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.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “MTG”
MTG-Jamendo Dataset
<p>We present the <a href="https://mtg.github.io/mtg-jamendo-dataset/">MTG-Jamendo Dataset</a>, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall.</p> <p>This repository contains metadata. For scripts and instructions on how to download and use the dataset please see the related <a href="https://github.com/MTG/mtg-jamendo-dataset">GitHub repository</a>.</p> <p><strong>Citation</strong></p> <p>If you use the MTG-Jamendo Dataset or part of it, please cite our <a href="http://mtg.upf.edu/node/3957">ICML2019 ML4MD paper</a>:</p> <pre><code>Bogdanov, D., Won M., Tovstogan P., Porter A., & Serra X. (2019). The MTG-Jamendo Dataset for Automatic Music Tagging. Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019).</code></pre> <p>BibTeX version: </p> <pre><code>@conference {bogdanov2019mtg, author = "Bogdanov, Dmitry and Won, Minz and Tovstogan, Philip and Porter, Alastair and Serra, Xavier", title = "The MTG-Jamendo Dataset for Automatic Music Tagging", booktitle = "Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019)", year = "2019", address = "Long Beach, CA, United States", url = "http://hdl.handle.net/10230/42015" } </code></pre> <p><strong>Acknowledgments</strong></p> <p>This work was funded by the predoctoral grant MDM-2015-0502-17-2 from the Spanish Ministry of Economy and Competitiveness linked to the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).</p> <p>This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068 "<a href="https://mip-frontiers.eu">MIP-Frontiers</a>".</p> <p>This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 688382 "<a href="https://www.audiocommons.org/">AudioCommons</a>".</p>
MTG Price Set
<p>Dataset containing core and expansion sets for MTG pulled from Scryfall on 2022-04-11. It includes a summary of each card and the price in EUR, USD and TIX.</p>
MTG-QBH: Query By Humming dataset
<p>This dataset includes 118 recordings of sung melodies. The recordings were made as part of the experiments on Query-by-Humming (QBH) reported in the following article:</p> <blockquote> <p>J. Salamon, J. Serrà and E. Gómez, "<a href="http://mtg.upf.edu/node/2657">Tonal Representations for Music Retrieval: From Version Identification to Query-by-Humming</a>", International Journal of Multimedia Information Retrieval, special issue on Hybrid Music Information Retrieval, In Press (accepted Nov. 2012). </p> </blockquote> <p>The recordings were made by 17 different subjects, 9 female and 8 male, whose musical experience ranged from none at all to amateur musicians. Subjects were presented with a list of songs out of which they were asked to select the ones they knew and sing part of the melody. The subjects were aware that the recordings will be used as queries in an experiment on QBH. There was no restriction as to how much of the melody should be sung nor which part of the melody should be sung, and the subjects were allowed to sing the melody with or without lyrics. The subjects did not listen to the original songs before recording the queries, and the recordings were all sung a capella without any accompaniment nor reference tone. To simulate a realistic QBH scenario, all recordings were done using a basic laptop microphone and no post-processing was applied. The duration of the recordings ranges from 11 to 98 seconds, with an average recording length of 26.8 seconds. </p> <p>In addition to the query recordings, three meta-data files are included, one describing the queries and two describing the music collections against which the queries were tested in the experiments described in the aforementioned article. Whilst the query recordings are included in this dataset, audio files for the music collections listed in the meta-data files are NOT included in this dataset, as they are protected by copyright law. If you wish to reproduce the experiments reported in the aforementioned paper, it is up to you to obtain the original audio files of these songs.</p> <p>All subjects have given their explicit approval for this dataset to be made public.</p> <p>Please Acknowledge MTG-QBH in Academic Research</p> <p><strong>Using this dataset</strong></p> <p>When the MTG-QBH dataset is used for academic research, we would highly appreciate if scientific publications of works partly based on the MTG-QBH dataset cite the above publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/mtg-qbh">https://www.upf.edu/web/mtg/mtg-qbh</a></p>
MTG-Audio Problems Detection on Sound Collections
<p>Manual annotation for the Audio Tagging Competition 2019 (<a href="https://www.kaggle.com/c/freesound-audio-tagging-2019">https://www.kaggle.com/c/freesound-audio-tagging-2019</a>) for audio problems described in Victor Badenas' Thesis from <a href="https://github.com/pirulok02/MTG-Audio-Problems-Detection">https://github.com/pirulok02/MTG-Audio-Problems-Detection</a></p>
Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall)
<p>Global tempo annotations used for training of the tempo estimation CNN presented in <a href="https://doi.org/10.5281/zenodo.1492353">A Single-step Approach to Musical Tempo Estimation using a Convolutional Neural Network</a>.</p>
Supplementary material 1 from: Basset Y, Donoso DA, Hajibabaei M, Wright MTG, Perez KHJ, Lamarre GPA, De León LF, Palacios-Vargas JG, Castaño-Meneses G, Rivera M, Perez F, Bobadilla R, Lopez Y, Ramirez JA, Barrios H (2020) Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites. Metabarcoding and Metagenomics 4: e58572. https://doi.org/10.3897/mbmg.4.58572
Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites
Supplementary material 2 from: Basset Y, Donoso DA, Hajibabaei M, Wright MTG, Perez KHJ, Lamarre GPA, De León LF, Palacios-Vargas JG, Castaño-Meneses G, Rivera M, Perez F, Bobadilla R, Lopez Y, Ramirez JA, Barrios H (2020) Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites. Metabarcoding and Metagenomics 4: e58572. https://doi.org/10.3897/mbmg.4.58572
Appendix S2
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - pool2_h1930001_MTG
GEO Series GSE168693. Homo sapiens. 3070 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - pool1_h1930002_MTG
GEO Series GSE168734. Homo sapiens. 3065 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - m3c9_h1930002_MTG
GEO Series GSE220451. Homo sapiens. 3035 samples. Type: Other; Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - pool96_h1930004_MTG
GEO Series GSE208051. Homo sapiens. 2982 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - m3c50_h1930004_MTG
GEO Series GSE232495. Homo sapiens. 2955 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - m3c8_h1930001_MTG
GEO Series GSE220431. Homo sapiens. 3027 samples. Type: Methylation profiling by high throughput sequencing; Other.
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.