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1,987 results for “mode”
Light intensity in reflection mode of PAAO (AJ-4-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 360 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-1-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s.</p>
Light intensity in reflection mode of PAAO (AJ-2-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 360 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-2-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s.</p>
Light intensity in reflection mode of PAAO (AJ-1-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 330 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-1-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s.</p>
Light intensity in reflection mode of PAAO (AJ-3-04-20 sample, 1st anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 360 us. Scans to average: 10. Spectrum is recorded every 2 s during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-3-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 1 hour.</p>
Spectrograms and frequencies of the first mode of Schumann Resonance according to multi-position monitoring parformed at the Ukrainian Antarctic Station (2002-2020) and at the Arctic SOUSY facility (2013-2020)
<p>This dataset contains the processed data used for the publication: ELECTROMAGNETIC SEASONS IN SCHUMANN RESONANCE RECORDS. The dataset contains the daily spectrograms and frequensies of first mode of Schumann Resonance (derived for North-South and East-West magnetic components) of ELF signals recorded at the Ukrainian “Akademik Vernadsky” Antarctic station (65.25° N and 64.25° W) 2002-2020, and at SOUSY Arctic facility (Svalbard 78.15° N and 16.05° E) 2013-2020.</p>
Exact solution and Majorana zero mode generation on a Kitaev chain composed out of noisy qubits
<p>Attached are the data sets in forms of python pickle files from the following submission https://arxiv.org/abs/2108.07235</p> <p>Abstract:</p> <p>Majorana zero modes were predicted to exist as edge states of a physical system called the Kitaev chain. Such zero modes should host particles that are their own antiparticles and could be used as a basis for a qubit that is to large extent immune to noise - the topological qubit. However, all attempts to prove their existence gave inconclusive results. Here, I experimentally show that Majorana zero modes do in fact exist on a Kitaev chain composed out of 3 noisy qubits on a publicly available quantum computer. The signature of Majorana zero modes is a degeneracy with the ground state which is not lifted by noise of the quantum computer. I also confirm that Majorana zero modes have a number of theoretically predicted features: a well-defined parity with switches at specific points and a non-conserved particle number. Furthermore, I show that Majorana zero modes favour long-range Majorana pairing at low chemical potential and short-range pairing at large values of the chemical potential. The results presented here are a most comprehensive set of validations ever conducted towards confirming the existence of Majorana zero modes in nature. I foresee that the findings presented here would allow any user with an internet connection to perform experiments with Majorana zero modes. Furthermore, the noisy intermediate scale quantum computing community can start building topological processors composed out of contemporary noisy qubits.</p>
Supplementary material for 'In-situ full-field measurements for 3D printed polymers during mode I interface failure'
<p>Additional raw data and correlation analysis output for 'In-situ full- field measurements for 3D printed polymers during mode I interface failure'. We provide the patterned images acquired by the stereo microscopic Correlated Solution system (tiff format) and the VIC3D analysis results (csv format) for one representative specimen with 0°- 0° stacking undergoing mode I interlayer failure.</p>
Experimental data and scripts used for the paper "Imperfect symmetry of real annular combustors: beating thermoacoustic modes and heteroclinic orbits"
<p>The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper.</p>
Experimental data on the effects of an azimuthal mean flow on the (thermo)acoustic modes in the annular electroacoustic feedback setup at TU Berlin
<p>Experimental data obtained in the presence of an azimuthal mean flow on the acoustic/thermoacoustic response in the annular electroacoustic feedback setup at TU Berlin. This dataset was used for the published article<br> S. C. Humbert, J. P. Moeck, A. Orchini, C. O. Paschereit, "Effect of an Azimuthal Mean Flow on the Structure and Stability of Thermoacoustic Modes in an Annular Combustor Model With Electroacoustic Feedback", J. Eng. Gas Turbines Power. June 2021, 143(6): 061026. Experimental data as well as Matlab scripts to use them are provided. Useful information is contained in "readme" files.</p>
Data and codes for "Perimeter modes of nanomechanical resonators exhibit quality factors exceeding 10^9 at room temperature"
<p>Data and codes for "Perimeter modes of nanomechanical resonators exhibit quality factors exceeding 10<sup>9</sup> at room temperature"</p>
Dynamics of Collective Modes in an unconventional Charge Density Wave system BaNi2As2 - Raw Data
<p>This repository includes two datasets included in the study "Dynamics of Collective Modes in an unconventional<br> Charge Density Wave system BaNi2As2". Two datasets are included:</p> <p>Temperature_dependent_reflectivity_changes.dat</p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat</p> <p>Temperature_dependent_reflectivity_changes.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 for sample temperatures between 13 K and 149K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective temperatures (constant fluence of 0.4 mJ cm<sup>−2</sup>).</p> <p> </p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 at 10 K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective fluences. Each signal has been normalized to the respective fluence.</p> <p> </p>
Majorana modes with side features in magnet-superconductor hybrid systems
<p>This repository contains the file parameters_Nb36Mn1_rel_fm_40bandTB.dat with all tight-binding parameters for the normal-state 40-band model, in our paper "Majorana modes with side features in magnet-superconductor hybrid systems".</p>
Original raw data to paper "Towards Fourier Domain Mode Locked frequency combs"
<p>The file contains the 4 original datasets used in the paper. The format is HD5. The data represents transients recorded with a Keysight 63GHz real time oscilloscope. The data represents the output from fast 35GHz and 50GHz diode base photo receivers detecting beat signals of two independent lasers.</p> <p> </p> <p>The filename "#A_Bnm_Cnm_Dus.h5" contains the following information:</p> <p> - A is the number of the measurement<br> - B is the bandwidth of the FDML output in nanometers<br> - C is the wavelength of the CW laser in nanometers<br> - D is the length of the measurement in micrometers</p> <p>The data set "#9_80nm_1290nm_200us.h5" is shown in:<br> - Figure 4e (as Measurement 3)</p> <p>The data set "#10_80nm_1290nm_200us.h5" is shown in:<br> - Figure 4e (as Measurement 4)</p> <p>The data set "#17_40nm_1305nm_200us.h5" is shown in:<br> - Figure 2b, c, d, e<br> - Figure 3<br> - Figure 4a, b, c, d, e(as Measurement 1)<br> - Figure S2<br> - Figure S3<br> - Figure S4</p> <p>The data set "#18_40nm_1305nm_200us.h5" is shown in:<br> - Figure 4e (as Measurement 2)</p> <p>Each dataset contains two collections of data.<br> One is the beatsignal between FDML laser and CW laser.<br> The second is the beatsignal between the two CW lasers during the measurement time of the first beat signal.</p>
Code and data for: Interpolant-based demosaicing routines for dual-mode visible/near-infrared imaging systems
<p><span>Dual-mode visible/near-infrared imaging systems, including a bioinspired six-channel design and more conventional four-channel implementations, have transitioned from a niche in surveillance to general use in machine vision. However, the demosaicing routines that transform the raw images from these sensors into processed images that can be consumed by humans or computers rely on assumptions that may not be appropriate when the two portions of the spectrum contribute different information about a scene. A solution can be found in a family of demosaicing routines that utilize interpolating polynomials and splines of different dimensionalities and orders to process images with minimal assumptions.</span></p>
Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022
<p>Data is in HDF5 format (with LZF compression). For specifics and details, please see <a href="http://github.com/GilesStrong/mode_diffprog_22_challenge">https://github.com/GilesStrong/mode_diffprog_22_challenge</a></p> <p>The training file contains two datasets:</p> <ul> <li>`'x0'`: a set of voxelwise X0 predictions (float32)</li> <li>`'targs'`: a set of voxelwise classes (int):</li> <li>0 = soil</li> <li>1 = wall</li> </ul> <p> </p> <p>The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position).</p> <p>All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels.</p> <p>The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer.</p> <p>It can be read using e.g. the code below:</p> <p> </p> <p><em>with h5py.File('train.h5', 'r') as f:</em></p> <p><em> inputs = h5['x0'][()]</em></p> <p><em> targets = h5['targs'][()]</em></p> <p>The test file only contains the X0 inputs:</p> <p><em>with h5py.File('test.h5', 'r') as h5:</em></p> <p><em> inputs = h5['x0'][()]</em></p> <p>The private testing sample also contains targets. The private and public splits can be recovered using:</p> <p><em>from sklearn.model_selection import train_test_split</em></p> <p><em>pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)</em></p> <p> </p>
A waveform dataset in continuous mode of the Montefeltro seismic network (MF) in central-northern Italy from 2018 to 2020
<p>The Montefeltro seismic network (FDSN Network code: 1S) was deployed in the Apennines area of<br> northern Marche and southern Emilia-Romagna regions (central Italy). The network has been set up<br> starting from December 2018, and the array consists of stations equipped with dynamic digitizers<br> and three component short/extended/broad band seismometers (Guralp 3D/40s, Lennartz 3D/5s,<br> SS20 3D/0.5s sensors). The temporary network records in continuous mode at 100 sps. The data are<br> used to analyse the seismicity and the spatio-temporal evolution of small seismic sequences,<br> occurring in the considered area and surrounding zones, strongly clustered in time and space.<br> Stations (registered in ISC) in this Network:<br> Station code Location Station name Data acquisition<br> Lat(N) Lon(E) Ele(m) Start End<br> MF01 43.82150 12.57190 368 Auditore (PU) 2018-11-29 2020-12-31<br> MF02 43.86540 12.21070 626 Sant'Agata Feltria (RN) 2019-04-19 2020-05-30<br> MF03 43.84860 12.47990 541 Monte Grimano (PU) 2019-10-07 2020-12-31<br> MF04 43.81030 12.05620 1043 Verghereto (FC) 2020-10-09 2020-12-31<br> The data of dataset files are miniseed formatted and subdivided by the following tree:<br> (1) the dataset is divided by years;<br> (2) the divided by years dataset is subdivided by stations;<br> (3) finally, the data are divided by days of each year in every station folder.</p> <p><br> Response information:<br> Station code Sensor Recorder Recorder period<br> Start End<br> MF01 Lennartz 3D/5s Reftek 130 2018-11-29 2019-10-07<br> MF01S Lennartz 3D/5s Sara SL06 2019-10-07 2020-12-31<br> MF02 Guralp CMG/20s Reftek 130 2019-04-19 2020-05-30<br> MF03 Guralp CMG/30s Reftek 130 2019-10-07 2020-12-31<br> MF04 Sara SS02/0.5s Sara SL06 2020-10-09 2020-12-31</p> <p>In this dataset the data recorded by MF01 station are been acquisited by different recorders,<br> the firth record period by Reftek 130 (MF01) and the second record period by Sara SL06 (MF01S).</p> <p>List for the responses of the Seismic Instruments<br> Guralp CMG/20s sensor response: RESP_XX_NS444_BHZ_CMG40T_20_50_800.txt<br> Guralp CMG/30s sensor response: RESP_XX_NS041_BHZ_CMG40T_30_800.txt<br> Lennartz 3D/5s sensor response: RESP_XX_NS484_SHZ_LE-3D5sMkIII_5_800.txt<br> Sara SS02/5s sensor response: RESP_XX_NS505_SHZ_SS02_5.txt<br> Reftek 130 datalogger response: RESP_XX_NR008_HHZ_130_1.txt<br> Sara SL06 datalogger response: RESP_XX_NS000_HHZ_SL06_88_L22x3_100_4.txt</p>
Container flows on road, rail and waterways along Rhine-Alpine corridor (Rhine section) at NUTS-2 level with cost-time-emissions estimates and accessibility-frequency-availability of modes
<p>The present dataset is used to estimate the heterogeneous mode choice preferences of shippers, that are presented in the following article :<br> "A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data"<br> (Nicolet, A., Negenborn, R. R. & Atasoy, B., A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data. IEEE Open Journal of Intelligent Transportation Systems, Vol. 3, 2022, pp. 650-661.)</p>
Robe de soirée longue à la mode
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MONACO: Modes of Narration and Attribution Corpus
<p><strong>MONACO: Modes of Narration and Attribution Corpus</strong></p> <p>This corpus is constructed by the project group Modes of Narration and Attribution (<a href="https://www.uni-goettingen.de/de/mona/626918.html">MONA</a>). We provide German literary texts annotated with three base phenomena: <strong>Generalising Interpretation</strong> (GI), <strong>Comment</strong>, and <strong>Non-fictional Speech</strong> (NfR), as well as <strong>Attribution</strong> on top of them.</p> <p><strong>DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies"</strong></p> <p>Online:</p> <ul> <li><a href="https://gepris.dfg.de/gepris/projekt/402743989">https://gepris.dfg.de/gepris/projekt/402743989</a></li> <li><a href="https://dfg-spp-cls.github.io/">https://dfg-spp-cls.github.io/</a></li> </ul> <p><strong>Teilprojekt: "Structuring Literature - Variants and Functions of Reflextive Passages in Narrative Fiction"</strong></p> <p>Online:</p> <ul> <li><a href="https://gepris.dfg.de/gepris/projekt/424264086">https://gepris.dfg.de/gepris/projekt/424264086</a></li> <li><a href="https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Structuring_Literature/">https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Structuring_Literature/</a></li> <li><a href="https://www.uni-goettingen.de/de/structuring+literature/626921.html">https://www.uni-goettingen.de/de/structuring+literature/626921.html</a></li> </ul>
Supplementary online material for KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern. Eclipse modelling of the triple and spectroscopic analysis
<p>Additional figures and data supplementary to the published (or soon-to-be-published) paper KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern Eclipse modelling of the triple and spectroscopic analysis.</p> <p> </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.