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”
[Model outputs] Identifying major hydrologic change drivers in a highly managed transboundary endorheic basin: integrating hydro‐ecological models and time‐series data mining techniques
Open the record for dataset details and reuse information.
Photochemical model output data associated with: How to identify exoplanet surfaces using atmospheric trace species in hydrogen-dominated atmospheres
Open the record for dataset details and reuse information.
Model code and output of CSIBv4 pan-Arctic sea ice-ocean DMS simulation
<p>here i deposit the model source code and output of the manuscript "Spatio-temporal variability in modelled bottom-ice and sea-surface dimethylsulfide concentrations and fluxes in the Arctic during 1979-2015" by Hayashida et al. (2020).</p> <p><strong>See "readme.txt" for information.</strong></p> <p>For question, contact Hakase Hayashida.</p>
Model output in the LS and SCS
<p>The model data or the samples used to produce the figures in the the manuscript "<em>An Examination of Circulation Characteristics in the Luzon Strait and the South China Sea using High-resolution Regional </em>Atmosphere-Ocean<em>Coupled Models</em>" submitted to <em>JGR-Oceans.</em></p>
Data and model output for figures in "Variable particle size distributions reduce the sensitivity of global export flux to climate change"
<p><strong>Associated publication</strong></p> <p>This dataset was used to generate analyses and figures in the following publication:</p> <p>Leung, S., Weber, T., Cram, J. A., & Deutsch, C. Variable particle size distributions reduce the sensitivity of global export flux to climate change. <em>Submitted to Biogeosciences.</em></p> <p><strong>Associated code</strong></p> <p>After downloading this dataset, run the associated MATLAB code at the following link to generate the figures and analyses in the above publication:</p> <p>https://doi.org/10.5281/zenodo.4117382</p>
Model input and output, performance measures and modifications in the source code for PALM simulations on Mäkelänkatu in Helsinki, Finland
<p>This dataset is for air quality simulations conducted on Mäkelänkatu in Helsinki, Finland, using the PALM model system 6.0.</p> <p>By default, simulations use modelled data as boundary conditions. This includes modelled meteorological data from MEPS (MetCoOp Ensemble Prediction System) and air pollutant background concentrations from the ADCHEM model. Alternatively, measured meteorology from the Kivenlahti mast in Espoo, Finland, and aerosol size distribution from the SMEAR III station in Kumpula, Helsinki, is applied.</p> <p>Simulations:</p> <ul> <li>9 June morning: <ul> <li>0609_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>0609_morning_smear: use modelled boundary conditions for meteorology and measured for aerosol size distribution</li> <li>0609_morning_wd_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at Kivenlahti. For aerosol size distribution, use the measured boundary conditions.</li> <li>0609_morning_wdk_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at SMEAR III. For aerosol size distribution, use the measured boundary conditions.</li> <li>precursor_0609_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_morning_allmet: precursor with measured boundary conditions for meteorology</li> <li>precursor_0609_morning_wd: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at Kivenlahti.</li> <li>precursor_0609_morning_wdk: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at SMEAR III.</li> </ul> </li> <li>9 June evening: <ul> <li>0609_evening: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_evening_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>precursor_0609_evening: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_evening_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> <li>12 December morning: <ul> <li>1207_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>1207_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution </li> <li>precursor_1207_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_1207_morning_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> </ul> <p> </p> <p>Datasets are given separately for the root (no suffix), parent (suffix _N02) and child (_N03) domain. The content is following:</p> <ul> <li>input_monitoring_output_usercode <ul> <li>Input data <ul> <li><run_identifier>_chemistry: emission data for gases</li> <li><run_identifier>_dynamic: initialisation and forcing data for meteorological variables and air pollutants</li> <li><run_identifier>_p3d: parameter file for model steering</li> <li><run_identifier>_salsa: emission data for aerosol particles</li> <li><run_identifier>_static: topography information</li> </ul> </li> <li>Simulation performance information <ul> <li><run_identifier>_cpu: information on the CPU time consumed</li> <li><run_identifier>_header: information about the selected model parameters</li> <li><run_identifier>_rc: time step control output</li> </ul> </li> <li>Output data <ul> <li><run_identifier>_av_masked_N03_M01.nc: temporally averaged wind speed data close to the ground</li> <li><run_identifier>_av_masked_N03_M04.nc: temporally averaged aerosol particle concentration data close to the ground</li> <li><run_identifier>_av_masked_N03_M06.nc: temporally averaged aerosol particle concentration data in a vertical column next to the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_av_masked_N03_M07.nc: temporally averaged aerosol particle concentration data in a vertical column on the other side of the street from the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_pr.nc: temporally vertical profile data on meteorological variables</li> <li><run_identifier>_ts.nc: flow statistics data</li> </ul> </li> <li>Modifications made to the source code (PALM revision, https://palm.muk.uni-hannover.de/trac/browser?rev=4416, last access: 10 Sept 2019) <ul> <li>chem_gasphase_mod.f90: chemical mechanism salsa+simple</li> <li>chem_emissions_mod.f90 (modifications indicated with "MONA")</li> <li>user_module.f90 (modification listed under "Current revisions")</li> <li>Makefile (modification listed under "Current revisions")</li> </ul> </li> </ul> </li> </ul> <p>See the PALM model webpage (https://palm.muk.uni-hannover.de) for details.</p> <p> </p>
Output from the PISCES Cobalt biogeochemical ocean model
<p>Output from the PISCES biogeochemical model used in Hawco et al. 2020 PNAS. This model was originally developed and published in Tagliabue A, et al. (2018) [The Role of External Inputs and Internal Cycling in Shaping the Global Ocean Cobalt Distribution: Insights From the First Cobalt Biogeochemical Model. Global Biogeochem Cycles 32(4):594–616.] but with the base model modified as described by Aumont O, et al. (2017) [Variable reactivity of particulate organic matter in a global ocean biogeochemical model. Biogeosciences 14(9):2321–2341.]. All fields are annual averages.</p> <p>For more information see www.pnas.org/cgi/doi/10.1073/pnas.2001393117</p>
Lagrangian Atmospheric Model output for the Control, Onset and Development experiments
<p><span><span><span><span><span><span><span><span><span><span><span>This dataset consists of model outputs from the Control, Onset and Development experiments. Each experiment has six ensembles which were integrated for three years. 6-hourly instantaneous surface (1000hPa) zonal and meridional wind speed (m/s) between 31°S-31°N is recorded in uv1000hPa_{Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. 6-hourly accumulated precipitation (mm) between 32°S-32°N is stored in precip_{ Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. Note the precip.*dat records accumulated rainfall for the past 6 hours. The missing data is recorded as nan.</span></span></span></span></span></span></span></span></span></span></span></p>
Model output data for "Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies"
<p>Empirical studies have documented both positive and negative density-dependent dispersal, yet most theoretical models predict positive density dependence as a mechanism to avoid competition. Several hypotheses have been proposed to explain the occurrence of negative density-dependent dispersal, but few of these have been formally modeled. Here, we developed an individual based model of the evolution of density-dependent dispersal. This model is novel in that it considers the effects of density on dispersal directly, and indirectly through effects on individual condition. Body condition is determined mechanistically, by having juveniles compete for resources in their natal patch. We found that the evolved dispersal strategy was a steep, increasing function of both density and condition. Interestingly, although populations evolved a positive density-dependent dispersal strategy, the simulated metapopulations exhibited negative density-dependent dispersal. This occurred because of the negative relationship between density and body condition: high density sites produced low condition individuals that lacked the resources required for dispersal. Our model therefore generates the novel hypothesis that observed negative density-dependent dispersal can occur when high density limits the ability of organisms to disperse. We suggest that future studies consider how phenotype is linked to the environment when investigating the evolution of dispersal.</p>
CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
<p>This is the subset of CESM1.2 model simulation output that was used for analysis and visualization of Yu and Fedorov [2020] (DOI:10.1029/2020GL088381). Please refer to README for details.</p>
GHGF-Flux forward model outputs
<p>Outputs from a tagged tracer simulation using the Greenhouse Gas Framework – Flux (GHGF-Flux) forward model for the four regions of interest, including Northeast China, Southeast China, North Bangladesh, and North India.</p>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
Observations of AOD and GEOS-Chem simulation model output dataset
<p>Archive of observations of AOD (MODIS Deep Blue, MAIAC, AERONET) used to evaluate the simulations of using high resolution offline dust emission, upon submission of the following manuscript:</p> <p>Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted. </p> <p>This dataset can also be accessed via this FTP ftp://stetson.phys.dal.ca/jmeng/GMD_OfflineDust_data/ </p> <p>The folder "GMD_OfflineDust_data" contains three folders, "AERONET", "MODIS" and "GC_output", representing AERONET AOD, MODIS AOD and model outputs respectively. </p>
Output Files from Reactive Transport Models (supplement to Solid Earth Manuscript 'se-2020-99')
<p>This repository includes the output files (in Tecplot format) of the numerical models in the paper entitled "<em><strong>Understanding controls on hydrothermal dolomitisation: insights from 3D Reactive Transport Modelling of geothermal convection</strong></em>".</p> <p>A key to the output data provided here is summarised in Data_Benjakul_et_al_2020.xlsx.</p> <p> </p> <p>We used <strong>TOUGHREACT</strong>, a numerical simulation program developed by the Lawrence Berkeley National Laboratory (LBNL) which is available at: <a href="https://tough.lbl.gov/licensing-download/toughreact-licensing-download/">https://tough.lbl.gov/licensing-download/toughreact-licensing-download/</a> to numerically model the convection of seawater in a single fault within carbonate-hosted system. The parameters used in the numerical models are given in the paper.</p> <p><a href="https://doi.org/10.5194/se-11-2439-2020">https://doi.org/10.5194/se-11-2439-2020</a></p> <p>The output files from TOUGHREACT (in Tecplot format) are visualised using <strong>trexplot</strong>, a python script developed by Hamish Robertson which can be found at: <a href="https://github.com/hammytheham/trexplot">https://github.com/hammytheham/trexplot</a> (last access: 5 December 2020).</p> <p> </p> <p>Please contact me via the following e-mail addresses in case you have any questions: <a href="mailto:rb17526@bristol.ac.uk">rb17526@bristol.ac.uk</a> or <a href="mailto:rbenjaku@gmail.com">rbenjaku@gmail.com</a></p>
Amory et al. (2021), Geoscientific Model Development : data, model outputs and source code
<p><strong>Data and model outputs for the replication of the analysis made in:</strong><br> (see the published version of this article in Geoscientific Model Development, 2021 - please cite this version if you use these data)<br> C. Amory, C. Kittel, L. Le Toumelin, C. Agosta, A. Delhasse, V. Favier, and X. Fettweis: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica, Geoscientific Model Development, accepted, 2021. </p> <p>See README.txt for a full description of the dataset content</p> <p>Please contact me at amory.charles@live.fr if you need other half-hourly outputs or for more details on the dataset</p>
Model outputs in the northern shelf of the South China Sea
<p>The data used in the figure of manuscript are available in Figure.rar file. The 201706XX_Chla_budget_2.mat files are the model outputs of the chlorophyll budget terms after process. </p>
SIASAR conceptual model v1 outputs obtained from Nicaragua and Honduras databases (last accessed: December 21, 2016)
<p>Provided results were obtained by applying SIASAR composite indicators v1, as described in http://hdl.handle.net/2117/77587</p> <p>All “NA” values were depurated from preliminary results</p> <p>Final results are presented for Nicaragua and Honduras independently</p> <p>Required input data is available at http://doi.org/10.5281/zenodo.571351</p>
Enhanced seismicity catalog and output files of geomechanical modeling in the changning shale gas field
Open the record for dataset details and reuse information.
LMDZ6iso output - Antarctic water stable isotopes in the global atmospheric model LMDZ6: from climatology to boundary layer processes
<h3><strong>Update of the Simulation</strong></h3> <p dir="ltr">This archive contains the output of a revised simulation, addressing issues identified in the original simulation described in the following article:</p> <p><em>Dutrievoz, N., Agosta, C., Risi, C., Vignon, É., Nguyen, S., Landais, A., ... & Prié, F. (2025). Antarctic water stable isotopes in the global atmospheric model LMDZ6: From climatology to boundary layer processes. Journal of Geophysical Research: Atmospheres, 130(5), e2024JD042073.</em></p> <p>All information is available here:<strong> <a href="https://docs.google.com/document/d/1x4FSGtNiJGqsMoks38yATzTQJ7T8sO8iXqeWagTU5yE/edit?usp=sharing">README</a></strong></p> <p>Researchers and colleagues are welcome to contact me at <strong>niels.dutrievoz@lsce.ipsl</strong>.<strong>fr</strong> for any further information regarding this updated simulation or potential collaborations. Please feel free to reach out if you are interested in daily or hourly output data from this simulation.</p> <p dir="ltr">I look forward to any exchanges that might arise from this work.</p> <p> </p>
Model output for PI, LGM, IS_albedo, IS_topo, LSC, ORB, GHG experiments
<p>Monthly mean outputs for LGM, PI, IS_topo, IS_albedo, GHG and ORB experiments.</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.