Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “magnetic tape”
Magnetic Tape Recorder Dataset
<p>This repository contains the datasets collected and used in the research project:</p> <p>O. Mikkonen, A. Wright, E. Moliner and V. Välimäki, “Neural Modeling Of Magnetic Tape Recorders,”<br> in <em>Proceedings of the International Conference on Digital Audio Effects (DAFx)</em>,<br> Copenhagen, Denmark, 4-7 September 2023.</p> <p>A pre-print of the article is available in <a href="https://arxiv.org/abs/2305.16862">arXiv</a>.<br> The code is open-source and published in <a href="https://github.com/01tot10/neural-tape-modeling">GitHub</a>.<br> The accompanying web page can be found from <a href="http://research.spa.aalto.fi/publications/papers/dafx23-neural-tape/">here</a>.</p> <p><strong>Overview</strong></p> <p>The data is divided into various subsets, stored in separate directories. The data contains both <em>toy data</em> generated using a software emulation of a reel-to-reel tape recorder, as well as <em>real data</em> collected from a physical device. The various subsets can be used for training, validating, and testing neural network behavior, similarly as was done in the research article.</p> <p><strong>Toy and Real Data</strong></p> <p>The <em>toy data</em> was generated using <a href="https://github.com/jatinchowdhury18/AnalogTapeModel/">CHOWTape</a>, a physically modeled reel-to-reel tape recorder. The subsets generated with the software emulation are denoted with the string `CHOWTAPE`. Two variants of the toy data was produced: in the first variant, the fluctuating delay produced by the simulated tape transport was disabled, and in the second kind, the delay was enabled. The latter variants are denoted with the string `WOWFLUTTER`.</p> <p>The <em>real data</em> is collected using an Akai 4000D reel-to-reel tape recorder. The corresponding subsets are denoted with the string `AKAI`. Two tape speeds were used during the recording: 3 3/4 IPS (inches per second) and 7 1/2 IPS, with the corresponding subsets denoted with '3.75IPS' and '7.5IPS' respectively. On top of this, two different brands of magnetic tape were used for capturing the datasets with different tape speeds: Maxell and Scotch, with the corresponding subsets denoted with 'MAXELL' and 'SCOTCH' respectively.</p> <p><strong>Directories</strong></p> <p>For training the models, a fraction of the inputs from <a href="https://zenodo.org/record/3824876">SignalTrain LA2A Dataset</a> was used. The training, validation, and testing can be replicated using the subsets:</p> <ul> <li>ReelToReel_Dataset_MiniPulse100_AKAI_*/ (hysteretic nonlinearity, real data)</li> <li>ReelToReel_Dataset_Mini192kHzPulse100_AKAI_*/ (delay generator, real data)</li> <li>Silence_AKAI_*/ (noise generator, real data)</li> <li>ReelToReel_Dataset_MiniPulse100_CHOWTAPE*/ (hysteretic nonlinearity, toy data)</li> <li>ReelToReel_Dataset_MiniPulse100_CHOWTAPE_F[0.6]_SL[60]_TRAJECTORIES/ (delay generator, toy data)</li> </ul> <p>For visualizing the model behavior, the following subsets can be used:</p> <ul> <li>LogSweepsContinuousPulse100_*/ (nonlinear magnitude responses)</li> <li>SinesFadedShortContinuousPulse100*/ (magnetic hysteresis curves)</li> </ul> <p><strong>Directory structure</strong></p> <p>Each directory/subset is made of up of further subdirectories that are most often used to separate the training, validation and test sets from each other. Thus, a typical directory will look like the following:<br> ```<br> [DIRECTORY_NAME]<br> ├── Train<br> │ ├── input_x_.wav<br> │ ...<br> │ ├── target_x_.wav<br> │ ...<br> └── Val<br> │ ├── input_y_.wav<br> │ ...<br> │ ├── target_y_.wav<br> │ ...<br> ├── Test<br> │ ├── input_z_.wav<br> │ ...<br> │ ├── target_z_.wav<br> │ ...<br> ```</p> <p>While not all of the audio is used for training purposes, all of the subsets share part of this structure to make the corresponding datasets compatible with the dataloader that was used.</p> <p>The input and target files denoted with the same number `x`, e.g. `input_100_.wav` and `target_100_.wav` make up a pair, such that the target audio is the input audio processed with one of the used effects. In some of the cases, a third file named `trajectory_x_.npy` can be found, which consists of the corresponding pre-extracted delay trajectory in the `NumPy` binary file format.</p> <p><strong>Revision History</strong></p> <ul> <li>Version 1.1.0 <ul> <li>Added high-resolution (192kHz) dataset for configuration (SCOTCH, 3.75 IPS)</li> </ul> </li> <li>Version 1.0.0 <ul> <li>Initial publish</li> </ul> </li> </ul>
Magnetic tape MA-9 / GRA-7 (Inv. 2017-403)
<p>This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine.</p> <p>object details:<br> name: MA-9 / GRA-71, magnetic tape<br> Inv.-No. of the Deutsches Museum: 2017-403</p> <p>file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.144<br> Y pixel spacing: 0.144<br> Z pixel spacing: 0.144<br> grid size x: 401<br> grid size y: 478<br> grid size z: 627<br> color depth: 16Bit</p> <p>ownership & image acquisition:<br> project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a><br> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a><br> IP holder: Deutsches Museum<br> license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a><br> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a><br> scanning device: High Energy CT XXL-CT<br> Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a><br> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)</p>
Analysis of the Magnetic Tape Bandage on Respiratory Functional Effects.
ClinicalTrials.gov study NCT05356299. IPD Sharing: NO. Countries: 1. Publications: 7.
Sensory and Vascular Effects of Kinesio and Magnetic Tape in Healthy Subjects
ClinicalTrials.gov study NCT07034456. IPD Sharing: YES. Countries: 0. Publications: 21.
Effects of Magnetic Tape on Isometric Hip Strength in Patients With Low Back Pain. RCT
ClinicalTrials.gov study NCT04893967. IPD Sharing: NO. Countries: 1. Publications: 0.
Changes in Blood Flow in the Application of Magnetic Tape in Patients With Low Back Pain. RCT
ClinicalTrials.gov study NCT04836468. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape Over Hip Cutaneous Nerves in Patients With Low Back Pain
ClinicalTrials.gov study NCT04845477. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape on the Autonomic Nervous System
ClinicalTrials.gov study NCT05504369. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape on Perceived Spine Pain and Over Ankle Dorsiflexion in Patients With Low Back Pain
ClinicalTrials.gov study NCT04875273. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape Over Ankle Dorsiflexion in Patients With Low Back Pain
ClinicalTrials.gov study NCT04883112. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape Over Internal Hip Rotation in Patients With Low Back Pain
ClinicalTrials.gov study NCT04873713. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape on Autonomic Nervous System in Patients With Low Back Pain
ClinicalTrials.gov study NCT04873726. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Magnetic Tape Over Hip Flexion in Patients With Low Back Pain.
ClinicalTrials.gov study NCT04878874. IPD Sharing: NO. Countries: 0. Publications: 0.
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </li> </ul>
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.