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28 results for “MSD”
MSD-A: Million Song Dataset for Artists
<p>The MSD-A is a dataset related to the Million Song Dataset (MSD). It is a collection of artist tags and biographies gathered from Last.fm for all the artists that have songs in the MSD. In addition, the MSD Taste Profile (recommendation dataset) is adapted to artists.</p> <p>We provide the biographies, tags, data splits, and feature embeddings to reproduce the experiments from the paper:</p> <p>Oramas S., Nieto O., Sordo M., & Serra X. (2017) A Deep Multimodal Approach for Cold-start Music Recommendation. https://arxiv.org/abs/1706.09739</p> <p>Source code is available at https://github.com/sergiooramas/tartarus</p> <p>The file dlrs-data.tar.gz in this zenodo version is corrupted. You can download the good file in this link:</p> <p>https://drive.google.com/open?id=0B-oq_x72w8NUbUpkMzZSc1JPd28</p>
Accelerogram (raw, msd), GLT4 seismic station (Galati, Romania), Vrancea (Romania) earthquake, 2020-04-25 01:04:18 ML=5.0 h=21.6 km
<p>Accelerogram (raw, msd) recorded at GLT4 seismic station (Galati, Romania).</p> <p>Vrancea (Romania) earthquake, 2020-04-25 01:04:18 (local time), ML=5.0, h=21.6 km.</p> <p>Recorded on a GeoSIG GMS-18 instrument.</p>
MSD-I: Million Song Dataset with Images for Multimodal Genre Classification
<p>The Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/) is a collection of metadata and precomputed audio features for 1 million songs. Along with this dataset, a dataset with annotations of 15 top-level genres with a single label per song was released. In our work, we combine the CD2c version of this genre datase (http://www.tagtraum.com/msd_genre_datasets.html) with a collection of album cover images. </p> <p><br> The final dataset contains 30,713 tracks from the MSD and their related album cover images, each annotated with a unique genre label among 15 classes. Based on an initial analysis on the images, we identified that this set of tracks is associated to 16,753 albums, yielding an average of 1.8 songs per album.</p> <p>We randomly divide the dataset into three parts: 70% for training, 15% for validation, and 15% for test, with no artist and album overlap across these sets. This is crucial to avoid possible overfitting, as the classifier may learn to predict the artist instead of the genre. </p> <p> </p> <p>Content:</p> <p>MSD-I dataset (mapping, metadata, annotations and links to images)<br> Data splits and feature vectors for TISMIR single-label classification experiments </p> <p>These data can be used together with the Tartarus deep learning python module https://github.com/sergiooramas/tartarus.</p> <p> </p> <p>Scientific References:</p> <p>Please cite the following paper if using MSD-I dataset or Tartarus software.</p> <p>Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).</p>
msd-errors - Data for the comparison Arrhenius figures for AgCrSe2
Results of the Arrhenius analysis of AgCrSe2 systems using Arrhenius and super-Arrhenius functions at a series of temperatures where only some subset of the full simulation is analysed.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/fc1f0cb96213ea5d811087618920b879fea11e9a">this GitHub repo</a>.
msd-errors - Data for diffusion coefficient and offset figures3
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
msd-errors - Data for scaling figures2
Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks). <br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
msd-errors - Data for diffusion coefficient and offset figures
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
msd-errors - Data for variance and covariance figure
Data for a figure that presents the variance and covariance for a true random walk. There is a comparison shown between the numerical result from 1024 unique random walks and the analysis, by kinisi, of the same 1024 random walks.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/6c81f0e8e5ee08bb2eaebd520ceb59213c5026f5">this GitHub repo</a>.
msd-errors - Data for AgCrSe2 figures
Data for the figures Results of the analysis of AgCrSe2 systems by kinisi at a series of temperatures.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/fc1af0c7016e63e39f7ae49931bc29f017e3fc59">this GitHub repo</a>.<br>
msd-errors - Data for the LLZO figure
Data for the figure showing the diffusion and Arrhenius plots for a short simulation of LLZO. <br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/56fb2caf0e78a22d0acc1b937c097cebbf5e915f">this GitHub repo</a>.<br>
msd-errors - Data for AgCrSe2 figures
Data for the figures Results of the analysis of AgCrSe2 systems by kinisi at a series of temperatures.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/56fb2caf0e78a22d0acc1b937c097cebbf5e915f">this GitHub repo</a>.<br>
Effect of MSD on FCR Among Gastric Cancer Survivors
ClinicalTrials.gov study NCT05606549. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Adding Azathioprine/Hydroxyurea Preconditioning to Alemtuzumab/TBI to Reduce Risk of Graft Failure in MSD HSCT in Adult SCD Patients
ClinicalTrials.gov study NCT05249452. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
PBI-MST-01 (NCT04541108) Substudy MSD-03: Intratumoral Microdosing of Pembrolizumab Alone and With MK-0482 or MK-4830 in HNSCC or STS
ClinicalTrials.gov study NCT06413095. IPD Sharing: NO. Countries: 1. Publications: 6.
msd-errors - LLZO simulation trajectories
<p>Simulation trajectories at a range of temperatures of LLZO, produced using MetalWalls.</p>
Clinical Validation of the Point-of-Care MSD Influenza Test
ClinicalTrials.gov study NCT00863343. IPD Sharing: Not stated. Countries: 1. Publications: 0.
PiPT in ADSM With MSD
ClinicalTrials.gov study NCT05132400. IPD Sharing: YES. Countries: 1. Publications: 0.
msd-errors - Data for the shorter AgCrSe2 figures
Results of the analysis of AgCrSe2 systems by kinisi at a series of temperatures where only some subset of the full simulation is analysed.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/fc1f0cb96213ea5d811087618920b879fea11e9a">this GitHub repo</a>.
msd-errors - Data for scaling figures
Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks). <br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/6c81f0e8e5ee08bb2eaebd520ceb59213c5026f5">this GitHub repo</a>.
A Study to Compare the Immune Response and Safety Elicited by Henogen's Adjuvanted Hepatitis B Vaccine Compared to Aventis Pasteur MSD's Hepatitis B Vaccine in Pre-Dialysis and Dialysis Patients Who R
ClinicalTrials.gov study NCT00291980. IPD Sharing: Not stated. Countries: 3. Publications: 0.
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