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2,208 results for “coupling”
Coupled High Frequency Measurements of Swash Sediment Transport and Morphodynamic data set produced at the CIEM flume, Hydralab IV
<p>The data set here presented aims to increase the understanding of the nearshore sediment dynamics. The experiments aimed at obtaining high quality data of hydrodynamics, sediment concentration and beach-face evolution with an intra-wave time scale. The specific objectives of CoSSedM access project were i) to obtain information of the effect of the wave group periods on the beach morphological evolution; ii) To obtain detailed sediment transport information at the inner surf and swash zones with different bi-chromatic wave conditions and iii) To obtain intra-wave measurements of beach-face evolution.</p> <p>The present work was developed in the framework of the HYDRALAB IV Transnational Access projects. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona. This is a wave flume 100 m long, 3 m wide, and 4.5 m deep. The working water depth was at around 2.5 m over the horizontal flume section and was varied slightly depending on the wave test. A beach was installed made of commercial well-sorted sand (d50 = 0.25 mm) with an overall mean beach gradient of approximately 1:15.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p> <p>More information can be found on the published papers:</p> <p>Alsina, J.M., Padilla, E.M. and Cáceres, I., 2016. Sediment transport and beach profile evolution induced by bi-chromatic wave groups with different group periods. Coastal Engineering, Vol. 114, 325-340.</p> <p>Van der Zanden, J.; Alsina, J.; Caceres, I.; Buijsrogge, R. H.; Ribberink, J. , 2015. Bed level motions and sheet flow processes in the swash zone : observations with a new conductivity-based concentration measuring technique (CCM+) . Coastal Engineering, Vol. 105, 47-65.</p>
Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= outputs from fits for obtaining spectroscopic constants) results discussed in the paper titled "Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states", by Avijit Shee, Trond Saue, Lucas Visscher and Andre Severo Pereira Gomes.</p>
Multi-scale modeling - WRF-CIM coupling
<p>In this dataset you can find WRF and CIM simulated data produced and used in the paper (<a href="https://www.sciencedirect.com/science/article/pii/S2212095518301688">Multi-scale modeling of the urban meteorology: Integration of a new canopy model in the WRF model</a>).</p> <p>More details on the datasets can be found in the Python Notebook.</p> <p>Additional data (namelists for ex.) can be obtained directly by contacting the authors.</p>
Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments"
<p>The data in this archive was acquired to investigate the coupling between cerebral blood flow and cerebral blood volume across different vascular compartments. These results will form the basis of a forthcoming publication. Please reference this dataset (version v1.1.0) if you use it in your work. Wesolowski R, Blockley NP, Driver ID, Francis ST, Gowland PA. Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments". Zenodo 2018. doi: 10.5281/zenodo.1411018. </p> <p>This dataset contains measurements of the haemodynamic responses of different compartments to a visual stimulus (8Hz red LED goggles, 19.2s ON, 40.8s OFF). Time course data are cycle averaged for the following haemodynamic properties;</p> <ol> <li>Arterial cerebral blood volume (CBVa) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBVa.</li> <li>Cerebral blood flow (CBF) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBF.</li> <li>Total cerebral blood volume (CBVtot) measured using bolus injections of a Gadolinium based contrast agent combined with T2* weighed gradient echo EPI.</li> </ol> <p>In addition, weighted mean and standard deviation of the changes in these parameters are presented using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. Weighting is performed with respect to the number of voxels present in each of the subjects regions of interest.</p> <p>Time course data were extracted from two different regions of interest;</p> <ol> <li>ROI<sub>CBF</sub>: defined using a CBF localiser</li> <li>ROI<sub>COMMON</sub>: defined using the overlap of CBF, CBV<sub>a</sub> and CBV<sub>tot</sub> localisers</li> </ol> <p>Furthermore, the transit times for CBF and CBVa were estimated for an average stimulus cycle and mean values extracted using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. These data were extracted from ROI<sub>CBF</sub>.</p> <p>Changes</p> <p>- Addition of propagation of uncertainty for CBVv and the Grubb constants alpha_tot and alpha_a.</p> <p> </p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for ionization energies) results discussed in the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes.</p> <p>In each archive file there is a README explaining how to use the bundled scripts to process the data.</p>
Product solubility control in cellooligosaccharide production by coupled cellobiose and cellodextrin phosphorylase
<p>We provide here the underlying data of the scientific publication "Product solubility control in cellooligosaccharide production by coupled cellobiose and cellodextrin phosphorylase". Please find the abstract below:</p> <p>Soluble cellodextrins (linear β‐1,4‐D‐gluco‐oligosaccharides) have interesting<br> applications as ingredients for human and animal nutrition. Their bottom‐up synthesis<br> from glucose is promising for bulk production, but to ensure a completely watersoluble<br> product via degree of polymerization (DP) control (DP ≤ 6) is challenging.<br> Here, we show biocatalytic production of cellodextrins with DP centered at 3 to 6<br> (~96 wt.% of total product) using coupled cellobiose and cellodextrin phosphorylase.<br> The cascade reaction, wherein glucose was elongated sequentially from α‐glucose<br> 1‐phosphate (αGlc1‐P), required optimization and control at two main points. First,<br> kinetic and thermodynamic restrictions upon αGlc1‐P utilization (200 mM; 45°C,<br> pH 7.0) were effectively overcome (53% → ≥90% conversion after 10 hrs of reaction)<br> by in situ removal of the phosphate released via precipitation with Mg2+. Second, the<br> product DP was controlled by the molar ratio of glucose/αGlc1‐P (∼0.25; 50mM<br> glucose) used in the reaction. In optimized conversion, soluble cellodextrins in a total<br> product concentration of 36 g/L were obtained through efficient utilization of the<br> substrates used (glucose: 98%; αGlc1‐P: ∼80%) after 1 hr of reaction. We also showed<br> that, by keeping the glucose concentration low (i.e., 1–10 mM; 200mM αGlc1‐P), the<br> reaction was shifted completely towards insoluble product formation (DP ∼9–10). In<br> summary, this study provides the basis for an efficient and product DP‐controlled<br> biocatalytic synthesis of cellodextrins from expedient substrates.</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
Raw data for "Coupled ptychography and tomography algorithm improves reconstruction of experimental data"
<p>Raw data used in "<a href="https://www.osapublishing.org/optica/abstract.cfm?uri=optica-6-10-1282"><em>Coupled ptychography and tomography algorithm improves reconstruction of experimental data</em></a>" by M. Kahnt, J. Becher, D. Brückner, Y. Fam, T. Sheppard, T. Weissenberger, F. Wittwer, J.-D. Grunwaldt, W. Schwieger and C.G. Schroer</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Figures 7-12 in Couples in phoretic copulation, a tool for male-female association in highly dimorphic insects of the wasp genus Dissomphalus Ashmead (Hymenoptera: Bethylidae)
Figures 7-12. (7-9) Female of Dissomphalus firmus from Panama: (7) habitus in lateral view; (8) head in dorsal view; (9) mesosoma in dorsal view. (10-12) Female of Dissomphalus rettenmeyeri from Panama: (10) habitus in lateral view; (11) head in dorsal view; (12) mesosoma in dorsal view. Scale bars: 100 µm.
Fig. 3 in The role of cladocerans in green and brown food web coupling
Fig. 3. The proportion of the contribution of POC, phytoplankton, and biofilm to cladoceran biomass in the three PIAP lagoons. Boxplots show the quartiles of 97.5%, 75%, 50%, 25%, and 2.5%. The lower and upper boxes indicate the 25% and 50% quartiles. Likewise, the lower and upper vertical lines indicate the 97.5% and 2.5% quartiles and the horizontal lines show the mode of contribution.
Data supporting publication: Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces
<p>Dataset for "Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces" Figure 1 to Figure 5.</p>
FIGURE 1 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 1. Simplification of the center of head movement as a joint in extinct Temnospondyli amphibian when biting. Elaborated from the original image (en.wikipedia.org/wiki/File:Jammerbergia_formops.jpg). Under license: CC BY-SA 3.0 (creativecommons.org/licenses/by-sa/3.0/).
FIGURE 4 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 4. Von Mises stress distribution in the skull for the Static Analysis in FEA in cases 1A, 2A, 3A, 1B, 2B and 3B.
FIGURE 2 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 2. Studied test cases of different feeding movements when applying a force F=800 N in the direction of the red arrow (when the force is perpendicular at the view the red arrow is a red dot). Case 1A, 2A and 3A with a fixed boundary condition in the condyle without the web of beams. Case 1B, 2B and 3B with the web of beams in the condyle and a fixed boundary condition.
South American Regional Indices on Convectively Coupled Waves
<p>This dataset provides regional indices for Convectively Coupled Waves (CCWs) over South America. The indices include data on wave activity a covering a specified temporal and spatial range. The data is valuable for researchers studying the influence of CCWs on regional climate and weather patterns.</p> <p><strong>Keywords</strong>: South American Regional Indices, Convectively Coupled Waves, Climate Data, CCWs Indices</p> <p><strong>Data Description</strong>:</p> <ul> <li><strong>Content</strong>: Includes indices for CCWs and time series data.</li> <li><strong>Format</strong>: Available in text and NetCDF format.</li> <li><strong>Coverage</strong>: Temporal coverage from 1979 to 2023 spatial coverage includes tropical South America domain.</li> </ul> <p><strong>Methodology</strong>: The indices are derived using EOF analisysis after filtering OLR.</p> <p><strong>Usage</strong>: The data can be used to analyze the seasonal and interannual variability of CCWs and their impact on South American weather patterns. Example applications include climate modeling, weather prediction, and atmospheric research.</p> <p><strong>Acknowledgments</strong>: This work was supported by NOAA grant number NA22OAR4310611 and NSF CAREER grant number 2236433 .</p> <p><strong>License</strong>: This dataset is licensed under Creative Commons Attribution 4.0 International License.</p> <p><strong>Contact Information</strong>: For more information, please contact : mayta@wisc.edu</p> <p> </p>
Large mass hierarchies from strongly-coupled dynamics—Data release
<p>This release contains data associated with the publication <a href="https://arxiv.org/abs/1605.04258">Large mass hierarchies from strongly-coupled dynamics</a> (<a href="https://doi.org/10.1007/JHEP06(2016)114">JHEP 06 (2016) 114</a>)</p> <p>It comprises four files:</p> <ul> <li><code>README.md</code>: Containing this information, and a more detailed description of the dataset.</li> <li><code>Fig4.csv</code>: the data shown in Figure 4 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of composite spin-0 and spin-2 states, and their excitations, computed for $c_1 = 0 = A_0$, as a function of $\Delta$, for $r_{UV} = 25$ and $r_{IR} = 10^{-6}$, normalised to the mass $M_0$ of the lightest scalar.</li> <li><code>Fig5.csv</code>: the data shown in Figure 5 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of the composite spin-0 and spin-2 states, computed for $c_1 = 0 = A_0$, as a function of $-A(r_{IR}) = \log (\Lambda_0 / \Lambda_{IR})$, for $\Delta = 1.5$, normalised to the mass $M_T$ of the lightest tensor. $A(r_{UV}) - A(r_{IR})$ is fixed to 8.</li> <li><code>DataRelease.nb</code>: A Mathematica notebook that will take the above two files and generate plots similar to those shown in <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>.</li> </ul> <p> </p>
data for Tuning the interlayer coupling in La0.7Sr0.3Mn0.95Ru0.05O3 LaNiO3 multilayers with perpendicular magnetic ansiotropy
<p>Here we upload the data for the manuscript "Tuning the interlayer coupling in<br>La0.7Sr0.3Mn0.95Ru0.05O3 / LaNiO3 multilayers with perpendicular magnetic anisotropy". The data underlying the figures of the main text and supplement materials are provided as txt files and embedded origin graphs. Hall voltage and Kerr ellipticity for the trilayers (Fig 2), reference samples (SFig2), SQUID for the multilayer (Fig 4, SFig6, SFig7), LSAT substrate (SFig3 and SFig 4), minor hysteresis loop of the multilayer (SFig 5), and XMCD for the multilayer (Fig 5, SFig 6).</p>
Interlayer coupling in isotopic heterobilayers of MoS2
<p><span>Heterostructure engineering of two-dimensional materials overlapped with isotope engineering provides a comprehensive platform to distinctly tune optoelectronic properties driven by interlayer interaction. A strong interlayer coupling is reported in a double-layered HS of two different sulfur isotope-modified adjacent MoS<sub>2</sub> monolayers (MLs) grown via 2-step chemical vapor deposition (CVD). The growth propagation reveal that the top layer of MoS2 grew at separate nucleation centers on the underlying crystal, forming distinct triangular domains and resulting in heterogeneity in the stacking arrangement. Raman analysis of layered HS depicts the merging of fingerprint Raman spectra of individual isotope-modified MoS<sub>2</sub><sup> </sup>ML implying a coupling factor. The interlayer coupling in double-layered HS is affirmed by low-frequency shear and breathing modes and the </span><span>variability in shear to breathing intensity ratio across different positions in HS indicates the potential for tuning interlayer coupling through stacking in a heterogeneous manner. </span><span>The hefty decrement of <em>A</em> exciton intensity in layered HS as well as the relative enhancement of <em>B </em>exciton in the photoluminescence spectra provide corroborative evidence for interlayer interaction. This is further ascertained by the observation of an additional slower decay channel in time-resolved photoluminescence measurements due to interlayer crossing recombination of the carriers in layered HS.</span></p>
Mesh and configuration files to perform coupled heat+fluid simulations on a realistic human eyeball geometry with Feel++
<h1>Run the simulation</h1> <h2>With slurm</h2> <p>Set up position and desired mesh in the `run.slurm` file. Then, submit the job with the following command:</p> <p><code>sbatch run.slurm</code><br><br></p> <h2>Without slurm</h2> <p>Run by hand the command of the <code>run.slurm</code> file.<br><code>POSITION=prone # prone supine standing</code><br><code>SOLVER_TYPE=simple # simple lsc</code><br><code>MESH_INDEX=M4 # M1 M2 M3 M4 M5</code></p> <p><code>mpirun -np 128 feelpp_toolbox_heatfluid \</code><br><code> --config-files eye-${POSITION}.cfg pc_${SOLVER_TYPE}.cfg \</code><br><code> --heat-fluid.json.patch='{ "op": "replace", "path": "/Meshes/heatfluid/Import/filename", "value": "$cfgdir/mesh/Mr/'${MESH_INDEX}'/Eye_Mesh3D_p$np.json" }' \</code><br><code> --heat-fluid.scalability-save=1 --heat-fluid.heat.scalability-save=1 --heat-fluid.fluid.scalability-save=1</code></p> <h2>Available meshes</h2> <p>The meshes are available and are already partitioned for parallel computing:</p> <p><code>M0 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M1 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M2 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M3 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M4 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M5 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M6 : 128, 256, 384, 512, 640, 768</code></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.