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53 results for “Biogeochemical Modeling”
Interview Transcripts on Ocean Modeling and Biogeochemical Modeling
<p>This data package contains interview transcripts of interviews that we conducted to understand the domain of ocean modeling with a focus on human interaction, processes and tooling to support our effort to create domain-specific languages to support the work of scientists in this domain.</p> <p>This data package contains the anonymized transcripts of interviews from two phases. The <strong>first phase</strong> was dedicated to collect any theme regarding the domain including processes to develop, maintain and use scientific models (in<br> particular in ocean sciences), as well as, human interaction, technical aspects, and other work environment related themes.</p> <p>The <strong>second phase</strong> focus on the development of biogeochemical models. However, we also included questions regarding the domain.</p>
Observational datasets for validation of Mediterranean Biogeochemical Copernicus Modelling System, period 2018-2020
<p>Datasets used for the validation of the biogeochemical component of the Mediterranean Analysis and Forecast center of the EU Copernicus Marine Service for the period 2018-2020.</p> <p>The list of datasets includes:</p> <p>1) the Delay Mode Satellite chlorophyll from https://data.marine.copernicus.eu/product/OCEANCOLOUR_MED_BGC_L3_NRT_009_141/description after interpolation to the 1/24° horizontal resolution, weekly averages and quality check with internal climatology</p> <p>2) the BGC-Argo float profiles of nitrate, chlorophyll and oxygen from Coriolis DAC (ftp://ftp.ifremer.fr/ifremer/argo; https://doi.org/10.17882/42182#76230) after an internal quality check procedure which is described in Salon et al., 2019. </p> <p>3) the climatological profiles for 16 subbasins of nitrate, phosphate, silicate, oxygen, DIC, alkalinity, pCO2 and pH computed from the Emodnet 2018 data collection and additional scientific datasets as described in Salon et al., 2019.</p> <p> </p> <p>Ref.: Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C. and Crise, A., 2019. Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. <em>Ocean Science</em>, <em>15</em>(4), pp.997-1022.</p> <p> </p>
Biogeochemical model projections years 1982-2007 in North East Atlantic
<p>Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal</p>
Fig. 3 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models
Fig. 3. The six stages of selective grazing, redrawn from Montagnes et al. (2008b).
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>
Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling
<p>Data used for the manuscript "Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling"</p>
Environmental (biogeochemical) conditions on the NW African coast from 3D reanalysis models
<p>This dataset contain hydrodynamic and biogeochemical variables extracted from the CMEMS service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>) covering the NW region of the African coast and the period 1993 to 2019. The environmental dataset include: nitrate concentration, phosphate concentration and chlorophyll-a concentration.</p>
Supplementary materials for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model"
<h2>Overview</h2> <p>This folder contains supplementary materials corresponding to the analysis conducted for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model". The folder is structured into two .zip files. <a href="../api/records/10720907/draft/files/ZENODO_PISCES_MLC.zip/content" target="_blank" rel="noopener noreferrer">ZENODO_PISCES_MLC.zip</a> contains the analysis presented in the paper. BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <h2>ZENODO_PISCES_MLC Folder Structure</h2> <h3>BDM</h3> <ul> <li><strong>MAREDAT_TUNED_SDM.csv</strong>: This file contains the BDM mesozooplankton biomass monthly climatology from MAREDAT data.</li> </ul> <h3>CODE</h3> <p>This directory contains Jupyter Notebook files (<code>.ipynb</code>) and related Python scripts used for data analysis and visualization. Below is a list of the files:</p> <ul> <li><strong>Code_Fig3_FigA8_FigA17.ipynb</strong>: Jupyter Notebook for generating figures 3, A8, and A17.</li> <li><strong>Code_Fig4.ipynb</strong>: Jupyter Notebook for generating figure 4.</li> <li><strong>Code_Fig5_FigA12_FigA13.ipynb</strong>: Jupyter Notebook for generating figures 5, A12, and A13.</li> <li><strong>Code_Fig6.ipynb</strong>: Jupyter Notebook for generating figure 6.</li> <li><strong>Code_Fig7.ipynb</strong>: Jupyter Notebook for generating figure 7.</li> <li><strong>Code_FigA10.ipynb</strong>: Jupyter Notebook for generating figure A10.</li> <li><strong>Code_FigA11.ipynb</strong>: Jupyter Notebook for generating figure A11.</li> <li><strong>Code_FigA14.ipynb</strong>: Jupyter Notebook for generating figure A14.</li> <li><strong>Code_FigA15.ipynb</strong>: Jupyter Notebook for generating figure A15.</li> <li><strong>Code_FigA16.ipynb</strong>: Jupyter Notebook for generating figure A16.</li> <li><strong>Code_FigA1.ipynb</strong>: Jupyter Notebook for generating figure A1.</li> <li><strong>Code_FigA2.ipynb</strong>: Jupyter Notebook for generating figure A2.</li> <li><strong>Code_FigA6_FigA7.ipynb</strong>: Jupyter Notebook for generating figures A6 and A7.</li> <li><strong>Code_FigA9.ipynb</strong>: Jupyter Notebook for generating figure A9.</li> <li><strong>Code_POC_metrics_not_in_the_paper.ipynb</strong>: Jupyter Notebook containing metrics related to particulate organic carbon (POC) not included in the paper.</li> <li><strong>Code_Table3.ipynb</strong>: Jupyter Notebook for generating table 3.</li> <li><strong>Code_Table4.ipynb</strong>: Jupyter Notebook for generating table 4.</li> <li><strong>Code_Table5.ipynb</strong>: Jupyter Notebook for generating table 5.</li> <li><strong>GlobalEstimatesAbstract.ipynb</strong>: Jupyter Notebook containing global estimates abstract.</li> <li><strong>mlctools</strong>: Python package containing utility functions for the analysis.</li> </ul> <h3>OBS</h3> <p>This directory contains observed data used in the analysis:</p> <ul> <li><strong>BATS_zooplankton.csv</strong>: Zooplankton data from the Bermuda Atlantic Time-series Study (BATS).</li> <li><strong>CHL2.nc</strong>: Chlorophyll data in NetCDF format.</li> <li><strong>climatology_n_0_5.nc</strong>: Climatological data in NetCDF format.</li> <li><strong>HOTS_zooplankton.csv</strong>: Zooplankton data from the Hawaii Ocean Time-series (HOTS).</li> </ul> <h3>OUTPUT</h3> <p>This directory contains output files from PISCES simulations (yearly, monthly and 5-day-average outputs). </p> <ul> <li><strong>0class</strong>: Output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>0classregrid</strong>: Regridded output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>10classes</strong>: Output files for the '10classes' classification corresponding to PISCES-MOG.</li> <li><strong>10classesregrid</strong>: Regridded output files from PISCES-MOG.</li> <li><strong>2classes</strong>: Output files from PISCES-MOG-2LS.</li> <li><strong>2classesregrid</strong>: Regridded output files from PISCES-MOG-2LS.</li> <li><strong>NOALLOregrid</strong>: Regridded output files from PISCES-MOG-NA.</li> </ul> <h3>PLOT</h3> <p>This directory contains plots generated during the analysis:</p> <h3>TEMP</h3> <p>This directory contains temporary files used during the analysis, including data files and matrices.</p> <h2>BDM-MAREDAT-ZENODO Folder </h2> <p>BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <p>For any inquiries or data access requests, please contact corentin.clerc -at- usys.ethz.ch</p>
The role of external inputs and internal cycling in shaping the global ocean cobalt distribution: insights from the first cobalt biogeochemical model
<p>Model output for cobalt biogeochemistry model on ORCA2 grid.</p>
Seasonal and Interannual Variability of Areal Extent of the Gulf Hypoxia from a Coupled Physical-Biogeochemical Model: A New Implication for Management Practice
<p>netcdf data and code for JGR manuscript: seasonal and interannual variability of areal extent of the Gulf Hypoxia from a coupled physical-biogeochemical model: A new implication for management practice</p>
ECCO-Darwin ED-SBS model and running instructions for "Biogeochemical river runoff drives intense coastal Arctic Ocean outgassing"
<p>Here is the model code and instructions file to run the ED-SBS simulations of "Biogeochemical river runoff drives intense Arctic Ocean outgassing" article.</p> <p>For more informations follow the instructions in the README.md file.</p>
Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"
<p>This dataset contains the numerical model output files that were used in the analysis presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)", submitted to Geoscientific Model Development.</p>
Data used in the physical-biogeochemical model of Danjiangkou Reservoir
<p>The date set includes meteorological, hydrological, water quality, and organic carbon loading data obtained in 2009 for the Danjiangkou Reservoir in China. The data were used to set the boundary conditions of the physical-biogeochemical model, which was adopted to simulate reservoir methane dynamics in the Danjiangkou Reservoir.</p>
Data for "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations"
<p>Data, figure generation scripts, and zero-dimensional version of the 17 species Biogeochemical Flux Model (BFM17) for the paper "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations" submitted to Geoscientific Model Development. </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>
A computationally efficient method for parameter sensitivity analysis of microbially-explicit biogeochemical models accounting for long-term behavior
<p>The dataset is for the manuscript entitled "A computationally efficient method for parameter sensitivity analysis of microbially-explicit biogeochemical models accounting for long-term behavior".</p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
Model input for initial submission of "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to GMD
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in the initual submission "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to Geoscientific Model Development.</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information:https://doi.org/10.48670/moi-00021. Refer to the manuscript for references to other data sources.</p> <p>Codes for generating regional MOM6 initial conditions, boundary conditions and other necessary model inputs as well as diagnostic scripts are maintained on the NOAA CEFI GitHub Repository: https://github.com/NOAA-GFDL/CEFI-regional-MOM6/. </p>
Implementing Riverine Biogeochemical Inputs in ECCO-Darwin: A Sensitivity Analysis of Terrestrial Fluxes in a Data-Assimilative Global Ocean Biogeochemistry Model
<p>Resolving riverine biogeochemical inputs in ocean biogeochemistry models is pivotal for capturing the spatiotemporal variability of nutrients and carbon in coastal regions and in the global ocean. ECCO-Darwin is a pioneering data-assimilative global-ocean biogeochemistry model, which, to date, has focused on the pelagic zone. In this work, we use an optimized version of ECCO-Darwin to perform a sensitivity analysis to quantify the response of the open ocean and coastal margins to lateral inputs of carbon and nutrients. We generate riverine inputs by combining point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral inputs from 5171 watersheds worldwide. While adding carbon and nutrients along with freshwater improves biogeochemical skill in river plume regions and coastal waters, the open-ocean response may be overestimated due to an excess of carbon and nutrients advected offshore. This highlights the need for a more nuanced representation of land-to-ocean and nearshore processes for quantifying how global ocean primary production and carbon cycling respond to land-to-ocean inputs.</p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)".</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information: \url{https://doi.org/10.48670/moi-00021}, \url{https://doi.org/10.48670/moi-00148}. Refer to the manuscript for references to other data sources.</p>
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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.