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307 results for “nonlinearity”
Incremental Linearization for Satisfiability and Verification Modulo Nonlinear Arithmetic and Transcendental Functions
<p>The tarball contains Satisfiability Modulo Theories (SMT) and Verification Modulo Theories (VMT) benchmarks for the theories of Nonlinear Real Arithmetic (NRA) and NRA extended with Transcendental Functions (NTA). These benchmarks have been collected in the following works:</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Invariant Checking of NRA Transition Systems via Incremental Reduction to LRA with EUF". In proc. Tools and Algorithms for the Construction and Analysis of Systems, TACAS'17, 2017.</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Satisfiability Modulo Transcendental Functions via Incremental Linearization". In proc. Int. Conference on Automated Deduction, CADE, 2017.</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Incremental Linearization for Satisfiability and Verification Modulo Nonlinear Arithmetic and Transcendental Functions". ACM Transactions on Computational Logics. 2018. To appear.</p>
Data for "Nonlinear Trapping Stiffness of Mid-Air Single-Axis Acoustic Levitators"
<p>Data associated with the manuscript entitled "Nonlinear Trapping Stiffness of Mid-Air Single-Axis Acoustic Levitators".</p>
Nonlinear imaging microscopy for assessing structural and photochemical modifications upon laser removal of dammar varnish on photosensitive substrates
<p>Varnish layers are commonly used to protect painted surfaces from atmospheric pollution, oxidation<br> and improve the aesthetic appearance of the artwork by providing an even surface finish, brilliance and<br> depth to the colours. However, the outer varnish layers suffer from progressive deterioration due to<br> aging and the continuous exposure to aggressive environmental conditions, imposing the need of their<br> removal for rectifying the optical and aesthetic properties of the painting and extend its lifetime. The<br> removal of the surface varnish layer, without affecting the painting substrate, comprises a delicate<br> intervention in cultural heritage (CH) conservation.<br> The main objective of this study is to determine by nonlinear imaging microscopy (NLM) the extent of<br> the photochemical damage that could be induced on underlying painting layers by laser removal of<br> varnish protective coatings. This will lead to the identification of the optimal laser cleaning conditions<br> that produce the minimum collateral damage to the painting layers.<br> The current study is undertaken using model samples constituted by bilayers, where the top varnish<br> layer (dammar) coats a bottom layer constituted by a doped synthetic polymer (polymetilmetacrilate,<br> PMMA doped with POPUP) film, the latter mimicking a paint layer. The target is to determine the<br> affected region as a function of depth of the doped polymer layer induced by laser ablation of the<br> varnish. To this aim we use the non destructive NLM imaging modalities of third harmonic generation<br> (THG) and multiphoton excitation fluorescence (MPEF) as novel diagnostic tools and a number of<br> laser conditions for varnish removal, namely different ultraviolet (UV) wavelengths and pulse durations.<br> Characterization of the samples by NLM is complemented by spectroscopic micro-Raman and laser<br> induced fluorescence (one-photon excitation) measurements. These provide a full characterization of<br> the lateral and in-depth chemical and morphological changes following laser removal of the varnish<br> protective layer.</p>
Nonlinear imaging microscopy for assessing structural and photochemical modifications upon laser removal of dammar varnish on photosensitive substrates
<p>The nonlinear optical microscopy (NLM) modalities of Multi-Photon Excited Fluorescence (MPEF) and Third Harmonic Generation (THG) have been combined in this work to characterize as a function of depth with micrometric resolution the type and extent of morphological and photochemical modifications that take place upon ultraviolet (UV) pulsed laser removal of a dammar varnish layer applied on a photosensitive substrate. The latter consists on a layer of the synthetic polymer polymethyl methacrylate doped with a photosensitizer, the aromatic compound 1,4-di[2-(5-phenyloxazolyl)] benzene, that strongly fluoresces upon UV light illumination. A number of laser conditions for partial or total elimination of the varnish coating were explored, namely different wavelengths (266, 248 and 213 nm) and pulse durations, in the nanosecond, picosecond and femtosecond ranges. Changes in the MPEF signals upon laser ablation of the outermost varnish layer successfully signpost photochemical modifications of the varnish or of the photosensitive under-layer, and their dependence with the laser ablation parameters, i.e., wavelength and pulse duration. In turn, THG signals mark the presence of layer boundaries and the reduction by laser ablation of the thickness of the varnish coating. The obtained MPEF and THG data are complemented by morphological observation by optical microscopy and measurements of laser induced fluorescence and micro-Raman spectra of the samples before and after laser ablation at the selected laser irradiation conditions. The results acquired through these nondestructive NLM imaging techniques serve to understand the phenomena that are induced upon laser ablation and to determine the best operating conditions that ensure controlled removal of the varnish with minimal morphological and chemical modifications to the under-layers. This research is of direct application to the UV pulsed laser cleaning of paintings and demonstrates the potential of NLM as a novel assessment tool for non-destructive, on line monitoring of the laser cleaning process.</p>
Automated discovery of reprogrammable nonlinear dynamic metamaterials — Data
<p>This dataset includes optimization and experimental data complementing the paper:</p> <p><a href="https://doi.org/10.1038/s41563-024-02008-6" target="_blank" rel="noopener">G. Bordiga, E. Medina, S. Jafarzadeh, C. Boesch, R. P. Adams, V. Tournat, K. Bertoldi. Automated discovery of reprogrammable nonlinear dynamic metamaterials. <em>Nature Materials.</em> (2024)</a>.</p> <p>Optimization and post-processing data in this dataset were generated using the code <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> developed for the paper. This dataset can be loaded and visualized using <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> with the following steps:</p> <ul> <li>Install <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Download <code>data.zip</code> from this dataset.</li> <li>Extract <code>data.zip</code> and place its content in a <code>data</code> folder in the root of <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Load the data associated with the design problems shown in the paper using the <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/notebooks" target="_blank" rel="noopener">notebooks</a>.</li> </ul> <p>For more information on each problem, please refer to the <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">README</a>.</p> <p>Videos illustrating the solved design problems can be viewed at <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/videos" target="_blank" rel="noopener">DifFlexMM/videos</a>.</p>
Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)
<p>Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).</p> <p>My thesis was structured along the following objectives:</p> <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>
Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2
<p>This data set contains all resources for the research project "<span>Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2" (published in Nature Communications (2024)).</span></p>
Nonlinear bi-color holography using plasmonic metasurfaces
<p>Dataset of the publication “Nonlinear bi-color holography using plasmonic metasurfaces“, Daniel Frese, Qunshuo Wei, Yongtian Wang,<br> Mirko Cinchetti, Lingling Huang, and Thomas Zentgraf, ACS Photonics (2021), 8(4), pp. 1013-1019 ( <a href="https://doi.org/10.1021/acsphotonics.1c00028">https://doi.org/10.1021/acsphotonics.1c00028</a>). The files includes the data on which the plots shown in figure 2, 3, and 4 are based.</p>
Two-story frame with Bouc-Wen hysteretic links as a multi-degree of freedom nonlinear response simulator
<p><strong>Two-story frame with Bouc-Wen hysteretic links as a multi-degree of freedom nonlinear response simulator</strong></p> <p>Standardized datasets for tasks related to system identification applications, reduced-order or surrogate modelling applications. A multi-degree of freedom nonlinear response simulator benchmark proposed in the <em><strong>5th Edition of the Workshop on Nonlinear System Identification Benchmarks</strong></em> (April 2021, <a href="https://sites.google.com/view/nonlinear-benchmark/benchmarks">Link</a>).</p> <p>The open-access software implementation of the frame can be found in this <a href="https://github.com/KosVla/NonlinearBoucWenFrameBenchmark"><em><strong>Github repository</strong></em></a>.</p>
Dataset for the publication "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities"
<p>This is dataset for paper published:</p> <p>G. D’Avanzo <em>et al</em>., "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 71, pp. 1-12, 2022, Art no. 9001312, doi: 10.1109/TIM.2022.3147883.</p>
Data for: Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations
<p>Magnetic Resonance Imaging measurement data used in our work about "Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p><br> Further information about the individual datasets:</p> <p>data_GSM_t1<br> Type: Gold-Standard T1 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|15<br> FOV [mm]: 200<br> T_INV [ms]: 30:250:2530</p> <p>data_GSM_t2<br> Type: Gold-Standard T2 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|(15:40:455)<br> FOV [mm]: 200</p> <p>data_05b_b1map<br> Type: B1 Map<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_05b_kspace<br> Type: Radial Single-Shot Dataset<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_06_irbssfp_long<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 10.8|5.4<br> FA [deg]: 45<br> T_RF [ms]: 2.5<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irbssfp_short<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 4.1|2.58<br> FA [deg]: 6<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_s03_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_s03_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.75|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_5ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 6.14|3.07<br> FA [deg]: 35<br> T_RF [ms]: 2.5<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_1ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.5|2.75<br> FA [deg]: 35<br> T_RF [ms]: 2.1<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.0|2.5<br> FA [deg]: 35<br> T_RF [ms]: 1.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_2ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.6|2.3<br> FA [deg]: 35<br> T_RF [ms]: 1.2<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4|2<br> FA [deg]: 35<br> T_RF [ms]: 0.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_4ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 3.8|1.9<br> FA [deg]: 35<br> T_RF [ms]: 0.4<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p> </p>
Local response and emerging nonlinear elastic length scale in biopolymer matrices
<p>Dataset corresponding to the underlying numerical and experimental data of the research article "Local response and emerging nonlinear elastic length scale in biopolymer matrices". </p> <p>This repository contains four categories of data, each contained in a folder: <br> - Fiber Network Simulations <br> - Finite Elements Simulations <br> - Optical Tweezer Experiments<br> - Traction Force Microscopy<br> In each folder, a README.txt document provides a detailed description of the content.</p> <p>We would like to acknowledge the support from the NIH (1R01GM140108), MathWorks, and the Jeptha H. and Emily V. Wade Award at the Massachusetts Institute of Technology. H.Y. acknowledges the MathWorks Mechanical Engineering Fellowship. M.G. acknowledges the Sloan Research Fellowship. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 891217 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 201269156 - SFB 1032 (Project B12) (E.B. and C.P.B.). P.R. is supported by France 2030, the French National Research Agency (ANR-16-CONV-0001), and the Excellence Initiative of Aix- Marseille University—A*MIDEX.</p>
Accompanying data for the paper "Reduced order modeling of geometrically nonlinear rotating structures using the direct parametrisation of invariant manifolds"
<p>Links</p><ul><li>isSupplementTo <i>publication-article</i> <a href="https://doi.org/10.46298/jtcam.10430">https://doi.org/10.46298/jtcam.10430</a></li><li>isSupplementedBy <i>software</i> <a href="https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147">https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147</a></li></ul><p>Language</p><ul><li>English</li></ul><p>License</p><ul><li>Creative Commons Attribution 4.0</li></ul><p>Contributions</p><ul><li>Adrien MARTIN carried out the main part of study, defined the examples, performed the numerical simulations and drafted the manuscript;</li><li>Andrea OPRENI and Alessandra VIZZACCARO developed the methodology and built the main parts of the Julia code implementing the reduction method;</li><li>Andrea OPRENI developed the first version of the HBFEM code which has been updated for rotation in collaboration with Adrien MARTIN;</li><li>Marielle DEBEURRE performed all the simulations shown in Appendix C related to the Timoshenko beam model with continuation;</li><li>Loïc SALLES supervised the work, discussed applications to blades, and helped in designing and understanding the twisted plate model;</li><li>Attilio FRANGI supervised the work and help in the development of the methodology;</li><li>Olivier THOMAS helped in all discussions related to the comparisons with the thin beam example and wrote Appendix C;</li><li>Cyril TOUZE supervised the work, carried out most of the writing and developed the methodology;</li></ul><p>All authors read and approved the final manuscript.</p><p>Data collection: period and details</p><ul><li>Datasets produced between September and December 2022</li></ul><p>Funding sources</p><ul><li>Funding from AID (Agence de l'Innovation de Défense), project REMODEL, contract number 2020 65 0057 ENSTA</li></ul><p>Data structure and information</p><ul><li>README.md: Contains the general information concerning this dataset</li></ul><p>Figures</p><ul><li>fig_1: description of the rotating beam</li><li>fig_2(a,b,c,d): Linear characteristics of the rotating cantilever beam</li><li>fig_3(a,b): FRC of the rotating cantilever beam around 1F mode</li><li>fig_4: Convergence of the non-autonomous part of DPIM for the 1F mode</li><li>fig_5(a,b,c,d,e,f): Interpolation of the coefficients of the autonomous ROM</li><li>fig_6(a,c): Hardening/softening behaviour of the rotating beam; fig 6b is a zoom on fig 6a</li><li>fig_7(a,b,c): Comparisons of FRCs obtained from interpolated ROMs with FOM solution</li><li>fig_8a: FRC of the rotating cantilever beam around 2F mode; fig 8b is a zoom of fig 8a</li><li>fig_9(a,b,c,d): fig 9 a-b-c : geometry of the blade and some modes and static displacements; fig 9d : Campbell diagram of the blade</li><li>fig_10: FRC of the twisted plate</li><li>fig_11(a,b,c): Computing time and convergence analysis with respect to mesh refinement for the fan blade</li></ul><p>fig_12(a,b,c,d): FRC of interpolated ROMs with increasing degrees compared to reference solution</p><p>fig_A_1: Campbell diagram of the beam : impact of Coriolis effects</p><ul><li>fig_C_3(a,b,c,d,e,f,g,h,i): Comparison of the results on the beam studied between DPIM and article from Thomas for 1F and 2F modes</li><li>fig_C_2(a, b): Comparison of the results on the beam studied between : DPIM, article from Thomas and results from Debeurre</li></ul>
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
<p>Neurons enhance their computational power by combining linear and nonlinear transformations in extended dendritic trees. Rich, spatially distributed processing is rarely associated with individual synapses, but the cone photoreceptor synapse may be an exception. Graded voltages temporally modulate vesicle fusion at a cone's ~20 ribbon active zones. The transmitter then flows into a common, glia-free volume where bipolar cell dendrites are organized by type in successive tiers. Using super-resolution microscopy and tracking vesicle fusion and postsynaptic response at the quantal level in the thirteen-lined ground squirrel, <em>Ictidomys</em> <em>tridecemlineatus</em>, we show that certain bipolar cell types respond to individual fusion events in the stream while other types respond to degrees of locally coincident events, creating a gradient across tiers that are increasingly nonlinear. Nonlinearities emerge from a combination of factors specific to each bipolar cell type including diffusion distance, contact number, receptor affinity, and proximity to transporters. Complex computations related to feature detection begin within the first visual synapse.</p>
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
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Data from: Machine learning without a processor: Emergent learning in a nonlinear analog network
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Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
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Data from: Geometrical frustration in nonlinear mechanics of screw dislocation
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Accounting for nonlinear responses to traits improves range shift predictions
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Nonlinear scattering of 90-degree pitch angle electrons in the outer radiation belt by large-amplitude EMIC waves
<p>This file is a collection of numeric data files which were used to create all figures in the paper (under review as of December 19, 2019). All the data were obtained from numerically solving the Lorentz equation of motion for 5 MeV electrons under EMIC waves superposed onto a dipolar background magnetic field. </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.