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619 results for “configuration”
NEMO4.2 eORCA1 configuration files for stable millennial ocean simulations
<p>This data repository contains the configuration files specific to the three model experiments described in the study entitled 'Effects of improved tidal mixing in NEMO one-degree global ocean model'. These experiments, called OLD, NEW and TRA, employ NEMO version 4.2.0 and the eORCA1 global mesh. They have been run for 1000 years under CORE version 2 normal year atmospheric forcing (https://data1.gfdl.noaa.gov/nomads/forms/core/COREv2/CNYF_v2.html).</p> <p>The files provided are: routines modified (compared to released NEMO 4.2.0 code), namelists, initial conditions, other input fields, and restarts for year 1001 of experiment TRA. </p> <p>Namelist filenames include either '_ref' or '_cfg'; the latter overwrite the former before the model reads the full namelists.</p>
HydroLight configuration files of annular irradiances dataset 2021-08-02
<p>This publication contains the configuration files of the HydroLight software (5.2) in ASCII text file format (.txt) related to <a href="https://doi.org/10.5281/zenodo.5041192">this</a> publication.</p> <p>Each file corresponds to the input file (the Iroot.txt file for the run) which is found in the HE5\run\batch directory.</p> <p>The input file format is described in detail in Appendix A of the HE52 Technical Documentation: https://www.sequoiasci.com/wp-content/uploads/2013/07/HE52TechDoc.pdf.</p> <p>The 3024 configurations are described in the following lists:</p> <p> </p> <p><strong>Configuration</strong></p> <p><strong>Name Value Step</strong></p> <p>Wavelength 400 nm to 700 nm 5 nm</p> <p>Solar zenith angle 0° to 80° 10°</p> <p>Cloud coverage 0% to 100% 20%</p> <p>Chl 0 mgm<sup>-3</sup> to 67 mgm<sup>-3</sup> (*)</p> <p>CDOM af(380) 0 m<sup>-1</sup> to 22.5 m<sup>-1</sup> (*)</p> <p>Mineral 0 gm<sup>-3</sup> to 38.7 gm<sup>-3</sup> (*)</p> <p>Wind speed 0</p> <p>Bottom Infinitely deep</p> <p> </p> <p>(*) Described in table 1 of this paper: (to edit)</p> <p><strong>Depth resolution configuration</strong></p> <p><strong>Value Step</strong></p> <p>2 cm to 50 cm 2 cm</p> <p>50 cm to 2 m 5 cm</p> <p>2 m to 3 m 10 cm</p> <p>3 m to 4 m 20 cm</p> <p>4 m to 10 m 50 cm</p> <p>10 m to 15 m 1 m</p> <p>15 m to 20 m 5 m</p>
Dataset for Algorithms and Complexity for Counting Configurations in Steiner Triple Systems
<p>This dataset contains the classification of full n-line configurations (for all n <= 13, filename: "full_line_config_<n>.txt.gz") and w_3 configurations (for all w <= 16, filename: "w_3_config_<w>.txt.gz") together with the sizes of minimum generating sets. Each file lists "m<s>" so that s is the size of the minimum generating set of the subsequent configuration, which is denoted by writing the points of each of its lines row-wise. For example</p> <p>m3<br> 0 1 4<br> 0 2 6<br> 0 3 5<br> 1 2 5<br> 1 3 6<br> 2 3 4<br> 4 5 6</p> <p>is the Fano plane and its minimum generating set has size 3.</p> <p>Additionally, the file "fulllineconjecture.txt.gz" contains the 623 Steiner triple systems of order 25 (i.e., all rows which contain curly brackets) used in Theorem 7 in the paper below, followed by a row starting with 1 and then describing the number of occurrences of all 179 full n-line configurations for n <= 8, i.e., first the number of occurrences of Pasch configurations, then mitre configurations, then the 5 full 6-line configurations, the 19 full 7-line configurations, and finally the 153 full 8-line configurations contained in the STS(25) in the preceding row. The ordering follows the ordering within the files "full_line_config_<n>.txt.gz". This file is built so that omitting all lines with curly brackets is a valid gap code and results in a prove of said theorem (i.e., zgrep -v "{" fulllineconjecture.txt.gz | gap yields 180).</p> <p><br> Further details can be found in the corresponding publication</p> <p>"Algorithms and Complexity for Counting Configurations in Steiner Triple Systems"</p> <p>by Daniel Heinlein and Patric R. J. Östergård.</p> <p>All files are compressed with gzip.</p>
An Empirical Study of Container Image Configurations and Their Impact on Start Times (Container Image Data)
<p>Dataset with the container image metadata used for our IEEE/ACM CCGRID 2023 paper "An Empirical Study of Container Image Configurations and Their Impact on Start Times".</p> <p>Abstract of the paper: A core selling point of application containers is their fast start times compared to other virtualization approaches like virtual machines. Predictable and fast container start times are crucial for improving and guaranteeing the performance of containerized cloud, serverless, and edge applications. While previous work has investigated container starts, there remains a lack of understanding of how start times may vary across container configurations. We address this shortcoming by presenting and analyzing a dataset of approximately 200,000 open-source Docker Hub images featuring different image configurations (e.g., image size and exposed ports). Leveraging this dataset, we investigate the start times of containers in two environments and identify the most influential features. Our experiments show that container start times can vary between hundreds of milliseconds and tens of seconds in the same environment. Moreover, we conclude that no single dominant configuration feature determines a container's start time and that hardware and software parameters must be considered together for an accurate assessment.</p> <p>Dataset description: Our images dataset contains 200,986 entries with 21 features associated to each container image. In the following, we describe the meaning of each feature. Further information is available in <a href="https://github.com/opencontainers/image-spec">OCI Image Specification</a> and the <a href="https://docs.docker.com/engine/reference/run/">Docker Run Documentation</a>. Besides the 20 features grouped in the five categories below, each dataset entry has a image_id, which is used to uniquely identify the dataset entry.</p> <p>Features</p> <p>Metadata features (prefix: meta)</p> <ul> <li><strong>meta_repo_digest</strong> : The repo digest is a SHA-256 hash which is used to uniquely identify and pull the image from Docker Hub</li> <li><strong>meta_architecture</strong> : The CPU architecture which the binaries in the image are built to run on</li> <li><strong>meta_os</strong> : The name of the operating system which the image is built to run on</li> <li><strong>meta_docker_version</strong> : The Docker version used to built this image</li> </ul> <p>I/O stream features (prefix: io)</p> <ul> <li><strong>io_attach_stdin</strong> : boolean setting to determine whether the console should be attached to the process stdin stream</li> <li><strong>io_attach_stdout</strong> : boolean setting to determine whether the console should be attached to the process stdout stream</li> <li><strong>io_attach_stderr</strong> : boolean setting to determine whether the console should be attached to the process stderr stream</li> <li><strong>io_tty</strong> : boolean setting to determine whether the console should pretend to be a TTY when attached</li> <li><strong>io_open_std_in</strong> : boolean setting to determine whether the process stdin stream should be kept open even if console not attached</li> <li><strong>io_std_in_once</strong> : boolean setting to determine whether the process retrieved input from the stdin stream at least once</li> </ul> <p>Start command features (prefix: cmd)</p> <ul> <li><strong>cmd_args</strong> : Length of list of arguments to use as the command to execute when the container starts</li> <li><strong>cmd_envvars</strong> : Environment variables set per default when the container starts</li> <li><strong>cmd_additional_args</strong> : Length of list for additional arguments to the containers entrypoint</li> </ul> <p>File system features (prefix: fs)</p> <ul> <li><strong>fs_volumes</strong> : Number of volumes to create/use by default</li> <li><strong>fs_size</strong> : Size of this image in bytes</li> <li><strong>fs_virtual_size</strong> : Virtual size of this image in bytes (equals size)</li> <li><strong>fs_graph_driver_name</strong> : Name of the image's graph driver</li> <li><strong>fs_root_fs_type</strong> : Name of the file system type used in the image</li> <li><strong>fs_layers</strong> : Number of root file system layers</li> </ul> <p>Networking features (prefix: net)</p> <ul> <li><strong>net_ports</strong> : Number of ports to expose per default</li> </ul> <p> </p> <p>Dataset acquisition: The dataset has been acquired from Docker Hub using a web crawler. We used substring matches with the <a href="https://hub.docker.com/explore">Docker Hub Explore function</a>. As search strings, we used all letter combination with sizes 1 to 3, meaning that our first search string was 'a' and our last was 'zzz'. We included both results from the 'recently updated' and the 'most popular' selection. We came up with an initial list of 286,294 image names. We then tested we could pull and start these images once. These tests have been conducted from April to June 2022. We sorted out all images that were either not pullable or startable and retrieved all total of 200,986 valid images. In the following, we describe the error types that we encountered and that let to the removal of the causing image from the dataset:</p> <ul> <li>The image manifest was unknown when we tried to download it meaning that is has been renamed or deleted from the time when our web crawler was running</li> <li>The entrypoint command required a dependency that was missing in the image and therefore the container could not be started</li> <li>The image did not specify an entrypoint command and could therefore not be started</li> <li>The image declared an invalid root file system type</li> <li>The image had a malformed root file system</li> <li>The image configuration was incomplete and therefore not all required data could be obtained</li> </ul> <p>See also our CodeOcean capsule with the processing scripts for our paper: https://doi.org/10.24433/CO.4595026.v2</p>
SUMMA/mizuRoute model configurations, parameters, and ensemble statistics for representative cryosphere basins
<p>Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological datasets enables convenient uncertainty characterization, which however is rarely explored in large-domain research. Tang et al. (2023) analyze how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Dataset for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables.</p>
Model results and configuration files for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>This repository includes files as described below:</p> <p><strong>1. namelist_CBMZ09_example.input, namelist_MOZART202_example.input:</strong></p> <p>Two WRF-chem namelist files for CBMZ and MOZART simulation.</p> <p>They are modified according to the namelist from <a href="https://github.com/wrfchem-leeds/WRFotron">https://github.com/wrfchem-leeds/WRFotron</a>.</p> <p><strong>2. wps_namelist_example.wps:</strong></p> <p>namelist for WRF Preprocessing System (WPS)</p> <p><strong>3. temporal_hourly_scale_factor_emission.csv:</strong></p> <p>Hourly scale factors for emissions.</p> <p>Hourly allocation is applied to all emission data (i.e., emissions for 2017, 2030 and perturbated emissions of NOx, VOCs).</p> <p><strong>4. vertical_emission_ratio.csv</strong></p> <p>Vertical shares (ratios) of emissions.</p> <p>Emissions from sectors of power and industry are vertically allocated based on this file. Vertical allocation is conducted for all emission data.</p> <p>These shares are suggested by MICS-ASIA III intercomparison framework.</p> <p><strong>5. 01_2030_2017_simulations.zip: </strong></p> <p>Simulated MDA8 ozone under future (2030) and 2017 emission scenarios by the two chemical mechanisms (i.e., CBMZ, MOZART).</p> <p><strong>6. 02_perturbations_of_NOxVOCs.zip:</strong></p> <p>Simulated MDA8 ozone given perturbations of NOx and VOCs emissions by the two chemical mechanisms.</p> <p><strong>7. 03_hourly_diff_O3_NOx_OH_HNO3.zip: </strong></p> <p>Differences of hourly simulated concentrations of O3, NOx, OH and HNO3 during July in the Base-2017 scenario between CBMZ and MOZART (CBMZ - MOZART).</p>
NEMO 4.0.7 ORCA1L75 - CNR ISMAR configuration files
<p>The dataset contains all static, ancillary, dynamic and forcing files to run tests with the ORCA1 configuration of NEMO (v4.0.7) for testing the CNR ISMAR setup (2010-2019)</p>
H2020 ENODISE: SISW Numerical Aeroacoustic Database Configuration B2
<p>This database contains the aeroacoustic numerical results generated by Siemens Industry Software (SISW) for the B2 configuration investigated in the framework of the European project ENODISE. In this configuration, a six-blade propeller mounted on a support is studied for two operating points with and without flow. The operating points correspond to conditions 65 and 66 of the experimental campaign carried out at Ecole Centrale of Lyon (see H2020 Enodise: Experimental dataset configuration B2 ECL, https://zenodo.org/record/7925336).</p> <p>The two investigated operating points are:</p> <ul> <li>Uinf = 0 studied numerically using detached-eddy simulation combined with finite-element method for acoustics</li> <li>Uinf = 22 m/s studied numerically using large-eddy simulation combined with finite-element method for acoustics</li> </ul> <p>The SISW aeroacoustic results have been obtained using CFD software Simcenter STAR-CCM+ coupled with acoustic software Simcenter 3D Acoustics. The hydrodynamic and acoustic interactions between the propeller and its support are taken into account in the numerical simulations.</p> <p>More information about the numerical setup and results can be found in the documentation included in the database.</p> <p>The data are saved in dat files.</p>
H2020 ENODISE: ONERA Numerical Aeroacoustic Database of Configuration C
<p>This database contains the acoustic prediction results realized by ONERA of the configuration C investigated in the framework of the European project ENODISE. In this configuration, contra-rotating propellers are investigated in static condition with and without shroud. The results presented here can be compared to the measurements carried out by von Karman Institute for Fluid Dynamics in anechoic facility ALCOVES (https://zenodo.org/record/7966211).</p> <p>The investigated operating point as calculated in the prediction is:</p> <ul> <li>Shrouded, RPM=6000, d=3cm.</li> <li>Unshrouded, RPM=6000, d=3cm.</li> </ul> <p>Predictions were obtained using the ProLB code based on a Lattice Botlzmann Method.</p> <p>Description of the database is proposed in the document <em>Database_ENODISE_ONERA_ConfigurationC.pdf.</em></p>
H2020 Enodise: TUD/NLR Experimental database configuration B3
<p>This dataset considers experimental aerodynamic and aeroacoustic data for configuration B3 as defined in the H2020 Enodise project (https://www.vki.ac.be/index.php/about-enodise):</p> <p>Configuration B3 has been split into two subconfigurations (B3-VTOL and B3-FLAP). The available data for two subconfigurations are given below.</p> <p><strong>B3-VTOL (side-by-side rotor setup):</strong></p> <p>- Time-resolved 2D2C Velocity fields</p> <p>- 4D-PTV tracks </p> <p><strong>B3-FLAP (over-the-wing distributed propulsion):</strong></p> <p>- Propeller thrust and torque (for isolated propeller, over-the-wing propeller, over-the-wing distributed propellers)</p> <p>- Acoustic microphone data (for isolated propeller, over-the-wing propeller, over-the-wing distributed propellers)</p> <p>- Wing static pressures (for over-the-wing propeller, over-the-wing distributed propellers)</p> <p>- Time-averaged 2D3C Velocity fields (for over-the-wing propeller)</p> <p>- Time-averaged 2D2C Velocity fields (for over-the-wing distributed propellers)</p> <p> </p>
H2020 ENODISE: UTWE Configuration A Experimental Databases
<p>This database contains the datasets for the aeroacoustic experiments conducted at the University of Twente on the H2020 ENODISE project, Task 3.1, Configuration A1 - Wall mounted.</p> <p>Configuration A1 consists of a propeller ingesting a zero-pressure-gradient boundary layer. Far-field acoustics are measured using two microphone arrays, to assess directivity and sound pressure levels. Near-field measurements consist of surface static pressure and pressure fluctuations. Propeller force and torque is measured using a load cell. Flow-field measurements are obtained through hotwire anemometry.</p> <p>This forms part of Deliverable D3.3, see report for further information regarding measurement techniques. Included are descriptive READMEs. The data is in the HDF5 format and comprises mean, spectral, and other processed data types in engineering units.</p>
H2020 ENODISE: NLR Numerical aeroacoustic database configuration B3-flap
<p>This dataset considers numerical aerodynamic and aeroacoustic data for an over-the-wing mounted propeller, denoted configuration B3-flap as defined in the H2020 ENODISE project (https://www.vki.ac.be/index.php/about-enodise).</p> <p>The dataset consists of</p> <p>- Aerodynamic data: propeller thrust and torque</p> <p>- Acoustic data: pressure Fourier modes at 1, 2, and 3 BPF</p> <p>both for the isolated propeller and the over-the-wing mounted propeller.</p> <p>An overview of the performed numerical simulation and a description of the dataset is given in the included PDF file (ENODISE_NLR_B3_flap_URANS.pdf). This file also includes the aerodynamic data.</p> <p>Experiments have also been performed by NLR for this configuration. See https://zenodo.org/record/8283630 for details and the experimental dataset.</p>
Phantom measurement data for 'Configuration-based electrical properties tomography', Iyyakkunnel et al. (2021)
<p>This dataset contains the phantom bSSFP measurement data used in the published article Iyyakkunnel et al., 'Configuration-based electrical properties tomography', Magn Reson Med. 2021;85:1855–1864 (doi: 10.1002/mrm.28542). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br> The data includes the magnitude and phase measurements for eight phase-cycled scans (in dicom (.dcm) format). The RF phase increment for the phase cycled scans corresponds to 0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°. For further measurement details, please refer to the mentioned original article.</p>
H2020 ENODISE: DLR Analytical Aeroacoustic Database BLI Configuration A1 and A2
<p>This database contains the acoustic prediction results of DLR for a single operating point of the A1 and A2 configurations investigated in the framework of the European project ENODISE. In these configurations, a single two-bladed propeller is immersed in a boundary layer. The present results can be compared to the measurements carried out by the University of Bristol and the University of Twente for different boundary layer characteristics. The experimental results are saved elsewhere on the ZENODO repository.</p> <p>The investigated operating point as calculated in the prediction is:</p> <ul> <li>Uinf = 33 m/s, 6500 rpm, advance ratio J=1.</li> </ul> <p>The experimental measurements were conducted at a slightly lower freestream velocity.</p> <p>The DLR prediction results were obtained using the analytical approach implemented in the DLR in-house program PropNoise coupled to the blade element momentum theory. The calculations were informed by the hot-wire measurements carried out in the boundary-layer as the propeller was removed.</p> <p>Refer to the two references cited below to obtain more information about the theory that was applied to obtain the results: </p> <ol> <li> S. Guérin, T. Lade, L. Castelucci, I. Zaman, Tonal noise emission by a low-Mach low-Reynolds number propeller ingesting a boundary layer, 29th International Congress on Sound and Vibration, 10-13 July 2023, Prague (CZ).</li> <li> S. Guérin, T. Lade, L. Castelucci, I. Zaman, Broadban noise emission by a low-Mach low-Reynolds number propeller ingesting a boundary layer, Inter-Noise 2023, 20-23 August 2023, Chiba (Great Tokyo), Japan.</li> </ol> <p>The results for tonal and broadband noise are saved separately. The DLR results are saved into an h5 file, which can be read with e.g. python.</p> <p>Further details can be found in the file <em>DLR_documentation_A1_A2.pptx</em></p>
H2020 ENODISE: DLR Analytical Aeroacoustic Database Configuration B1
<p>This database contains the acoustic prediction results of DLR for a single operating point of the B1 configuration investigated in the framework of the European project ENODISE. In this configuration, three identical propellers with 6 blades are mounted at leading-edge of a wing (puller configuration). The results presented here can be compared to the measurements carried out by the Technical University of Delft, when these are available. The experimental results should be also saved on the ZENODO repository (use the key word ENODISE).</p> <p>The investigated operating point as calculated in the prediction is:</p> <ul> <li>Uinf = 30 m/s, advance ratio J=0.8.</li> </ul> <p>The DLR prediction results were obtained using the analytical approach implemented in the DLR in-house program PropNoise coupled to the blade element momentum theory.</p> <p>The interaction with the wing is accounted for in a simplistic way as explained in the detailed documentation <em>B_documentation.pptx.</em></p> <p>Only, the results for tonal noise are available in this database. Three cases can be investigated separately: a single isolated propeller, 3 distributed propellers, three distributed propellers interacting with the wing.</p> <p> </p> <p>The DLR results are saved in h5 files, which can be read with e.g. Python.</p>
Configuration files for model stations presented in the manuscript "Sensitivity of shelf sea marine ecosystems to temporal resolution meteorological forcing"
<p>This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations</p> <p><br> GOTM code is freely available from: <br> https://github.com/gotm-model/code</p> <p><br> FABM code is freely available from:<br> https://github.com/fabm-model/fabm.git</p> <p><br> ERSEM code is freely available from:</p> <p><a href="https://www.pml.ac.uk/Modelling_at_PML/Access_Code">https://www.pml.ac.uk/Modelling_at_PML/Access_Code</a><br> </p> <p>Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above. </p> <p>Versions/commits for the model code used to create results presented in this manuscript are:</p> <p>GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f </p> <p>FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498 </p> <p>ERSEM: Version 19.04</p> <p> </p> <p> </p>
Dataset for the paper "Slavic morphosyntax is primarily determined by its geographic location and contact configuration", Scando-Slavica Journal
<p>This is the raw dataset for the paper "Slavic morphosyntax is primarily determined by its geographic location and contact configuration", Scando-Slavica</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>
Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study
<p>Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study</p>
Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams
<p>Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams</p>
ScienceDex guides
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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.