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1,836 results for “emotion”
A list of color, emotion, and human body part concepts
<p>The list includes 220 human body part, color, and emotion concepts. The concepts are based on the available concept sets in <a href="https://concepticon.clld.org/">Concepticon</a> (List et al. <a href="https://aclanthology.org/L16-1379/">2016</a>, <a href="https://doi.org/10.5281/zenodo.596412">2021</a>) and were tagged as <em>human body part </em>(136 concepts), <em>emotion </em>(62 concepts), or <em>color </em>(22 concepts).</p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]
<p>Dataset for the journal article <em>How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.</em></p>
Digital Approaches to Analyzing and Translating Emotion: What Is Love?
<p>This repository contains the data used for and created during the research for the article "Digital Approaches to Analyzing and Translating Emotion: What Is Love?" in Karen Sonik and Ulrike Steinert (eds.),<em> The Routledge Handbook of Emotions in the Ancient Near East</em> (London: Routledge, 2022), pp. 88–116, <a href="https://doi.org/10.4324/9780367822873-6">https://doi.org/10.4324/9780367822873-6</a>.</p> <p><strong>Lists/</strong> contains various lists used for and created while analyzing love words in Akkadian.</p> <p><strong>Networks and figures/</strong> contains the networks and figures produced during our research.</p> <p><strong>Oracc data/</strong> contains the raw data extracted from the Oracc texts.</p> <p><strong>PMI and fastText results/</strong> contains the results produced with PMI and fastText.</p> <p><strong>Texts/</strong> contains the text file used for producing the results with PMI and fastText.</p> <p>We gratefully acknowledge that our research has been funded by the Academy of Finland (decision numbers 298647, 312051, and 330727). Our research data originates from the Open Richly Annotated Cuneiform Corpus (Oracc). We thank Oracc for their efforts in making linguistically annotated cuneiform texts available online. We are indebted to everyone who has been involved in creating this research data, including the authors of the original publications and the researchers who have made the data Oracc-compatible and enriched it through lemmatizations and by adding other metadata (for a list of projects and their contributors, see the file OraccCredits.txt). In the context of this article and dataset, we want to acknowledge the work of the Munich Open-access Cuneiform Corpus Initiative (PIs Karen Radner and Jamie Novotny), the Royal Inscriptions of the Neo-Assyrian Period project (PI Grant Frame), and the Akkadian Love Literature project (Nathan Wasserman and Yigal Bloch) in particular.</p>
Emotion-Antecedent Appraisal Checks: EEG and EMG datasets for Novelty and Pleasantness
<p>The Electroencaphalography (EEG) and facial Electromyography (EMG) signals included in this data set were collected in the context of a previous study conducted by van Peer, Grandjean and Scherer (2014). That study addressed three fundamental questions regarding the mechanisms underlying the appraisal process: Whether appraisal criteria are processed (a) in a fixed sequence, (b) independent of each other, and (c) by different neural structures or circuits. In that study, an oddball paradigm with affective pictures was used to experimentally manipulate novelty and intrinsic pleasantness appraisals. EEG was recorded during task performance, together with facial EMG, to measure, respectively, cognitive processing and efferent responses stemming from the appraisal manipulations. The data set made here publicly available contains the exact same data used by Coutinho, Gentsch, van Peer, Scherer and Schuller (to appear). The only difference in relation to the original data is that the some of the pre-processing steps (i.e., the processing of the raw data) were changed in order to improve the detection of artifacts. The full details of the original study, data collected, pre-processing steps and final data set are included in the paper distributed with the data (study1_dataset.pdf).</p> <p><strong>References</strong></p> <p>Coutinho E, Gentsch k, van Peer JM, Scherer KR & Schuller BW (to appear). Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography. <em>PloS One</em>.</p> <p>van Peer JM, Grandjean D, Scherer KR (2014). Sequential unfolding of appraisals: EEG evidence for the interaction of novelty and pleasantness. <em>Emotion, </em>14(1), 51-63.</p>
Emotion-Antecedent Appraisal Checks: EEG and EMG datasets for Goal Conduciveness, Control and Power
<p>The Electroencaphalography (EEG) and facial Electromyography (EMG) signals included in this dataset was collected in the context of a previous study (Gentsch, Grandjean & Scherer, 2013). This dataset contains the exact data used in Coutinho, Gentsch, van Peer, Scherer & Schuller (to appear). The only difference in relation to the original data is that the some of the pre-processing steps (i.e., the processing of the the raw data) were changed. The full details of the data collected and pre-processing are included in a file distributed with the data (dataset-details.pdf).</p> <p>References</p> <p>Coutinho, Gentsch, van Peer, Scherer & Schuller (to appear). Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography. PloS One.</p> <p>Gentsch K, Grandjean D, Scherer KR. Temporal dynamics of event-related potentials related to goal conduciveness and power appraisals. Psychophysiology. 2013;50(10):1010–1022. </p>
Experimental datasets for sentiment analysis and emotion mining - Emotion Mining Toolkit (EMTk)
<p><strong>Description</strong></p> <p>Datasets for sentiment analysis and emotion mining, distributed with the Emotion Mining Toolkit (EMTk) Docker container (see <a href="https://collab-uniba.github.io/EMTk">https://collab-uniba.github.io/EMTk</a> for more):</p> <ul> <li>Stack Overflow - A couple of gold standards of 4,000+ posts, manually annotated for mining both emotions and polarity.</li> <li>Jira - A gold standard of ~4,000 issues, manually annotated for emotions.</li> </ul> <p><strong>Citation</strong></p> <p>Please, see the references below for the papers to cite. Do not cite this Zenodo upload directly.</p>
Images associated to the paper "Evaluating the Sensitivity to Virtual Characters Facial Asymmetry in Emotion Synthesis"
<p>We conducted an experiment by presenting 64 pairs of static facial expressions, one symmetric and one asymmetric, illustrating eight emotions (three basic and five complex ones) alternatively for a male and a female character.<br> Each emotion was presented four times by swapping the symmetric and asymmetric positions and by mirroring the asymmetrical expression. Participants were asked to grade, on a continuous scale, the correctness of each facial expression with respect to a short definition</p>
A Lexicon of Emotions in Turkish
<p>A Turkish lexicon of emotions prepared for dictionary function of MaxQDA software. This lexicon has been prepared as a part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.896311” </p>
DUX: A dataset of User Interactions and User Emotions
<p>User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users' emotions based on their interactions alone would not be intrusive to users and could be easily implemented in most applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.</p>
Data and code for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter"
<p>This is the code and data for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter" by Müller-Hansen et al. (Global Environmental Change, 2023).</p><p>This archive contains the following materials:</p><ul><li>Data set of tweets</li><li>Table of subqueries for searching Twitter</li><li>Code for figure generation</li></ul><p>Please see the Readme for further details.</p><p> </p>
Emotion regulation in the Ageing Brain, University of Reading, BBSRC
Open the record for dataset details and reuse information.
Data and supplementary material in support of article "Can robots express facial emotions dominantly enough for use in dementia care?"
<p>Data and supplementary material in support of article "Vlachos, E. and Tan, Z. H. (2020). Can robots express facial emotions dominantly enough for use in dementia care?, <em>International Psychogeriatrics</em>, Cambridge University Press". </p> <p>Our objective is to evaluate the recognition, and denomination of the six basic emotional facial expressions as displayed by a social robot to persons with dementia, and to compare it with the results from the evaluation of static photographs of humans from the Paul Ekman database in order to investigate the differences in recognition rates among the two stimuli.</p>
Emotion and Diversity in Online Publics - Supporting Materials
<p>Supporting materials for the article "Counterpublic, Deliberative Sphere, Incubator, Battleground: Emotion and Diversity in Online Publics".</p> <p>"</p>
Emotional imagination of negative situations: functional neuroimaging in anorexia and bulimia
<p>Clinical dataset (SPSS 20 file) 64 subjects.</p> <p>fMRI con images 64 subjects described in the clinical dataset.</p> <p> </p>
French Emotional Speech Database - Oréau
<p>This document presents the French emotional speech database - Oréau, recorded in a quiet environment. The database is designed for general study of emotional speech and analysis of emotion characteristics for speech synthesis purposes. It contains 79 utterances which could be used in everyday life in the classroom. Between 10 and 13 utterances were written for each of the 7 emotions in French language by 32 non-professional speakers.</p> <p>2 versions are available, the first one contains 502 sentences. A perception test was performed to evaluate the recognition of emotions and their naturalness. 90% of utterances (434 utterances) were correctly identified and retained after the test and various analyses, which constitutes the second version of database.</p>
Dataset for article: Perakakis P., Idrissi S., Vila J., Ivanov Ch.P. (2012). Dynamical patterns of human postural responses to emotional stimuli. Psychophysiology, 49 (9), pp. 1225–1229
<p>Dataset for article: Perakakis P., Idrissi S., Vila J., Ivanov Ch.P. (2012). Dynamical patterns of human postural responses to emotional stimuli. Psychophysiology, 49 (9), pp. 1225–1229</p>
The influence of art expertise and training on emotion and preference ratings for representational and abstract artworks.
<p>Across cultures and throughout recorded history, humans have produced visual art. This raises the question of why people report such an emotional response to artworks and find some works more beautiful or compelling than others. In the current study we investigated the interplay between art expertise, and emotional and preference judgments. Sixty participants (40 novices, 20 art experts) rated a set of 150 abstract artworks and portraits during two occasions: in a laboratory setting and in a museum. Before commencing their second session, half of the art novices received a brief training on stylistic and art historical aspects of abstract art and portraiture. Results showed that art experts rated the artworks higher than novices on aesthetic facets (beauty and wanting), but no group differences were observed on affective evaluations (valence and arousal). The training session made a small effect on ratings of preference compared to the non-trained group of novices. Overall, these findings are consistent with the idea that affective components of art appreciation are less driven by expertise and largely consistent across observers, while more cognitive aspects of aesthetic viewing depend on viewer characteristics such as art expertise.</p>
Historic Embodied Emotions Model (HEEM) dataset
<p>First release of the HEEM dataset.</p>
VnEmoLex: A Vietnamese emotion lexicon for sentiment intensity analysis
<p>VnEmoLex is the moderate-sized data set annotated for eight basic emotions: joy, sadness, anger, fear, trust, disgust, surprise and anticipation for Vietnamese. It is built on the NRC Word-Emotion Association Lexicon (EmoLex)<sup>1 </sup>and the Viet Wordnet<sup>2</sup> . VnEmoLex has total 12,795 words of which 4431 words are from the EmoLex dictionary, 8364 words are taken from the Viet Wordnet.</p> <p><sup>1 </sup>http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm</p> <p><sup>2 </sup>http://http://viet.wordnet.vn/wnms/</p>
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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.