cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/x2sk2k/0yhlls
title: output_Figure_3B.tab

description: :unav
DOI: 10.25625/x2sk2k/2htlph
title: data_Figure_2F.mat

description: :unav
DOI: 10.25625/x2sk2k/bhoj3l
title: data_Figure_S2A.mat

description: :unav
DOI: 10.25625/x2sk2k/i0nvzt
title: data_Figure_S2C.tab

description: :unav
DOI: 10.25625/x2sk2k/m5qkte
title: data_Figure_S1A.mat

description: :unav
DOI: 10.25625/x2sk2k/p2uzm9
title: output_Figure_3.pdf

description: :unav
DOI: 10.25625/x2sk2k/3ofh4z
title: output_Figure_S1A.pdf

description: :unav
DOI: 10.25625/x2sk2k/gb2tpi
title: output_Figure_4AB.tab

description: :unav
DOI: 10.25625/x2sk2k/kip8x6
title: data_Figure_S2B.mat

description: :unav
DOI: 10.25625/x2sk2k/mqywt0
title: Figure_4.m

description: :unav
DOI: 10.25625/x2sk2k/ue4mio
title: data_Figure_4B.mat

description: :unav
DOI: 10.25625/x2sk2k/a5iatz
title: output_Figure_S1C.pdf

description: :unav
DOI: 10.25625/x2sk2k/jtcqjr
title: data_Figure_3B.tab

description: :unav
DOI: 10.25625/x2sk2k/ndmwl8
title: data_Figure_S2A.mat

description: :unav
DOI: 10.25625/x2sk2k/sfzkh8
title: Figure_3.m

description: :unav
DOI: 10.25625/x2sk2k/wg0hfv
title: data_Figure_S1B.tab

description: :unav
DOI: 10.25625/x2sk2k/1krhsm
title: Figure_S.m

description: :unav
DOI: 10.25625/x2sk2k/66xkpg
title: data_Figure_2.mat

description: :unav
DOI: 10.25625/x2sk2k/bfsge7
title: data_Figure_S2C.mat

description: :unav
DOI: 10.25625/x2sk2k/ohjpc0
title: data_Figure_3AB.mat

description: :unav
DOI: 10.25625/x2sk2k/ste59z
title: data_Figure_S1ABC.mat

description: :unav
DOI: 10.25625/x2sk2k/u8hbdj
title: data_Figure_S5.mat

description: :unav
DOI: 10.25625/x2sk2k/aek4nk
title: data_Figure_5A.mat

description: :unav
DOI: 10.25625/x2sk2k/oaoilx
title: data_Figure_5B.mat

description: :unav
DOI: 10.25625/x2sk2k/oc3eua
title: data_Figure_5C.mat

description: :unav
DOI: 10.25625/x2sk2k/w1ivu1
title: Figure_5.m

description: :unav
DOI: 10.25625/x2sk2k/xkxq2n
title: data_Figure_4A.mat

description: :unav
DOI: 10.25625/x2sk2k/zounce
title: data_Figure_3C.mat

description: :unav
DOI: 10.25625/x2sk2k/1st0cr
title: Figure_4.m

description: :unav
DOI: 10.25625/x2sk2k/cretui
title: Figure_2.m

description: :unav
DOI: 10.25625/x2sk2k/dol6x7
title: Figure_3.m

description: :unav
DOI: 10.25625/x2sk2k/pf1v4j
title: data_Figure_4B.mat

description: :unav
DOI: 10.25625/x2sk2k/rcqclb
title: data_Figure_S2B.mat

description: :unav
DOI: 10.25625/x2sk2k
title: Replication Data for: Assessing cognitive flexibility in humans and rhesus macaques with visual motion and neutral distractors

description: This is the official data repository to reproduce all figures and statistical analysis for project: "Assessing cognitive flexibility in humans and rhesus macaques with visual motion and neutral distractors" by Yurt P, Calapai A., Mundry R., Treue S. September 2022. Matlab (2020a) is required, as well as the gramm package for data visualization (https://github.com/piermorel/gramm)
DOI: 10.25625/x2sk2k/zjhjbh
title: README.txt

description: :unav
DOI: 10.34740/kaggle/dsv/4659567
title: YouTube Daily
DOI: 10.21227/1npe-tw43
title: The Properties and Sensing Mechanism of PdNPs-decorated Silicon Nanobelt Devices for H2 Sensing at Room Temperature

description: This study reports the properties and the sensing mechanism of Pd nanoparticles (PdNPs) decorated n+/n-/n+ double-junction silicon nanobelt (SNB) device as hydrogen (H2) gas sensor. The SNB devices are prepared via CMOS process. Plasma-enhanced atomic layer deposition (PEALD) is adopted for PdNPs deposition as sensing material on the Al2O3 dielectric of SNB devices.PEALD of PdNPs provides high conformity and fine control of the particle size. The PdNPs-decorated SNB devices working at room temperature are characterizedat H2 concentration ranging from 10 to 1000 ppm. Instead of using the traditional steady-state response, the slope of response and the corresponding sensing mechanism are presented to estimate concentration and shorten the response time. More than 60 % improvement in response time has been achieved for 10 to 1000 ppm H2 detection. To reduce recovery time, device localized Joule heating (DLJH) with a bias of 11 V for 240 s is demonstrated to restore the device back to the baseline.At a bias of 1 V, H2 sensing at room temperature consumes only 68.39 μW.
DOI: 10.14278/rodare.1958
title: Data for: The influence of sedimentary heterogeneity on the diffusion of radionuclides in the sandy facies of Opalinus Clay at the field scale

description: Radionuclide migration in clay-rich formations is typically dominated by diffusion considering the low permeability of these formations. An accurate estimation of radionuclide migration in host rocks using numerical tools plays a key role in the safety assessment of disposal concepts for nuclear waste. In the sandy facies of the Opalinus Clay (SF-OPA), the spatial variability of the pore space network and compositional heterogeneity at the pore scale (nm to µm) cause heterogeneous diffusion at the core scale (cm to dm). Such heterogeneous diffusion patterns affect the migration of radionuclides in various sedimentary layers even above the core scale (m). In this work, we study the heterogeneous diffusion of cations based on a two-dimensional (2D) structural model at the m-scale. As key parameters for the diffusive transport calculation, the effective diffusion coefficients in different sedimentary layers are quantified based on our previous developed up-scaling workflow from pore- to core-scale simulation combined with the multi-scale digital rock models. The heterogeneous effective diffusivities are then implemented into the large-scale structural model for diffusive transport simulation using the FEM-based OpenGeoSys-6 simulator. The sensitivity analysis focuses on the effects of the SF-OPA bedding angle and the effect of different layer-succession layout with different canister emplacement on the spatio-temporal evolution of radionuclide diffusion front line. Results show that the moving distance of the diffusion front is farther away from the canister center, along the direction with the neighboring layer having lower diffusion coefficient within the total simulation time of 2000 years. When the bedding angle increases, the diffusion front moves farther in in vertical upward direction direction, which has less retardation effect for the radionuclide from the ground surface point. For different layer-succession layout with different canister emplacement, the smallest migration distance of the diffusion front line is 1.65 m. Within 2000 years, for the conceptual model 2B that the canister is emplaced in the layer with the highest diffusivity coefficient, the diffusion front can migrate 0.19 m farther along vertical downward direction due to the influence of the neighboring layer. The numerical results provide insight into the effects of rocks heterogeneity on diffusion of radionuclides, contributing to enhanced long-term predictability of radionuclide migration in SF-OPA as potential host rock for a deep geological repository.
DOI: 10.14278/rodare.1959
title: Data for: The influence of sedimentary heterogeneity on the diffusion of radionuclides in the sandy facies of Opalinus Clay at the field scale

description: Radionuclide migration in clay-rich formations is typically dominated by diffusion considering the low permeability of these formations. An accurate estimation of radionuclide migration in host rocks using numerical tools plays a key role in the safety assessment of disposal concepts for nuclear waste. In the sandy facies of the Opalinus Clay (SF-OPA), the spatial variability of the pore space network and compositional heterogeneity at the pore scale (nm to µm) cause heterogeneous diffusion at the core scale (cm to dm). Such heterogeneous diffusion patterns affect the migration of radionuclides in various sedimentary layers even above the core scale (m). In this work, we study the heterogeneous diffusion of cations based on a two-dimensional (2D) structural model at the m-scale. As key parameters for the diffusive transport calculation, the effective diffusion coefficients in different sedimentary layers are quantified based on our previous developed up-scaling workflow from pore- to core-scale simulation combined with the multi-scale digital rock models. The heterogeneous effective diffusivities are then implemented into the large-scale structural model for diffusive transport simulation using the FEM-based OpenGeoSys-6 simulator. The sensitivity analysis focuses on the effects of the SF-OPA bedding angle and the effect of different layer-succession layout with different canister emplacement on the spatio-temporal evolution of radionuclide diffusion front line. Results show that the moving distance of the diffusion front is farther away from the canister center, along the direction with the neighboring layer having lower diffusion coefficient within the total simulation time of 2000 years. When the bedding angle increases, the diffusion front moves farther in in vertical upward direction direction, which has less retardation effect for the radionuclide from the ground surface point. For different layer-succession layout with different canister emplacement, the smallest migration distance of the diffusion front line is 1.65 m. Within 2000 years, for the conceptual model 2B that the canister is emplaced in the layer with the highest diffusivity coefficient, the diffusion front can migrate 0.19 m farther along vertical downward direction due to the influence of the neighboring layer. The numerical results provide insight into the effects of rocks heterogeneity on diffusion of radionuclides, contributing to enhanced long-term predictability of radionuclide migration in SF-OPA as potential host rock for a deep geological repository.
DOI: 10.6084/m9.figshare.21673817.v1
title: Supplementary Material for: Osimertinib in the Treatment of EGFR Mutation-Positive Advanced Non-Small Cell Lung Cancer: A Meta-Analysis

description: <b><i>Background:</i></b> Non-small cell lung cancer (NSCLC) accounts for about 85% of generally reported lung cancer patients. <b><i>Objectives:</i></b> This is a systematic review of the clinical efficacy and safety of osimertinib in treating epidermal growth factor receptor (EGFR) mutation-positive advanced NSCLC. <b><i>Methods:</i></b> A network search was completed for clinical research literature (from inception of each database to May 30, 2020) on osimertinib for EGFR mutation-positive advanced NSCLC. Strict inclusion and exclusion criteria were formulated to screen the literature. After data extraction, RevMan 5.3 software was utilized for quality evaluation and meta-analysis. The primary endpoints were objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events of grades 3 and 4. <b><i>Results:</i></b> Finally, 6 eligible articles and a total of 1,848 patients containing 1,123 in experimental groups and 725 in control groups were included. Meta-analysis indicated that ORR (odds ratio [OR] = 3.40, 95% CI 1.64∼7.01, <i>p</i> = 0.0009), DCR (OR = 4.36, 95% CI 3.09∼6.15, <i>p</i> &lt; 0.00001), PFS (HR = 0.36, 95% CI 0.27∼0.47, <i>p</i> &lt; 0.00001), and OS (OR = 0.58, 95% CI 0.46∼0.72, <i>p</i> &lt; 0.00001) of the experimental group were prominently better than the control group. Adverse events of grades 3 and 4 mainly incorporated decreased nausea, rash, stomatitis, and vomiting, which were dramatically relieved compared with the control group. <b><i>Conclusion:</i></b> Osimertinib is currently an appreciably effective and well-tolerated therapeutic avenue for EGFR mutation-positive advanced NSCLC.
DOI: 10.57841/casdc.0000023
title: 1
DOI: 10.5281/zenodo.831189
title: Mumu: Multimodal Music Dataset

description: MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. 

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. 

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.


	MuMu dataset (mapping, metadata, annotations and text reviews)
	Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments 


These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

<strong>Scientific References</strong>

Please cite the following paper if using MuMu dataset or Tartarus library.

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916
description: This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
description: {"references": ["Oramas et al. (2017). Dataset associated to https://arxiv.org/abs/1707.04916"]}
DOI: 10.5281/zenodo.1003066
title: Mumu: Multimodal Music Dataset

description: MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. 

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. 

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.


	MuMu dataset (mapping, metadata, annotations and text reviews)
	Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments 


These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

<strong>Scientific References</strong>

Please cite the following paper if using MuMu dataset or Tartarus library.

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916
description: This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
description: {"references": ["Oramas et al. (2017). Dataset associated to https://arxiv.org/abs/1707.04916"]}
DOI: 10.5281/zenodo.1236906
title: Mumu: Multimodal Music Dataset

description: MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. 

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. 

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.


	MuMu dataset (mapping, metadata, annotations and text reviews)
	Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments 


These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

<strong>Scientific References</strong>

Please cite the following papers if using MuMu dataset or Tartarus library.

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).

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916
description: This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
description: {"references": ["Oramas et al. (2017). Dataset associated to https://arxiv.org/abs/1707.04916"]}
DOI: 10.5281/zenodo.7399400
title: MuMu: Multimodal Music Dataset

description: MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues. MuMu dataset (mapping, metadata, annotations and text reviews) Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus. NOTE: This version provides simplified files with metadata and splits. <strong>Scientific References</strong> Please cite the following papers if using MuMu dataset or Tartarus library. 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). Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916
description: This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
description: {"references": ["Oramas et al. (2017). Dataset associated to https://arxiv.org/abs/1707.04916"]}
DOI: 10.5281/zenodo.831188
title: MuMu: Multimodal Music Dataset

description: MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues. MuMu dataset (mapping, metadata, annotations and text reviews) Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus. NOTE: This version provides simplified files with metadata and splits. <strong>Scientific References</strong> Please cite the following papers if using MuMu dataset or Tartarus library. 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). Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916
description: This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
description: {"references": ["Oramas et al. (2017). Dataset associated to https://arxiv.org/abs/1707.04916"]}