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1,938 results for “Resonances”
Dataset for: Non-Markovian effects of two-level systems in a niobium coaxial resonator with asingle-photon lifetime of 10 milliseconds
<p> Datasets for the publication "Non-Markovian effects of two-level systems in a niobium coaxial resonator with asingle-photon lifetime of 10 milliseconds ", Physical Review Applied (2021). The upload contains the data as well as the evalationb routines to repdroduce the figures in the publication.</p>
Open Dataset for publication: Resonant laser ionization and mass separation of Ac-225
<p>The dataset for publication in preparation:</p> <p>Resonant laser ionization and mass separation of Ac-225.</p> <p> </p> <p>Includes all data analysed for results given in the publication.</p>
Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics
<p>We share experimental data for Figs. 2,4,5,6,11,12,13 of arxiv manuscript arXiv:2211.12469 [physics.app-ph] entitled "Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics".</p>
Normalized daily average Schumann resonance (SR) intensity data from 13 to 31 January, 2019
<p>Data are provided for the whole globe (T), South America (SA), Africa (AF) and Asia (AS).</p> <p>File prepared by Tamás Bozóki (20 January, 2023)<br> Contact: bozoki.tamas@epss.hu</p>
Engineering Fano-resonant hybrid metastructures with ultra-high sensing performances
<p>Here the data related to our recent work that uses metasurfaces designed on metamaterials to excite Fano resonances and Rabi Split analogue modes for sensing.</p> <p> </p> <p>In this repository the following data are available:</p> <ul> <li>The Reflectance, Transmittance and Absorbance (1-T-R) evaluated over all the diffraction angles (from 0° to 89°) for the proposed structure (ring + cross) and the other tested structures, available in the .mat file.</li> <li>The .m file to repeat all the simulations are available in main folder. The available Matlab codes allow reproducing numerical simulations using Reticolo (RCWA) as the main framework to do the computation.</li> <li>Use "getrefractiveindex.m" to interpolate refractive index on finest wavelength range. Please, note that according to the uploaded codes this function have to be in the same folder of the loaded refractive indices. </li> <li>You can use the function "textprogressbar.m" to get a progress bar in real time to know the status of the current simulation. Please, note that this function have to stay in the "/RETICOLO V8/reticolo_allege" folder. </li> <li>One of the available Matlab code allows displaying the electric and magnetic field evaluated in the structure section or on the top view. </li> <li>The sensing test performed for the reported structures using different surrounding medium, for this purpose it is possible to use the codes that allow studying the parameters variation, the incident angle variation or just a single run changing the refractive index of the surrounding medium. </li> </ul> <p>The proposed code works using the Reticolo main code. To download it please visit the following link <a href="https://www.lp2n.institutoptique.fr/light-complex-nanostructures">https://www.lp2n.institutoptique.fr/light-complex-nanostructures</a> or using Zenodo <a href="https://zenodo.org/record/5905381#.Y8rvbC9abpA">https://zenodo.org/record/5905381#.Y8rvbC9abpA</a></p> <p>Once Reticolo has been downloaded copy and paste its path in the proposed Matlab code for Fano resonances on ENZ cavities as reported in the example codes.</p> <p>In this repository you will find the refractive indices that have been used.</p>
Reverberant Magnetic Resonance Elastographic Using a single Mechanical Driver
<p>Reverberant elastography provides fast and robust estimates of shear modulus. However, reverberant elastography uses multiple mechanical drivers, hampering clinical utility. In this work, we hypothesize that a single mechanical driver can generate reverberant shear fields in constrained organs such as the brain. To corroborate this hypothesis, we imaged the brain of a healthy volunteer; and two constrained phantoms containing spherical inclusions with diameters ranging from 4-18 mm. As a secondary goal, we assessed the feasibility of recovering shear modulus from a single component of the reverberant wave field. Viable reverberant and subzone elastograms were produced only when obtained at 50 and 100 Hz in phantoms. Different levels of reverberance were exhibited in different displacement components (70-82% for phantoms and 87-93% for the clinical case); however, wavefields obtained when imaging at 50 Hz and 100 Hz were not significantly different (p>0.05). Errors incurred in reverberant elastograms varied from 5% to 65% when imaging at 50 Hz and 2% to 55% when imaging at 100 Hz. Errors incurred in subzone elastograms ranged from 4% to 18% at 50 Hz and 5% and 50% at 100 Hz. The contrast-to-noise ratio of reverberant elastograms ranged from 20 dB to 44 dB compared to 25 dB to 31 dB in subzone elastograms. The accuracy of the elastograms acquired from the phantom containing internal shear wave reflectors did not differ noticeably. The global brain stiffness estimated from reverberant and subzone elastograms was 2.36 ± 0.95 kPa and 2.5 ± 1.1 kPa, respectively, when imaging at 50 Hz, and 2.56 ± 0.828 kPa and 2.89 ± 1.3 kPa respectively, when imaging at 70 Hz. The phantom study revealed that performance varied depending on the component of displacement used to compute reverberant elastograms; however, the clinical study demonstrated similar performance of reverberant and subzone elastograms</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>
Surface acoustic wave resonators on thin film piezoelectric substrates in the quantum regime - data archive
<p>The Archive contains all raw and processed (fitted) data that is used in the manuscript "surface acoustic wave resonators on thin film piezoelectric substrates in the quantum regime" submitted to IOP Materials for quantum technology.</p>
Data for: Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". 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>).<br> </p> <p>Further information about the individual datasets:</p> <p>data_invivo_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_invivo_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_invivo_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp_shim<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7<br> missing shim break</p> <p>data_vfa_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_vfa_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_vfa_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_20<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 20<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_40<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 40<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_45<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_50<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 50<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_60<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 60<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_70<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 70<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_77<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 77<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p>
Data Supplement: GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic Resonance Image (MRI) Reconstruction in Julia
<p><strong>Dataset for GIRFReco.jl Paper</strong><br> <br> Please download this and extract to an appropriate location prior to running the demonstration code in GIRFReco.jl. The extracted folder will serve as the root directory in the demo code.</p>
Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"
<p>Simulation data used in the publication Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper </p>
Understanding unconventional magnetic order in a candidate axion insulator by resonant elastic x-ray scattering - Data set
<p>This data set includes the resonant elastic xray scattering (REXS) data collected at the I16 (Diamond Light Source, United Kingdom) and P09 (DESY, Germany) beamline, along with the magnetization data.</p> <p>I16 Data<br> - Temperature dependence of the L=15 reflection<br> - Temperature dependence of the L=14.333 reflection<br> - 00L dependence (6K - 20K)<br> - Azimuthal dependence of L=15 reflection<br> - Azimuthal dependence of L=13.667 reflection</p> <p>P09 Data<br> - Field dependence of L=15 reflection (pi-pi channel)<br> - Field dependence of L=15 reflection (pi-sigma channel)<br> - Field dependence of L=14.333 reflection (pi-pi channel)<br> - Field dependence of L=14.333 reflection (pi-sigma channel)</p> <p>Magnetization Data<br> - Field Dependence of the Magnetization<br> </p>
Data for 'Probing resonating valence bonds on a programmable germanium quantum simulator' by Chien-An Wang et al.
<p>Data and python scripts for 'Probing resonating valence bonds on a programmable germanium quantum simulator' by Chien-An Wang et <em>al</em>.</p> <p>Corresponding authors Corentin Déprez (C.C.Deprez@tudelft.nl) // Menno Veldhorst (M.Veldhorst@tudelft.nl)</p>
Over-coupled resonator for broadband surface enhanced infrared absorption (SEIRA)
<p>This repository contains all datasets and MATLAB codes used in the paper "Over-coupled resonator for broadband surface enhanced infrared absorption (SEIRA)"</p> <p>3 folders can be found</p> <ul> <li>BMM contains a main python script that reproduces the electromagnetic computations of the article</li> <li>CMT : contains 3 scripts <ul> <li><strong>Absorption2D.m</strong> computes a 2D map of the absorption from the resonator from eq.1 of the main text as a function of f and (ω − ωr )/γnr .</li> <li><strong>DeltaRm.m</strong> plots the analytical expression of Delta Rm computed in the SI as a function of f.</li> <li><strong>DRanalytics.m</strong> plots the reflexion with the absorber as a function of omega. The expression is derived from the coupled mode formalism as described in the supplemental information.</li> </ul> </li> <li>Data_exp: contains raw experimental datasets of the figures.</li> <li>over_coupling_package : contains scripts to reproduce the electromagnetic simulations presented in the paper</li> </ul> <p>===============================</p> <p>PAPER ABSTRACT:</p> <p>Detection of molecules is a key issue for many applications. Surface enhanced infrared absorption (SEIRA) uses arrays of resonant nanoantennas with good quality factors which can be used to locally enhance the illumination of molecules. The technique has proved to be an effective tool to detect small amount of material. However nanoresonators can detect molecules on a narrow bandwidth so that a set of resonators is necessary to identify a molecule fingerprint. Here, we introduce an alternative paradigm and use low quality factor resonators with large radiative losses (over-coupled resonators). The bandwidth enables to detect all absorption lines between 5 and 10 µm, reproducing the molecular absorption spectrum. Counterintuitively, despite a lower quality factor, the system sensitivity is improved and we report a reflectivity variation as large as one percent per nanometer of molecular layer of PMMA. This paves the way to specific identification of molecules. We illustrate the potential of the technique with the detection of the explosive precursor 2,4-dinitrotoluene (DNT). There is a fair agreement with electromagnetic simulations and we also introduce an analytic model of the SEIRA signal obtained in the over-coupling regime.</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
<p>The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because mapping of embryonic brain activity in vivo is challenging. Here we applied for the first time Manganese Enhanced Magnetic Resonance Imaging (MEMRI), a functional imaging method, to the eggs of domestic chicks. We revealed both spontaneous and light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo.</p>
Data of findings in the article "Optomechanical ring resonator for efficient microwave-optical frequency conversion" by I.T. Chen et al.
<p>Data of findings in the article "Optomechanical ring resonator for efficient microwave-optical frequency conversion" by I.T. Chen et al.</p>
Research data of "Quantum resonant optical bistability with a narrow atomic transition: bistability phase diagram in the bad cavity regime"
<p>The data set includes the matlab programs, measured data and drawings used for the figures in D Rivero et al 2023 New J. Phys. 25 093053</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
Open the record for dataset details and reuse information.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): nuclear magnetic resonance spectra of soils, 2009 and 2013
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes nuclear magnetic resonance spectra of surface organic, deep organic, and mineral layers of control and experimentally warmed soils sampled in 2009 and 2013.
An archive of data from Resonant Column and Cyclic Torsional Shear Tests performed on Italian Clays
<p>A large data-set of index and dynamic parameters measured from resonant column (RC) and cyclic torsional shear(CTS) tests on 170 undisturbed isotropically consolidated fine-grained specimens deriving from 90 sites in Central and Northern Italy is made available. Tests were all performed over the past 20 years at the Geotechnical Laboratory of the Civil and Environmental Engineering Department of the Florence University using the same apparatus and following the same standardized procedures.</p> <p>The experimental data are organized in an excel file (named as “Italian_Clays_Archive.xlsx”). For each tested sample, the main physical, index and dynamic properties measured are archived with the code number of the sample (No) in the sheet named as “Dataset” as well as any information available about the borehole from which the sample has been taken. The list and the meaning of the symbols used can be found in the sheet named as “Legend”. Other sheets containing borehole stratigraphy are named as “XX-ST” (where “XX” stands as the bore-hole code, BH) and they can be recalled directly from the “Dataset” sheet. Note that stratigraphy is given in its original format, when available. However, depth and thickness of each layer can be easily deduced by the figure provided and the soil lithology is well represented by the symbol used that are those generally adopted internationally. Finally, the sheets named as "YY-CTS-STEPZ" (where “YY” and “Z” stand as the sample code, No, and the step number, respectively) contain the shear stress and strain values measured after CTS tests at different steps (i.e. amplitudes of the cyclic dynamic torsional loading applied) during the 1st, 5th, 15th, 20th and 25<sup>th</sup>.and/or and/or the corresponding shear modulus and damping ratio calculated from the same cycles.</p> <p>The selected samples were taken mostly in Holocene and Pleistocene fluvio-lacustrine soil deposits at depths ranging from 1 m to 75 m below ground level and they mainly consist of normally and over-consolidated clayey silts or clays (1 < OCR < 9.4) of medium-to-high plasticity (4 < PI < 84), with very low-to high consistency (-1< Ic < 1.9) and initial void ratio, e<sub>0</sub>, ranging between 0.175 and 2.456. The database also includes some samples of organic clays of low consistency, very high water content and void ratio and low unit weight. The initial (small strain) values of shear modulus, G<sub>0</sub>, and damping ratio, D<sub>0</sub>, range between 21 MPa and 292 MPa and between 0.8% and 5.1%, respectively. The smallest and the largest shear strain values induced by RC and CTS tests are 1.9x10<sup>-5</sup> % and 6.3x10<sup>-1</sup>%, respectively.</p>
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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.