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129 results for “lagrangian”
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1990-2002 (2 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1970-1989 (1 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
A 3-km model configuration of the southern Benguela Current upwelling system: ROMS model data and Pyticles Lagrangian data
<p>This dataset contains model output data from the Regional Ocean Modelling System (ROMS) configuration of the southern Benguela upwelling system (SBUS) to study the interannual variability of Lagrangian transport in the SBUS. This is a 3-km model resolution that ran for 22 years from 1989-2011 period with the first 3 years considered as spin-up. The model outputs were archived at a daily frequency. The 3-km model was nested in a 7.5 km model resolution described by Ragoasha et.al., 2019.</p> <p>The model output data provided here is a monthly climatology (1995-2011) NetCDF file of the surface temperature, salinity, the velocity fields (<em>u,v & w</em>), and sea surface height (SSH). The file that contains the model grid is also provided.</p> <p>An eddy detection and tracking algorithm were also performed on the daily 3-km SSH model outputs to study mean eddy characteristics of the region for the 1992-2011 period. The file contains identifications of the Eddies detected and tracked in out model domain, their position (longitude and latitude), vorticity, amplitude, propagation and rotational speed.</p> <p> </p> <p>An example of a Pyticles (Gula et al., 2014; Ragoasha et.al., 2019) Lagrangian output subset for 3000 Lagrangian drifters tracked for 60 days. The drifters were released in the upper 100 m depth at an across-shore transect off Cape Point (34<sup>o</sup>S). A Matlab file is also provided for monthly (1992-2011) percentage of drifters that reach St Helena Bay (32<sup>o</sup>S) from Cape Point. </p> <p> </p> <p> </p> <p><strong>Dataset provided:</strong></p> <p>Monthly climatology file: “<em>roms_avg_Y1995M1-Y2011M12.nc”</em></p> <p>Model grid file: “<em>grid_roms_avg_r3km.nc”</em></p> <p>Eddy tracking file: “<em>TRA02_SEL01_DET02_eddies_r3km_1992M1_2011M12.nc”</em></p> <p>Pyticles Lagrangian experiment output example file: “<em>Pyticles_Y2010M10.nc”</em></p> <p>Monthly transport success Matlab file: <em>"R3km_monthly_transport_1992_2011.mat"</em></p> <p> </p> <p> </p> <p><strong>Citations:</strong></p> <p> </p> <p><strong>Ragoasha, N</strong>., Herbette, S., Cambon, G., Reason, C., Roy, C., 2019. Lagrangian pathways in the southern Benguela upwelling system. <em>Journal of Marine Systems</em>, 195: 50-66.</p> <p> </p> <p>Gula, J., Molemaker, M. J., & McWilliams, J. C., 2014. Submesoscale Cold Filaments in the Gulf Stream. <em>Journal of Physical Oceanography.,</em> 44 (10), 2617–2643. DOI: 10.1175/JPO-D-14-0029.1</p> <p> </p> <p><strong>Corresponding author:</strong></p> <p>M.N. Ragoasha, ORCID identifier: 0000-0002-1500-6259. Email: moagaboragoasha@gmail.com</p> <p> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors acknowledge the funding of N. Ragoasha’s PhD by the South-Africa’s National Research Foundation (NRF, South Africa) and the French Institute for Research and Sustainable Development (IRD, France). This work was also supported by the French National Program LEFE/INSU under the project’s name Benguela Upwelling Innershelf</p> <p>647 Circulation (BUIC). This work was granted access to the HPC resources of [TGCC/CINES/IDRIS] under the allocation 2017- [DARI n<sup>◦</sup>A0020107443] attributed by GENCI (Grand Equipement National de Calcul Intensif).</p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
Dataset for illustrative examples using the Lagrangian Atmospheric moisTure and heaT trackINg (LATTIN) tool
<p>This dataset provides the FLEXPART outputs for the illustrative examples of LATTIN usage. See the LATTIN GitHub repository (https://github.com/apalarcon/LATTIN) for details.</p><p>It was generated using FLEXPART model v9 fed by ERA-Interim reanalysis at the Environmental Physics Laboratory (EPhysLab) at the University of Vigo. See the list of publications of the EPhysLab research group for details on these simulations (https://ephyslab.uvigo.es/moisturetransport/index.php/Publications). </p>
Country-ocean-moisture-flows-reconciled-with-ERA5-reanalysis obtained processing Lagrangian moisture connections
<p>The dataset "Reconciled global atmospheric moisture flows between countries/oceans and subcontinents" presents tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance, and provides robust estimates of terrestrial moisture recycling and net moisture flows to support global water governance analysis.</p> <p>This repository is supplement to a study by De Petrillo & Fahrländer et al. (2025), which describes the development of the reconciliation framework, includes a perfromance analysis of the method and shows an exemplary case study on the published data. </p> <p>The atmospheric moisture flows are sourced from the UTrack atmospheric moisture flow dataset by Tuinenburg et al. (2020a) (dataset access: Tuinenburg et al., 2020b) and reconciled with ERA5 precipitation and evaporation data (Hersbach et al., 2020) on the mean annual basis in the period 2008-2017, by means of a post-processing framework, based on the Iterative Proportional Fitting (IPF) algorithm.</p> <p>NOTE: The final dataset is available in form of bilateral matrices (country/ocean and subcontinent/ocean) and in form of direct flows (flow edges). Supporting material to read the dataset is in the folder "List" . Processed ERA5 data (where the precipitation-evaporation annual balance is met) and input data to generate the figures are also available.</p> <p>References:</p> <p>De Petrillo, E., Fahrländer, S., Tuninetti, M., Andersen, L.S., Monaco, L., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric moisture flows to close the global freshwater cycle.<em> </em><em>Commun Earth Environ <strong>6</strong>, 347 (2025). </em><a href="https://doi.org/10.1038/s43247-025-02289-y">https://doi.org/10.1038/s43247-025-02289-y</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020a). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., Staal, A. (2020b): Global evaporation to precipitation flows obtained with Lagrangian atmospheric moisture tracking. PANGAEA, <a href="https://github.com/ObbeTuinenburg/UTrack_global_database">https://doi.pangaea.de/10.1594/PANGAEA.912710</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p> <p> </p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y1 & Y2
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 1996 (Y1) and 1997 (Y2).</p>
The Lagrangian particle trajectory output and the metadata of the Southern Ocean Biogeochemical Divide location for 'Localizing the Southern Ocean Biogeochemical Divide'
<p>1. Trajectory files<br> <br> The Lagrangian particle trajectory output files from virtual particle release experiments at the surface and 500m depth using Connectivity Modeling System (Paris et al. 2013, https://github.com/beatrixparis/connectivity-modeling-system) run offline in the ACCESS-OM2-01 model (Kiss et al., 2020), a global 0.1° ocean sea-ice model, with a JRA55-do repeat year neutral state atmospheric forcing (Stewart et al., 2020).<br> <br> These trajectory datasets are compressed to two .rar format files for 2 depths, which were outputted from the Connectivity Modeling System v2.0 (CMS) in NetCDF format. Trajectory files include latitude, longitude, depth, interpolated along-track salinity and interpolated along-track temperature for each particle which are outputted every five days in the virtual particle tracking experiment. In addition, these datasets also contain the "exitcode" and release date information of each particle. More information please see in the CMS user guide. Other experiment setup files included in each release directory are "nest_1.nml", "runconf.list" and "ibm.list".<br> <br> These datasets are the original data output by the CMS. Limited by multiple nodes and maximum running time on the supercomputer, the surface release experiment is composed of 5 consecutive sub-experiments, and the 500m release experiment is composed of 3 consecutive sub-experiments. Each sub-experiment contains 48 independent output NetCDF files. <br> <br> 2. SOBD files<br> <br> These two .rar SOBD files are original arrays of the percentage of the upper cell minus the lower cell (i.e., the SOBD percentage) at surface and 500m depth (as presented in Fig.3 in Localizing the Southern Ocean Biogeochemical Divide).<br> <br> We provide original arrays in both .csv and .npz formats. More information can be found in the "Readme.txt" file in each .rar file.<br> <br> Citation of associated paper: Y. Xie, V. Tamsitt, L. T. Bach Localizing the Southern Ocean Biogeochemical Divide. <strong><em>to be submitted to Geophysical Research Letters</em></strong></p> <p><br> References:</p> <p>Kiss, A. E., Hogg, A. M., Hannah, N., Dias, F. B., Brassington, G. B., Chamberlain, A., . . . Stewart, K. D. (2020). ACCESS-OM2 v1 . 0 : a global ocean – sea ice model at three resolutions. <strong><em>Geoscientific Model Development</em></strong>,13, 401–442. doi: https://doi.org/10.5194/gmd-13-401-2020</p> <p>Paris, A. C. B., Vaz, A. C., Helgers, J., & Wood, S.(2017).Connectivity Modeling System User 's Guide CMS v 2 . 0. Retrieved from https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Stewart, K. D., Hogg, A. M. C., England, M. H., & Waugh, D. W.(2020).Response of the Southern Ocean Overturning Circulation to Extreme Southern408Annular Mode Conditions. <strong><em>Geophysical Research Letters</em></strong>,47(22), 1–10. doi:10.1029/2020GL091103</p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
Lagrangian particles in turbulence: An experimental data set
<p>This data set contains the coordinates of Lagrangian trajectories in a quasi-homogeneous isotropic turbulent flow. The trajectories were measured in a water tank experiment using the 3D-PTV method through the MyPTV open-source software (https://github.com/ronshnapp/MyPTV). The total duration of this data set corresponds to 20 seconds of recording at a rate of 500 Hz, and it holds roughly 575,000 trajectories.</p> <p> <br> The data is stored in two text files in a tab-separated format, where each file coresponds to 10 seconds of recording. Each row in the files outlines a single measurement point from the experiment. The various columns correspond to the following information:<br> 1 - trajectory_id <br> 2 - x [mm] <br> 3 - y [mm]<br> 4 - z [mm]<br> 5 - vx [mm/frame]<br> 6 - vy [mm/frame]<br> 7 - vz [mm/frame]<br> 8 - ax [mm/frame^2]<br> 9 - ay [mm/frame^2]<br> 10 - az [mm/frame^2]<br> 11 - time [frame]<br> where trajectory_id uniquely marks samples that correspond to the same physical trajectory; x, y, and z are the position components in the three orthogonal space directions; vx, vy, and vz correspond to the velocity components; ax, ay, and az that correspond to the acceleration components.</p> <p><br> The flow in the experiment was forced using a system of 8 propellers, powered by DC motors that were positioned at the corners of the cylindrical, octagonally shaped, water tank. The propellers were changing their direction of rotation at random time intervals with an average interval duration of 0.1 seconds. The root mean square of the turbulent flow fluctuations is about 100 millimeters per second. There is a time-averaged secondary circulation with a magnitude of roughly 66% of the root mean squared fluctuation strength. The Taylor microscale Reynolds number is estimated as about 188. </p>
A Lagrangian study of interfaces at the edges of cumulus clouds
<p>The upload contains DNS data that has been used to produce the results published in Nair et al, 'A Lagrangian study of interfaces in cumulus clouds,' Journal of the Atmospheric Sciences, 2021. Please read the README file for all necessary information on how to read and analyze the data.</p>
Drifter deployment strategies to determine Lagrangian surface convergence in submesoscale flows
<p>Data used for the preparation of the manuscript "Drifter deployment strategies to determine Lagrangian surface convergence in submesoscale flows".</p>
Lagrangian trajectory dataset for AMOC lower limb
<p>These Lagrangian trajectory files were generated by TRACMASS, a Lagrangian parcel tracing algorithm, using data from a high-resolution (1/12<sup>o</sup>) ocean sea-ice hindcast. Two set of experiments were performed to trace the Atlantic Meridional Overturning Circulation (AMOC) lower limb; 1) Initiated only southward trajectories across the Fram Strait <strong>(fs)</strong> that corresponds to Arctic outflow and 2) traced only northward trajectories across the easten Subpolar North Atlantic (SPNA) Section which corresponds to Atlantic inflow and associated with the North Atlantic Current <strong>(nac)</strong>.</p> <p>_ini.csv = store positions and properties of trajectories at the starting location</p> <p>_run.csv = store positions and properties of trajectories during the trajectory simulation</p> <p>_out.csv = store positions and properties of trajectories at the ending location</p> <p>_rerun.csv = This file is used to select trajectories that have reached a particular ending section. Column 2 in this file contain kill zone flag. Flag 1 means trajectories reaching the surface, 2 indicates trajectories reaching the Fram Strait , 3 means trajectories reaching the eastern SPNA section and finally 4 illustrate trajectories aprroaching the Barents Sea.</p> <p>TRACMASS documentation is available at <strong>https://www.tracmass.org/docs.html</strong></p>
FESOM-REcoM model data: Lagrangian particle trajectories
<p>This data set includes the results of Lagrangian particle tracking experiments with FESOM1.4-REcoM2. Particles were seeded at 596 positions near the Filchner Ice Shelf front at 78°S between Berkner Island and Coats Land and tracked forwards and backwards using daily mean model output. Particles were seeded every 10th day in 1990 and 1991 (historical, forward), 2009 and 2008 (historical, backward), 2080 and 2081 (future/SSP5-8.5 scenario, forward), and 2099 and 2098 (future/SSP5-8.5 scenario, backward). Particles were tracked outside of ice-shelf cavities for 20 (19) years or until the the particle left the domain of interest in the north (62°S), west (65°W), or east (2°E). </p> <p>The following information for each particle is stored twice a day: longitude (blon in files), latitude (blat), time (bday and time), depth (bdepth), temperature (btemp), salinity (bsalt), density (bsigma0; potential density anomaly referenced to 0dbar) dissolved inorganic carbon (bdic), and total carbon (btotc).</p> <p>Each *tar.gz archive contains one experiment, i.e., all trajectories for e.g., forward/1990-2009 (seeding days 1,11,...,361 in 1990). For each seeding day, there are 12 *nc files, which together constitute the 596 particles seeded on a given day.</p> <p> </p> <p><strong>Naming convention / *tar.gz files: </strong></p> <p>Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_<em>EXPERIMENT</em>_<em>START_END_YEAR</em>.tar.gz</p> <p><em>EXPERIMENT</em>: bw (backward) or fw (forward)</p> <p><em>START_END_YEAR</em>: 2009_1990, 2008_1990, 2098_2080, or 2099_2080 for backward experiments; 1990_2009, 1990_2008, 2080_2099, or 2081_2099 for forward experiments</p> <p>(EXAMPLE: Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_fw_1990_2009.tar.gz)</p> <p><strong>Naming convention / *nc files: </strong></p> <p>drifter_start_Filchner_ice_shelf_day<em>SEEDING_DAY</em>_<em>START_END_YEAR</em>_<em>NUM_FILE</em>_reduced.nc</p> <p><em>SEEDING_DAY: </em>1, 11, ..., 361</p> <p><em>START_END_YEAR: </em>same as above</p> <p><em>NUM_FILE: </em>1, ..., 12</p> <p>(EXAMPLE: drifter_start_Filchner_ice_shelf_day1_1990_2009_1_reduced.nc)</p> <p> </p> <p><strong>NOTE:</strong> The following files are duplicates and also contained in the larger *tar archives described above (disregard them if all tar archives starting with "Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_" have been downloaded):</p> <p>Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_1991_2009.tar.gz</p> <p>Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_2080_2099.tar.gz</p> <p> </p> <p><strong>The Lagrangian particle trajectories have been analyzed here:</strong> </p> <p>Nissen, C., Timmermann, R., van Caspel, M., and Wekerle, C.: Altered Weddell Sea warm- and dense-water pathways in response to 21st-century climate change, Ocean Sci., 20, 85–101, <a href="https://doi.org/10.5194/os-20-85-2024">https://doi.org/10.5194/os-20-85-2024</a>, 2024</p> <p><strong>The Eulerian fields underlying the Lagrangian experiments are described in more detail here: </strong></p> <p>Nissen, C., Timmermann, R., Hoppema, M. <em>et al.</em> Abruptly attenuated carbon sequestration with Weddell Sea dense waters by 2100. <em>Nat Commun</em> <strong>13</strong>, 3402 (2022). <a href="https://doi.org/10.1038/s41467-022-30671-3">https://doi.org/10.1038/s41467-022-30671-3</a> </p> <p>Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. <em>J. Climate</em>, <strong>36</strong>, 6613–6630, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>. </p> <p>Original model output is available at the World Data Center for Climate (WDCC) under the following DOIs:</p> <ul> <li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li> <li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li> </ul>
SBC LTER: Regional Oceanic Modeling System (ROMS) Setup Files, Code, and Lagrangian Model Setup Files
This data contains all the necessary code, grid, forcing, initial, and boundary condition files for running the UCLA version of the Regional Oceanic Modeling System (ROMS) for the Santa Barbara Channel nested solution set that is analyzed in Dauhajre and McWilliams (2019): Nearshore Lagrangian Connectivity: Resolution Sensitivity and Submesoscale Influence. Along with this abstract and separate methods, a brief readme file (README_ROMS) contains other relevant details for running the simulations. The ROMS files in this data correspond to a one-way grid-nesting with the following horizontal resolutions: dx=1km, 300m, 100m, 36m that correspond to R1km, R300m, R100m, and R36m in the publication. In the data directories here, these grids have the following prefixes for all relevant files (grids, forcing, initial condition, and boundary conditions): “usw1” (dx=1km), “usw2” (dx=300m), “usw3” (dx=100m), and “usw4sbc” (dx=36m). The ROMS code is in fortran and requires compilation, with compilation instructions in the directory /src_ROMS_UCLA_2018 in the file “compile.sh”. All grid, forcing, initial, and boundary condition files are in netcdf form. The grid files are in the directory “grids”. Note that the R1km grid has 2 files: usw1_grd.nc and usw1_grd_samp.nc. The latter is a smaller version of the grid that focuses on the Santa Barbara Channel that can be used for the offline Lagrangian model for faster computation (which requires an analogous sub-sampling of the ROMS output). For running ROMS, usw1_grd.nc needs to be used as it corresponds to the entire domain. The atmospheric forcing, derived from a Weather Research and Forecasting model at dx=6km resolution is given in the directory “forcing_files”; all atmospheric forcing files are interpolated to the relevant ROMS grid and formatted to be used as inputs in ROMS. Each grid contains a file corresponding to precipitation (e.g., usw1_prec.nc), radiation (e.g., usw1_rad.nc), atmospheric temperature and specific humidity (
Computing Eddy-Driven Effective Diffusivity Using Lagrangian Particles Dataset
<p>This archive contains netCDF files corresponding to datasets used in the<br> Ocean Modelling paper entitled<br> "Computing Eddy-Driven Effective Diffusivity Using Lagrangian Particles" by<br> Phillip J. Wolfram and Todd D. Ringler.</p> <p>Archives are presented for particle reset times of 3, 5, 10, and 15 days,<br> corresponding to data sources used to produce figures in the manuscript. </p> <p>The data is in standard netCDF file format, which is readily readable using<br> standard netCDF tools or via the python libraries netCDF4 and xarray.</p> <p>Please see the README.md (with updates at https://gist.github.com/pwolfram/c9f3d8b6c37385732bd1b105b5267e6a).</p> <p> </p>
Dataset from Lagrangian bio-optical drifters during four experiments in coastal and open ocean waters
Open the record for dataset details and reuse information.
Lagrangian particle release experiments from Prend et al. (2024)
<p>This repository contains netCDF files with results from the Lagrangian particle release experiments presented in Prend et al. (2024). Please reach out to the corresponding author, Channing Prend (cprend@uw.edu) with any questions about the file contents. </p>
Data and scripts accompanying the paper "University of Warsaw Lagrangian Cloud Model (UWLCM) 2.0"
<p>The archive contains datasets, run scripts and plotting scripts used when preparing the paper:<br> P. Dziekan and P. Zmijewski "University of Warsaw Lagrangian Cloud Model (UWLCM) 2.0: Adaptation of a mixed Eulerian-Lagrangian numerical model for heterogeneous computing clusters"<br> submitted to Geoscientific Model Development on 19.11.2021.<br> </p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y13 & Y14
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2008 (Y13) and 2009 (Y14).</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.