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773 results for “bounds”
A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process, and Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform
<p>The data contained in this repository was used in the production of the publication "A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process" (<a href="https://doi.org/10.1088/1367-2630/ac3048">https://doi.org/10.1088/1367-2630/ac3048</a>) and "Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform" (<a href="https://doi.org/10.1016/j.physletb.2025.139247">https://doi.org/10.1016/j.physletb.2025.139247</a>).</p>
Coefficients for Tight Logarithmic Approximations and Bounds for Generic Capacity Integrals
<p>This is a supplementary dataset for the publication:</p> <p>I. M. Tanash and T. Riihonen, "Tight Logarithmic Approximations and Bounds for Generic Capacity Integrals and Their Applications to Statistical Analysis of Wireless Systems," in <em>IEEE Transactions on Communications</em>, 2022, doi: 10.1109/TCOMM.2022.3198435.</p> <p>The dataset contains the sets of optimized coefficients for the novel minimax approximations of the Nakagami and lognormal capacity integrals in terms of absolute error. The proposed approximations have the form of a weighted sum of logarithmic functions. The optimized coefficients are found for a wide range of the corresponding fading parameters, namely m for the Nakagami capacity integral and σ (standard deviation) for the lognormal capacity integral. Please note that the optimized coefficients in the provided dataset for the lognormal capacity integral are calculated for σdB (standard deviation in decibels) so σ=0.1 log_e(10) σdB in Eq. 5.</p> <p>The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected capacity integral, the parameter's value, and the number of terms. See help func_extract_coef for more information.</p> <p>The Matlab script (general_any_func) implements the theory presented in the corresponding journal paper: More specifically, it implements solving Eq. 22 to calculate the optimized coefficients of Eq. 7 for the Nakagami capacity integral. The code also provides general comments on how to generalize it to obtain the optimized coefficients of any communication system in terms of absolute error. Number of supplementary Matlab functions (general_any_func, func_abs_gen_any_func, calc_d_gen, calc_Cappr_gen, calc_d_gen_derivative, calc_Cappr_gen_derivative, Gauss_Laguerre, and peakseek) are provided herein and are used in the main Matlab script.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function (func_extract_coef.m) in extracting the required coefficients from the dataset, to calculate and plot the corresponding absolute error which is shown by figure Example.jpg.</p>
Measurement of the bound-state beta decay of 205Tl(81+): intermediate and result data
<p>The data presented here is the intermediate and result data from the measurement of the bound-state beta decay of 205Tl(81+), experiment G-20-0E121, which was performed at the Experimental Storage Ring (ESR) at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt (Germany) in the frame of FAIR Phase-0. The experimental measurement was done from the 26th March 2020 to 6th April 2020.</p> <p><strong>Intermediate Data:</strong> During the experiment, the ESR monitored the beam via three main detectors: the non-destructive 245 MHz Schottky resonator, the DC Current Transformer (DCCT), and a Multi-Wire Proportional Chamber (MWPC). In particular:</p> <ol> <li>Schottky data: the integrated Schottky noise power density for the 205Tl(81+) peak and the 205Pb(82+) peak is provided for each storage measurement. The full Schottky spectrum can be made available on request. As described in the related works below, the Schottky data became saturated above a certain threshold due to a mismatched amplifier in the NTCAP DAQ. This results in non-exponential decay of Schottky peaks for high intensities. </li> <li>DCCT data: the entire beam current in the ring was monitored using the DCCT. The DCCT was recorded using a scalar counter and thus has a non-zero offset value, which we determined to be 28.199 µA from a period with no beam. The DCCT is intended to be used as a diagnostic tool, and is not as precise as other, purpose-built detectors.</li> <li>MWPC data: for most storage measurements, a MWPC detector was placed downstream of the gas target on the outside of the ring to detect electron recombination products. The provided data is the detection rate on the anode.</li> <li>The gas target density, as recorder by a scaler counter, is also included.</li> </ol> <p>The intermediate data on all three of these detectors plus the gas target density is provided in the tar.gz repository, with individual ROOT files for each storage time. The ROOT files have the naming format "Storage time_MMDD_HH.root". Each file contains 5 TGraph objects.</p> <p><strong>Result Data: </strong>The result data provides all the necessary individual measurement and correction values to extract a bound-state beta-decay rate from the corrected ratios. It is provided in two forms:</p> <ol> <li>BSBD_205Tl-result_data.ods is an ODS table for easy visualisation.</li> <li>BSBD_205Tl-final_vals.txt is a text file used by the Monte Carlo analysis script provided in <a href="https://doi.org/10.5281/zenodo.11560338" target="_blank" rel="noopener">DOI 10.5281/zenodo.11560338</a>.</li> </ol> <p>The "Ratio" column is the corrected 205Pb/205Tl decay ratio for each storage, following Equation (1) of <a href="https://www.nature.com/articles/s41586-024-08130-4" target="_blank" rel="noopener">Leckenby et al. (2024) Nature 635:321–326</a>. 1 sigma error bars, both including and not-including the estimated contamination variation, are provided.</p> <p>Please refer to the related works below or contact the authors for more details.</p>
Dataset containing binominal lexemes in Harakmbut (isolate, Peru), for "The derivational use of classifiers in Western Amazonia" and "When the alienability contrast fails to surface in adnominal possession: Bound nouns in Harakmbut"
<p>This is the dataset used, amongst others, in the paper: Van linden, An. Forthcoming. When the alienability contrast fails to surface in adnominal possession: Bound nouns in Harakmbut. Special Issue “Re-assessing the explanatory potential of alienability contrasts”, guest-edited by Françoise Rose & An Van linden. <em>Linguistics – An Interdisciplinary Journal of the Language Sciences</em>. [<a href="https://doi.org/10.1515/ling-2022-0039">https://doi.org/10.1515/ling-2022-0039</a>]</p> <p>For more details, see the ReadMe file.</p>
Bounding box coordinates of von Karman vortex street
<p>Those files contain bounding box coordinates of von Karman vortex street annotated by VoTT. The von Karman vortex street is annotated as one object in vortex_street.tar.gz, while each vortex in the von Karman vortex street is annotated as one object in vortices.tar.gz. The original video file is from https://doi.org/10.1063/1.4921683.1 to 1.4921683.9. See also the reference.</p>
All-atom 500-nano seconds Molecular Dynamics Simulations of SARS-CoV-2 Spike Receptor-binding Domain bound with ACE2
<p>Data includes all of the trajectories (1000) of classical all-atom molecular dynamics (MD) simulations of of SARS-CoV2 Spike Protein/ACE2 complex (PDB ID: 6M0J). In order to decrease the size of the file only protein rajectories were provided. Simulation has been performed with Desmond. Protein was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 500 nano-seconds (ns) production run was performed for the simulation.</p>
CHARMM27 dynamics simulation trajectories of α-conotoxin LsIA and its C-terminal carboxylated analogue bound at α3β2 nAChR
<p>The whole simulation trajectories (28 individual trajectories with 27ns for each) contain the coordinates and parameters of atoms with time for α-conotoxin LsIA and its C-terminal carboxylated analogue anchored to rat α3β2 nAChR, respectively. The GROMACS 4.6.5 with the CHARMM27 force field is used for the simulation. The trajectory (.xtc) files are saved every 100ps time for each protein complex only. The portable binary run input (.tpr) files are also uploaded with the data. </p>
X-ray diffraction images for L-threonine dehydrogenase from Trypanosoma brucei with NAD and pyruvate bound.
<p>X-ray diffraction images which were collected at ESRF (Grenoble) using an ADSC 315r CCD detector on beamline ID29 on 11th November 2009. More details are given in the uploaded notes. </p>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors
<p>This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.</p>
X-Ray Diffraction data from Membrane transport protein AcrB, V612F mutant with bound minocycline, source of 9FHC structure
<p>Crystals were grown of the membrane transport protein AcrB, V612F mutant, with bound minocycline. </p> <p>X-ray diffraction data of this upload: 400 frames of 0.5° width were collected on 2007-04-30 at the X06SA beamline of Swiss Light Source at Paul-Scherrer-Institute (Switzerland).</p> <p>The data can be processed with XDS; XDS.INP is provided as part of the upload.</p> <p>The data are the basis of the PDB 9FHC structure.</p>
Improved upper bounds for permutation flowshop scheduling benchmarks (Taillard and VRF)
<p>Optimal makespans and permutation schedules (found and proven optimal by Branch-and-Bound) for Taillard instances Ta112, Ta116 (500 jobs, 20 machines) and 74 instances of the VRF benchmark.</p>
Data supporting publication: Revealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy
<p>This repository includes the data corresponding to the figures shown in the journal article entitledRevealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy, published in Advanced Materials on 02.08.2024</p>
Cholec80-Boxes: Bounding-Box Labels for Surgical Tools in Five Cholecystectomy Videos
<p> </p> <p>The dataset is descriped in a pending publication titled "Cholec80-Boxes: Bounding-box Labeling Data for Surgical Tools in Cholecystectomy Images". The dataset was used in the following studies titled:</p> <ul> <li>"Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach".</li> <li>"Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches".</li> <li>"Analysing attention convolutional neural network for surgical tool localisation: A feasibility study".</li> </ul> <p>The dataset consists of cholecystectomy images and bounding-box labels for surgical tools. These images were extracted from five videos of the Cholec80 dataset (Twinanda et al., 2016) at a rate of 1 Hz. The images are stored in '.png' format with a resolution of 854*480 pixels. Each video’s images are organized in a separate folder. The labeling data are stored in a CSV file, which contains the region of interest (ROI) labels for each surgical tool visible in the extracted images. Additionally, the CSV file provides information about each labeled image. Table 1 presents a content description of the 'ROI_Labels.csv' file.</p> <p><strong>Table 1:</strong> Description of 'ROI_Labels.csv' file.</p> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Description</strong></td> <td><strong>Type</strong></td> </tr> <tr> <td><em>Surgery_num</em></td> <td>Procedure number in the Cholec80 dataset from which the image was extracted.</td> <td>Integer</td> </tr> <tr> <td><em>Dir</em></td> <td>Directory of the image folder.</td> <td>String</td> </tr> <tr> <td><em>FrameName</em></td> <td>Image name in the format '<em>Video_SS_fffff.png', </em>where <em>SS is the Surgery_num and fffff is the frame number in the video.</em></td> <td>String</td> </tr> <tr> <td><em>NumBBox_inFrame</em></td> <td>The bounding-box number in the image.</td> <td>Integer</td> </tr> <tr> <td><em>ToolName</em></td> <td>Name of the surgical tool.</td> <td>String</td> </tr> <tr> <td><em>BBox</em>_<em>X</em></td> <td>X-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Y</em></td> <td>Y-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Width</em></td> <td>Bounding box width.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Height</em></td> <td>Bounding box height.</td> <td>Integer</td> </tr> </tbody> </table> <p> </p> <p><strong>Citing This Dataset:</strong></p> <p>When using this dataset, please cite the following publications:</p> <ul> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Laufer, B., Krueger-Ziolek, S., Neumuth, T. & Moeller, K. (2023). Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches. <em>Sensors</em>, <em>23</em>(4), 1958.<br><br></li> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Neumuth, T., & Möller, K. (2023). Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach. IFAC-PapersOnLine, 56(2), 5626-5631.</li> <li> <p>Abdulbaki Alshirbaji, T., Arabian, H., Jalal, N. A., Battistel, A., Docherty, P. D., Neumuth, T., & Moeller, K. Cholec80-Boxes: Bounding-box labeling data for surgical tools in cholecystectomy images. (<em>to be submitted</em>). </p> </li> <li>Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., & Padoy, N. (2016). Endonet: a deep architecture for recognition tasks on laparoscopic videos. <em>IEEE transactions on medical imaging</em>, <em>36</em>(1), 86-97.</li> </ul>
Brightfield images of cells and spheroids in wells annotated with bounding boxes
<p>The images in this dataset show cells in different developmental stages upon forming spheroids. They can go through several developmental sages: Starting from cells, they turninto compacted objects and then into spheroids. Ultimately, they can die and disintegrate. The objects are annotated with bounding boxes that carry these respective labels. The dataset in its current form can be upload to an OMERO server using the omero-cli-transfer package - simply download the zip file, log into your omero server and use the `omero transfer unpack` command as shown on the <a href="https://github.com/ome/omero-cli-transfer">omero-cli-transfer documentation</a>.</p> <p>The dataset can be used to train object detection models such as a <a href="https://docs.ultralytics.com/models/yolov8/">yolo classifier </a>- the linked repository provides a tutorial on how to do so.</p>
New upper bounds for some instances from benchmark for vector packing problem
<p>This dataset is a result of the research: Đorđe Stakić, Miodrag Živković, Ana Anokić, "A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem", Information Technology and Control, 2021, 50(4), 808-826, <a href="https://doi.org/10.5755/j01.itc.50.4.29009">https://doi.org/10.5755/j01.itc.50.4.29009</a> Files are given by algorithm described in it. </p> <p>This dataset consists of 14 solutions with better bounds for instances described in paper: Heßler, K., Gschwind, T., Irnich, S. Stabilized branch-and-price algorithms for vector packing problems. European Journal of Operational Research, 2018, 271(2), 401-419. <a href="https://doi.org/10.1016/j.ejor.2018.04.047">https://doi.org/10.1016/j.ejor.2018.04.047</a> </p> <p>File structure: </p> <p>Instance name: UB: solution (bins with indices of items)</p> <ul> <li>CL_04_100_06: 627</li> <li>CL_04_100_08: 642</li> <li>CL_04_200_01: 1293</li> <li>CL_05_100_06: 314</li> <li>CL_05_100_08: 321</li> <li>CL_05_100_10: 327</li> <li>CL_05_200_02: 627</li> <li>CL_05_200_03: 633</li> <li>CL_05_200_04: 630</li> <li>CL_05_200_05: 632</li> <li>CL_05_200_06: 627</li> <li>CL_05_200_07: 634</li> <li>CL_05_200_08: 635</li> <li>CL_05_200_10: 632</li> </ul>
Ancillary files for "Reinterpreting the ATLAS bounds on heavy neutral leptons in a realistic neutrino oscillation model [arXiv: 2107.12980]"
<p><em>(Description copied from Appendix A "Ancillary files" of the companion paper)</em></p> <p>In order to simplify the interpretation of experimental results within realistic HNL models, we are including a number of data files along with the present publication. They can be used to generate the relevant signal samples, or to implement the extrapolation method presented in section 3.2.</p> <p><strong>Card files for the Monte-Carlo event generation</strong></p> <p>The /attachments/card_files folder contains the MadGraph card files (ending in .dat) and scripts (ending in .txt) for generating the signal samples used in this analysis, as well as for computing the total HNL width. Due to the OSSF veto, only processes with no opposite-charge same-flavor lepton pairs have been included. Additional relevant processes can easily be added by modifying the <em>generate</em> and <em>add process</em> lines in the *.txt files. All samples (except the ones used to compute the HNL width, which are generated at parton level) are generated at leading order, include up to two hard jets, and are showered and hadronized using Pythia 8. This is essential for obtaining a realistic W spectrum. The shower parameters could probably benefit from further tuning, and further improvements in the W spectrum accuracy are expected at NLO (using a suitable model). To allow computing the signal efficiencies, all cuts have been disabled in the run card (with the exception of the maximum <span class="math-tex">\(|\eta_{\mathrm{jet}}|\)</span> which needs to be set to 5 for correct matching).</p> <p><strong>Signal cross sections</strong></p> <p>The cross sections for the various processes considered in this analysis, as well as the total HNL width (both computed using MadGraph as described in section 3.2), are provided as JSON files in the /attachments/cross_sections folder.</p> <p>The file total_hnl_width.json contains the total HNL width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> (expressed in GeV), computed for the 5 mass points used in this analysis, and under the assumption of unit mixing with a single flavor <span class="math-tex">\(\alpha\)</span>, for each flavor. The total HNL width can then be computed for any combinations of mixing angles using eq. (3.2). The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N\)</span>, and the second one the flavor <span class="math-tex">\(\alpha\)</span> for which the total width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> has been computed for a unit mixing angle <span class="math-tex">\(|\Theta_{\alpha}|^2 = 1\)</span> (with <em>Wtot_e</em> for <span class="math-tex">\(\alpha=e\)</span>, <em>Wtot_mu</em> for <span class="math-tex">\(\mu\)</span> and <em>Wtot_tau</em> for <span class="math-tex">\(\tau\)</span>).</p> <p>The file cross_sections.json contains the reference cross sections <span class="math-tex">\(\sigma_P^{\mathrm{ref}}\)</span> (in pb) for all the processes <em>P</em> considered in this analysis, expressed for <span class="math-tex">\(|\Theta|_{\mathrm{ref}}^2 = 1\)</span> and <span class="math-tex">\(\Gamma_{\mathrm{ref}} = 10^{-5}\,\mathrm{GeV}\)</span>. The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N \)</span> and the second the process <em>P</em>. The correspondence between the key and the physical process can be found in table 7.</p> <p><strong>Signal efficiencies</strong></p> <p>The efficiencies resulting from the event selection described in section 3.1, as well as their parametrization according to eq. (3.6) (as discussed in section 3.3) can respectively be found in the files efficiencies.json and fitted_efficiencies.json in the /attachments/efficiencies folder.</p> <p>The file efficiencies.json is organized as follows. The data is located in a triply nested dictionary under the data key: the first level corresponds to the HNL mass hypothesis <span class="math-tex">\(M_N\)</span>, the second to the process key (cf. table 7) and the third to the <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bin for which the efficiency is computed. The values of the bottom-most dictionary are lists containing the efficiencies for a number of HNL lifetimes, as listed in meters in levels/lifetime.</p> <p>Finally, the file fitted_efficiencies.json is also organized as a triply nested dictionary, with the first level corresponding to the HNL mass <span class="math-tex">\(M_N\)</span>, the second to the process key, and where the third level denotes the fit parameter from eq. (3.6). tau0 is for <span class="math-tex">\(\tau_0\)</span>, epsilon0_total for <span class="math-tex">\(\epsilon_0\)</span> (the unbinned prompt efficiency), and epsilon0_binned is a list containing the prompt efficiencies <span class="math-tex">\(\epsilon_{0,b}\)</span> for the five <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bins <em>b</em> (in the same order as in efficiencies.json). The layout described here (or a similar one) can be used by experiments to report their signal efficiencies in a way that allows theorists to compute the expected signal for arbitrary choices of mixing angles.</p>
Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein
<p>These data support the manuscript entitled "Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein" by Heller, Shukla, Figueiredo, and Hansen.</p><p>This data should be used with the code provided on GitHub at https://github.com/hansenlab-ucl/R2_IDP_small_mol. Once downloaded, this directory should be extracted using the following command:</p><p> tar -xzvf Data.tar.gz</p><p>The directory should be saved with the name 'Data' placed in the same directory as the GitHub README.md file.</p><p><strong>This dataset contains: </strong><br><i>Nuclear Magnetic Resonance (NMR) spectroscopy data files (.ft2 format) including: </i></p><p>* 1H 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of the protein, non-structural protein 5A, domains 2 and 3 (NS5A-D2D3), in 1H_1D_ft2_data/</p><p>* 1H pseudo-2D Diffusion Ordered SpectroscopY (DOSY) data of 5-fluoroindole (50 uM) with and without NS5A-D2D3 (75 uM) in 1H_DOSY_data/</p><p>* 1H-15N Heteronuclear Single Quantum Coherence (HSQC) measurements of NS5A-D2D3 (40 uM) in the absence and presence of 5-fluoroindole (160 and 320 uM) in 1H_15N_HSQC_ft2_and_metadata/</p><p>* 19F 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_1D_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-lattice, R1,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R1eff_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-spin, R2,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R2eff_ft2_data/</p><p><i>Circular Dichroism (CD) data files (.txt format) including: </i></p><p>* CD measurements of NS5A-D2D3 at increasing concentrations in CD_data/no_molecule/</p><p>* CD measurements of NS5A-D2D3 with and without the small molecule, 5-fluoroindole CD_data/with_molecule/</p><p><i>Metadata </i></p><p>* Metadata from the Biological Magnetic Resonance Data Bank (https://bmrb.io/) used to determine scaling factors for the calculation of chemical shift perturbations in 1H_15N_HSQC_ft2_and_metadata/</p>
The Upper Bound of Information Diffusion in Code Review
<p>More details on <a href="https://github.com/michaeldorner/information-diffusion-boundaries-in-code-review">https://github.com/michaeldorner/information-diffusion-boundaries-in-code-review</a></p>
Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"
<p>The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.</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.