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.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “FVCOM”
Great Lakes WRF-FVCOM model ensemble outputs: Summer 2018 daily LST and T2m
<p>Postprocessed model data for the paper: "Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model"</p> <p>Perturbed Physics Ensemble outputs from a coupled lake-atmosphere-land Great Lakes regional model: <br>- Time period: May, June, July of 2018 <br>- Computational domain: Great Lakes region as contained within <a href="../api/records/10806629/draft/files/wrf_grid.nc/content" target="_blank" rel="noopener noreferrer">wrf_grid.nc</a> (atmosphere-land) and <a href="../api/records/10806629/draft/files/fvcom_grid.nc/content" target="_blank" rel="noopener noreferrer">fvcom_grid.nc</a> (lake).<br>- Quantities of interest: lake surface temperature and 2-m near-surface air temperature<br>- Training set: "<a href="../api/records/10806629/draft/files/wfv_global_daily_temperature_training_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_training_set.pkl</a>" [18 members]. Associated with "<a href="../api/records/10806629/draft/files/perturbation_matrix_9variables_korobov18.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_korobov18.nc</a>" input model configuration matrix.<br>- Test set: "<a href="https://zenodo.org/api/records/13863491/draft/files/wfv_global_daily_temperature_test_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_test_set.pkl</a>" [9 members]. Associated with "<a href="https://zenodo.org/api/records/13863491/draft/files/perturbation_matrix_9variables_latin_hypercube9.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_latin_hypercube9.nc</a>" input model configuration matrix.</p>
Plymouth sound and surroundings surface temperature and salinity from FVCOM model
<p>Output of surface temperature and salinity from a high-resolution model of the Plymouth sound and surrounding areas, monthly files covering from May - October 2016. The model setup is as for the operational model (https://www.plymouthmarineforecasts.org/) except with observed river flows and boundary forcing was bias corrected against observations from the L4 buoy timeseries (https://www.westernchannelobservatory.org.uk/). This dataset was used in calculations of coastal CO2 flux in:</p> <p>bg-2021-166<br> Title: Tidal mixing of estuarine and coastal waters in the Western English Channel is a control on spatial and temporal variability in seawater CO2<br> Author(s): Richard Peter Sims et al.</p> <p>The code and model mesh data required to create plots therein are also included in plot_data.zip</p>
FVCOM_Petermann_Discharge_Input_Files
<h2>Overview</h2> <p>This repository contains all the input files that are required to run the subglacial discharge experiments for the Petermann Ice Shelf-fjord system using the Finite Volume Community Ocean Model (FVCOM). You can access the corresponding peer-reviewed, open-access research article here: <a href="https://doi.org/10.1038/s41467-025-59469-9" target="_blank" rel="noopener">Enhanced subglacial discharge amplifies Petermann Ice Shelf melting when ocean thermal forcing saturates</a></p> <h2>Pre-requisites</h2> <p>The open source code Finite Volume Community Ocean Model version 4.0 (FVCOM v4.0) augmented by an ice shelf (Zhou and Hattermann, 2020) and sea ice module (Prakash et al., 2022) is required to conduct the numerical experiments, and is made publicly available at <a href="https://doi.org/10.5281/zenodo.15084570" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.15084570</a>. A detailed description of our high-resolution (200 m) unstructured grid 3-D regional model setup centered on the Petermann ice shelf and fjord is available from Prakash et al. (2022) and Prakash et al. (2023).</p> <h2>File description</h2> <p>The model input files can be classified under 2 broad categories, namely </p> <ul> <li>static, and </li> <li>time-varying.</li> </ul> <p>Static : These include the following files</p> <ol> <li><em>zisf.dat</em> (realistic Petermann ice shelf draft derived from BedMachine v3 (Morlighem et al., 2017)), </li> <li><em>dep.dat</em> (smoothed bathymetry, which has been modified to provide an improved (with respect to BedMachine v3 (Morlighem et al., 2017)) representation of the sub-ice shelf topography), </li> <li><em>cor.dat</em> (Coriolis),</li> <li><em>sigma.dat </em>(terrain-following vertical coordinate system),</li> <li><em>grd.dat</em> (FVCOM Petermann Fjord regional model grid)</li> </ol> <p>Time-varying: These include the following files</p> <p>(a) Atmospheric Forcing</p> <ol> <li><em>ocnice_YYYY_wnd.nc</em> (wind forcing), </li> <li><em>ocnice_YYYY_fwf.nc</em> (freshwater flux), and</li> <li><em>ocnice_YYYY_hf.nc</em> (heat flux), </li> </ol> <p>where YYYY represents the forcing year, e.g. 2014.</p> <p>(b) Sea ice forcing</p> <ol> <li><em>ocnice_YYYY_icenudge.nc</em> (includes sea ice concentration and thickness, bulk ice salinity, and sea ice velocities),</li> </ol> <p>where YYYY represents the forcing year, e.g. 2014.</p> <p>(c) Ocean boundary conditions</p> <ol> <li><em>pf_l_bcorr_climaYY.nc</em> (bias-corrected lateral ocean boundary conditions which includes temperature, salinity, ocean velocities, and sea surface elevation)</li> </ol> <p>where YY represents the forcing year, e.g. 14 (for 2014).</p> <p>In addition to these two broad categories, a restart file (<em>*_restart_*.nc</em>) is included in each run directory, which is used to provide "realistic" (a.k.a. "hotstart") initial ocean conditions. The restart file in the directory <em>control_run_2014</em> includes a "realistic" initial (July 01, 2014 00:00:00 UTC) ocean condition derived from a 4-km pan-Arctic (A4) ROMS model (Hattermann et al., 2016). Thereafter, the FVCOM model solution on January 01, 2015 00:00:00 UTC is used to "hotstart" the 2015 calendar year. Lastly, the stable FVCOM model solution from January 01, 2016 00:00:00 UTC is used to "hotstart" the 2016 calendar year, as well as the discharge experiments. Note that the discharge experiments depart from the control experiment (<em>control_run_2016</em>) by introducing subglacial discharge of varying magnitudes. All other conditions are kept the same, in order to isolate and investigate the impact of subglacial discharge on the basal melting of the Petermann ice shelf. The namelist file (*.nml) for each run is also included in the corresponding directory.</p> <p>To minimize storage space, each run directory is populated with only the input files that are unique to that run. For e.g., <em>control_run_2015/2016</em> and the <em>discharge_experiments</em> do not include the static files from <em>control_run_2014</em>, however, they are required to conduct each of these runs. Likewise, <em>discharge_experiments</em> do not include the time-varying forcing files, since they are identical to the ones contained in <em>control_run_2016</em>. The <em>discharge_experiments</em> do however include the discharge forcings for the present, RCP 8.5, and median(present,RCP 8.5) scenarios. These are the riverdata (riverdata.nc (present), riverdata_expC.nc (median), and riverdata_expD.nc (RCP 8.5)) and the corresponding rivernamelist (riverNamelist.nml, riverNamelist_expC.nml, riverNamelist_expD.nml) files. Note that a rivernamelist is different from a namelist file, in that it only contains information about the subglacial discharge.</p> <p>Development of the smoothed model topography datasets for our unstructured grid (horizontal resolutions of 200 m, 2km, and 4km) regional model domain, including localized improvements made within the Petermann Fjord; the ocean nesting and surface forcing methodologies (models used, downscaling procedure etc.); bias-correction, among other technical details, are detailed in Prakash et al. (2022) and Prakash et al. (2023).</p>
3D simulation salinity intrusion of Vembanad Lake using FVCOM
<p>This Animation was created for the purpose of presenting an oral presentation at CMLRE, Kochi (OSICON 19, 12th December 2019).</p> <p>The paper entitled - "An Integrated Hydrological and Hydrodynamic Model for the Vembanad Lake, South West Coast of India" </p>
The ECS-FVCOM results and also the input variables of FMGDM, the initial particles, satellite pictures, and trajectories data
<p>The data for the manuscript: A Lagrangian-based Floating Macro<br> -algal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tides. <br> -----------------------------------------------------------------------------<br> Updata: 2021/03/18</p> <p>-----------------------------------------------------------------------------<br> Part 1: The ECS-FVCOM results and also the input variables of FMGDM<br> Part 2: The initial particles position imformation (lat, lon, depth)<br> Part 3: The satellite pictures of green tides in YS, 2014 and 2015</p> <p>Part 4: Drifters trajectories dataset (lat, lon)</p> <p> </p> <p>=============================================</p> <p>Version 5 updata: 2021/12/20</p> <p>Note: Modified and added some missing variables ('omega') in Part1,</p> <p> the results of ECS-FVCOM</p>
observed dataset and FVCOM-ERSEM modeling result of environmental factors and Chl-a concentration in CRE
<p>This data set is mainly used in the article "Formation and Breakdown of an Offshore Summer Cold-Water Zone and Its Ecological Effect on Phytoplankton". "2013-2018 data" was the dataset of obversed data in the Changjiang River Esturary (CRE) during the summer of 2013-2018. And the "cje1v_avg_2015.zip" was the result of three-dimensional physical-biogeochemical coupled model(FVCOM-ERSEM).</p>
DE BAY IRENE COMPOUND FLOODING USING DHSVM-FVCOM
<p>Data sets used in DHSVM and FVCOM model development and validation, and result analysis.</p>
Pearl River Delta FVCOM model mangrove forests scenarios
<p>Model data presented in: De Dominicis, M., Wolf, J., van Hespen, R., Zheng, P., Hu. Z. "Mangrove forests can be an effective coastal defence in the Pearl River Delta, China", <em>Communications Earth & Environment</em> (2023).</p> <p>To explore the effects of vegetation on storm surge dynamics and currents, we used a Finite Volume Community Ocean Model implementation for the South China Sea and the Pearl River Delta and simulated Typhoon Hato (2017) one of the strongest typhoons to affect the coastal areas of the Pearl River Delta in recent decades. We numerically modelled the protection capability of the mangrove wetlands in Shenzhen Bay and in the upper estuary river branches close to Guangzhou to determine their ability to mitigate coastal flooding. Additionally, we analyzed how the effectiveness of mangroves changes under different sea level rise scenarios. </p> <p>The dataset consists of water elevation and horizontal currents for 40 model experiments (see Table 1 in De Dominicis et al, 2023).</p>
Pearl River Delta FVCOM model sea level rise scenarios
<p>Model data presented in: De Dominicis, M., Wolf, J., Jevrejeva, S., Zheng, P., Hu. Z. "Future interactions between sea level rise, tides and storm surges in the world's largest urban area", Geophysical Research Letters (2020).</p> <p>An FVCOM (Finite Volume Community Ocean Model) implementation for the South China Sea and the Pearl River Delta is used to understand how future sea level rise, tides and typhoon storm surges can interact and affect coastal inundation. Four future sea level rise scenarios have been analyzed: 0.3 m, 0.5 m, 0.9 m and 2.1 m. Two typhoons that impacted the PRD have been modelled: Hato (2017) and Mangkhut (2018).</p> <p>The dataset consists of water elevation data for 40 model experiments:</p> <ul> <li>EXP01-10 allow to study tide and sea level rise interactions. The Pearl River Delta model has been run for 1 month forced only by tides and sea level rise at the model boundary.</li> <li>EXP11-15 (for Hato) and EXP26-30 (for Mangkhut) allow the study of tide, surge and sea level rise interactions; in these experiments the model has been fully forced by atmospheric forcing, tides and sea level rise.</li> <li>EXP16-20 (for Hato) and EXP31-35 (for Mangkhut) allow the study of tide and sea level rise interactions and are needed for the calculation of the surge, the model has been forced by tides and sea level rise only at the model boundary. The surge can be calculated as the difference between the fully forced run (EXP11-15 and EXP26-30) and the corresponding tide-only forced run (EXP16-20 and EXP31-35) for a given sea level rise.</li> <li>EXP21-25 (for Hato) and EXP35-40 (for Mangkhut) allow the study of the surge and sea level rise interactions only (no tides); for these experiments the model has been forced by atmospheric forcing and sea level rise at the model boundary.</li> </ul>
Example simulation showing spatial and temporal variations in surface carbon biomass of plankton functional groups during a Spring bloom as shown by a 3D hydrodynamic-biogeochemical model (FVCOM-ERSEM), with and without integration of the mixoplankton paradigm.
<p>The outputs are from simulations from using the FVCOM hydrodynamic model coupled to two different versions of ERSEM – (i) ERSEM and (ii) ERSEM-PB (the latter includes the implementation of the mixoplankton paradigm through integration of the 'Perfect Beast' PB model; Flynn and Mitra 2009 <em>Journal of Plankton Research</em>).</p> <p>The FVCOM domain was configured to represent Lyme Bay: a protected bay on the South Coast of England. This region is an important area for shellfish aquaculture. The domain was configured at 350 m – 5 km high-resolution, resolving sub-km scale dynamics in the area. A nested modelling approach of increasing model resolution was set up using two model domains. For the coupled hydrodynamic-biogeochemical model, a parent domain of 1.5 km – 10 km resolution was used to drive Lyme Bay model domain. The atmospheric forcing was provided by a 3-step downscaling of GFS global datasets to reach the 3 km of the final model domain using the Weather Research Forecast (WRF) model. Hydrodynamic boundary conditions are extracted from the European Copernicus Marine System North West European Shelf Forecast system. River flows were extracted from a National scale hydrology model run by the Center for Hydrology and Ecology in the UK. Simulations were initialised at Jan 1<sup>st</sup> 2005, and spun up for 3 months prior to the output of the data visualised in these videos. </p> <p>The 6 videos portray spatial and temporal variation of daily averaged surface carbon biomass (μgC L<sup>-1</sup>) during the month of April 2005 for the different plankton functional types (FTs) as follows:</p> <ul> <li>Video 1: all phytoplankton FTs in standard ERSEM grouped together. These thus include diatoms, nano-, pico- and micro- plankton; i.e., these simulations do not discriminate between phytoplankton and constitutive mixoplankton (CM).</li> <li>Video 2: phytoplankton FT in ERSEM-PB now considering only diatoms and picoplankton (i.e., cyanobacteria) only; CM are now included in Video 3 outputs.</li> <li>Video 3: all mixoplankton FTs grouped together in ERSEM-PB. These outputs thus include biomasses of micro-CM, nano-CM and NCM.</li> <li>Video 4: all zooplankton FTs grouped together in standard ERSEM. Thus, these include nanoflagellates, meso- and micro- zooplankton and thus includes the primary producing non-constitutive mixoplankton</li> <li>Video 5: zooplankton FT representing only the heterotrophic nano- and micro- zooplankton in ERSEM-PB.</li> <li>Video 6: spatio-temporal variability between the constitutive and non-constitutive mixoplankton functional groupings within FVCOM-ERSEM-PB. </li> </ul> <p>For further information about the mixoplankton paradigm, please see the following open access publications and references there in:</p> <p>Mitra A, Caron DA, Faure E, Flynn KJ, Leles SG, Hansen PJ, McManus GB, Not F, Gomes HR, Santoferrara L, Stoecker DK, Tillmann U (2023) <strong>The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</strong>. <em>Journal of Eukaryotic Microbiology</em>, e12972. <a href="https://doi.org/10.1111/jeu.12972">https://doi.org/10.1111/jeu.12972</a></p> <p>Glibert PM, Mitra A (2022) <strong>From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean</strong>. <em>Limnology and Oceanography</em> 67: 585-597 <a href="https://doi.org.10.1002/lno.12018">https://doi.org.10.1002/lno.12018</a> </p> <p>Mitra A, Irigoien X (2022) <strong>Mixoplankton – Marine Organisms that break the rules</strong>. EU Researcher. <a href="https://issuu.com/euresearcher/docs/mixitin_eur28_h_res">https://issuu.com/euresearcher/docs/mixitin_eur28_h_res</a> </p> <p>Flynn KJ, Mitra A, Anestis K, Anschütz AA, Calbet A, et al. (2019) <strong>Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now?</strong> <em>Journal of Plankton Research</em> 41: 375-391 <a href="https://doi.org/10.1093/plankt/fbz026">https://doi.org/10.1093/plankt/fbz026</a></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.