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695 results for “model output”
GFDL AM2.1 Model Output -Changes in Monsoon Meteorological Elements Due to the Warming of the Tropical Indian Ocean
<p>Changes in Climatological Mean Stream Function, Eddy Flux, Wind Field, Moisture Flux, and Precipitation from May to September Due to the Warming of the Indian Ocean</p>
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
<p>Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.</p>
Model outputs from a NEMO-PISCES configuration of the tropical Atlantic Ocean
<p>Model outputs from a NEMO-PISCES configuration of the tropical Atlantic Ocean (35°S-35°N, 100°W-20°E), from 1998 to 2007. It contains monthly climatologies (10-years averages) of several ocean and biogeochemical variables, as well as monthly Chlorophyll, for 4 sensitivity experiments. These 4 simulations are described and analyzed in the paper: "On the importance of riverine organic matter for the Amazon plume: a modeling study" by Gévaudan et al., under review in JGR: Oceans. A preprint is available here: https://doi.org/10.22541/essoar.172081327.75230541/v2.</p>
RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution.<strong><br></strong></p> <p><strong>Software</strong></p> <p>The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-03</p> <ul> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (2011), River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a></p> <p>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., et al. (2004). The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p>
ICON-Coast model output for a study on the impact of Arctic coastal erosion on sea water carbonate saturation.
<p>Primary output of the ocean-biogeochemistry model ICON-Coast that has been used to create the figures in a manuscript on the impact of Arctic coastal erosion on sea water carbonate saturation.</p>
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</p>
Output of WRF BEP+BEM model and Pseudo Global Warming composites
<p>Output for the submitted article "Assessing the intensity of heatwaves in a warming climate at the urban scale: A case study of the Metropolitan Area of Barcelona"</p> <p>Sergi Ventura1, JR Miro2, Ricard Segura-Barrero1, Fei Chen3, Alberto Martilli4, Changhai Liu5, Kyoko Ikeda5, Gara Villalba1,6,*</p> <p><br>1 Sostenipra Research Group (SGR 01412), Institute of Environmental Sciences and Technology (MDM-2015-0552), Z Building, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain<br>2 Department of Territory and Sustainability, Meteorological Service of Catalonia, Generalitat de Catalunya, Barcelona, Spain<br>3 Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China<br>4 Research Center for Energy, Environment and Technology, CIEMAT, Madrid, Spain<br>5 National Center for Atmospheric Research, Boulder, CO 80301, USA<br>6 Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain</p> <p><br>* Corresponding author: gara.villalba@uab.cat</p> <p>All rights lie with the authors.</p> <p> </p> <p>We use WRF BEP+BEM to investigate the sensitivity of multiple heat wave periods to climate change under the SSP370 scenario. The simulations show mean temperature increases of 2.5 ºC by the mid-21st-century and 4.2 ºC by the end of the century in the Metropolitan Area of Barcelona, a Mediterranean region in northeastern Spain.</p> <p>This Zenodo repository contains the following:</p> <ul> <li> <p><strong>met_em initial and boundary conditions for the Metropolitan Area of Barcelona</strong>:<br><code>met_em</code> files including the initial and boundary conditions used for modeling a historical heatwave (2020), as well as projected PGW-MID (2070) and PGW-END (2100) periods.</p> </li> <li> <p><strong>wrfout_d03, model output</strong>:<br>WRF output at 1 km resolution for the Metropolitan Area of Barcelona on <strong>August 1st, 2020</strong> (a historical heatwave day), representing the historical, mid-century (2070), and end-of-century (2100) periods.</p> </li> <li> <p><strong>PGW composites</strong>:<br>NetCDF-format plots of <strong>relative humidity (RH)</strong> and <strong>temperature (T)</strong> maximum and minimum values. These are provided for the historical control period (CTL), mid-century (MID), and end-of-century (END), across the four analyzed synoptic weather patterns (SS, SA, DA, and DAU).</p> </li> </ul> <p> </p> <p> </p>
SCAR DistAnt Ecological Model Output Repository
<p>This repository provides access to a collection of ecological model outputs (species distribution and similar models) from Antarctica and the Southern Ocean. It is a project of the SCAR <a href="https://scar.org/science/egabi/home/" rel="nofollow">Expert Group on Biodiversity Informatics</a> in conjunction with <a href="https://www.belspo.be/belspo/impuls/project_en.stm#ADVANCE" rel="nofollow">ADVANCE</a> (Royal Belgian Institute of Natural Sciences) and the Integrated Digital East Antarctica program at the <a href="https://www.antarctica.gov.au/science/" rel="nofollow">Australian Antarctic Division</a>.</p> <p><strong>Please note:</strong> the inclusion of a layer in this collection is not an endorsement of its quality or suitability for your intended purpose. Users should consult the associated publication for details on the source data and modelling processes. We encourage users to contact the original publication authors to discuss their intended use of these layers.</p> <p>Model outputs are provided as cloud-optimized geotiffs (COGs), with generally one file per species. The COG will have multiple bands if the original model predictions include uncertainty estimates or multiple model output variables. Each output has been kept on its original coordinate reference system (map projection) and spatial resolution.</p> <p>There is a <a href="https://github.com/SCAR/distant/blob/master/metadata.csv">table of minimal metadata</a> that describes the layers in the collection. This metadata is intended to be sufficient for users to find potential layers of interest, and make an initial evaluation of their high-level characteristics such as taxonomic details, spatial coverage and resolution, and model output types. Our metadata is NOT intended to provide a comprehensive description of each layer: users are referred to the original publication for that level of detail (see the layer’s <code>reference</code> entry).</p> <div> <h3>Citing</h3> The layers in this collection have been re-released under their original license where applicable, or a CC-BY licence otherwise. Please cite when using, and also cite the original data sources used. For example:</div> <div> <p>Kovacs J (2020) A model of my favourite Southern Ocean species. <em>Journal of Southern Ocean Stuff</em> <strong>123</strong>:1–10. Data obtained from the SCAR DistAnt Ecological Model Output Repository, doi:10.5281/zenodo.10910076</p> </div> <p>Individual data sources might have varying licence conditions: consult the <code>licence</code> field for details.</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
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>
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>
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>
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>
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>
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.