMS/Hyogo_et_al_2022 for complete model output. Complete datasets can also be obtained by rerunning the simulation.)</p> <p><strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /forcing/era_xx/ .<br> cp ../build/mitgcmuv .<br> qsub run_omp_high_t1.pbs</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.