cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4660565
title: Ulm-TSST dataset - raw data (MuSe2021)

description: <strong>Ulm-TSST dataset of the 2nd Multimodal Sentiment Challenge! </strong><br> The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines. We introduce the novel dataset Ulm-TSST database, supplying a multimodal richly annotated dataset of external dimensional ratings of emotion and mental well-being. After a brief period of preparation the subjects are asked to give an oral presentation, within a job-interview setting. Ulm-TSST includes biological recordings, such as Electrocardiogram (ECG), Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM) as well as continuous arousal and valence annotations. With 105 participants (69.5% female) aged between 18 and 39 years, a total of 10 hours were accumulated.
DOI: 10.5281/zenodo.6415480
title: Puntuacion de comida para gatos

description: This dataset contains information about cat food, specifically its name, ingredients punctuation, nutrition punctuation and its total.
DOI: 10.5281/zenodo.6415479
title: Puntuacion de comida para gatos

description: This dataset contains information about cat food, specifically its name, ingredients punctuation, nutrition punctuation and its total.
DOI: 10.6084/m9.figshare.19551820
title: Eyetracking DATAfiles of "Bouchon, Hochmann & Toro (inpress) The emergence of the consonant bias in Spanish learning infants in the first year of life. JECP

description: .topd and .mat files for 2 exp: <br> EXPERIMENT 1 - 5 month olds data - 8 mo - 12 mo <br> EXPERIMENT 2 - 5 month olds data - 8 mo - 12 mo
DOI: 10.6084/m9.figshare.19551820.v5
title: Eyetracking DATAfiles of "Bouchon, Hochmann & Toro (inpress) The emergence of the consonant bias in Spanish learning infants in the first year of life. JECP

description: .topd and .mat files for 2 exp: <br> EXPERIMENT 1 - 5 month olds data - 8 mo - 12 mo <br> EXPERIMENT 2 - 5 month olds data - 8 mo - 12 mo
DOI: 10.6084/m9.figshare.19551820.v4
title: Eyetracking DATAfiles of "Bouchon, Hochmann & Toro (inpress) The emergence of the consonant bias in Spanish learning infants in the first year of life. JECP

description: .topd and .mat files for 2 exp: <br> EXPERIMENT 1 - 5 month olds data - 8 mo - 12 mo <br> EXPERIMENT 2 - 5 month olds data - 8 mo - 12 mo
DOI: 10.6073/pasta/02e2764efb408a8b66b7f486eac14dc0
title: Multiple Element Limitation in Northeast Hardwood Ecosystems (MELNHE): Salt Exchangeable Cation Extractions from Hubbard Brook and Bartlett sites

description: Soil element concentrations (Na, Mg, K, Ca, Al, Mn, Fe, Si, Sr, and Ba) were measured in salt exchangeable extracts of soil samples taken in July 2017 in the MELNHE study, specifically in Bartlett stands C1-C9 and Hubbard Brook stands HBM and HBO. Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. Additional analysis data on these samples can be found in the dataset "Soil properties in the MELNHE study at Hubbard Brook Experimental Forest, Bartlett Experimental Forest and Jeffers Brook, central NH USA, 2009 - present" (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=165). This work is a contribution of the Hubbard Brook Ecosystem Study. Hubbard Brook is part of the LTER network, which is supported by the US National Science Foundation. The Hubbard Brook Experimental Forest is operated and maintained by the US Department of Agriculture, Forest Service, Northern Research Station.
DOI: 10.5281/zenodo.4767114
title: MuSe-Stress: Multimodal Emotional Stress (MuSe2021)

description: MuSe-Stress sub-challenge of the 2nd Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2021. Predicting valence and arousal of individuals in a stressed disposition, as induced by the Trier Social Stress Test (TSST) protocol. Available modalities: audio, video, text and physiological signals (respiratory rate, ECG, BPM).
DOI: 10.5281/zenodo.4659751
title: MuSe-Stress: Multimodal Emotional Stress (MuSe2021)

description: MuSe-Stress sub-challenge of the 2nd Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2021. Predicting valence and arousal of individuals in a stressed disposition, as induced by the Trier Social Stress Test (TSST) protocol. Available modalities: audio, video, text and physiological signals (respiratory rate, ECG, BPM).
DOI: 10.5281/zenodo.6424223
title: Results of the 3D detection of cracks in tested disc-shaped specimens

description: 3D images of fatigue crack obtained by laboratory tomography and synchrotron tomography within bi-disc specimens.
DOI: 10.5281/zenodo.6351386
title: Results of the 3D detection of cracks in tested disc-shaped specimens

description: 3D images of fatigue crack obtained by laboratory tomography and synchrotron tomography within bi-disc specimens.
DOI: 10.5281/zenodo.4654371
title: MuSe-Sent: Multimodal Sentiment Classification in-the-Wild (MuSe2021)

description: <strong>MuSe-Sent of the 2nd Multimodal Sentiment in-the-Wild Challenge!</strong><br> Predicting five advanced intensity classes for each of the emotional dimensions (valence, arousal) for segments of audio-video-text data. This package includes only MuSe-Sent features (all partitions) and labels of the training and development set (test scoring via the MuSe website). More: https://www.muse-challenge.org/muse2021 <br> <strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines. We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (&gt;36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of close-up, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.
DOI: 10.5281/zenodo.4654370
title: MuSe-Sent: Multimodal Sentiment Classification in-the-Wild (MuSe2021)

description: <strong>MuSe-Sent of the 2nd Multimodal Sentiment in-the-Wild Challenge!</strong><br> Predicting five advanced intensity classes for each of the emotional dimensions (valence, arousal) for segments of audio-video-text data. This package includes only MuSe-Sent features (all partitions) and labels of the training and development set (test scoring via the MuSe website). More: https://www.muse-challenge.org/muse2021 <br> <strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines. We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (&gt;36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of close-up, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.
DOI: 10.25345/c5c24qr72
title: MassIVE MSV000089225 - HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery
title: Strazar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR, Brown EM, Eraslan B, Ortiz K, Clauser KR, Carr SA, Xavier RJ, Graham DB. 2022. T cell responses are exquisitely antigen-specific and directed against peptide epitopes displayed by human leukocyte antigen (HLA) on the surface of presenting cells. In particular, class II HLA (HLA-II) is remarkably polymorphic, which allows for presentation of diverse peptide antigens to T cells, but also forms the basis for genetic associations with diverse immunopathologies across the spectrum of infectious disease and autoimmunity. Here, we employ monoallelic immunopeptidomics to retrieve over 200,000 unique peptides presented by 41 HLA-II heterodimers covering major alleles across diverse ancestries. We leveraged this expansive dataset to develop computational models that predict peptide antigens based on HLA-II binding properties and infer informative features of the protein antigens from which these peptides derive. Combining both peptide and (contextual) protein features, we develop Context Aware Predictor of T cell Antigens (CAPTAn) to discover novel T cell epitopes from prokaryotes in the human microbiome and the viral pandemic pathogen SARS-CoV-2.
DOI: 10.5281/zenodo.4134758
title: MuSe-Trust: Multimodal Trustworthiness Sub-challenge (MuSe2020)

description: <strong>MuSe-Trust of MuSe2020:</strong> Predicting the level of trustworthiness of user-generated audio-visual content in a sequential manner utilising a diverse range of features and (optional) emotional (arousal and valence) predictions. This package includes only MuSe-Trust features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). <strong>General:</strong> The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (&gt;36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.
description: Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Bjoern W. Schuller, Iulia Lefter, Erik Cambria, and Ioannis Kompatsiaris. 2020. MuSe 2020 Challenge and Workshop: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media: Emotional Car Reviews in-the-wild. In Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop (MuSe'20). ACM, USA, 35–44. DOI: https://doi.org/10.1145/3423327.3423673
DOI: 10.5281/zenodo.4134757
title: MuSe-Trust: Multimodal Trustworthiness Sub-challenge (MuSe2020)

description: <strong>MuSe-Trust of MuSe2020:</strong> Predicting the level of trustworthiness of user-generated audio-visual content in a sequential manner utilising a diverse range of features and (optional) emotional (arousal and valence) predictions. This package includes only MuSe-Trust features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). <strong>General:</strong> The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (&gt;36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.
description: Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Bjoern W. Schuller, Iulia Lefter, Erik Cambria, and Ioannis Kompatsiaris. 2020. MuSe 2020 Challenge and Workshop: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media: Emotional Car Reviews in-the-wild. In Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop (MuSe'20). ACM, USA, 35–44. DOI: https://doi.org/10.1145/3423327.3423673
DOI: 10.5281/zenodo.6326950
title: Data for the publication "Assessing the potential for simplification in global climate model cloud microphysics"

description: This repository contains the data for the paper: Authors: Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann<br> Titel: Assessing the potential for simplification in global climate model cloud microphysics<br> Date: 2022 Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.5506588)
DOI: 10.5281/zenodo.5506533
title: Data for the publication "Assessing the potential for simplification in global climate model cloud microphysics"

description: This repository contains the data for the paper: Authors: Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann<br> Titel: Assessing the potential for simplification in global climate model cloud microphysics<br> Date: 2022 Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.5506588)
DOI: 10.5281/zenodo.4651164
title: Multimodal Sentiment Analysis in Car Reviews dataset - raw data (MuSe-CAR raw)

description: <strong>General</strong>: The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (&gt;36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.
description: Stappen, Lukas, Alice Baird, Lea Schumann, and Björn Schuller. "The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements." arXiv preprint arXiv:2101.06053 (2021).
DOI: 10.5281/zenodo.4134765
title: Multimodal Sentiment Analysis in Car Reviews dataset - raw data (MuSe-CAR raw)

description: <strong>General</strong>: The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (&gt;36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.
description: Stappen, Lukas, Alice Baird, Lea Schumann, and Björn Schuller. "The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements." arXiv preprint arXiv:2101.06053 (2021).
DOI: 10.6084/m9.figshare.19539184
title: Integrated Network Pharmacology and Experimental Validation to Investigate the Potential Mechanisms of Luteolin on RSV-Induced Lung Inflammation

description: Raw data and pictures
DOI: 10.6084/m9.figshare.19539184.v2
title: Integrated Network Pharmacology and Experimental Validation to Investigate the Potential Mechanisms of Luteolin on RSV-Induced Lung Inflammation

description: Raw data and pictures
DOI: 10.6084/m9.figshare.19539184.v1
title: Integrated Network Pharmacology and Experimental Validation to Investigate the Potential Mechanisms of Luteolin on RSV-Induced Lung Inflammation

description: Raw data and pictures
DOI: 10.7298/6r80-8a15
title: Data from: Carbon allocation to root exudates is maintained in mature temperate tree species under drought
DOI: 10.13011/m3-xvd5-6363
title: Measuring and Modeling Microsegregation in High Pressure Die Cast Mg-Al Alloys
DOI: 10.5281/zenodo.6408603
title: Face mask detection and masked facial recognition dataset (MDMFR Dataset)

description: The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (MDMFR, 2022) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, illumination conditions, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (Plastiliq, 2022).