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750 results for “coherence”
Exponential approximation of the coherence contribution to the thermal conductivity of complex clathrate-type crystals
<p>The low-temperature properties of guest-host crystals, such as clathrates and skutterudites, offer a rich playground for discovering novel physical phenomena and developing new materials with unique properties. The temperature dependence of thermal conductivity in these materials can exhibit both crystal-like and glass-like behavior, which reflects the properties of the phonon excitations and various scattering mechanisms. The ultra-low thermal conductivity of clathrate crystals is closely related to the concept of minimal thermal conductivity, which is determined by the intrinsic phonon scattering in the material. In this work, the temperature dependence of thermal conductivity for both crystal-like and glass-like behavior of different structural types of clathrates and skutterudites was analyzed using the ”Unified theory of thermal transport in crystals and glasses” of M. Simoncelli, N. Marzari & F. Mauri. A method was proposed and tested for the coherence contribution related to wave-like tunneling and loss of coherence between different vibrational eigenstates. The temperature dependence of the coherence contribution to thermal conductivity was approximated by the exponential function of an Arrhenius type with characteristic energy <em>E</em> and characteristic minimal thermal conductivity parameter <em>κ</em><sub>0</sub>. The coherence contribution is intertwined with other phonon scattering mechanisms, and over a wide temperature range, its temperature dependence is universal with parameters depending on the crystal structure, positional disorder, and impurity doping. This work provides insights into the temperature dependence of thermal conductivity in guest-host materials and its importance for designing and optimizing their properties for various applications, such as thermoelectric generators</p>
Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle Dataset
<p>This data repository presents representative data of the data we use in the paper "Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle" on the study of the USAF-1951 resolution target measurements. It includes a raw dataset of 3,000 measurements used for the reconstruction given in Fig. 2e-h.</p> <p>Directory Structure<br>LED Repository: This repository contains images using an LED to identify the fiber core centers for the sampling of the measurements. </p> <p>Core reflection Directory: Features back-reflected core measurements captured when illuminating the laser core by core. This dataset is collected without the USAF target at the distal facet, providing baseline data for the core reflection so that it can be removed from the measurements digitally.</p> <p>Measurements Directory: Consists of the raw data for the measurements taken with the USAF target placed at a 700-micron distance. This directory is the core of our dataset, offering raw, unprocessed measurements crucial for the analysis presented in our paper.</p> <p>Contact_LED_transmission: Contains a contact transmission image of the resolution target, captured with the target placed at contact distance and illuminated by an LED from behind. This serves as a reference image for transmission (Fig.2h).<br>Data Usage</p> <p>For more information, refer to Weinberg, Gil, et al. "<a href="https://arxiv.org/abs/2402.00148">Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle</a>." arXiv preprint arXiv:2402.00148 (2024).</p>
refering rawdata and code of "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)"
<p>Corresponding raw data and codes that produce all relative video and imaging results for real-time monitoring the physicochemical phenomena of microfluidics, microorganism's group, and chemical reactions, supporting and verifying the research article "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)".</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 1
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 1. It contains data for the S1 simulation.</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 2
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 2. It contains the remaining of data for the S1 simulation, the data of the reference simulations RefC and RefW, and the data for the sensitivity analysis of the supplementary material.</p>
Fast and accurate spectral estimation axial super resolution optical coherence tomography
<p>This depository contains the data and code underlying the results of the publication '<em>Fast and accurate spectral estimation axial super resolution optical coherence tomography</em>' in Optics Express (doi.org/<em>10.1364</em>/<em>OE</em>.<em>439761). </em>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.</p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes the Matlab scripts and functions in this depository that were used in the publication. For reproducing the figures of the publication, refer to the scripts <em>SE_OCT_figure(..).m</em>. For understanding the method and applying it on other datasets from the reader, <em>Bscan_reconstruction.m </em>and <em>Cscan_reconstruction.m</em> are the most convenient scripts to start with. For simulating OCT data as presented in the publication, <em>OCT_simulations.m</em> could be applied. Details on the variables and parameters, such as number of iterations, grid interpolation factor and number of data chunks are commented on in the code itself and should be understandable with the publication as reference. </p> <p>Table 2 describes the datasets that have been used for the publication and are free for the readers to be used with their methods. Table 3 then gives a brief explanation of the variables that are contained in the dataset <em>.mat</em> files.</p> <table> <caption>Table 1. The Matlab scripts in this depository with brief description.</caption> <thead> <tr> <th scope="col">script name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>Bscan_reconstruction.m</td> <td>This script loads a B-scan .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>Cscan_resonstruction.m</td> <td>This script loads a 3Ddata .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>oct_ar.m</td> <td>This function applies the auto-regressive spectral estimation on the input data.</td> </tr> <tr> <td>oct_iaa.m</td> <td>This function applies RFIAA or FIAA on the input data.</td> </tr> <tr> <td>fiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying FIAA (without the recursive scheme) and within rfiaa_oct.m for the first line. This function applies FIAA on a single A-scan.</td> </tr> <tr> <td>rfiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying RFIAA (with the recursive scheme) on a chunk of data. It initializes the first line of the chunk with fiaa_oct.m, and then it applies rec_fiaa_oct.m with the initialization taken from the previous scanline.</td> </tr> <tr> <td>rec_fiaa_oct.m</td> <td>This function applies RFIAA on a single A-scan, taking the initialization from the previous scanline as extra input parameter. </td> </tr> <tr> <td>RayleighThreshold.m</td> <td>This function automatically determines the lower limit of the dynamic range for plotting an OCT image. It fits a Rayleigh distribution on the input data (preferably noise, but also a full image could be used) and returns a threshold in dB. </td> </tr> <tr> <td>morgenstemning.m</td> <td>This function defines the colormap as used in the publication.</td> </tr> <tr> <td>Bscan_reconstruction_function.m</td> <td>This function takes the interference OCT signal, reference spectra and reconstruction parameters as input and returns the reconstructed images according to the four methods in the publication. This function is used in the scripts for reproducing the figures in the publication. It follows the same structure as the script <em>Bscan_reconstruction.m.</em></td> </tr> <tr> <td>SE_OCT_figure3.m</td> <td>This script does the processing for and plots figure 3 in the manuscript. For this script, the .zip file <em>wedge_simulation_data </em>needs to be unpacked and placed as folder in the folder where this script is executed.</td> </tr> <tr> <td>SE_OCT_figure4.m</td> <td>This script reproduces figure 4 in the publication.</td> </tr> <tr> <td>SE_OCT_figure5.m</td> <td>This script reproduces figure 5 in the publication.</td> </tr> <tr> <td>SE_OCT_figure6.m</td> <td> <p>This script reproduces figure 6 in the publication</p> </td> </tr> <tr> <td>OCT_simulations.m</td> <td>This script reproduces the OCT simulations as described in the publication. As the noise is random, any new realization might slightly differ from the data in the publication.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Table 2. The OCT datasets contained in this depository with a brief description. Table 3 describes the variables that are contained in each of these datasets.</caption> <thead> <tr> <th scope="col">dataset name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>wedge_Bscan_data.mat</td> <td>Experimental data from the wedge phantom as visualized in figure 3 of the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>wedge_simulation_data.zip</td> <td>This zipped folder contains 16 simulation datasets with different noise levels, which form the basis of Figure 3 (f) in the publication.</td> </tr> <tr> <td>interfaces_simulation_Bscan_data.mat</td> <td>This file contains the simulation data for 8 interfaces with decreasing intensity and forms the basis of Figure 4 in the publication.</td> </tr> <tr> <td>layered_phantom_Bscan_data.mat</td> <td>This file contains the experimental data from the layered phantom, as used in Figure 4 (c-d) in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>onion_Bscan_data.mat</td> <td>This file contains the experimental data from the onion sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>skin_Bscan_data.mat</td> <td>This file contains the experimental data from the skin sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>intralipid_Bscan_data.mat</td> <td>This file contains the experimental data from the intralipid sample as used in Figure 6 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>speckle_simulation_Bscan_data.mat</td> <td>This file contains simulation data for 3 speckle regions as used in Figure 6 in the publication.</td> </tr> <tr> <td>reference_spectrum.mat</td> <td>This file just contains a spectrum from the used experimental setup which is used as input for the simulations.</td> </tr> <tr> <td>onion_3Ddata.mat</td> <td>This file contains 3D data of the onion sample, which is used for visualization 1. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> <tr> <td>skin_3Ddata.mat</td> <td>This file contains 3D data of the skin sample, which is used for visualization 2. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> </tbody> </table> <table> <caption>Table 3. This table contains the variables in the .mat files and their description.</caption> <thead> <tr> <th scope="col">variable name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td><em>iRawdata</em></td> <td>OCT interference spectra interpolated to a linear grid in k-domain, before subtracting the reference spectrum</td> </tr> <tr> <td><em>sk</em></td> <td>the reference spectrum, interpolated to a linear grid in k-domain</td> </tr> <tr> <td><em>phasep</em></td> <td>4 polynomial coeficients, which can be used in 'polyval' to correct for dispersion</td> </tr> <tr> <td><em>sizeX</em></td> <td>the lateral size of the scan in mm</td> </tr> <tr> <td><em>sizeY</em></td> <td>(only for 3D datasets) the lateral size in the direction perpendicular to x in mm</td> </tr> <tr> <td><em>sizeZ</em></td> <td>the axial field of view (one-sided) before range reduction in mm </td> </tr> <tr> <td><em>ROIp</em></td> <td>the best axial region of interest for this dataset to apply RFIAA on a reduced reconstruction range (in pixels of the DFT reconstruction without zero-padding)</td> </tr> </tbody> </table> <p> </p>
Data for: Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics
<p>Data for publication: A. Scheidegger, J. Vanicek, and N. Golubev, Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics, https://doi.org/10.1063/5.0076609</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model
<p>This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy.</p> <p>Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,</p> <p><strong>Abstract (Manuscript)</strong></p> <p>In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect.</p> <p><strong>Readme data files</strong></p> <p><em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em></p> <p>Radar coherence quarterly statistics - Albacete (Spain)</p> <p>Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020</p> <p>File naming (.tif files) per year (<em>YYYY</em>):</p> <ul> <li>Mean coherence: <em>mean_YYYY_1to4.tif</em></li> <li>Standard deviation coherence: <em>std_YYYY_1to4.tif</em></li> <li>Range coherence: <em>range_YYYY_1to4.tif</em></li> <li>Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em></li> <li>Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em></li> <li>Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em></li> </ul> <p>Each file consists of 4 bands:</p> <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> <p>Statistics are based on radar coherence data, which is scaled between >0 and 1<br> No data: 0-values</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>SIGPAC_data_Albacete_2018_to_2020.zip</em></p> <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> <p>More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> <p>Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p>Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> <p>Pixel values:</p> <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em></p> <p>Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files):</p> <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> <p>Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em></p> <p>Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI):</p> <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> <p>Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020)</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p>
Data - Coherent combining of low-power optical signals based on optically amplified error feedback
<p>This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Data of publication A Frequency-Multiplexed Coherent Electro-optic Memory in Rare Earth Doped Nanoparticles
<p>Data corresponding to main text figures of publication : A. Fossati, S. Liu, J. Karlsson, A. Ikesue, A. Tallaire, A. Ferrier, D. Serrano, and P. Goldner, <em>A Frequency-Multiplexed Coherent Electro-Optic Memory in Rare Earth Doped Nanoparticles</em>, Nano Lett. <strong>20</strong>, 7087 (2020).</p>
Data from: A coherent biogeographic framework for Old World Neogene and Pleistocene mammals
<p>In order to understand mammalian evolution and compute a wide range of biodiversity indices, we commonly use spatial division adapted to ecological and evolutionary constraints called bioregion. While commonly conducted by neontologists, the establishment of bioregions in palaeontology is generally a secondary analysis, shaped on subjective time scales and areas specific to the investigated questions and groups. This heterogeneity, coupled with the scale-dependency of biodiversity indices, prevents the clear identification of macroecological and macroevolutionary trends for large taxonomic groups like extinct mammals. Here we tackle this issue by providing a coherent framework for Neogene and Pleistocene mammals of the Old World following two steps: (A) a temporal scale adapted to mammalian evolutionary history (i.e. evolutionary fauna) is defined by poly-cohort analysis, and (B) bioregions are then computed for each evolutionary fauna by clustering, ordination and intermediate approaches at multiples spatial scales (i.e. continental to regional) for Eurasia and Africa. Additionally, providing a coherent framework for a wide range of mammalian datasets, our results show: (1) the synchronous emergence and fall of five mammalian evolutionary faunas identified at chronological scales varying from the epoch to the geological stage; (2) a transition from a longitudinal to a latitudinal biogeographical structuring between the Miocene and Pliocene, especially in Europe; (3) the long-term affinity of southern Asian with African faunas, in sharp contrast with the modern Palearctic bioregion extension; and (4) the establishment of a vast Mediterranean bioregion from fragmented areas in late Miocene to its full extent in the Pleistocene.</p>
Directional excitation of a high-density magnon gas using coherently driven spin waves
<p>Data corresponding to the figures of the main text of: "Directional excitation of a high-density magnon gas using coherently driven spin waves "</p>
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders
<p>This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. <em>et al.</em> Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. <em>Eur Arch Psychiatry Clin Neurosci</em> (2022). https://doi.org/10.1007/s00406-022-01455-z</p> <p> </p>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
Coherent enhancement of optical remission in diffusive media
<p>Experimental data sets associated with "Coherent enhancement of optical remission in diffusive media" (https://doi.org/10.1073/pnas.2207089119).</p>
Data and code for Coherent Correlation Imaging: Resolving fluctuating states of matter
<p>Data and code to generate the figures in the main text and the Extended data figures.</p>
Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements
<p>All data presented in the figures of "Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements".</p>
Spatially Coherent 3D Distributions of HI and CO in the Milky Way - Data Products
<p>Data products from the joint reconstruction of Galactic HI and H2 (via CO).</p> <h3>Primary data products:</h3> <p>These are the posterior samples of the <strong>"densities"</strong> (HI and H2) in cm^-3 and <strong>"auxiliary"</strong> fields (i.e. the three components of the Galactic velocity field and the two spatially resolved line-widths) in km/s on our Sun-centered HEALPix-times-radius grid. These files also contain two tables with the centres and edges of the pixelisation in radial direction. The nearest (farthest) bin is at approximately 50 pc (28 kpc). The HEALPix dimension is ordered using the "nested" scheme.</p> <ul> <li><em>samples_densities_hpixr.fits </em></li> <li><em>samples_auxiliary_hpixr.fits</em></li> </ul> <h3>Interpolated to a regular grid:</h3> <p>For convenience, we also provide versions linearly interpolated to regular, Cartesian grids. Due to the strongly inhomogeneous original numerical grid, these interpolated versions contain regions of significant over/undersampling. To mitigate this a little, we provide a <strong>"local"</strong> (800 x 800 x 320 grid points with -1.25 kpc < x < 1.25 kpc, -1.25 kpc < y < 1.25 kpc and -0.5 kpc < z < 0.5 kpc) and a <strong>"global"</strong> (1250 x 1250 x 125 grid points with -12 kpc < x < 28 kpc, -20 kpc < y < 20 kpc, -2 kpc < z < 2 kpc) version. The origin (0,0,0) is defined by the position of the Sun and positive x points towards the Galactic centre.</p> <p>In an attempt to keep the file sizes reasonable, we provide the mean and standard deviation of each field instead of all eight individual samples.</p> <ul> <li><em>mean_std_densities_xyz_global.fits</em></li> <li><em>mean_std_densities_xyz_local.fits</em></li> <li><em>mean_std_auxiliary_xyz_global.fits</em></li> <li><em>mean_std_auxiliary_xyz_local.fits</em></li> </ul>
Research data for: On the persistent shape and coherence of pulsating auroral patches
<p>Data for the research article: On the persistent shape and coherence of pulsating auroral patches in Journal of Geophysical Research: Space physics. For example all-sky imager movies of 557.7 nm pulsating/fluctuating aurora on 1 March 2012. </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.