cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5522/04/13194155
title: Pupil data from the publication, Cortical State Fluctuations during Sensory Decision Making, by Jacobs et al in Current Biology

description: This site contains the pupil data from the publication Cortical State Fluctuations during Sensory Decision Making, by Jacobs et al in Current Biology.<br>This data is from the behavioural tasks described in the publication. The data consist of the results from a custom pupil size extraction algorithm, and requires further preprocessing. The code to perform the preprocessing is available with the companion code (https://github.com/eakjacobs/Jacobs_et_al_CurrentBiology). The raw data can also be shared upon request (they are not uploaded here because of the large size of the videos); please get in touch via email. The timestamps associated with the results here are also not yet aligned to the behavioural data; the companion code takes care of this as well.<br><br>The data is organised by experimental subject; most subjects were recorded from on multiple days, which form subfolders within the subject folder. Within a day, there may have been several experiments, which are again contained in subfolders within the day folder. The companion code expects this data organisation.<br>If there are any problems or questions, please raise an issue on GitHub.<br><br>Please follow this link for more information: https://doi.org/10.6084/m9.figshare.13084805<br><br>The research article can be found (freely available) at: https://www.cell.com/current-biology/fulltext/S0960-9822(20)31437-8
DOI: 10.6084/m9.figshare.13130303
title: Dataset for Enhancement of EPC migration by high-dose lisinopril is superior compared to captopril and ramipril

description: Dataset for Enhancement of EPC migration by high-dose lisinopril is superior compared to captopril and ramipril
DOI: 10.6084/m9.figshare.13130303.v1
title: Dataset for Enhancement of EPC migration by high-dose lisinopril is superior compared to captopril and ramipril

description: Dataset for Enhancement of EPC migration by high-dose lisinopril is superior compared to captopril and ramipril
DOI: 10.5522/04/13194155.v1
title: Pupil data from the publication, Cortical State Fluctuations during Sensory Decision Making, by Jacobs et al in Current Biology

description: This site contains the pupil data from the publication Cortical State Fluctuations during Sensory Decision Making, by Jacobs et al in Current Biology.<br>This data is from the behavioural tasks described in the publication. The data consist of the results from a custom pupil size extraction algorithm, and requires further preprocessing. The code to perform the preprocessing is available with the companion code (https://github.com/eakjacobs/Jacobs_et_al_CurrentBiology). The raw data can also be shared upon request (they are not uploaded here because of the large size of the videos); please get in touch via email. The timestamps associated with the results here are also not yet aligned to the behavioural data; the companion code takes care of this as well.<br><br>The data is organised by experimental subject; most subjects were recorded from on multiple days, which form subfolders within the subject folder. Within a day, there may have been several experiments, which are again contained in subfolders within the day folder. The companion code expects this data organisation.<br>If there are any problems or questions, please raise an issue on GitHub.<br><br>Please follow this link for more information: https://doi.org/10.6084/m9.figshare.13084805<br><br>The research article can be found (freely available) at: https://www.cell.com/current-biology/fulltext/S0960-9822(20)31437-8
DOI: 10.5281/zenodo.4247586
title: Matlab Algorithm for Systematic Vertical Separation Measurements of Tectonic Fault Scarps

description: We have studied the topography along the Lost River Fault in Idaho (USA) and in particular the rupture zone generated by the strong 1983 Borah Peak earthquake (M<sub>w</sub> 6.9) almost 40 years after its occurrence. Our first reason for acquiring these data was to systematically measure vertical separation (VS) along the fault.<br> To do this we have developed and implemented a scarp analysis algorithm in MATLAB (www.mathworks.com) which enables users to measure the vertical separation from a topographic profile. The code constructs a topographic profile along the preferred trend (generally perpendicular to the fault trace) and projects the best-fit lines to each of the hanging wall and footwall flat to the fault location. The vector difference between the intersection of these lines with a vertical plane at the fault location is the vertical separation. Please see the code guide for additional detail.
DOI: 10.5281/zenodo.4247585
title: Matlab Algorithm for Systematic Vertical Separation Measurements of Tectonic Fault Scarps

description: We have studied the topography along the Lost River Fault in Idaho (USA) and in particular the rupture zone generated by the strong 1983 Borah Peak earthquake (M<sub>w</sub> 6.9) almost 40 years after its occurrence. Our first reason for acquiring these data was to systematically measure vertical separation (VS) along the fault.<br> To do this we have developed and implemented a scarp analysis algorithm in MATLAB (www.mathworks.com) which enables users to measure the vertical separation from a topographic profile. The code constructs a topographic profile along the preferred trend (generally perpendicular to the fault trace) and projects the best-fit lines to each of the hanging wall and footwall flat to the fault location. The vector difference between the intersection of these lines with a vertical plane at the fault location is the vertical separation. Please see the code guide for additional detail.
DOI: 10.5281/zenodo.3889149
title: dim-sim

description: The <strong>dim-sim</strong> dataset is a collection of user-annotated music similarity triplet ratings used to evaluate music similarity search and related algorithms. Our similarity ratings are linked to the Million Song Dataset (MSD) and were collected for the following paper: Disentangled Multidimensional Metric Learning for Music Similarity<br> Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, and Juhan Nam.<br> Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, 2020. <pre><code>@inproceedings{Lee2019MusicSimilarity, title={Disentangled Multidimensional Metric Learning For Music Similarity}, author={Lee, Jongpil and Bryan, Nicholas J. and Salamon, Justin and Jin, Zeyu, and Nam, Juhan}, booktitle={Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2020}, organization={IEEE} }</code></pre> We kindly request that articles and other works in which this dataset is used cite the paper as listed above. Please see our paper or visit https://jongpillee.github.io/multi-dim-music-sim for more information.
DOI: 10.5281/zenodo.3648287
title: Contextual Tags for music auto-tagging

description: The dataset is composed of 15 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, chill, club, dance, gym, happy, night, party, relax, running, sad, sleep, summer, work, workout". For each track one or multiple tags are associated with it indicating that users listen to the track in the associated context. The creation of the dataset and the initial baseline of an auto-tagging model is described in the paper: Ibrahim, Karim M., Jimena Royo-Letelier, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC." <em>2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em>. IEEE, 2020. The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api .
DOI: 10.5281/zenodo.3889148
title: dim-sim

description: The <strong>dim-sim</strong> dataset is a collection of user-annotated music similarity triplet ratings used to evaluate music similarity search and related algorithms. Our similarity ratings are linked to the Million Song Dataset (MSD) and were collected for the following paper: Disentangled Multidimensional Metric Learning for Music Similarity<br> Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, and Juhan Nam.<br> Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, 2020. <pre><code>@inproceedings{Lee2019MusicSimilarity, title={Disentangled Multidimensional Metric Learning For Music Similarity}, author={Lee, Jongpil and Bryan, Nicholas J. and Salamon, Justin and Jin, Zeyu, and Nam, Juhan}, booktitle={Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2020}, organization={IEEE} }</code></pre> We kindly request that articles and other works in which this dataset is used cite the paper as listed above. Please see our paper or visit https://jongpillee.github.io/multi-dim-music-sim for more information.
DOI: 10.5281/zenodo.3648286
title: Contextual Tags for music auto-tagging

description: The dataset is composed of 15 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, chill, club, dance, gym, happy, night, party, relax, running, sad, sleep, summer, work, workout". For each track one or multiple tags are associated with it indicating that users listen to the track in the associated context. The creation of the dataset and the initial baseline of an auto-tagging model is described in the paper: Ibrahim, Karim M., Jimena Royo-Letelier, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC." <em>2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em>. IEEE, 2020. The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api .
DOI: 10.5281/zenodo.3553625
title: New crowdsourced annotations for the GiantSteps Tempo dataset

description: Raw beat and derived tempo annotations from A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music.
DOI: 10.5281/zenodo.3553624
title: New crowdsourced annotations for the GiantSteps Tempo dataset

description: Raw beat and derived tempo annotations from A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music.
DOI: 10.5281/zenodo.3553592
title: Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall)

description: Global tempo annotations used for training of the tempo estimation CNN presented in A Single-step Approach to Musical Tempo Estimation using a Convolutional Neural Network.
DOI: 10.5281/zenodo.3553591
title: Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall)

description: Global tempo annotations used for training of the tempo estimation CNN presented in A Single-step Approach to Musical Tempo Estimation using a Convolutional Neural Network.
DOI: 10.5281/zenodo.3260499
title: A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music

description: Dataset accompanying the paper published at ISMIR 2019. See included README file for details.
DOI: 10.5281/zenodo.3260498
title: A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music

description: Dataset accompanying the paper published at ISMIR 2019. See included README file for details.
DOI: 10.5281/zenodo.3256373
title: DMSA Extracted Features

description: Pre-computed features for DMSA (https://github.com/stefan-balke/dmsa).
DOI: 10.5281/zenodo.2747436
title: DAMP-MVP: Digital Archive of Mobile Performances - Smule Multilingual Vocal Performance 300x30x2

description: The Smule 300x30x2 dataset contains recordings of sung karaoke tracks, lyrics text files, and some metadata describing the songs being performed by each singer. This dataset was collected from performances on Smule by selecting the most popular singers, female and male, of the 300 most popular arrangements in 30 countries. The most popular arrangements were determined by counting song starts or joins (duet/group) of recordings for each arrangement, within the country of interest. The term "arrangement" is used, because there might be multiple arrangements of the same song. The most popular performances and singers of those arrangements were determined by counting Listens (and/or Loves, if no complete Listens) for all performances of each arrangement. Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).
DOI: 10.5281/zenodo.3256372
title: DMSA Extracted Features

description: Pre-computed features for DMSA (https://github.com/stefan-balke/dmsa).
DOI: 10.5281/zenodo.2616690
title: DAMP-VPB: Digital Archive of Mobile Performances - Smule Vocal Performances Balanced

description: The DAMP-balanced dataset contains 24874 solo singing performances from 5429 singers singing a collection of 14 songs.<br> The structure of the DAMP-balanced is that the last 4 songs are designed to be the test set, and the first 10 songs<br> could be partitioned into any 6/4 train/validation split (permutation) that the singers in train and validation set sang<br> the same 6/4 songs collections according to the 6/4 split (the number of total recordings for train and validation set<br> are different from split to split, since there are different number of singers that all sang the same 6/4 split for<br> different split). For example, a subset from the dataset splitting the 14 songs into 6/4/4 train/validation/test sets having 276/88/224<br> performances sang by 46/22/56 singers could be extracted from the dataset and be used for machine learning tasks.<br> Each singer in the train, validation and test set, sang each of the 6/4/4 songs once respectively, thus making the<br> collections of performances "balanced" respect to the song collections. List of songs: one call away - Charlie Puth(3912) say you won`t let go - James Arthur(3255) allof me - John Legend(2856) closer -The Chainsmokers(2873) seven years - Lukas Graham(2942) despacito- Luis Fonsi(1287) more than words - Extreme(586) lostboy - Ruth B.(1183) loveyourself - Justin Bieber(3019) rockabye- Clean Bandit(2737) part of your world - Jodi Benson(56) when I was your man - Bruno Mars(56) chandelier- Sia(56) cups - Anna Kendrick(56) Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).
DOI: 10.5281/zenodo.2747435
title: DAMP-MVP: Digital Archive of Mobile Performances - Smule Multilingual Vocal Performance 300x30x2

description: The Smule 300x30x2 dataset contains recordings of sung karaoke tracks, lyrics text files, and some metadata describing the songs being performed by each singer. This dataset was collected from performances on Smule by selecting the most popular singers, female and male, of the 300 most popular arrangements in 30 countries. The most popular arrangements were determined by counting song starts or joins (duet/group) of recordings for each arrangement, within the country of interest. The term "arrangement" is used, because there might be multiple arrangements of the same song. The most popular performances and singers of those arrangements were determined by counting Listens (and/or Loves, if no complete Listens) for all performances of each arrangement. Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).
DOI: 10.5281/zenodo.2616689
title: DAMP-VPB: Digital Archive of Mobile Performances - Smule Vocal Performances Balanced

description: The DAMP-balanced dataset contains 24874 solo singing performances from 5429 singers singing a collection of 14 songs.<br> The structure of the DAMP-balanced is that the last 4 songs are designed to be the test set, and the first 10 songs<br> could be partitioned into any 6/4 train/validation split (permutation) that the singers in train and validation set sang<br> the same 6/4 songs collections according to the 6/4 split (the number of total recordings for train and validation set<br> are different from split to split, since there are different number of singers that all sang the same 6/4 split for<br> different split). For example, a subset from the dataset splitting the 14 songs into 6/4/4 train/validation/test sets having 276/88/224<br> performances sang by 46/22/56 singers could be extracted from the dataset and be used for machine learning tasks.<br> Each singer in the train, validation and test set, sang each of the 6/4/4 songs once respectively, thus making the<br> collections of performances "balanced" respect to the song collections. List of songs: one call away - Charlie Puth(3912) say you won`t let go - James Arthur(3255) allof me - John Legend(2856) closer -The Chainsmokers(2873) seven years - Lukas Graham(2942) despacito- Luis Fonsi(1287) more than words - Extreme(586) lostboy - Ruth B.(1183) loveyourself - Justin Bieber(3019) rockabye- Clean Bandit(2737) part of your world - Jodi Benson(56) when I was your man - Bruno Mars(56) chandelier- Sia(56) cups - Anna Kendrick(56) Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).
DOI: 10.5281/zenodo.2585988
title: Jamendo Corpus for Singing Voice Detection

description: This is a public corpus of 93 creative-commons licensed music pieces annotated<br> by voice (sung or spoken) and no-voice.
description: {"references": ["https://perso.telecom-paristech.fr/grichard/Publications/Icassp08_ramona.pdf"]}
DOI: 10.5281/zenodo.2585987
title: Jamendo Corpus for Singing Voice Detection

description: This is a public corpus of 93 creative-commons licensed music pieces annotated<br> by voice (sung or spoken) and no-voice.
description: {"references": ["https://perso.telecom-paristech.fr/grichard/Publications/Icassp08_ramona.pdf"]}
DOI: 10.5281/zenodo.1715175
title: MedleyDB 2.0 Audio

description: Audio files for the second release of the MedleyDB multitrack dataset (MedleyDB 2.0). <strong>Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: </strong><em>Metadata</em> can be found here, <em>Annotations</em> can be found here. *THE MULTITRACKS IN THIS RELEASE ARE NEW. THIS DOES NOT CONTAIN ANY OF THE MULTITRACKS FROM THE ORIGINAL RELEASE OF MEDLEYDB!* For detailed information about the dataset, please visit MedleyDB's website. If you make use of MedleyDB 2.0 for academic purposes, please cite the following white paper: <em>Rachel M. Bittner, Julia Wilkins, Hanna Yip and Juan P. Bello, "MedleyDB 2.0: New Data and a System for Sustainable Data Collection" Late breaking/demo extended abstract, 17th International Society for Music Information Retrieval (ISMIR) conference, August 2016.</em>
description: {"references": ["Rachel M. Bittner,\u00a0Julia Wilkins,\u00a0Hanna Yip\u00a0and\u00a0Juan P. Bello, \"MedleyDB 2.0: New Data and a System for Sustainable Data Collection\" Late breaking/demo extended abstract, 17th International Society for Music Information Retrieval (ISMIR) conference, August 2016."]}
DOI: 10.5281/zenodo.1715174
title: MedleyDB 2.0 Audio

description: Audio files for the second release of the MedleyDB multitrack dataset (MedleyDB 2.0). <strong>Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: </strong><em>Metadata</em> can be found here, <em>Annotations</em> can be found here. *THE MULTITRACKS IN THIS RELEASE ARE NEW. THIS DOES NOT CONTAIN ANY OF THE MULTITRACKS FROM THE ORIGINAL RELEASE OF MEDLEYDB!* For detailed information about the dataset, please visit MedleyDB's website. If you make use of MedleyDB 2.0 for academic purposes, please cite the following white paper: <em>Rachel M. Bittner, Julia Wilkins, Hanna Yip and Juan P. Bello, "MedleyDB 2.0: New Data and a System for Sustainable Data Collection" Late breaking/demo extended abstract, 17th International Society for Music Information Retrieval (ISMIR) conference, August 2016.</em>
description: {"references": ["Rachel M. Bittner,\u00a0Julia Wilkins,\u00a0Hanna Yip\u00a0and\u00a0Juan P. Bello, \"MedleyDB 2.0: New Data and a System for Sustainable Data Collection\" Late breaking/demo extended abstract, 17th International Society for Music Information Retrieval (ISMIR) conference, August 2016."]}
DOI: 10.5281/zenodo.1649325
title: MedleyDB Audio: A Dataset of Multitrack Audio for Music Research

description: Audio files for the MedleyDB multitrack dataset. <strong>Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: </strong><em>Metadata</em> can be found here, <em>Annotations</em> can be found here. For detailed information about the dataset, please visit MedleyDB's website. If you make use of MedleyDB for academic purposes, please cite the following publication:<br> <br> <em>R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.</em>
description: {"references": ["R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, \"MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research\", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014."]}
DOI: 10.5281/zenodo.1649324
title: MedleyDB Audio: A Dataset of Multitrack Audio for Music Research

description: Audio files for the MedleyDB multitrack dataset. <strong>Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: </strong><em>Metadata</em> can be found here, <em>Annotations</em> can be found here. For detailed information about the dataset, please visit MedleyDB's website. If you make use of MedleyDB for academic purposes, please cite the following publication:<br> <br> <em>R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.</em>
description: {"references": ["R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, \"MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research\", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014."]}