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.
41
datasets available to search
ShareScore release 0.7.1
Dataset results
41 results for “Musical Instruments”
YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations
<p>YM2413-MDB is an 80s FM video game music dataset with multi-label emotion annotations. It includes 669 audio and MIDI files of music from Sega and MSX PC games in the 80s using YM2413, a programmable sound generator based on FM. The collected game music is arranged with a subset of 15 monophonic instruments and one drum instrument. They were converted from binary commands of the YM2413 sound chip. Each song was labeled with 19 emotion tags by two annotators and validated by three verifiers to obtain refined tags</p> <p>For more detailed information about the dataset, please refer to our paper: <a href="https://arxiv.org/abs/2211.07131">YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations</a>.</p> <p><strong>File Description</strong></p> <p><strong>1) Pure data</strong></p> <p>- original_vgms: crawled vgm files from <a href="https://www.smspower.org/">SMS POWER</a> and <a href="https://vgmrips.net/packs/">VGMRIPs</a></p> <p>- wav: rendered vgm files using <a href="https://github.com/vgmrips/vgmplay">VGMPlay</a></p> <p> </p> <p><strong>2) MIDI data</strong></p> <p>- midi/vgmplay_log_to_midi: converted midi files</p> <p>- midi/adjust_tempo: add postprocessing(metrically aligned using wav_downbeat files) after midi conversion</p> <p>- midi/adjust_tempo_remove_delayed_inst: add postprocessing(metrically aligned using wav_downbeat files, remove delayed instrument) after midi conversion</p> <p> </p> <p><strong>3) Metadata</strong></p> <p>- emotion_annotation/verified_annotation.csv: contains emotion annotation for each songs</p> <p>- tags_kor_eng.txt: Korean <-> English tag dictionary</p> <p> </p> <p><strong>4) Useful middle-time step data</strong></p> <p>- wav_downbeat: extracted downbeat values using TCNBeatTracker of <a href="https://github.com/CPJKU/madmom">madmom</a></p> <p>- vgm_txts: disassembled vgm files as txt using <a href="https://github.com/vgmrips/vgmtools#vgm-text-writer-vgm2txt">vgm2txt</a></p> <p>- ydr: YM2413 Disassembly Raw(YDR). command list of vgm files. generated by reading vgm_txts</p> <p> </p> <p><strong>Update Log</strong></p> <p>- version 1.0.1: Fix ticks per beat value adjust to tempo where tempo values are not 150. Also, madmom downbeat files are updated from DBNBeatTracker(ISMIR, 2015) to TCNBeatTracker(Newer one EUSIPCO, 2019).</p> <p>- version 1.0.2: <strong><a href="https://github.com/jech2/YM2413-MDB/issues/2">Wrong emotion tag issue in the verification annotation file was fixed.</a></strong></p>
China traditional music instrument dataset
<p>The FolkMusic dataset is a Chinese traditional music dataset mainly used for training instrument recognition models and performance evaluation. The dataset covers 15 traditional Chinese musical instruments, including Ba, Flute, Dongxiao, Erhu, Guqin, Guzheng, Hulusi, Liuqin, Pipa, Sanxian, Sheng, Suona, Yangqin, Zhongruan, and Falling Qin. The music clips in each instrument are saved as .mp3 files, which are recorded via two channels with a sampling rate of 44100Hz. The duration of these music clips are 3s, and a single instrument plays each music clip.</p>
Methods of analysis of selected instrumental music pieces of the Sonnleithner Collection of Tyrol
<p>Methods of analysis of selected instrumental music pieces of the Sonnleithner Collection of Tyrol;</p><p>AAWM 2022 (Analytical Approaches to World Music Conferences);</p><p>10th International Workshop on Folk Music Analysis (FMA 2022);</p><p>https://conferences.iftawm.org/</p><p> </p>
Therapeutic Instrumental Music Performance With Sensory-Enhanced Motor Imagery in Chronic Post-Stroke Rehabilitation
ClinicalTrials.gov study NCT03246217. IPD Sharing: NO. Countries: 1. Publications: 1.
Biosafety of Musical Instruments in the ICU
ClinicalTrials.gov study NCT05988853. IPD Sharing: NO. Countries: 1. Publications: 1.
A Hopf Adaptive Oscillator Analog Circuit as a musical instrument
Open the record for dataset details and reuse information.
Moisture and mold-proof characteristics of surface modified wood for musical instrument soundboards
<p>Wood is the major material for musical instrument soundboards fabrication on account of practical and cultural reasons. However, as a natural material, wood is easy to be degraded due to moisture or fungi corrosion. The traditional wood protection methods are normally meant for the structural materials, which might not suitable for the soundboard materials. Therefore, in this work, a novel nanomaterial-based modification method was applied to effectively protect wood from moisture and fungi, without causing changes to the acoustic properties of wood. The modified wood could do a better work in constructing soundboards with long lifespan.</p>
Moisture and mold-proof characteristics of surface modified wood for musical instrument soundboards
Open the record for dataset details and reuse information.
Towards Molecular Musical Instruments: Interactive Sonifications of 17-Alanine, Graphene and Carbon Nanotubes
<p><strong>Supplementary materials for the paper:</strong></p> <p>Thomas J. Mitchell, Alex J. Jones, Michael B. O’Connor, Mark D. Wonnacott, David R. Glowacki, and Joseph Hyde. 2020. Towards Molecular Musical Instruments: Interactive Sonifications of 17-Alanine, Graphene and Carbon Nanotubes. In Proceedings of the 15th International Audio Mostly Conference (AM’20), September 15–17, 2020, Graz, Austria. ACM, New York, NY, USA, 8 pages. <a href="https://gate.sc/?url=https%3A%2F%2Fdoi.org%2F10.1145%2F3411109.3411143&token=4f57ce-1-1596010611306">doi.org/10.1145/3411109.3411143</a></p> <p><strong>17-Alanine.mov:</strong></p> <p>Video was created by Alex J. Jones at the University of Bristol</p> <p>An immersive and interactive VR view of simulated 17-Alanine molecule with accompanying sonification.</p> <p><strong>GrapheneMusic.aif:</strong></p> <p>Composed by Joseph Hyde (<a href="https://gate.sc/?url=https%3A%2F%2Fwww.josephhyde.co.uk%2F&token=21d2c3-1-1596010611306">www.josephhyde.co.uk/</a>)</p> <p><strong>Example1.wav:</strong></p> <p>Molecule: 17-Alanine<br> Friction: 60<br> Timestep: 0.5<br> A: 0.1 D: 0.1 S: 1 R: 3<br> Wavetable Method: Relative<br> Note: C3<br> Velocity: 127<br> Temperature: 5<br> Automation: None</p> <p><strong>Example2. wav:</strong></p> <p>Molecule: 17-Alanine<br> Temperature: various<br> Friction: 60<br> Timestep: 0.5<br> Note: C3<br> Velocity: 127<br> A: 0.1 D: 0.1 S: 1 R: 3<br> Wavetable Method: Relative<br> Automation: Temperature</p> <p><strong>Example3.wav:</strong></p> <p>Molecule: 17-Alanine<br> Temperature: various<br> Friction: 100<br> Timestep: 0.78<br> Note: C3<br> Velocity: 127<br> A: 0.1 D: 0.1 S: 1 R: 0.5<br> Wavetable Method: Relative<br> Automation: Temperature</p>
Instrumental background music mitigates self-control fatigue and improves performance in prolonged mental work (Dataset)
<p>This repository contains the raw data used for a research study titled "Instrumental Background Music Facilitates Task Endurance and Cognitive Performance: Insights from Ego Depletion". This repository contains two Microsoft Excel files, as follows:</p> <ul> <li><em>ed_music_feature_analysis.csv</em></li> <li><em>ed_raw_data.xlsx</em></li> <li><em>ed_appendices [Anonymous].pdf</em></li> </ul> <p><strong>Files Description</strong></p> <p><em><strong>File: ed_music_feature_analysis.csv</strong></em></p> <p>This file contains the specific feature analysis for each music stimulus (N = 17) used in the experiment. These features (retrieved from Spotify's "Get Track's Audio Features" API) are:</p> <ul> <li>energy level</li> <li>valence</li> <li>tempo</li> </ul> <p>The Spotify ID of each music track is also recorded in this file, to facilitate future replication of the experiment.</p> <p><em><strong>File: ed_raw_data.xlsx</strong></em></p> <p>This file contains the raw data from the experiment (N = 41). There are two tabs in this file. The "data" tab records all raw data, whilst the "index" tab contains all the abbreviations (with their explanations) used in the document, and when relevant, a brief description of how the outcome measures were calculated.</p> <p>In the "data" tab, each row corresponds to a single participant and each column to a single variable. The variables about sample characteristics include:</p> <ul> <li>Age</li> <li>Self-identified gender</li> <li>Area of study</li> <li>Level of English proficiency</li> <li>Whether the participant has specific neurodiversity</li> <li>Personality traits</li> <li>Musicianship</li> <li>Self-reported everyday music listening habits</li> <li>Self-reported perceived (positive and negative) impact of music listening while studying</li> </ul> <p>The variables about experimental conditions and outcomes measures include:</p> <ul> <li>Each participant's assigned experimental condition (music or control)</li> <li>Reading comprehension performance (measured in terms of the number of questions answered and a final score)</li> <li>Pre- and post-reading positive affect, negative affect, and motivation for cognition</li> <li>(for the music condition) personal perception of the music they heard during the experiment</li> <li>Level of ego depletion (measured based on Stroop interference)</li> <li>Verbal working memory (measured based on Reading Span Task performance)</li> <li>Divided attention capacity (measured based on Category Switch Task performance)</li> </ul> <p><em>Notes.</em></p> <p><em>1. Empty cells in between data are missing data (due to participant absentees).</em></p> <p><em>2. See the "index" tab in <strong>ed_raw_data.xlsx </strong>for </em>a <em>detailed presentation and explanation of each recorded data.</em></p> <p><strong><em>File: </em><em>ed_appendices [Anonymous].pdf</em></strong></p> <p>This file contains the four appendices mentioned in the main article.</p>
Musical Instrument Rondador 3D
El rondador is a Ecuadorian musical instrument that is made from a local plant called "Carrizo" This especific model has 25 tubes each one with different heights and thickness. The .rar file contains: - High and low meshes in .fbx and .obj format - PBR textures maps (2K resolution )for any 3d software, unity 5 and unreal engine 4. - .blend file - hdri image file Source: Objaverse 1.0 / Sketchfab
Investigating CNN-Based Instrument Family Recognition for Western Classical Music Recordings
<p>This repository contains the data used for experiment 2 (both patch- and file-based) to reproduce the results from the ISMIR paper.</p> <p>If you wish to know more about the dataset and experiment 1, please contact us.</p>
Indian Instrumental Music in Hypertension
ClinicalTrials.gov study NCT02147366. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effects of Instrumental Music on Intraoperative Parameters and Postoperative Pain and Side Effects
ClinicalTrials.gov study NCT05886231. IPD Sharing: NO. Countries: 1. Publications: 0.
MINERVA: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain
<p>These folders contains all the data and trained models (including a detailed README), needed to replicate the results from the following publication:</p> <blockquote> <p>Matthia Sabatelli, Nikolay Banar, Marie Cocriamont, Eva Coudyzer, Karine Lasaracina, Walter Daelemans, Pierre Geurts & Mike Kestemont, "Advances in Digital Music Iconography. Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain". Digital Humanities Quarterly (2020).</p> </blockquote> <p>In this paper, we present MINERVA, the first benchmark dataset for the detection of musical instruments in non-photorealistic, unrestricted image collections from the realm of the visual arts. This effort is situated against the scholarly background of music iconography, an interdisciplinary field at the intersection of musicology and art history. We benchmark a number of state-of-the-art systems for image classification and object detection. Our results demonstrate the feasibility of the task but also highlight the significant challenges which this artistic material poses to computer vision. All the corresponding code, necessary for extending or replicating our work, is freely available for reuse (CC-BY) from an <a href="https://github.com/paintception/MINeRVA">open code repository</a>.</p> <p>This work has been generously funded by the Belgian Federal Research Agency BELSPO under the BRAIN-be program (project title: 'INSIGHT: Intelligent Neural Systems as Integrated Heritage Tools').</p> <p>Project website: <a href="https://hosting.uantwerpen.be/insight/">https://hosting.uantwerpen.be/insight/</a></p>
Instrumental Music Part 1
<p>Music</p>
Music Instrumental
Open the record for dataset details and reuse information.
Music Instrumental
<p>Music</p>
Music Instrumental
<p>music</p>
Instrumental Music
<p>Music composition</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.