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1,836 results for “emotion”
Emotion recognition: social appraisal and offset task, 8-12 yo.
<p>Data from social appraisal task and offset task in 57 8-12 yo. school children, with pedagogy school type information.</p>
eMOTIONAL Cities metadata catalogue
<p>eMOTIONAL Cities Metadata catalogue</p> <p>OGC API Records endpoint <a title="eMOTIONAL Cities Metadata catalogue" href="https://emotional.byteroad.net/catalogue" target="_blank" rel="noopener">https://emotional.byteroad.net/catalogue</a></p>
Multiclass Emotion Model Dataset
<p>This dataset is a compilation of three publicly available datasets: DailyDialog, emotion-stimulus and ISEAR. It is divided into 70% training set and 30% test set.</p> <p>The references for the three datasets are:</p> <ol> <li>Li, Y., Su, H., Shen, X., Li, W., Cao, Z., & Niu, S. (2017). DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. Retrieved from http://arxiv.org/abs/1710.03957</li> <li>Ghazi, D., Inkpen, D., & Szpakowicz, S. (2015). Detecting emotion stimuli in emotion-bearing sentences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9042, pp. 152–165). Springer Verlag. https://doi.org/10.1007/978-3-319-18117-2_12.</li> <li>Scherer KR, Wallbott HG. Evidence for universality and cultural variation of differential emotion response patterning. J Pers Soc Psychol. 1994; 66(2): 310. [Downloaded from https://www.kaggle.com/shrivastava/isears-dataset]</li> </ol>
Biological Motion and Emotion: Investigating the Impact of Tempo Manipulations
<p>The video set includes 26 audiovisual or visual-only stimuli that served in the perceptual experiment of the study "Comparing original and tempo-manipulatd biological movements: From Kinematics to multimodal emotion and time perception". Among the videos, 18 were experimental stimuli while 8 were stimuli in catch trials. The experimental stimuli were named after the following format: "Manipulation_Modality", while catch trial stimuli were named after "Manipulation_Duration". Slow represents 86BPM, medium represents 130BPM, while fast represents 195BPM. "Fast2Slow_Audiovisual.mp4", for instance, stands for an audiovisual stimuli that was originally recorded in 195BPM and decelerated to 86BPM. </p>
EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation
<p>EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in <strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains <strong>1,087</strong> music clips from 387 songs and <strong>clip-level</strong> emotion labels annotated by four dedicated annotators. </p> <p>For more detailed information about the dataset, please refer to our paper: <a href="https://arxiv.org/abs/2108.01374"><strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong></a>. </p> <p><strong>File Description</strong></p> <ul> <li><em><strong>midis/</strong></em>: midi clips transcribed using GiantMIDI. <ul> <li>Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song.</li> </ul> </li> <li><em><strong>metadata/</strong></em>: metadata from YouTube. (Got when crawling)</li> <li> <p><em><strong>songs_lists/</strong></em>: YouTube URLs of songs.</p> </li> <li> <p><em><strong>tagging_lists/</strong></em>: raw tagging result for each sample.</p> </li> <li> <p><em><strong>label.csv</strong></em>: metadata that records filename, 4Q label, and annotator.</p> </li> <li> <p><em><strong>metadata_by_song.csv</strong></em>: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test.</p> </li> <li> <p><em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files.</p> </li> </ul> <p>------</p> <p><strong>2.2 Update</strong></p> <ul> <li>Add tagging files in <em><strong>tagging_lists/</strong></em> that are missing in the previous version.</li> <li>Add <em><strong>timestamps.json</strong></em> for easier usage. It records all the timestamps in dict format. You can see <em><strong>scripts/load_timestamp.ipynb</strong></em> for the format example.</li> <li>Add <em><strong>scripts/timestamp2clip.py</strong></em>: After the raw audio are crawled and put in <em><strong>audios/raw</strong></em>, you can use this script to get audio clips. The script will read <em><strong>timestamps.json</strong></em> and use the timestamp to extract clips. The clips will be saved to <em><strong>audios/seg</strong> </em>folder.</li> <li>remove 7 midi files that were added by mistake, and also corrected the number in <em><strong>metadata_by_song.csv</strong></em>.</li> </ul> <p> </p> <p><strong>2.1 Update</strong></p> <p>Add one file and one folder:</p> <ul> <li><em><strong>key_mode_tempo.csv</strong></em>: key, mode, and tempo information extracted from files.</li> <li><strong><em>CP_events/</em></strong>: CP events used in our paper. Extracted using this <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/representations/uncond/cp/corpus2events.py">script</a>, and add the emotion event to the front.</li> </ul> <p>Modify one folder:</p> <ul> <li>The <strong><em>REMI_events/</em></strong> files in version 2.0 contain some information that is not related to the paper, so remove it.</li> </ul> <p> </p> <p><strong>2.0 Update</strong></p> <p>Add two new folders:</p> <ul> <li><strong><em>corpus/</em></strong>: processed data that following <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/Dataset.md">the preprocessing flow</a>. (Please notice that although we have <code>1078</code> clips in our dataset, we lost some clips during steps 1~4 of the flow, so the final number of clips in this <strong><code>corpus</code></strong> is <code>1052</code>, and that's the number we used for training the generative model.)</li> <li><strong><em>REMI_events/</em></strong>: REMI event for each midi file. They are generated using this <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/representations/uncond/remi/corpus2events.py">script</a>.</li> </ul> <p>-------- </p> <p> </p> <p> </p> <p> </p> <p><strong>Cite this dataset</strong></p> <pre><code>@inproceedings{{EMOPIA}, author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan}, title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation}, booktitle = {Proc. Int. Society for Music Information Retrieval Conf.}, year = {2021} }</code></pre>
Examining the representation of landscape and its emotional value in German-Swiss fiction around 1900
<p>This repository provides access to the main datasets used in Grisot, G & Herrmann, J. B. (2023) <em>Examining the representation of landscape and its emotional value in German-Swiss fiction around 1900.</em> The data set includes the following item:</p> <p>(1) <em>all_entities_new.Rdata</em>: comprehensive list of labelled spatial entities;</p> <p>(2) <em>corpus_summary_table.xlsx</em>: a summary of the corpus content;</p> <p>(3) <em>corpus_clean_partitioned.zip</em>: the entire corpus after removing stopwords;</p> <p>(4) <em>sentiment_lexicons.RData</em>: the sentiment lexicons for German used in our analyses;</p> <p>(5) tables with details of various analyses (<em>proportions_spatial_entities.xlsx, proportions_spatial_entities_by_country.xlsx, Spatial_entities_in_Corpus_by_category.xlsx</em>);</p> <p>(6) <em>Sentiment_lexicons_info.xlsx</em>: information about the content of the sentment lexicons.</p> <p>The study for which this data were collected explored the representation and affective encoding of fictional space in a corpus of 125 Swiss literary prose texts of the 19th and early 20th Century written in German, offering thus a contribution to both spatial and affective literary studies. Motivated by questions about the iconic dichotomy between urban and rural in literary works – and in Swiss literature in particular – we used computational methods to examine the types of space encoded in German-Swiss literature as well as the affective encoding associated with these space types. Taking into account the complexity of cultural perceptions and representations of space across history, we explore questions about affective encoding of rural, urban, and natural fictional spaces, as well as about their role in the construction of ‘Swiss’ national literature.</p>
Watching with Argus Eyes: Characterization of emotional and physiological responding in adults exposed to childhood maltreatment and/or recent adversity
<p><em>Background:</em><br> Exposure to adverse experiences is a well-established major risk factor for affective psychopathology. The vulnerability of deleterious sequelae is assumed in maladaptive processes of the defensive system, particularly in emotional processing. More specifically, childhood maltreatment has been suggested to be associated with the recruitment of specific and distinct defensive response profiles. To date it remains unclear whether these are specific or generalisable to recent adversity in adulthood.</p> <p><br> <em>Method:</em><br> This preregistered study aimed to investigate the impact of exposure to childhood and recent adversity on emotional processing in 685 healthy adults with the “Affective Startle Modulation” Paradigm (ASM).</p> <p><br> <em>Results:</em><br> First, we replicated higher trait anxiety and depression levels in individuals exposed to both types of adversity. Second, we observed blunted general skin conductance reactivity in individuals exposed to recent adversity. Third, individuals <em>exposed to childhood maltreatment</em> showed reduced, while individuals <em>exposed to recent adversity</em> showed increased discrimination between pictures of negative and neutral valence, compared to non-exposed individuals in SCR. No association between exposure to adversity and fear potentiated startle was observed. Furthermore, explorative analyses revealed moderate dimensional and categorical agreement between two childhood maltreatment questionnaires and provide insight into potential adversity-type specific effects.</p> <p><br> <em>Conclusion:</em><br> Our results support experience-dependent plasticity in sympathetic nervous system reactivity and suggest distinct response profiles in affective modulation in individuals exposed to early versus recent adversity. We emphasise the need to further explore distinct adversity profiles to further our understanding on specific psychophysiological profiles and their potential implication for prevention and intervention.</p>
Perception and Experience of Appraisals and Consumption Emotions in Reviews (PEACE-Reviews) Pilot Dataset
<p>This pilot dataset contains review text responses and associated emotional ratings about the usage of particular products by participants (USA) recruited from a crowdsourcing platform called Prolific. This pilot study is part of a larger project of constructing a dataset that has people's review text of particular products labelled with emotional variables such as cognitive appraisals, and emotional intensity. The aim of this pilot is to pilot different methods that elicit participants to write their emotional experiences when using a product. </p> <p><strong>Variables</strong></p> <p>Column A- condition that the participant was assigned to. [1- presence of emotion prompts, review format; 2- presence of emotion prompts, question format; 3- absence of emotion prompts, review format; absence of emotion prompts, question format]</p> <p>Column B- product reviewed</p> <p>Column C- cost of product reviewed</p> <p>Column D- emotion felt while using the product (this only applies to condition 1 and 3)</p> <p>Columns E - K are the text responses from participants.</p> <p>Column E- review text responses of the product (this only applies to condition 1 and 3)</p> <p>Column F- how important is the product in text (this only applies to condition 2 and 4)</p> <p>Column G- how did the participants feel when using the product in text (this only applies to condition 2 and 4)</p> <p>Column H- Why did the participants feel the way they are feeling when using the product in text (this only applies to condition 2)</p> <p>Column I- whether the product is consistent with what the participants' wanted in text (this only applies to condition 2 and 4)</p> <p>Column J- whether using the product was pleasant in text (this only applies to condition 2 and 4)</p> <p>Column K- whether the participants' understood what was happening when they were using the product in text (this only applies to condition 2 and 4)</p> <p>Columns L - AE are the rating of the cognitive appraisals on a Likert scale of 1-7. (7 means that the participants endorse more on that appraisal). These ratings correspond to how the participants' perceive the situation of using the product that they have talked about. Missing data correspond to the participants' rating as 'not applicable'. </p> <p>Column L- self-control- To what extent did you think you had control over the situation?</p> <p>Column M- pleasantness- To what extent did you think that the situation was pleasant?</p> <p>Column N- goal congruence- To what extent was the situation consistent with what you wanted?</p> <p>Column O- expectedness- To what extent did you expect the situation to occur?</p> <p>Column P- fairness- To what extent did you think the situation was fair?</p> <p>Column Q- certainty- To what extent did you understand what was happening in the situation?</p> <p>Column R- coping potential- To what extent were you able to cope with any negative consequences of the situation?</p> <p>Column S- goal relevance- To what extent did you think that the situation was relevant to what you wanted?</p> <p>Column T- other-agency- To what extent did you think that someone else other than you was responsible for what was happening in the situation?</p> <p>Column U- difficulty- To what extent did you think that the situation was difficult?</p> <p>Column V- self-agency- To what extent did you think that you were responsible for what was happening in the situation?</p> <p>Column W- attentional activity- To what extent did you think that you needed to attend to the situation further?</p> <p>Column X- circumstances-control- To what extent did you think that circumstances beyond anyone's control were controlling what was happening in the situation?</p> <p>Column Y- positive future expectancy- To what extent did you think that the situation would get better?</p> <p>Column Z- other-control- To what extent did you think that other people were controlling what was happening in the situation?</p> <p>Column AA- effort- To what extent did you think that you needed to exert effort to deal with the situation?</p> <p>Column AB- problems- To what extent did you think that there were problems that had to be solved before you could get what you wanted?</p> <p>Column AC- external normative significance- To what extent did you think that the situation was consistent with external and social norms?</p> <p>Column AD- circumstances-agency- To what extent did you think that circumstances beyond anyone's control were responsible for what was happening in the situation?</p> <p>Column AE- familiarity- To what extent did you think that the situation was familiar?</p> <p>Columns AF - AH are some variables pertaining the participants' experience of using the product on a Likert scale of 1-7.</p> <p>Column AF-How much effort did you put into researching about the product/service before purchasing it?</p> <p>Column AG- To what extent would you recommend the product/service that you have recalled to someone else?</p> <p>Column AH- To what extent would you purchase again the product/service that you have recalled?</p> <p>Columns AI - AP are the emotional intensity rated for each emotion on a Likert scale of 1-7 (7 means that the participant strongly felt that emotion when using the product)</p> <p>Columns AQ - AW are demographic variables (note that participants are recruited from USA)</p> <p>Column AQ- gender</p> <p>Column AR- age group</p> <p>Column AS- race</p> <p>Column AT- whether the participant is of Spanish, Hispanic, or Latino origin</p> <p>Column AU- native language</p> <p>Column AV- highest education level</p> <p>Column AW- total household income before tax during the past 12 months</p>
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional - Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_IC_T6-Post
<p>Answers given by students in the sixth grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón (Spain) in the spring of the school year 2015-16. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de sexto curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional: Versión para Niños (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2015-16. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional – Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_IC_T3-Post
<p>Answers given by students in the third grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón (Spain) in the spring of the school year 2012-13. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de tercer curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2012-13. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional – Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_CC-CS_T6-Post
<p>Answers given by students in the sixth grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón and Seville (Spain) in the spring of the school year 2014-15. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de sexto curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón y Sevilla (España) en primavera del curso escolar 2014-15. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a></p>
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional - Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_IC_T5-Post
<p>Answers given by students in the fifth grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón (Spain) in the spring of the school year 2014-15. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de quinto curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional: Versión para Niños (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2014-15. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
eSEEd: emotional State Estimation based on Eye-tracking dataset
<p>We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br><br>TO USE THIS DATASET PLEASE CITE:<br>Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. <em>Brain Sci.</em> 2023, <em>13</em>, 589. https://doi.org/10.3390/brainsci13040589</p>
Appendix: Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition
<p>Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition".</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>
Knocking on a yellow door: interactions among knocking sounds, colours, and emotions.
<p>Everyday sounds play a particular role in some media, such as video games and movies, where they are associated with visual contents. </p> <p>In this context, we present the audiovisual stimuli used for a study (n=60) done to investigate the hypothesis that the association between knocking sounds and emotions changes if the knocking sound is performed on a door of a different colour. This study draws inspiration from a previous experiment on knocking sound by Pauletto and co-workers.<br> </p> <p>The audio-visual materials consist of 5 professionally performed emotional audios taken from Barahona-Rìos & Pauletto (2020) synchronised with a video of a hand knocking on a door of 5 different colours, for a total of 25 videos.</p>
IFEED: Interactive Facial Expression and Emotion Detection Dataset
<p>Interactive Facial Expression and Emotion Detection (IFEED) is an annotated dataset that can be used to train, validate, and test Deep Learning models for facial expression and emotion recognition. It contains pre-filtered and analysed images of the interactions between the six main characters of the Friends television series, obtained from the video recordings of the Multimodal EmotionLines Dataset (MELD).</p> <p>The images were obtained by decomposing the videos into multiple frames and extracting the facial expression of the correctly identified characters. A team composed of 14 researchers manually verified and annotated the processed data into several classes: Angry, Sad, Happy, Fearful, Disgusted, Surprised and Neutral.</p> <p>IFEED can be valuable for the development of intelligent facial expression recognition solutions and emotion detection software, enabling binary or multi-class classification, or even anomaly detection or clustering tasks. The images with ambiguous or very subtle facial expressions can be repurposed for adversarial learning. The dataset can be combined with additional data recordings to create more complete and extensive datasets and improve the generalization of robust deep learning models.</p>
childTale-A: A corpus of eighty fairy tales from the 7th edition by the Brothers Grimm, manually annotated for textually encoded emotions
<p>The childTale-A corpus is a collection of eighty fairy tales, a core set of the Grimms’ Children's and Household Tales as introduced in Herrmann & Lüdtke (2023). Within the CHYLSA project, textually encoded emotions were annotated in each sentence in each of the eighty fairy tales. Annotations were collected for the dimensions <em>valence</em> and <em>arousal</em>, as well as for the six basic emotions <em>anger</em>, <em>disgust</em>, <em>fear</em>, <em>joy</em>, <em>sadness</em>, and <em>surprise</em>. Each fairy tale was annotated by two persons (for details see Hermann & Lüdtke, 2023).</p> <p>In detail, this dataset contains:</p> <ul> <li>instructions and texts used for annotation (zip-files): <ul> <li>all N=80 fairy tales as txt-files (with normalised orthography)</li> <li>instructions (in German) for the valence and arousal annotation as well as for the annotation of the six basic emotions</li> </ul> </li> <li>scripts for preparing and analysing annotations for textually encoded emotions: <ul> <li>Python scripts to prepare Excel files for annotation (including a script to separate texts into individual sentences)</li> <li>R-scripts for data preparation, calculation of inter-rater reliability and smoothing of the valence annotations (discrete cosine transformation (DCT) with length normalisation) <ul> </ul> </li> </ul> </li> <li>sentence-level data (for each sentence in each of the eighty fairy tales): <ul> <li>annotations for the dimensions valence and arousal (continuous values)</li> <li>categorisation of each sentence (as negative, neutral or positive) based on the continuous valence annotation</li> <li>annotations on the occurrence of the six basic emotions anger, disgust, fear, joy, sadness, and surprise</li> </ul> </li> <li>transformed and length-normalised valence annotations as basis for the <em>Emotional Arcs</em> (DCT and length normalisation results in one hundred data points for each fairy tale, saved in a separate data file)</li> <li>text-level data (for each of the eighty fairy tales): <ul> <li>general information, for example title in German and English, number of sentences, Kinder- und Hausmärchen-ID (KMH-ID), corpus ID</li> <li>results of the analysis of the annotated data with values for: <ul> <li><em>Average Valence</em> and <em>Average Arousal</em></li> <li>inter-rater reliability index (Krippendorff's alpha coefficient) for the valence and arousal annotations</li> <li>proportions of positive, negative and neutral sentences</li> <li><em>Emotion Potential</em> (percent of both positive and negative sentences)</li> <li><em>Valence Span</em>, <em>Arousal Span</em> and range of the <em>Emotional Arc</em></li> <li><em>Emotion Profile</em> (relative frequency for each of the six basic emotions <em>anger</em>, <em>disgust</em>, <em>fear</em>, <em>joy</em>, <em>sadness</em>, and <em>surprise)</em></li> <li>inter-rater reliability indices (Krippendorff's alpha coefficient and the percentage of agreement) for each of the six basic emotions</li> </ul> </li> </ul> </li> </ul> <p>Reference: <br> Herrmann, Berenike & Lüdtke, Jana (2023). A Fairy Tale Gold Standard. Annotation and Analysis of Emotions in the Children's and Household Tales by the Brothers Grimm.<em> </em>Zeitschrift für digitale Geisteswissenschaften (ZfdG). DOI: 10.17175/2023_005.</p> <p> </p> <p>DFG Schwerpunktprogramm SPP 2207 “Computational Literary Studies“<br> Online:</p> <ol> <li><a href="https://gepris.dfg.de/gepris/projekt/402743989">https://gepris.dfg.de/gepris/projekt/402743989</a></li> <li><a href="https://dfg-spp-cls.github.io/">https://dfg-spp-cls.github.io<em>/</em></a></li> </ol> <p>Teilprojekt: „CHYLSA - Children’s and Youth Literature Sentiment Analysis“</p> <p>Online:</p> <ol> <li><a href="https://gepris.dfg.de/gepris/projekt/424250469">https://gepris.dfg.de/gepris/projekt/424250469</a></li> <li><a href="https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-CHYLSA/">https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-CHYLSA/</a></li> </ol>
Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data
<p>These are the data and resources used for a Twitch Emote recommendation system using a Word2Vec model. The nature and exploration of the data is described in Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data. To protect the privacy of the users whose messages were scraped to build this corpus, names and timestamps have been removed and only the message bodies are included. However, a tutorial for this project is included on the project GitHub: https://github.com/KoroshM/Emote-Recommender.<br> <br> embeddings.tsv and labeled_metadata.tsv may be used in TensorFlow's embedding projector to visualize the embedding space.<br> <br> Note: Model files are the following:<br> embeddings.tsv<br> labeled_metadata.tsv<br> model<br> model.model**<br> model.wv.vectors.npy</p> <p>**Located here: https://drive.google.com/drive/folders/1RZC4JA4CpAcwoo6dOwq_jobTd6dNi_n2?usp=sharing</p>
GamER: Gameplay Physiological Signal Dataset for Emotion Recognition
<p>A dataset of physiological signals (EEG, ECG, EDA, EOG and Respiration) collected from 48 subjects while they were playing simple games designed to elicit emotional responses.</p> <p>Data were collected using the Biosignals PLUX Researcher kit, which allows up to 10 hours of signal recordings at up to 3kHz sampling rate and 16-bit resolution per channel, while recording data from up to eight sensors simultaneously.</p> <p>The dataset consists of 3 files:</p> <ol> <li>data.zip: Contains the physiological signals. They are organized in 1 folder per subject, which contains the recorded data in .h5 and .txt format as provided by the Researcher kit.</li> <li>emotion_annotation.csv: Contains the emotion annotation that was produced by a psychologist based on the subjects self-reports and by examining the video of the data collection process (the videos are not included for privacy reasons)</li> <li>video_offsets.csv: Additional information for synchronizing the video-based annotations with the signals.</li> </ol> <p>For more information, refer to the accompanying publication.</p> <p><em>Disclaimer: All subjects have provided written consent for the publication of their anonymized physiological signals.</em></p>
Multi-view emotional expressions dataset
<p>Multi-view emotional expressions dataset (MEED) using 2D pose estimation.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.