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.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “Musical Performance”
Background music and cognitive task performance: systematic review dataset
<p>This repository contains the raw data used for a systematic review of the impact of background music on cognitive task performance (Cheah et al., 2022). Our intention is to facilitate future updates to this work.</p><p><strong>Contents description</strong></p><p>This repository contains eight Microsoft Excel files, each containing the synthesised data pertaining to each of the six cognitive domains analysed in the review, as well as task difficulty, and population characteristics:</p><ul><li><i>raw-data-attention</i></li><li><i>raw-data-inhibition</i></li><li><i>raw-data-language</i></li><li><i>raw-data-memory</i></li><li><i>raw-data-thinking</i></li><li><i>raw-data-processing-speed</i></li><li><i>raw-data-task-difficulty</i></li><li><i>raw-data--population</i></li></ul><p><strong>Files description</strong></p><p><i>Tabs organisation</i></p><p>The files pertaining to each cognitive domain include individual tabs for each cognitive task analysed (c.f. Figure 2 in the original paper for the list of cognitive tasks). The file with the population characteristics data also contains separate tabs for each characteristic (extraversion, music training, gender, and working memory capacity).</p><p><i>Tabs contents</i></p><p>In all files and tabs, each row corresponds to the data of a test. The same article can have more than one row if it reports multiple tests. For instance, the study by Cassidy and MacDonald (2007; cf. <i>Memory.xlsx</i>, tab: <i>Memory-all</i>) contains two experiments (immediate and delayed free recall) each with multiple test (immediate free recall: tests 25 – 32; delayed free recall: tests 58 – 61). Each test (one per row), in this experiment, pertains to comparisons between conditions where the background music has different levels of arousal, between groups of participants with different extraversion levels, between different tasks material (words or paragraphs) and different combinations of the previous (e.g., high arousing music vs silence test among extraverts whilst completing an immediate free recall task involving paragraphs; cf. test 30).</p><p>The columns are organised as follows:</p><ul><li>"TESTS": the index of the test in a particular tab (for easy reference);</li><li>"ID": abbreviation of the cognitive tasks involved in a specific experiment (see glossary for meaning);</li><li>"REFERENCE": the article where the data was taken from (see main publications for list of articles);</li><li>"CONDITIONS": an abbreviated description of the music condition of a given test;</li><li>"MEANS (music)": the average performance across all participants in a given experiment with background music;</li><li>"MEANS (silence)": the average performance across all participants in a given experiment without background music.</li></ul><p>Then, in horizontal arrangement, we also include groups of two columns that breakdown specific comparisons related to each test (i.e., all tests comparing the same two types of condition, e.g., L-BgM vs I-BgM, will appear under the same set of columns). For each one, we indicate mean difference between the respective conditions ("MD" column) and the direction of effect ("Standard Metric" column). Each file also contains a "Glossary" tab that explains all the abbreviations used in each document.</p><p><strong>Bibliography</strong></p><p>Cheah, Y., Wong, H. K., Spitzer, M., & Coutinho, E. (2022). Background music and cognitive task performance: A systematic review of task, music and population impact. <i>Music & Science, 5</i>(1), 1-38. <a href="https://doi.org/10.1177/0305735699272005">https://doi.org/10.1177/20592043221134392</a></p>
CROCUS: Dataset of Musical Performance Critique
<p>CROCUS (CRitique dOCUmentS): Dataset of Musical Performance Critique Documents (in Japanese) CC BY-NC-ND 4.0</p> <p>This open dataset contains 90 musical performances and 239 critiques of those performances.</p> <p>For more information, please visit the project page below.<br> <a href="https://masaki-cb.github.io/crocus/">https://masaki-cb.github.io/crocus/</a></p> <p>--</p> <p>Naming Rule:<br> For Recording Data<br> PieceID-PlayerID.wav<br> e.g. Perfomance of beethoven's piece by player 1<br> n01-bee-sym3-p01.wav</p> <p>For Critique Data<br> PieceID-PlayerID-CriticID.wav<br> e.g. Critique by teacher 3 on perfomance of beethoven's piece by player 1<br> n01-bee-sym3-p01-c03.txt</p> <p>List of Piece<br> PieceID Composer Title<br> n01-bee-sym3 L. v. Beethoven Symphony No.3 in E flat Major 'Eroica', Op.55<br> n02-ros-silk G. A. Rossini La scala di seta, Overture<br> n03-sch-sym8 F. Schubert Symphony No.8 in B Minor, D.759 'Unfinished'<br> n04-bra-vcon J. Brahms Violin Concerto in D Major, Op.77<br> n05-tch-sym4 P. I. Tchaikovsky Symphony No.4 in f Minor, Op.36<br> n06-tch-swan P. I. Tchaikovsky "The Swan Lake", Ballet Suite, Op.20a<br> n07-rim-sche N. Rimsky-Korsakov "Scheherazade", Symphonic Suite, Op.35<br> n08-str-donj R. Strauss "Don Juan", Symphonic Poem, Op.20<br> n09-rav-tomb M. Ravel Le Tombeau de Couperin I.Prelude<br> n10-pro-pete S. Prokofiev "Peter and the Wolf", Symphonic Tale, Op.67</p>
Musical Performance Critique Documents for Guitar
<p>CROCUS (CRitique dOCUmentS): Dataset of Musical Performance Critique Documents (in Japanese) CC BY-NC-ND 4.0</p> <p>This open dataset contains 84 guitar performances and 252 critiques of those performances.</p> <p>For more information, please visit the project page below.<br><a href="https://masaki-cb.github.io/crocus/">https://masaki-cb.github.io/crocus/</a></p> <p> </p> <p>n01 F. Sor Etude, Op. 31-1<br>n02 F. Sor Etude, Op. 35-22<br>n03 M. Carcassi Etude, Op. 60-3<br>n04 Anonymous Romanza<br>n05 F. Tárrega Lagrima<br>n06 L. Walker Kleine Romanze<br>n07 J. S. Sagreras Maria Luisan</p> <p> </p>
JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting
<p>Release of the MIDI data pairs that constitute the JAZZVAR dataset. See below for the abstract of the publication.</p> <p>The data is also available transposed to C/Am and subsequently, to all keys, with accompanying metadata.</p> <p>Abstract:</p> <p>Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pairs of Variation and Original MIDI segments. Each Variation in the dataset is accompanied by a corresponding Original segment containing the melody and chords from the original jazz standard. Our approach differs from many existing jazz datasets in the music information retrieval (MIR) community, which often focus on improvisation sections within jazz performances. In this paper, we outline the curation process for obtaining and sorting the repertoire, the pipeline for creating the Original and Variation pairs, and our analysis of the dataset. We also introduce a new generative music task, Music Overpainting, and present a baseline Transformer model trained on the JAZZVAR dataset for this task. Other potential applications of our dataset include expressive performance analysis and performer identification.</p>
Dataset: Warner Music Group Corp. (WMG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music
<p>Dataset accompanying the paper published at ISMIR 2019.</p> <p>See included README file for details.</p>
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
R code and dataset to "Monetizing Spillover Effects in the Creative Industries: the Impact of Live Music Performances on Youtube Searches"
<p>Content:</p> <ol> <li>The script<strong> main_script.R</strong> includes code to run a regression discontinuity (RD) design and validation and falsification of estimated results</li> <li>The folder <strong>data</strong> contains two files: <ol> <li>bands_2016_2019.csv: a dataset of performers with additional information for each one.</li> <li>festivals_2016_2019.csv: a dataset of video search activity (as retrieved from Google Trends) for performers in file bands_2016_2019.csv</li> </ol> </li> <li>The folder <strong>source</strong> contains two additional R scripts: <ol> <li>data_preparation.R: generates the long dataset used to estimate RD effects</li> <li>status_simulation.R: randomly assigns treattment status to performers and estimates RD effects. Note this may take a long time to run. Parallel code is used: the number of cores has been set to 4. </li> </ol> </li> <li>The folder simulation_results contains simulated data after running the script status_simulation.R.</li> </ol>
Audio-visual perceived arousal in music performance
<p>This dataset and R code are are linked to the manuscript:</p> <p>Järveläinen, H. Sound-accompanying movements enhance perceived arousal in music performance even from a distance. Proc. Sound and Music Computing conference SMC, Saint-Etienne, France, June 2022, (in press).</p> <p>The dataset Odf.RData contains functional measurements of perceived arousal in music performance, and the respective INLA model fit. The measurements are based on three-minute recordings from a solo piece for the English horn as specified in the article.</p>
Data from: Creating a multi-track classical music performance dataset for multi-modal music analysis: challenges, insights, and applications
We introduce a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground- truth annotation files including frame-level and note-level tran- scriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches for addressing the challenges involved in maintaining synchronization and ex- pressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset against existing widely-used music audio datasets. We anticipate that the dataset will be useful for the devel- opment and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multi-modal tasks. We bench- mark two existing MIR tasks (multi-pitch analysis and score- informed source separation) on the dataset and compare against other existing music audio datasets. Additionally, we consider two novel multi-modal MIR tasks (visually informed multi-pitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measures and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.
Life vs. Digital Music Interventions Performed by Professionals Throughout Pregnancy to Increase Mental Health for Mothers and Their Offspring
ClinicalTrials.gov study NCT07003048. IPD Sharing: NO. Countries: 1. Publications: 8.
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.
Music and Brain Stimulation for Upper Extremity Performance in Patients With Corticobasal Syndrome
ClinicalTrials.gov study NCT05073471. IPD Sharing: NO. Countries: 1. Publications: 23.
Effects of Music-with-Movement on Cognitive and Physical Performance of People With Potentially Reversible Cognitive Frailty: a Randomised Controlled Trial
ClinicalTrials.gov study NCT06791720. IPD Sharing: NO. Countries: 1. Publications: 10.
Effects of Music on Mental and Physical Performance in Young Basketball Players
ClinicalTrials.gov study NCT06515197. IPD Sharing: YES. Countries: 1. Publications: 3.
EffectS of prEferred Music on Laparoscopic performancE
ClinicalTrials.gov study NCT04111679. IPD Sharing: NO. Countries: 1. Publications: 1.
The Influence of TaKeTiNa Music Therapy, Traditional Chinese Acupuncture and Clown Theatrical Performance on Quality of Life and the Therapeutic Process of Patients Undergoing Allogenic Stem Cell Tran
ClinicalTrials.gov study NCT02976558. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Effect of Music on Surgical Performance During Artificial Intelligence-Based Simulation Training
ClinicalTrials.gov study NCT07111481. IPD Sharing: YES. Countries: 1. Publications: 7.
The Application of Virtual Reality Exposure Versus Relaxation Training in Music Performance Anxiety
ClinicalTrials.gov study NCT05735860. IPD Sharing: Not stated. Countries: 1. Publications: 1.
tDCS and Musical Performance in Young Orchestra Musicians
ClinicalTrials.gov study NCT06958081. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
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.