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Determining haploblocks and haplotypes in the MAGIC winter wheat population WM-800 based on the wheat 15k Infinium and the 135k Affymetrix SNP arrays
<p><span>Haplotypes are derived from single nucleotide polymorphisms (SNPs). They are beneficial (i) to remove redundant sequence information in genetic populations and, more important, (ii) to distinguish more than two variants/alleles at a genomic locus. A haploblock locus, made of multiple haplotypes, is very useful in multiparent-advanced-generation-intercross (MAGIC) populations, where, ideally, multiple founder alleles need to be distinguished at each locus to subsequently carry out efficient genome-wide association analysis studies (GWAS). </span></p> <p><span>In this regard, the dataset contains genotype matrices (made of SNP, haploblock and haplotype data) for 800 lines of the MAGIC WHEAT population WM-800 (Sannemann et al. 2018). The datasets are based on genotyping the lines with both the already published wheat 15k Infinium SNP array (Sannemann et al. 2018) and the new wheat 135k Affymetrix SNP array.</span></p>
Using a whole-body 31P birdcage transmit coil and 16-element receive array for human cardiac metabolic imaging at 7T
<p>The uploaded data relate to the PLoS ONE manuscript of Valkovič et al. entitled "Using a whole-body 31P birdcage transmit coil and 16-element receive array for human cardiac metabolic imaging at 7T." 2017<br> The data consist of the models used for simulating electromagnetic fields, in CST Studio 2016 format, for the whole-body coil and the surface coil, and of the the phantom and anonnymised in vivo data, in Siemens DICOM format, acquired as described in the manuscript.</p>
Indices and sign arrays for floretion multiplication up to order 8.
<div> <div> <div> <p>A floretion is a type of hypercomplex number that extends the concept of quaternions. One main characteristic of floretions is their representation in an octal number system, where the digits are limited to {1, 2, 4, 7} which, when endowed with multiplication, become a discrete mathematical group. The number of digits in octal is the "order" of the group. These numbers can be associated with geometric shapes, specifically equilateral triangles, which are used to represent complex operations and properties visually.</p> <p>In mathematical operations, floretions employ logic operations like XOR for multiplication, following rules akin to quaternion multiplication but extended into higher dimensions and complexity. Floretions can be manipulated algebraically or graphically, each method offering unique insights into their structure and behavior.</p> <p>The development and use of floretions incorporate both algebraic structures and geometric representations, bridging a unique connection between number theory and visual geometry.</p> <p><a title="Floretion Calculator " href="https://floretions.com/">Floretion Calculator: floretions.com</a></p> <p><a href="https://github.com/Floretion-Inquisitor/floretions">Floretions at GitHub</a></p> <p> </p> <p> </p> </div> </div> </div> <p><strong>Dataset Description for Floretion Multiplication (up to order 8):</strong></p> <p>This collection of datasets, stored in numpy binary format, is designed to facilitate efficient multiplication of floretions up to order 8. The matrices included—indices and signs—are optimized for rapid computation of the product <strong>z=x⋅y</strong> using standard dot products, enhancing both speed and efficiency.</p> <p>For a foundational understanding, consider quaternion multiplication (order 1 floretions):</p> <h3>x = x[0]i + x[1]j + x[2]k + x[3]e </h3> <h3>y = y[0]i + y[1]j + y[2]k + y[3]e<code><br></code></h3> <p><strong>Multiplication Outcome</strong>: The coefficient for base vector "<strong>i</strong>" in <strong>z=x⋅y</strong> is calculated as:</p> <div><code>x[0]y[3]+x[1]y[2]−x[2]y[1]+x[3]y[0]=np.dot(np.multiply([1,−1,1,1],[x[3],x[2],x[1],x[0]]),[y[0],y[1],y[2],y[3]])</code></div> <p>Notice that <code>[1,−1,1,1]</code> above is the first row in the "signs" matrix and the indices of the coefficients of <strong>x</strong> are given by the first row of the "indices" matrix, below. </p> <h3>"Signs" Matrix<br>[[ 1 -1 1 1]<br>[ 1 1 -1 1]<br>[-1 1 1 1]<br>[-1 -1 -1 1]]</h3> <h3>"Indices" Matrix<br>[[3 2 1 0]<br>[2 3 0 1]<br>[1 0 3 2]<br>[0 1 2 3]]</h3> <p>These precomputed matrices streamline the computation of floretion products across various orders. For order 7 floretions, the data is split into 4 segments, for order 8 into 64 segments to keep file sizes managable. </p> <p> </p> <ul> <li> <div> <ul> <li> </li> </ul> </div> </li> </ul> <p> </p> <p> </p> <p> </p>
Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy
<p>Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy</p> <p>The link to the main publication with detailed information will be added later.</p> <p>The three videos show liv eand offline processing with the CheeTah T3, ASI Serval, ASI Accos, LiberTEM and CEOS Panta Rhei. They are also available at https://www.youtube.com/playlist?list=PLZCH_qD2RkB5oFdBc12xcYUcbmWZpJQHh</p> <p>benchmark.ipynb is a Jupyter notebook that was used to test the performance, as reported in the paper.</p> <p>raw_csr.zip is a test dataset recorded on a gold grid in the CSR format that can be opened by LiberTEM</p> <p>benchmark.ipynb is a Jupyter notebook that shows how the test dataset can be opened and processed with common Python packages and with LiberTEM.</p>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Dataset of Room Impulse Responses from Baffled Microphone Arrays and Sound Sources at Three Elevations
<p>This data set contains a collection of impulse responses (stored in SOFA format) from <em>spherical microphone arrays</em> (<strong>SMA</strong>s), <em>equatorial microphone arrays</em> (<strong>EMA</strong>s), and <em>non-spherical microphone arrays</em> (<strong>XMA</strong>s). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see <strong>DATA</strong> section for a full description).</p> <p>The original purpose of the microphone array data is the binaural rendering in the <em>spherical harmonics</em> (<strong>SH</strong>) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) for 360 horizontal head orientations of a <em>G.R.A.S KEMAR</em> acoustic dummy head are provided as a reference for each scenario.</p> <p>Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.</p> <p> </p> <p><strong>======<br> DATA<br>======</strong></p> <p>This archive contains the processed impulse response sets of various measurement configurations, as described in this section.</p> <p>Directory "resources/ARIR_processed/":</p> <ul> <li>Post-processed SMA and EMA impulse responses <ul> <li><strong>"_SMA*_"</strong> or <strong>"_EMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 1x <em>DPA 4060</em> microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius</li> <li>High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations): <ul> <li>Hall: <strong>1202</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 29</li> <li>Others: <strong>2702</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 44</li> </ul> </li> <li>Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved): <ul> <li>SH order 29: <strong>1742 channels</strong> (t-design grid) for SMA; <strong>59</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 12: <strong>314</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>25</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 8: <strong>146</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>17</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 4: <strong>42</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>9</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 2: <strong>14</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>5</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 1: <strong>6</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>3</strong> <strong>channels</strong> (equiangular grid) for EMA</li> </ul> </li> </ul> </li> <li>Post-processed XMA impulse responses <ul> <li><strong>"_XMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 18x <em>Rode Lavalier GO</em> microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)</li> <li>High-resolution data (measured simultaneously): <ul> <li><strong>18 channels</strong> for maximum SH order 8</li> </ul> </li> <li>Lower-resolution data via integer subsets of microphones: <ul> <li>SH order 4: <strong>9 channels</strong></li> <li>SH order 2: <strong>6 channels</strong></li> </ul> </li> <li>Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)</li> <li>Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)</li> </ul> </li> <li>Generated XMA calibration filters and equalization filters <ul> <li><strong>"_x_nm_"</strong> and <strong>"_e_nm_"</strong> in the file name</li> <li>In proprietary Matlab format</li> <li>Time-domain representation of filters in the respective orders of "real" spherical harmonics</li> </ul> </li> <li>Post-processed binaural impulse responses <ul> <li><strong>"_KEMAR_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From <em>G.R.A.S KEMAR</em> dummy head with large pinna</li> <li>For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)</li> </ul> </li> <li>Thereby, impulse response sets are included for five acoustic environments <ul> <li><strong>"Simulation_"</strong>: Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)</li> <li><strong>"Anechoic_"</strong>: Anechoic measurement of a <em>Genelec 8030A</em> loudspeaker at the same height of the array</li> <li>"<strong>LabDry_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)</li> <li><strong>"LabWet_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)</li> <li><strong>"Hall_"</strong>: Room measurement in a very reverberant hall of a <em>Genelec 8030A</em> loudspeaker at three different source heights</li> </ul> </li> <li>Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis) <ul> <li><strong>"_SrcHigh"</strong>: The source is located above the horizon of the receiver</li> <li>"<strong>_SrcEar"</strong>: The source and receiver are located at the same height</li> <li><strong>"_SrcLow"</strong>: The source is located below the horizon of the receiver</li> </ul> </li> <li>Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included <ul> <li><strong>"Anechoic_MicSMAnoTape_"</strong>: SMA measurement microphone without the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicSMAwithTape_"</strong>: SMA measurement microphone with the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicXMAmic19_"</strong>: XMA measurement microphone (the source was compensated)</li> <li><strong>"Anechoic_SrcFreeField_"</strong>: Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)</li> <li><strong>"Anechoic_SrcFreeField+MicSMAnoTape_"</strong>: Measurement source and SMA measurement microphone without the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicSMAwithTape_"</strong>: Measurement source and SMA measurement microphone with the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicXMAmic19_"</strong>: Measurement source and XMA measurement microphone</li> <li>Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior): <ul> <li>Anechoic KEMAR: Source</li> <li>Anechoic SMA/EMA/XMA: Source and array microphones</li> <li>Rooms KEMAR: None</li> <li>Rooms SMA/EMA/XMA: Array microphones</li> <li>There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>===========<br> DATA_RAW<br>===========</strong></p> <p><strong>This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data.</strong></p> <p>The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section <strong>CODE_AND_PLOTS</strong>). The data yields the final impulse responses (see section <strong>DATA</strong>), as described in this section.</p> <p>Directory "resources/ARIR_raw/":</p> <ul> <li>Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data</li> <li>In proprietary Matlab format, separate for every measurement position of each configuration</li> <li>Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.</li> <li>Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.</li> </ul> <p>Directory "resources/ARIR_raw/Logs_temp_humidity/":</p> <ul> <li>Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements</li> <li>In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section <strong>CODE_AND_PLOTS</strong>)</li> <li>This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.</li> </ul> <p> </p> <p><strong>==================<br> CODE_AND_PLOTS<br>==================</strong></p> <p>This archive contains the code required to gather the raw acoustic measurement data (see section <strong>DATA_RAW</strong>), the code to post-process and yield the final impulse response data (see section <strong>DATA</strong>), and the resulting plots as described in this section.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab and Python functions that are utilized in the code</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the code header.</li> </ul> <p>Directory "plots/":</p> <ul> <li>Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.</li> </ul> <p>Shell script "x1_Start_Jupyter.sh":</p> <ul> <li>Prepare a Python environment with the required tools described as dependencies.</li> <li>Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.</li> </ul> <p>Python Jupyter notebook "x1a_Measure_Microphones.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1b_Measure_BRIRs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.</li> <li>Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1c_Measure_SMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.</li> <li>Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1d_Measure_XMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired scattering body orientations.</li> <li>Perform a series of acoustic measurements of the XMA microphones at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Matlab script "x1e_Simulate_SMAs.m":</p> <ul> <li>Simulate a plane wave impinging from an arbitrary direction on SMAs and EMAs with a desired sampling grid in an anechoic environment.</li> <li>The simulations are helpful to evaluate the rendering method and to investigate the influence of different sampling grids and equalization methods on the rendered binaural signals.</li> </ul> <p>Matlab script "x2_Gather_And_Plot_Measurements.m":</p> <ul> <li>Gather the stored single files with individually measured impulse responses and the according metadata into a combined data set.</li> <li>The initial impulse responses can be recomputed with pre- and post-processing parameters tuned towards the specific acoustic scenario, including compensation of provided source and receiver impulse responses.</li> <li>Many plots may be generated during the processing to validate the input and output data.</li> </ul> <p>Matlab script "x2a_Compare_Measurement_Lengths.m":</p> <ul> <li>Compare the length of the resulting impulse responses of designated measurement configurations.</li> <li>This may be helpful for the tuning of pre-processing and post-processing parameters of the measured impulse responses.</li> </ul> <p>Matlab script "x3_Subsample_Measurements.m":</p> <ul> <li>Spatially subsample a high-resolution directional impulse response data set into a different (lower-resolution) sampling grid in the spherical harmonics domain.</li> <li>This is suitable for array and HRIR data sets.</li> <li>The script also compares the subsampled data against a reference set if available. In the current data set, an evaluation is performed for an anechoic simulation and a room measurement of an SMA at SH order 8.</li> </ul> <p>Matlab script "x3a_Gather_XMA_Measurements.m":</p> <ul> <li> <p>Transform anechoic XMA measurement data from SOFA into the data format required by the processing scripts to calculate the respective calibration and equalization filters.</p> </li> </ul> <p>Readme file "x3b_Generate_XMA_Filters.txt":</p> <ul> <li>The code for this functionality follows the publication [1] but is currently not polished enough for publication. Please contact Jens Ahrens (jens.ahrens@chalmers.se) for questions regarding this functionality.</li> <li>[1] J. Ahrens, H. Helmholz, D. Lou Alon, and S. V. Amengual Garí, “Spherical Harmonic Decomposition of a Sound Field Using Microphones on a Circumferential Contour Around a Non-Spherical Baffle,” <em>IEEE/ACM Trans. Audio, Speech, Lang. Process.</em>, vol. 30, pp. 3110–3119, 2022, doi: 10.1109/TASLP.2022.3209940.</li> </ul> <p>Matlab script "x3c_Gather_XMA_Filters.m":</p> <ul> <li>Rename the files containing the computed calibration and equalization filters into a suitable convention for this collection of scripts.</li> <li>The generated name includes an incremental index to track different versions of provided filter sets.</li> </ul> <p>Matlab script "x3d_Compare_XMA_Filters.m":</p> <ul> <li> <p>Generate various time domain and frequency domain plots to compare different versions of the generated XMA calibration and equalization filters.</p> </li> </ul> <p> </p> <p><strong>================<br> DOCUMENTATION<br>================</strong></p> <p>This archive contains additional documentation of the setups and processes while conducting the acoustic measurements, as described in this section.</p> <p>Directory "documentation/":</p> <ul> <li>Various photographs of the different room, source, and receiver arrangements of the data set</li> <li>The room dimensions and source and receiver positions are documented in the form of the original measurement notes (this may be improved in the future).</li> </ul> <p> </p>
Experiment resources for "Quality of Binaural Rendering From Baffled Microphone Arrays Evaluated Without an Explicit Reference"
<div>This data set contains the following resources to reproduce the listening experiment and statistical analysis of the referenced manuscript:</div> <div> <ul> <li>The <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) of all listening conditions presented in the perceptual experiment.</li> <li>The tools to create the listening test infrastructure, including <em>Pure Data</em> (<strong>Pd</strong>) patches and configuration files for the <em>SoundScape Renderer</em> (<strong>SSR</strong>) and <em>graphical user interface</em> (<strong>GUI</strong>).</li> <li>The raw response data as gathered from the experiment subjects.</li> <li>The R and Stan scripts to perform the statistical analysis and generate the resulting plots and tables.</li> </ul> </div> <div> <p> </p> <p>The archive contains the following components described below.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab, R, Stan, and Pd functions that are utilized in the code and experimental setup</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the file header.</li> </ul> </div> <div> <p>Directory "plots/4_Equalization/":</p> <ul> <li>Plots of all available headphone equalization filters as generated by the following Matlab scripts.</li> </ul> <p>Directory "plots/8_User_study/":</p> <ul> <li>Plots of the raw and analyzed experimental results as generated by the following Matlab and R scripts.</li> </ul> </div> <div> <p>Directory "resources/BRIR_auralization/":</p> <ul> <li>Audio files with static binaural auralizations of all listening conditions presented in the perceptual experiment as generated by the following Matlab scripts.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted" do not include a headphone equalization.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted+Sennheiser_HD650_lin" include the equalization for the <em>Sennheiser HD650</em> headphones employed in the listening experiment. The files are identical to the annotated <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/" target="_blank" rel="noopener">listening examples</a> published for the manuscript.</li> </ul> <p>Directory "resources/BRIR_rendered/":</p> <ul> <li>BRIRs and rendering parameters of all listening conditions presented in the perceptual experiment as generated from the associated <a href="../doi/10.5281/zenodo.8206570" target="_blank" rel="noopener">data set</a> and <a href="https://github.com/HaHeho/baffled-arrays-to-binaural/releases/tag/v2024.JAES" target="_blank" rel="noopener">rendering code</a>.</li> <li>Scene configuration files for the SSR with all listening conditions presented in the perceptual experiment as generated from the following Matlab scripts.</li> </ul> <p>Directory "resources/HPCF_KEMAR/":</p> <ul> <li>Impulse responses of equalization filters for various headphones on the G.R.A.S KEMAR acoustic dummy head as measured for this experiment.</li> </ul> <p>Directory "resources/User_study/":</p> <ul> <li>Various resources for the listening experiment.</li> <li>The files in "Exp1_analysis" include intermediate and final statistical analysis results as generated from the following R scripts.</li> <li>"Exp1_config.json" contains the configuration of the study GUI with conditions presented in the listening experiment.</li> <li>"Exp1_Introduction.pdf" contains the instructions presented to the subjects at the start of the listening experiment.</li> <li>"Exp1_Part2_data_strings.xls" contains all subjects' raw perceptual response data gathered from the listening experiment.</li> <li>"Questionnaire.pdf" contains the questionnaire given to the subjects at the end of the listening experiment.</li> </ul> <p>Matlab script "x4_Gather_Headphone_Compensations.m":</p> <ul> <li>Generate plots of measured headphone compensation filters. Furthermore, the generated minimum phase filters and filters to yield a linear phase response from the headphones are extracted as separate WAV files.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Collect several specified pre-rendered binaural room impulse response sets into an ASD file. The SSR can load this scene to present all gathered configurations in direct comparison with head tracking.</li> </ul> </div> <div> <p>Readme file "x6a_Normalize_SSR_Loudnesses.txt":</p> </div> <div> <div> <div> <ul> <li>Ideally, the rendering script would implement a measure to provide a reliable estimation of the binaural loudness of the rendered configuration. This could be used to normalize all stimuli levels. However, such a measure is currently not available or implemented.</li> <li>Therefore, tuning the stimuli loudness for the user study was performed beforehand by ear. The adjusted playback levels are set in a modified SSR configuration file for the listening experiment.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Perform convolution of (rendered) binaural room impulse responses with a source audio signal. This is done for a specified selection of static head orientations and a continuous rotation over all horizontal head orientations.</li> <li>The resulting auralizations are published as <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/">supplementary materials</a> to the manuscript.</li> </ul> <p>Shell script "x7_Start_Study_GUI.sh":</p> <ul> <li>Initialize all required components to perform the perceptual user study, including: <ul> <li>SSR to perform the real-time rendering of the BRIRs with head tracking</li> <li>SSR to extract head-tracking data (in case a Polhemus tracker is used)</li> <li>Pd to extract head-tracking data (in case a Supperware tracker is used)</li> <li>Pd to perform real-time convolution to apply headphone compensation</li> <li>Pd to receive OSC messages from the study GUI</li> <li>Pd to trigger audio file playback from received OSC messages</li> <li>Pd to translate OSC messages into FUDI messages for the SSR</li> <li>The GUI to be used by the participants and implement the study procedure</li> </ul> </li> <li>Some static configuration variables can be adjusted, whereas other parameters are chosen during script execution.</li> </ul> <p>Matlab script "x8_Gather_Study_Data.m":</p> <ul> <li>Transform the raw result data from the questionnaire (*.xls) and the study GUI (*.json) into a compact format (*.xls) that can be loaded to plot the raw data and imported by software for the subsequent statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>Matlab script "x8a_Plot_Study_Data.m":</p> <ul> <li>Generate a set of violin plots to visualize the initial distribution of the raw perceptual data. The data is split by specified attributes and plotted separately for visual inspection.</li> <li>The data may also be transformed into ranks for a first distribution inspection. Note that implementing the ranking method, notably how ties are resolved, may differ from the technique employed in the statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>R script "x8b_Analyze_Exp1_Data.R":</p> <ul> <li>Perform the statistical analysis by transforming the observed subject ratings into a predicted distribution of ranks using a hierarchical generalized linear regression model.</li> <li>Executing the statistical model may take some time due to the Bayesian framework employing Markov-chain Monte Carlo simulations.</li> <li>Data is exported at various intermediate steps to be loaded and visualized by the following R script.</li> </ul> <p>R markdown script "x8c_Plot_Exp1_Results.Rmd":</p> <ul> <li>Generate various plots and data tables of the observed data and the predicted results to visualize the distribution and influence of different analysis parameters.</li> <li>Some of the resulting plots were used in the manuscript.</li> <li>"x8c_Plot_Exp1_Results.html" conveniently summarizes all plots and data tables generated by "knitting" the R markdown script.</li> </ul> </div> </div> </div>
2D Array of Pulse Coupled Oscillators Driving Cilia to Demonstrate Metachronal Waves
<p>The video demonstrates metachornal waves from an 2D array of delay locked pulse coupled oscillators, each mapping to a cilia.<br>Within the triangle structure, the oscillators are coupled to their nearest neighbors.</p>
Bottom-up Fabrication of 2D Rydberg Exciton Arrays in Cuprous Oxide
Open the record for dataset details and reuse information.
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p>
Object Shape Recognition and Targeting Using 4x4 Ultrasonic Sensor Array
Open the record for dataset details and reuse information.
Stacked multiband receiver function waveforms for global study of mantle transition zone discontinuities using dense arrays
<p>Please see the readme for a full explanation. This dataset includes the following 1) the nine preferred parameters and their uncertainties 2) the group and stacked observed and best modeled receiver function waveforms and 3) a list of the network codes and stations. </p>
Dataset for "AACVD Synthesized Tungsten Oxide-NWs loaded with Osmium oxide as Gas Sensor Array: Enhancing Detection with PCA and ANNs"
<p>Dataset with the gas senisng mesurements for Osmium oxide decorated tunsten oxide gas sensors</p>
Codes, Catalogues and Data for "Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"
<p><strong>Codes, Catalogues and Data available for:</strong> <br>"Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"</p> <p><strong>Catalog</strong> folder: Contains the CMM (beam-forming based) event catalogue as well as event and station information for the PNR-1z site.</p> <p><strong>Classification Test</strong> folder: Jupyter notebooks that run the classification tests and mseed input data of isolated phases (P, S, Noise).</p> <p><strong>DL_model_catalogues</strong> folder: Contains full catalogues for each DL phase picker (GPD, U-GPD, EQT and PhaseNet) and the LinMEF-filtered catalogues.</p> <p><strong>Model_run_docs</strong> folder: Util/core files for PhaseNet and EQTransformer to read data with different sampling frequencies (i.e., not 100 Hz)</p> <p><strong>Data</strong> folder: Contains one hour of continuous downhole data (11th December 2018, 9am-10am) from the PNR-1z dataset.</p>
Datasets of microtremor arrays and phase velocities of Rayleigh waves
<p>Observed microtremor array data / Synthetic and observed phase-velocity data used in Cho and Iwata [2018, submitted to JGR].</p>
Microtremor array data
<p>Microtremor array data that are used for analyses with ESAC and nc-SPAC methods, which include both observed and numerically-synthesized waveforms.</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Evaluation Datasets
<p>This package consists of two evaluation datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These evaluation datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>. The corresponding development datasets can be downloaded <a href="https://doi.org/10.5281/zenodo.2599196">here</a>.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <ul> <li>The <strong>foa_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> evaluation dataset.</li> <li>The <strong>mic_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation dataset.</li> </ul> <p>-- Version 2 updates --</p> <p>The<a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection-results"> DCASE 2019 sound event localization and detection task has now ended</a>. Hence we are releasing the reference labels for the evaluation dataset in this version.</p> <ul> <li>The <strong><em>metadata_eval.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation datasets. </li> <li>The <strong>short2longnames.txt</strong> file consists of the corresponding names for each recording in the dataset in the <a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection#development-dataset">development-set format</a>, i.e., including the information of the impulse response location and the maximum number of overlapping sound events in the recording.</li> </ul> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p> <p> </p> <p> </p>
Dataset of Denmark Strait Mooring Array 2014/15
<p><strong>Data from Denmark Strait mooring array 2014/15 as used and described in Moritz et al. 2019: "Mesoscale Eddies observed at the Denmark Strait Sill" (submitted to JGR Oceans May 2019).</strong></p> <p>The mooring array deployed in 2014/15 consisted of five current meters labeled DS1, DS20, DS2, DS21, DS22. DS1 and DS2 are the long-term moorings maintained by the Marine and Freshwater Research Institute (MFRI), Reykjavik, Iceland, and the University of Hamburg, Hamburg, Germany, respectively. Both consist of an upward-looking ADCP mounted within a floatation body, additionally equipped with a SeaBird MicroCAT SBE 37 below the ADCP, providing temperature and salinity information. For the experiment in 2014/15 DS20 had the same design and was positioned between DS1 and DS2. Further west, an ADCP and a MicroCAT were deployed as DS21 in a bottom mounted trawl proof frame. Mooring DS22 had an Aanderaa RCM8 and a MicroCAT and completed the array on the Greenlandic shelf.</p> <p>From the ADCPs we obtained vertical velocity profiles spanning 400 m. The RCM performed single-point measurements at a height of 22 m above the bottom. The five moorings were separated by 5 to 10 km, thus spanning approximately 30 km from the deepest point of the passage towards the Greenlandic shelf. The temporal resolution of our data set is hourly and covers 300 days.</p> <p>The data were processed, quality controlled and corrected. The correction of the data took into account a low velocity bias that affected the measurements of the Long Ranger ADCPs. The coordinate system was rotated such that the v-component is oriented along the mean flow direction and the u-component across the mean flow. Positive v is upstream (northeastward) and negative v is directed downstream (southwestward). Positive u is towards Iceland (southeastward) and negative u is directed towards Greenland (northwestward).</p> <p>The data are provided in MATLAB format:</p> <p>---------------------------------------------------------------------------------------<br> array_2014_15_meta.mat: <br> moor_name: Mooring name<br> moor_lat: Latitude of mooring position [°N]<br> moor_lon: Longitude of mooring position [°E]<br> moor_dist: distance between moorings relative to DS1 [km]<br> moor_bottom_depth: bottom depth at mooring position [m]</p> <p>--------------------------------------------------------------------------------------- <br> array_2014_15_velocity_mat:<br> DSxx.uq: hourly u-component velocity (Dimension: Time,Depth) [m/s] <br> oriented across mean flow direction, i.e.<br> positive u is directed southeastward<br> negative u is directed northwestward<br> <br> DSxx.vq: hourly v-component velocity (Dimension: Time,Depth) [m/s]<br> oriented parallel to mean flow direction, i.e.<br> positive v is directed upstream (northeastward)<br> negative v is directed downstream (southwestward)<br> <br> DSxx.z: depth above bottom [m] <br> <br> DSxx.mtime: numerical time vector (Matlab datenum format)<br> <br> Please note: DS22 is a point measurement at 472 m.<br> <br> <br> --------------------------------------------------------------------------------------- <br> array_2014_15_temperature.mat:<br> temperature: hourly near-bottom temperature [°C]<br> time: numerical time vector (Matlab datenum format)</p> <p>--------------------------------------------------------------------------------------- <br> array_2014_15_u_v_100mab_mat:<br> u_100: hourly u-component velocity at 100 m above bottom [m/s]<br> oriented across mean flow direction, i.e.<br> positive u is directed southeastward<br> negative u is directed northwestward<br> <br> v_100: hourly v-component velocity at 100 m above bottom [m/s]<br> oriented parallel to mean flow direction, i.e.<br> positive v is directed upstream (northeastward)<br> negative v is directed downstream (southwestward)<br> <br> u_100_r: hourly u-component velocity at 100 m above bottom [m/s]<br> oriented in East-West direction, i.e.<br> positive u is directed eastward<br> negative u is directed westward<br> <br> v_100_r: hourly v-component velocity at 100 m above bottom [m/s]<br> oriented in North-South direction, i.e.<br> positive v is directed northward<br> negative v is directed southward <br> <br> time: numerical time vector (Matlab datenum format) <br> --------------------------------------------------------------------------------------- </p> <p> </p> <p> </p> <p> </p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
MetTLM 20NRM01 TU/e Dataset: Dependence of Temporal Frequency and Chromaticity on the Visibility of the Phantom Array Effect
<p>The dataset has the following format: 20 (Participants) by 18 (= 3 Chromaticities × 6 Temporal Frequencies)</p> <p><strong>Chromaticities</strong>: <strong>R</strong>ed (<strong>R</strong>); <strong>G</strong>reen (<strong>G</strong>); Warm <strong>W</strong>hite (<strong>W</strong>)</p> <p><strong>Temporal Frequencies</strong>: <em>F1</em> = <strong>80</strong> Hz; <em>F2</em> = <strong>300</strong> Hz; <em>F3</em> = <strong>600</strong> Hz; <em>F4</em> = <strong>900</strong> Hz; <em>F5</em> = <strong>1200</strong> Hz; <em>F6</em> = <strong>1800</strong> Hz.</p> <table> <tbody> <tr> <td> </td> <td> <p> <strong>R</strong></p> <p><em>F1</em></p> </td> <td> <p><strong>R</strong></p> <p><em>F2</em></p> </td> <td> <p><strong>R</strong></p> <p><em>F3</em></p> </td> <td> <p><strong>R</strong></p> <p><em>F4</em></p> </td> <td> <p><strong>R</strong></p> <p><em>F5</em></p> </td> <td> <p><strong>R</strong></p> <p><em>F6</em></p> </td> <td> <p><strong>G</strong></p> <p><em>F1</em></p> </td> <td> <p><strong>G</strong></p> <p><em>F2</em></p> </td> <td> <p><strong>G</strong></p> <p><em>F3</em></p> </td> <td> <p><strong>G</strong></p> <p><em>F4</em></p> </td> <td> <p><strong>G</strong></p> <p><em>F5</em></p> </td> <td> <p><strong>G</strong></p> <p><em>F6</em></p> </td> <td> <p><strong>W</strong></p> <p><em>F1</em></p> </td> <td> <p><strong>W</strong></p> <p><em>F2</em></p> </td> <td> <p><strong>W</strong></p> <p><em>F3</em></p> </td> <td> <p><strong>W</strong></p> <p><em>F4</em></p> </td> <td> <p><strong>W</strong></p> <p><em>F5</em></p> </td> <td> <p><strong>W</strong></p> <p><em>F6</em></p> </td> </tr> <tr> <td>P01</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P02</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P03</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P04</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>...</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P19</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>P20</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p>Due to the fractional factorial 3 (colour) × 6 (temporal frequency) mixed design, there are some empty cells. The details are described in Table 2 of the publication.</p> <p>The values in the table represent the visibility thresholds in Modulation Depth (MD). For example, the values of 0.05, 0.1, and 1 means a MD of 5%, 10% and 100% respectively.</p> <p><br><br></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.