Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
695
datasets available to search
ShareScore release 0.9.0
Dataset results
695 results for “model output”
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
Regional model (ACM) output used to elucidate coastal ocean carbon transport processes in the northwest North Atlantic (Rutherford and Fennel, 2022; GRL)
<p>Key variables from the regional Atlantic Canada model (ACM) used to elucidate coastal ocean carbon transport in the northwest North Atlantic. The dataset includes all model variables required to reproduce the results in Rutherford & Fennel (2022, <em>GRL</em>). See RutherfordandFennel_GRL_ACM_data_README.txt for more details.</p>
Post-processed output for paper "Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model"
<p>This dataset contains processed output of the ACCESS-OM2-01 global ocean-sea ice model.</p> <p>The processed output was used for analyses of the paper:</p> <p>Huneke W. G. C., Morrison A. K., Hogg A. McC. Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model, 2022, Journal of Physical Oceanography, doi:10.1175/JPO-D-21-0143.1</p> <p>The Github repository github.com/wghuneke/ASC_SpatialTemporalVariability contains Jupyter notebooks and instructions for how to produce these files from the raw model output.</p> <p>Contact wilma.huneke@anu.edu.au if you want access to the ACCESS-OM2-01 raw model output itself.</p>
Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"
<p># Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"<br> This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation.</p> <p>### REFERENCES<br> Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be<br> Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version<br> 1.86.0, <URL: https://github.com/pik-piam/madrat>.</p> <p>rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL:<br> https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>.</p> <p>Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov<br> R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E,<br> Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL:<br> https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>.</p> <p>Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>.</p> <p>Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder<br> F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL:<br> https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>.</p> <p>## LICENSE<br> This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode.</p> <p>## CONTACT<br> karstens@pik-potsdam.de</p>
Outputs of the "Passenger demand mobility model" developed for South Asia
<p>This dataset comprises outputs and intermediate calculations of the passenger mobility demand model developed for South Asian regions. The dataset includes the following: </p> <ul> <li>'Total_PDT.zip' folder contains files of the type <em><strong>total_pdt_scename_j_m.csv</strong></em>: <ul> <li> These CSV files are the model output that contain passenger distance travelled (PDT) by mode in each area type and distance category for daily travel; and PDT by mode for long distance travel in different scenarios (scen_name), type of urbanisation (j), and mode share trajectory (m).</li> <li><strong>scename</strong>- This refers to the scenarios considered which are as follows - 'Base', 'NP', 'BP', 'TOD and 'TECH'</li> <li><strong>j</strong>- This refers to the type of urbanisation trajectories considered and are defined as follows: <ul> <li>1 - <a href="../api/records/10303828/draft/files/Urbanisation_type_trajectory_1.csv/content">Urbanisation_type_trajectory_1.csv</a></li> <li>2 - <a href="../api/records/10303828/draft/files/Urbanisation_type_trajectory_2.csv/content">Urbanisation_type_trajectory_2.csv</a></li> </ul> </li> <li><strong>m</strong>- This refers to the mode share trajectories considered in this study and are defined as follows: <ul> <li>1- Business as Usual (BaU)</li> <li>2- Car-oriented future (COF)</li> <li>3- Sustainable future (SF)</li> </ul> </li> </ul> </li> </ul> <ul> <li>Trip rates calculated for SSP 2- <a href="../api/records/10303828/draft/files/trip_rate_2.csv/content">trip_rate_2.csv</a></li> </ul> <p> </p>
Sea ice model outputs developed in "Modelling Antarctic sea ice variability using a brittle rheology"
<p>Output files from the BBM, mEVP and mEVP+ runs developed in the paper entlited "Modelling Antarctic sea ice variability using a brittle rheology". </p> <p>Paper abstract:</p> <p>Sea ice is a composite solid material that sustains large fracture features at scales from metres to kilometres which can play an important role in coupled atmosphere-ocean processes. To model these features, brittle sea ice physics, via the Brittle-Bingham-Maxwell (BBM) rheology, has been implemented in the Lagrangian neXt generation Sea Ice Model (neXtSIM). In Arctic-only simulations the BBM rheology has shown a capacity to represent observationally consistent sea ice fracture patterns and breakup across a wide range of time and length scales. Still, it has not been tested whether this approach is suitable for the modelling of Antarctic sea ice. Here, we introduce a new 50-km grid-spacing Antarctic configuration of neXtSIM, neXtSIM-Ant, using the BBM rheology. We evaluate this simulation against observations of sea ice extent, drift, and thickness and compare it with identically-forced neXtSIM simulations that use the standard modified Elastic-Visco-Plastic (mEVP) rheology. In general, using BBM results in thicker sea ice and an improved representation of sea ice drift than mEVP. We suggest that this is related to short-duration breakup events caused by Antarctic storms that are not captured in the viscous-plastic model. </p>
Climate model (CM2.6) and regional model (ACM) outputs 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 variables 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.txt</em> for more details.</p>
CarloTopo synthetic GeoModeller model and relevant MCUE outputs
<p>Companion dataset to the paper:</p> <p><em>Topological Analysis in Monte Carlo Simulation for Uncertainty Estimation</em><em>, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell</p> <p>This dataset contains twoarchives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:</p> <ol> <li>CarloTopo.zip contains a synthetic GeoModeller three dimensional model.</li> <li>High_confidence_CarloTopo.zip contains MCUE outputs for Gaussian perturbation and topological clustering.</li> <li>Low_confidence_CarloTopo.zip contains MCUE outputs for Uniform perturbation and topological clustering.</li> </ol> <p>In the output archive files:</p> <ul> <li>xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)</li> <li>.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)</li> <li>.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @:https://www.paraview.org/download)</li> <li>the frequency.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet</li> <li>.csv files are the probabilistic geological models</li> <li>.task files are GeoModeller's API files</li> <li>.txt files are logs from CURE</li> </ul>
Farfish FFDB Model outputs and indicators
<p> Data summary, estimates and projections stored in the FarFish FFDB</p>
Water potential data and model output
<ol> <li>Classifying the diverse ways that plants respond to hydrologic stress into generalizable 'water-use strategies' has long been an eco-physiological research goal. While many schemes for describing water-use strategies have proven to be quite useful, they are also associated with uncertainties regarding their theoretical basis and their connection to plant carbon and water relations. In this review, we discuss the factors that shape plant water stress responses and assess the approaches used to classify a plant's water-use strategy, paying particular attention to the popular but controversial concept of a continuum from isohydry to anisohydry.</li> <li>A generalizable and predictive framework for assessing plant water-use strategies has been historically elusive, yet recent advances in plant physiology and hydraulics provide the field with a way past these obstacles. Specifically, we promote the idea that many metrics that quantify water-use strategies are highly dynamic and emergent from the interaction between plant traits and environmental conditions, and that this complexity has historically hindered the development of a generalizable water-use strategy framework.</li> <li>This idea is explored using a plant hydraulics model to identify: 1) distinct temporal phases in plant hydraulic regulation during drought that underpin dynamic water-use responses, and 2) how variation in both traits and environmental forcings can significantly alter common metrics used to characterize plant water-use strategies. This modeling exercise can bridge the divide between various conceptualizations of water-use strategies and provides targeted hypotheses to advance understanding and quantification of plant water status regulation across spatial and temporal scales.</li> <li>Finally, we describe research frontiers that are necessary to improve the predictive capacity of the plant water-use strategies concept, including further investigation into the belowground determinants of plant water relations, targeted data collection efforts, and the potential to scale these concepts from individuals to whole regions.</li> </ol>
MetROMS model output for manuscript "Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf"
<p>This dataset contains output of the MetROMS ocean/sea-ice/ice-shelf model.</p><p>The output was used for analyses of the manuscript:</p><p>Huneke W. G. C., Hobbs W.R., Klocker A., Naughten K. A., 2023, Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf, Geophysical Research Letters, 50, e2023GL105435, https://doi.org/10.1029/2023GL105435</p><p>The Github repository https://github.com/wghuneke/MetROMS_BasalMelt_Perturbation/tree/main contains analysis (python) scripts for processing and visualising the model output.</p><p>Contact wilma.huneke@anu.edu.au if you need further information.</p>
LarsAckermann GMD Iceberg 2023 - model output SPINUP
<p>This dataset contains model output from the experiment ID "spinup" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061</p><p>---------------------------------------------------------</p><p>It contains:</p><p>mean output over the last 100 model years of:<br> - 3D ocean temperature<br> - 3D salinity<br> - 2D sea-ice height<br> - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins<br> - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basins</p><p>timeseries over 700 years of:<br> - global ocean temperature<br> - AABW for the IndoPacific sector<br> - global AABW</p><p>yearly data of:<br> - 3D ocean temperature<br> - 3D vertical velocity<br> - 3D vertical bolus velocity<br> - 3D Väisälä frequency<br> - 2D iceberg freshwater and heat fluxes<br> - buoys files with information about iceberg velocity, location, and dimension</p>
MITgcm model setup and output for "Submesoscale variability and basal melting in ice shelf cavities of the Amundsen Sea"
<p>Here, it contains the results of the high-res eastern AMS simulation. A detailed description of the model configuration and model output is provided by Nakayama et al.,2019.</p><p>Nakayama, Yoshihiro, Georgy Manucharyan, Hong Zhang, Pierre Dutrieux, Hector S. Torres, Patrice Klein, Helene Seroussi, Michael Schodlok, Eric Rignot, and Dimitris Menemenlis. "Pathways of ocean heat towards Pine Island and Thwaites grounding lines." <i>Scientific reports</i> 9, no. 1 (2019): 16649.</p><p>Due to space limitations, please access NASA data for all other model daily outputs (Registration is required). https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/AMS_200m. </p><p>Contents can be downloaded easily using wget (see link below).<br>https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf</p><p>(Contents)<br>code.zip (code to run this simulation)<br>input_ctrl.zip (input file required for this simulation <br>input_nomelt.zip (input file required for this simulation <br>results_ctrl.zip (days 30 and 60))<br>results_nomelt.zip (days 30 and 60))<br>Caution: You need to download heavier input files to rerun this simulation from ECCO-Drive. <br><br>(How to build and run)<br>mkdir build<br>./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br>make depend<br>make -j 16<br>cd ..<br>mkdir test<br>cd test<br>ln -sf ../input/* .<br>ln -sf /nobackup/hzhang1/forcing/era_xx_it33/ .<br>cp ../build/mitgcm_uv .<br>qsub run8_sandy_tracer_init_cont_2.pbs</p>
Water potential data and model output
Open the record for dataset details and reuse information.
Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
Dynamics and Chemistry of the Summer Stratosphere Model Output
DCOTSS-Model-Output features numerical model output for the Dynamics and Chemistry of the Summer Stratosphere sub-orbital campaign. Featured in this product are trajectory calculations, convection-permitting model simulations, and chemistry model output. Air parcel trajectories are computed using the TRAJ3D trajectory model. Two types of trajectory products are created: flight trajectories and overshoot trajectories. Flight trajectories will be initialized every second along each Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) flight track and run backwards for up to 10 days. Overshoot trajectories will be initialized in overshoot volumes identified from both GridRad radar and GOES satellite data every 10 minutes and run forward for up to 5 days. Convection allowing model simulations are carried out using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). These will aid in the evaluation of aircraft observations and evaluate the ability of numerical models to represent overshooting convection and transport. Photodissociation frequencies (J values) will also be computed using a radiative transfer model of the UV and Visible (UV/Vis) spectral regions. Data collection for this product is ongoing and currently only features the first deployment.Each summer the North American Monsoon Anticyclone (NAMA) dominates the circulation of the North-Western Hemisphere and acts to partially confine and isolate air from the surrounding atmosphere. Strong convective storms in the NAMA regularly reach altitudes deep into the lower stratosphere, with some ascending above 20 km. These storms carry water and pollutants from the troposphere into the otherwise very dry stratosphere, where they can have a significant impact on radiative and chemical processes, potentially including destruction of stratospheric ozone. The DCOTSS field campaign is a NASA Earth Venture Suborbital research project aimed at investigating these thunderstorms. DCOTSS utilizes NASA’s ER-2 aircraft and conducted two ~8-week science deployments based out of Salina, KS spanning early to late summer.
GPP model output from PHACE experiment
<p>Gross Primary Production model output data from teh PHACE experiment.</p>
Model outputs and scripts to reproduce figures of a manuscript in revision in Paleoceanography & Paleoclimatology
<p>The model outputs are in Model/outputs and the scripts to make figures are in Model/scripts.</p>
Model Output from Coupled LPJ-GUESS & Madingley Model System
Open the record for dataset details and reuse information.
interactive and prescribed snow depth driven climate model simulations output
<p>compressed dataset of model simulation output for current and future climates</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.