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40,091 results for “recordings”
A dataset recorded during development of a tempo-based brain-computer music interface
Open the record for dataset details and reuse information.
A dataset recording joint EEG-fMRI during affective music listening
Open the record for dataset details and reuse information.
Data for "What have biological records ever done for us? A systematic scoping review"
<p>These files contain data used in "What have biological records ever done for us? A systematic scoping review" (Gaul et al. 2020). </p>
Remote detection and recording of atomic-scale spin dynamics
<p>This folder contains all data and all processing files for the paper titled "Remote detection and recording of atomic-scale spin dynamics". View full paper here: https://www.nature.com/articles/s42005-020-0361-z</p>
68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>Data provided by Meitian Wang at PSI and master file revised May 2020 for full NXmx compliance.</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5 nexus_details.start_time=2018-01-00T00:00:00.000 nexus_details.end_time=2018-01-00T00:00:02.720Z nexus_details.end_time_estimated=2018-01-00T00:00:02.720Z nexus_details.sample_name=Lysozyme nexus_details.total_flux=1000000000000</p> <p>Some notes about the parameters:<br> - Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.<br> - Wavelength is a single wavelength for the whole dataset, but options exist to do 1 wavelength per image, or a whole spectrum per image.<br> - Start and end times are example timestamps for illustration. End times are estimated for 68 frames using a 25 Hz recording rate.<br> - Total flux of 1e12 photons is an estimate.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p>
WWV Doppler Shift Recording by AD8Y, 2.5 MHz, no GPSDO
<p>Doppler shift data from WWV on 2.5 MHz.</p> <p><strong>Callsign:</strong> AD8Y</p> <p><strong>Radio:</strong> Icom 7610</p> <p><strong>GPSDO:</strong> No</p> <p><strong>Antenna: </strong>Dipole, shorter than 1/2 wavelength.</p> <p><strong>Coordinates:</strong> (41.4067594 -75.6675467)</p> <p><strong>Elevation:</strong> 914.8 feet</p> <p> </p> <p>This dataset is part of the Frequency Analysis Network project, collected according to the same process as the data for the Festival of Frequency Measurement (10.5281/zenodo.3707210). </p>
WWV Doppler Shift Recording by AD8Y, 5 MHz, no GPSDO
<p>Doppler shift data from WWV on 5 MHz.</p> <p><strong>Callsign:</strong> AD8Y</p> <p><strong>Radio:</strong> Kenwood TS-450</p> <p><strong>GPSDO:</strong> No</p> <p><strong>Antenna:</strong> Random wire vertical</p> <p><strong>Coordinates:</strong> (41.4067594 -75.6675467)</p> <p><strong>Elevation:</strong> 914.8 feet</p> <p><strong>Notes:</strong> The TXCO is intermittently calibrated against a GPSDO, but the system cannot be run off the GPSDO because signal leakage from it overwhelms the signal from WWV. Data collection was interrupted on some dates, including 31 December 2019 and 16 January 2020. </p> <p> </p> <p>This dataset is part of the Frequency Analysis Network project, collected according to the same process as the data for the Festival of Frequency Measurement (10.5281/zenodo.3707210). </p>
Hassan #1 binaural recording in Darb al-Ahmar, Cairo (Egypt), 25-10-2011
<p>« Mics in the Ears » binaural experiment in Cairo (Egypt): Vincent Battesti & Nicolas Puig, social anthropologists, asked inhabitants of Cairo megapolis in Egypt to record the surrounding urban sounds during one of their daily journeys (without the researcher), equipped with binaural microphones and GPS device. Participants have recorded in different Cairo neighbourhoods, and are themselves from different generations, social and economic backgrounds, and different genders.</p> <p>The two sound files are one the raw sound recorded by one of them during this walk in a neighbourhood in Cairo (see name of the inhabitant and date and place of recording in the file name), and the other the description and comments he or she gave us a posteriori when listening to this previous raw sound he or she recorded. See <a href="https://vbat.org/article831">https://vbat.org/article831</a></p>
Hassan #2 Route binaural recording in Duwiqa, Cairo (Egypt), 28-09-2012
<p>« Mics in the Ears » binaural experiment in Cairo (Egypt): Vincent Battesti & Nicolas Puig, social anthropologists, asked inhabitants of Cairo megapolis in Egypt to record the surrounding urban sounds during one of their daily journeys (without the researcher), equipped with binaural microphones and GPS device. Participants have recorded in different Cairo neighbourhoods, and are themselves from different generations, social and economic backgrounds, and different genders.</p> <p>The two sound files are one the raw sound recorded by one of them during this walk in a neighbourhood in Cairo (see name of the inhabitant and date and place of recording in the file name), and the other the description and comments he or she gave us a posteriori when listening to this previous raw sound he or she recorded. See <a href="https://vbat.org/article831">https://vbat.org/article831</a></p>
Salma binaural recording, Wast al-Balad, Cairo (Egypt), 26-09-2012
<p>« Mics in the Ears » binaural experiment in Cairo (Egypt): Vincent Battesti & Nicolas Puig, social anthropologists, asked inhabitants of Cairo megapolis in Egypt to record the surrounding urban sounds during one of their daily journeys (without the researcher), equipped with binaural microphones and GPS device. Participants have recorded in different Cairo neighbourhoods, and are themselves from different generations, social and economic backgrounds, and different genders.</p> <p>The two sound files are one the raw sound recorded by one of them during this walk in a neighbourhood in Cairo (see name of the inhabitant and date and place of recording in the file name), and the other the description and comments he or she gave us a posteriori when listening to this previous raw sound he or she recorded. See <a href="https://vbat.org/article831">https://vbat.org/article831</a></p>
Samir binaural recording in Bashtil, Cairo (Egypt), 28-09-2012
<p>« Mics in the Ears » binaural experiment in Cairo (Egypt): Vincent Battesti & Nicolas Puig, social anthropologists, asked inhabitants of Cairo megapolis in Egypt to record the surrounding urban sounds during one of their daily journeys (without the researcher), equipped with binaural microphones and GPS device. Participants have recorded in different Cairo neighbourhoods, and are themselves from different generations, social and economic backgrounds, and different genders.</p> <p>The two sound files are one the raw sound recorded by one of them during this walk in a neighbourhood in Cairo (see name of the inhabitant and date and place of recording in the file name), and the other the description and comments he or she gave us a posteriori when listening to this previous raw sound he or she recorded. See <a href="https://vbat.org/article831">https://vbat.org/article831</a></p>
Shady binaural recording in Wast al-Balad, Cairo (Egypt), 26-09-2012
<p>« Mics in the Ears » binaural experiment in Cairo (Egypt): Vincent Battesti & Nicolas Puig, social anthropologists, asked inhabitants of Cairo megapolis in Egypt to record the surrounding urban sounds during one of their daily journeys (without the researcher), equipped with binaural microphones and GPS device. Participants have recorded in different Cairo neighbourhoods, and are themselves from different generations, social and economic backgrounds, and different genders.</p> <p>The two sound files are one the raw sound recorded by one of them during this walk in a neighbourhood in Cairo (see name of the inhabitant and date and place of recording in the file name), and the other the description and comments he or she gave us a posteriori when listening to this previous raw sound he or she recorded. See <a href="https://vbat.org/article831">https://vbat.org/article831</a></p>
Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions
<p>This repository contains raw surface Electromyography signals termed surface Electromyograms (<a href="https://en.wikipedia.org/wiki/Electromyography">sEMG</a>) recorded with 8 circular surface Ag/AgCl pairs of electrodes placed circumferentially around the forearm of the dominant arm in 10 able-bodied individuals (5 Females and 5 Males). The proposed method for processing sEMG data with subjects' characteristics and protocol can be found in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>For each subject, sEMG was recorded from <strong>three recording electrode array positions</strong> termed P1, P2, and P3 for 9 hand movements. We provide a compressed .7z folder with 10 sub-folders for each subject named by <strong>subject ID</strong> (ID1, ID2, ... ID10). Each sub-folder contains 27 .txt data files (for 9 movements × 3 electrode array positions), except for subject ID7 (there are 24 .txt records, since three records for wrist extension EX in P1, P2, and P3 positions got corrupted in subject ID7). Average size of 10 sub-folders is 167.50 ± 27.02 MB with maximum of 194 MB and minimum of 117 MB.</p> <p>The subjects performed following hand movements from the reference resting position –relaxation, R (explained in-detail in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): (1) spherical power grasp, PS, (2) three finger sphere grasp, 3F, (3) two finger prismatic grasp, PP, (4) wrist flexion, FL, (5) wrist extension, EX, (6) radial deviation, RD, (7) ulnar deviation, UD, and then forearm rotation i.e. (8) pronation, PR, and (9) supination, SU. PS, 3F, PP, FL, EX, RD, UD, PR, and SU correspond to <strong>type of hand movement</strong> in naming convention for .txt data files.</p> <p><a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">Hand movements YoutTube playlist</a> contains explanatory videos for 9 hand movements recorded in this study, and we also provide corresponding .wmv here in the "movies hand movements.7z". Naming convention for .wmv files is <strong>type of hand movement</strong> with both full name and abbreviation for the movement (for example "radialDeviation-RD.wmv").</p> <p>Naming convention for .txt data files within 10 sub-folders is: <strong>subjects ID _ type of hand movement _ recording electrode array position</strong> (for example: "ID1_3F_P1.txt" in sub-folder ID1, "ID9_RD_P3.txt" in sub-folder ID9).</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/4039550/files/EMG%20dataset.7z?download=1">EMG dataset.7z</a>, 267 .txt data files, text format</li> <li><a href="https://zenodo.org/record/4039550/files/movies%20hand%20movements.7z?download=1">movies hand movements.7z</a>, 9 .wmv files, explanatory hand movement videos (also available on <a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">YouTube</a>)</li> <li><a href="https://zenodo.org/record/4039550/files/README.txt?download=1">README.txt</a>, metadata for data files, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point* according to the following structure</strong></p> <ol> <li>column - CH1** (recorded samples from channel 1)</li> <li>column - CH2** (recorded samples from channel 2)</li> <li>column - CH3** (recorded samples from channel 3)</li> <li>column - CH4** (recorded samples from channel 4)</li> <li>column - CH5** (recorded samples from channel 5)</li> <li>column - CH6** (recorded samples from channel 6)</li> <li>column - CH7** (recorded samples from channel 7)</li> <li>column - CH8** (recorded samples from channel 8)</li> </ol> <p>* For subjects ID1 and ID2 three decimal places are provided, while for other subjects 6 decimal places in .txt data files are provided.</p> <p>** Each data file contains at least 10 repetitions of the corresponding movement. In cases where file contains >10 repetitions (overall 162 .txt data files), we used the first or the last ten for the analysis (except for two files where short and strong artifact appeared during the measurement procedure, and corresponding movement repetitions were discarded) presented in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>Sample rate was set at 1000 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. For more in-detail explanations of electrode array assemble and positioning for sEMG channels CH1, CH2, ... CH8, please refer to <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant preprint and dataset as:</p> <ol> <li> <p>Miljković, N. and Isaković, M.S., 2021. Effect of the sEMG electrode (re) placement and feature set size on the hand movement recognition. <em>Biomedical signal processing and control</em>, 64:102292. <em><a href="https://doi.org/10.1016/j.bspc.2020.102292">10.1016/j.bspc.2020.102292</a></em></p> </li> <li> <p>Miljković, N. and Isaković, M.S., 2020. Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions. [Data set]. <em>Zenodo</em> <em><a href="https://zenodo.org/record/4039550">10.5281/zenodo.4039550</a></em>.</p> </li> </ol> <p><strong>ACKNOWLEDGEMENTS</strong> (from <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): "Special appreciation the authors owe to Professor Mirjana B. Popović from the University of Belgrade for her kind support,precious guidance, and advice regarding this research which significantly improved the manuscript. Also, the authors would like to thank Dr Matija Štrbac from Tecnalia Serbia Ltd. for providing advice throughout the study.The authors thank all volunteers for their participation."</p>
X-ray diffraction images of bovine trypsin crystals recorded at the FemtoMAX beamline of Max IV synchrotron facility
<p>The deposition concerns bovine trypsin diffraction images in two wedges. Each image is recorded on a still crystal and separated by 0.1 deg rotation. The x4.tar.gz archive contains summed intensities from individual snapshots at the same orientation, whereas x4_single.tar.gz archive contains single snapshots/orientation. </p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18'30.96" N, 11°5'0.68" E)
<p>Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18’30.96” N, 11°5’0.68” E (Latitude Y 33.3086, Longitude X 11.083522).</p> <p>1. Conventional AMS radiocarbon dates and reservoir-corrected and 2σ range calibrated ages from Sebkha Boujmel (BJM2 core).</p> <p>2. Output of the age-depth model for BJM2 core indicating depth and associated mean date for each cm (cal yr BP). The age model was obtained by third-degree polynomial regression with 10k model iteration using the package Clam 2.2.</p> <p>3. Pollen percentage for the three ecological groups (Mediterranean, steppe and desert taxa). The percentages are calculated with respect to a basic sum that only includes these three groups. Pollen taxa and types from the same genus or family and with the same ecology are grouped; including Boraginaceae (Moltkiopsis ciliata, Onosma and Echium), Ephedra sp. (Ephedra fragilis-t. and Ephedra distachia-t.) and Zygophyllaceae (Fagonia, Nitraria and Zygophyllum). Percentage of aquatics pollen are calculated based on the total sum of pollen grains identified in each pollen spectrum.</p> <p>4. Pollen and clay mineralogy data from Sebkha Boujmel. Percentages of (1) <strong>fresh water</strong> (Cyperaceae, Glyceria, Juncus, Lemna, Potamogeton, Rumex aquaticus-t., Typha/Sparganium-t.) and <strong>(2) Mediterranean tree and shrub</strong> (Buxus, Ceratonia, Cistus, Juniperus, Lamiaceae, Myrtus, Nerium, Olea, Papaveraceae, Pinus, Pistacia, Quercus ilex-t., Quercus deciduous-t., Rhus tripartita-t.) pollen taxa. (3) <strong>Wet / dry (W / D) pollen ratio</strong> (Poaceae + Cyperaceae/Asteraceae Cichorioideae + Asteraceae Asteroideae + Amaranthaceae Cornulaca/Traganum-t.). (4) <strong>Percentages of desert pollen taxa</strong> (Apiaceae, Asphodelus, Asteraceae Asteroideae, Asteraceae Cichorioideae, Calligonum, Capparis, Cistanche, Cleome, Cornulaca/Traganum-t., Crassulaceae, Cucurbitaceae, Echium, Ephedra distachia-t., Ephedra fragilis-t., Fagonia, Helianthemum, Malvaceae, Moltkiopsis ciliata, Neurada, Nitraria, Onosma, Reaumuria, Tamarix and Zygophyllum). (5) <strong>Illite</strong> <strong>[%] (Ill) / Kaolinite [%] (Kln) ratio</strong> and (6) <strong>Palygorskite percentages [%] (Plg)</strong>.</p> <p>5. Pollen percentage of Artemisia and selected anthropogenic pollen indicators (APIs) including cultivated (Cerealia-t., Corchorus, Ficus, Olea, Phoenix, Vitis), nitrophilous (Aizoaceae, Emex, Peganum, Polygonum) and introduced (Acacia cyanophylla-t., Casuarina, Eucalyptus) plant taxa. Percentage are calculated based on the total sum of pollen grains identified in each pollen spectrum.</p> <p>6. Pollen counts for BJM2 core (pollen grain count for each taxon by sample). + Lycopodium (added), Lycopodium (counted) and Sample weight [gr].</p> <p>7. Clay Mineralogy of BJM2 sediment core. </p> <p>Smectite [%] (Sme), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Illite [%] (Ill), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Palygorskite [%] (Plg), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Kaolinite [%] (Kln), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Chlorite [%] (Chl), METHOD/DEVICE: X-ray diffraction, clay fraction</p>
Tetrode recording from the antennal lobe of a locust (Schistocerca americana)
<p><span>1</span></p> <p>1</p> <p>1A tetrode recording from the antennal lobe (the first olfactory relay) of a locust, Schistocerca americana. 20 seconds of data are contained in the file in HDF5 format. The data were filtered (before A/D conversion) between 300 and 5000 Hz and sampled at 15 kHz. See Pouzat, Mazor and Laurent (2002) Journal of Neuroscience Methods 122(1): 43--57 for recording details.</p>
Extracellular recordings from a locust (Schistocerca americana) antennal lobe.
<p>Raw data from a tetrode recording from the antennal lobe (the first olfactory relay) of a locust, Schistocerca americana.</p> <p>The data were filtered (before A/D conversion) between 300 and 5000 Hz and sampled at 15 kHz. See Pouzat, Mazor and Laurent (2002) Journal of Neuroscience Methods 122(1): 43--57 for recording details. The data were recorded with a "Michigan probe", now sold by NeuroNexus (http://neuronexus.com/). A picture of the probe--made of 16 channels making 4 tetrodes--can be seen on slide 3 of DOI:10.5281/zenodo.14660. Good data were visible only on one of the tetrodes made of channel 9 / 11 / 13 / 16 and only data from these channels were recorded. The "LabBook" attribute contains transcript of the actual lab book with details about the acquisition. In short: 1 hour and 40 minutes of spontaneous activity was recorded as well as responses to 150 stimulation with citral.</p> <p>Each data set has a "log_file_content" attribute containing a copy of actual log file automatically generated during the acquisition. The stimulation protocol as well as the precise times of beginning and end of trial acquisition can be found there.</p> <p>The data are in HDF5 format (http://www.hdfgroup.org/HDF5/).</p> <p>Recordings performed by Christophe Pouzat and Ofer Mazor in the laboratory of Gilles Laurent (California Institute of Technology) in February 2001.</p>
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