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Laypeople in Fire Protection: Their Motivation, Their Emotions, Their Value
<p>This online supplement contains a dataset (raw data) collected in a study by the University of Münster, Germany, as well as supplementary study materials, and the online appendix of our paper including additional results. The study is part of a larger research project (<a href="https://www.brawa.ovgu.de/en/">https://www.brawa.ovgu.de/en/</a>) and examined the motivation, emotions, and behavior of laypeople in fire protection.</p><p>Fire events, a specific type of emergency situation, occur regularly and often cost people's lives. Early fire detection and involvement of (trained) laypeople in very early stages is essential, especially in fire-prone historical buildings. In an interview study (N = 25) and a quantification study (N = 238), we examined laypeople's motivation to help, their emotions, and their value – especially regarding behaviors – during fires. In Study 1, based on in-depth interviews using the critical incident technique, we inductively developed three category systems: 1) Key motivators included one's emotional bond to the object, altruism, self-protection, and self-efficacy. 2) Laypeople's emotions, such as stress at first but also calmness, were categorized. 3) We also found that laypeople's primary value during fire incidents was their ability to provide support, e.g., knowledge of the building. In Study 2, we found quantitative support for the findings derived from Study 1. Additionally, we compared laypeople's and experts' assessments and compared reactions depending on fire size. Thus, our research shifts the focus from evacuation to active – and safe – involvement of laypeople. Our results are helpful for executives in emergency management, as they may find value in including laypeople when coping with emergencies.</p><p>The ethics committee of the Faculty of Psychology and Sports Science of the University of Münster approved the studies (under ID 2021-30-MT and ID 2021-14-MT) and we preregistered them with aspredicted.org (available at <a href="https://aspredicted.org/dd2p8.pdf">https://aspredicted.org/dd2p8.pdf</a> and <a href="https://aspredicted.org/m6zh5.pdf">https://aspredicted.org/m6zh5.pdf</a>). The studies were supported by the German Federal Ministry of Education and Research (grant number 13N15416).</p><p><strong>This online supplement includes:</strong></p><ul><li>An online supplement (online appendix) containing additional material and results for Study 1 and Study 2</li><li>One codebook describing all instructions and items in Study 2 (quantification study)</li><li>Raw (anonymized) data set and analysis script for Study 2 (Note: The raw data contains only the information of persons who were included in the analysis and who gave their informed consent. Some demographic information was deleted to ensure anonymity.)</li></ul>
Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic
Open the record for dataset details and reuse information.
Data Associated with Manuscript Titled "Conflicted Emotions Following Trust-Based Interaction"
<p>This is a dataset used in analyses of trust-based interaction behavior and emotional reactions described and interpreted in the associated manuscript titled "Conflicted Emotions Following Trust-Based Interaction".</p>
Data and code from "Facial blushing and feather fluffing are indicators of emotions in domestic fowl"
<p>Data to produce the manuscript and R codes use to analyse the data</p>
Multimodal datataset of EEG, ECG and audio recordings of individual musicians playing emotional music
<p>This dataset contains EEG, ECG and audio recordings of 11 individual musicians playing emotional music on their instrument. The dataset consists of two parts:</p> <ol> <li> <p>Experiment: Musicians’ self-reported ratings, audio recordings, and physiological recordings where the 11 expert musicians were asked to play at least 4~2-minute unfamiliar (non-popularly known) musical pieces. Participants were asked to play at least once one of the following emotions: happiness, sadness, relaxation, and anger. For the physiological recordings EEG, ECG, and GSR signals were recorded. Each musican’s data is denoted by MS_ followed by the order in which they were recorded.</p> </li> <li> <p>Self-report questionnaire: A self-assessment questionnaire and their answers where 11 expert musicians were asked to rate musical pieces recorded based on:</p> <ol> <li> <p>Objective valence & arousal they felt the piece had;</p> </li> <li> <p>Felt valence & arousal during playing.</p> </li> </ol> </li> </ol> <p> </p> <p>For a more detailed explanation of the dataset, its recording procedure, and its contents, see </p> <p>L. Turchet, B. O'Sullivan, R. Ortner & C. Gugher (2024). Emotion Recognition of Playing Musicians from EEG, ECG, and Acoustic Signals. IEEE Transactions on Human-Machine Systems.</p> <p><strong> </strong></p> <h2>File Listing</h2> <p>The following files are available (each explained in more detail below):</p> <div> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Format</p> </td> <td> <p>Contents</p> </td> </tr> <tr> <td> <p>EEG_ECG_data_for_each_musician</p> </td> <td> <p>mat</p> </td> <td> <p>This folder contains the raw EEG, ECG, & GSR data for all 11 musicians for each piece they played as well as a resting state recording, which was recorded while a neutral audio stimulus was played.</p> </td> </tr> <tr> <td> <p>audio_data_for_each_musician</p> </td> <td> <p>wav, JSON, csv</p> </td> <td> <p>This folder contains 3 subfolders: <br>1) wav_audio_files_original: This is the raw audio data recorded for each musician and includes all 56 pieces included in the paper reported above, please see below regarding rejected trials.</p> <p>2) wav_audio_files (original split_into_3_parts): Here, the 56 pieces are appropriately split into 3 separate parts.<br><br>3) analysis_audio_files: This folder contains the acoustic features extracted, 1714 acoustic features were extracted from each split trial. Each result is stored in .JSON, however, the collated results can be seen in all_results.csv.</p> </td> </tr> <tr> <td> <p>self_report_questionnaire</p> </td> <td> <p>pdf, xls</p> </td> <td> <p>Two files exist in this folder:<br>1. The self-reported questionnaire given to the musicians of the questionnaire during the experiment.<br>2. </p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p> <h2>File Details</h2> <p><strong> </strong></p> <h3>EEG_ECG_data_for_each_musician</h3> <p>These are the original raw data recordings. EEG data were recorded using a g.GAMMAcap2 by g.tec Medical Engineering, a 64-channel cap with g.SCARABEO active electrodes, with two g.GAMMAsys reference active ear clip electrodes. Two g.GAMMAbox electrode connector boxes were used to connect the active electrodes to two g.USBamp biosignal amplifiers with a sampling frequency of 256 Hz.<br><br>The following 31 EEG channels were used: Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F3, F4, F7, F8, Cz, C3, C4, CP3, CP4, CP5, CP6, P1, P2, P3, P4, P5, P6, P7, P8, PO7, PO8, O1, and O2. AFz was used as a ground electrode and Cz was used as a re-reference electrode. The right-side ear clip electrode was used as a reference electrode. </p> <p><br>ECG data were recorded using a single g.GAMMAclip active electrode clip connected directly to the g.GAMMAbox, sharing the same ground electrode with the EEG cap and placed on position V4 of the subjects.<br><br>GSR data was recorded using the g.GSRsensor² box which contains two small dry electrodes placed underneath the participant’s toes (due to the amount of hand movement required for playing an instrument). The g.GSRsensor² was connected directly to a g.GAMMAbox, using jumper cables connected to different ports in the g.USBAMPs to share the same reference and ground electrodes as the EEG cap. </p> <p><strong> </strong></p> <p>The locations of the channels and their corresponding number in the raw data is as follows:</p> <div> <table> <tbody> <tr> <td> <p>Channel Number</p> </td> <td> <p>Channel Name</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Time series</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>AF3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>AF4</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>AF7</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>AF8</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>CP3</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>CP4</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>CP5</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>CP6</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>P1</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>P2</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>P5</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>P6</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>P7</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>P8</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>O1</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>O2</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>Cz</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>Fp1</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Fp2</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>F3</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>F4</p> </td> </tr> <tr> <td> <p>23</p> </td> <td> <p>F7</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>F8</p> </td> </tr> <tr> <td> <p>25</p> </td> <td> <p>C3</p> </td> </tr> <tr> <td> <p>26</p> </td> <td> <p>C4</p> </td> </tr> <tr> <td> <p>27</p> </td> <td> <p>P3</p> </td> </tr> <tr> <td> <p>28</p> </td> <td> <p>P4</p> </td> </tr> <tr> <td> <p>29</p> </td> <td> <p>PO7</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>PO8</p> </td> </tr> <tr> <td> <p>31</p> </td> <td> <p>AFz</p> </td> </tr> <tr> <td> <p>32</p> </td> <td> <p>GSR</p> </td> </tr> <tr> <td> <p>33</p> </td> <td> <p>ECG</p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p> <h3>audio_data_for_each_musician/wav_audio_files_original</h3> <p>This folder contains all of the raw audio files recorded during the experiment. All pieces were recorded using the software Audacity and exported as WAV files encoded with a bit depth of 32-bits and a sampling rate of 44.1 kHz. 56 of the included trials are present in this folder.</p> <p><strong> </strong></p> <h3>audio_data_for_each_musician/wav_audio_files (original split_into_3_parts)</h3> <p>This folder contains the above mentioned 56 raw audio pieces separated into 3 appropriately sized recordings.</p> <p><strong> </strong></p> <p>audio_data_for_each_musician/analysis_audio_files<br><br>This folder contains the 1714 acoustic features from each of the 3 separated trials from 56 accepted pieces, these are denoted by MS_ followed by the order in which the musicians were recorded and the order in which the trails were split, the individual features are in JSON format. Within this folder, we have collated all results in a csv file (all_results.csv) where the columns show the intended emotion, trial name and number, followed by the names of the acoustic features. </p> <p><strong> </strong></p> <p>self_report_questionnaire</p> <p>This pdf is the questionnaire which each musician was given following each trial relating to the emotions communicated and felt during each trial. Most* questions in the questionnaire were multiple-choice and speak pretty much for themselves. The answers for which were collated and are described below.</p> <p><strong> </strong></p> <p>self_report_questionnaire_anwsers</p> <p>This .xls file contains the results of all the musicians self-reported ratings from the above described questionnaire. </p> <div> <table> <tbody> <tr> <td> <p>Column name</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Subject</p> </td> <td> <p>The subject code of the musician, denoted by MS_</p> </td> </tr> <tr> <td> <p>recording_filename</p> </td> <td> <p>The name of the trial denoted by MS_01_ followed by the trial number.</p> </td> </tr> <tr> <td> <p>intended_emotion</p> </td> <td> <p>The intended emotion the musician was instructed to communicate. The emotions are as follows:</p> <ol> <li> <p>Angry</p> </li> <li> <p>Sad</p> </li> <li> <p>Relaxed</p> </li> <li> <p>Happy</p> </li> </ol> <p>They are intended to be reported by using the valence-arousal space.</p> </td> </tr> <tr> <td> <p>valence_communicated</p> </td> <td> <p>The valence rating (integer between 1 and 5), that participants were asked to objectively rate how they thought the music played would be perceived. </p> </td> </tr> <tr> <td> <p>arousal_communicated</p> </td> <td> <p>As above, but relating to arousal rather than valence.</p> </td> </tr> <tr> <td> <p>valence_felt</p> </td> <td> <p>The valence rating (integer between 1 and 5), that participants were asked to rate how they felt while playing the piece.</p> </td> </tr> </tbody> </table> </div> <p> </p> <div> <table> <tbody> <tr> <td> <p>arousal_felt</p> </td> <td> <p>As above, but relating to arousal rather than valence.</p> </td> </tr> <tr> <td> <p>instrument_played</p> </td> <td> <p>The instrument played for each trial.</p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p>
Dataset of Emotional intelligence and mood in the beauty contest game
<p>This site provides access to the dataset in the article "Emotional Intelligence and Mood in the Beauty Contest Game: An Experimental Study." The study explores emotional intelligence and mood's impact on strategic decision-making within the Beauty Contest Game framework. Results offer compelling evidence of their influence on strategic guessing and reasoning depth. The dataset includes responses from 96 participants, assessing emotional intelligence, mood categories, and strategic decision details. This resource is crucial for understanding and validating study findings, shedding light on decision-making dynamics and implications for psychology, economics, and organizational behavior. The dataset's availability encourages further research on emotions in decision-making, serving as a foundation for exploring the interrelationship between emotional intelligence, mood, and strategic behavior.</p>
Emotion4MIDI: A Lyrics-Based Emotion-Labeled Symbolic Music Dataset
<p>This dataset includes emotion labels for the publicly available MIDI dataset, namely Lakh MIDI Dataset and Reddit MIDI dataset. The values represent the probability of containing a particular emotion. For a single song, more than one emotion can be present, hence the values don't add up to 1.</p>
Data and code associated with the publication "Emotional states elicited by wolf videos are diverse and explain general attitudes towards wolves", Arbieu et al., People and Nature 2024
<p>This folder contains the data and R scripts needed to replicate the analysis of the publication entitled "<span>Emotional states elicited by wolf videos are diverse and explain general attitudes towards wolves". <span>This dataset represents a social survey in rural populations of 24 randomly selected cities in France (n=795) to (i) quantify emotional diversity and (ii)<span> test the relationship between emotional states and attitudes towards wolves, accounting for individual and regional factors. <span>All </span>data were collected between November 2018 and May 2019. </span></span></span></p>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
The effect of ethical leadership on organizational outcomes in the hospitality industry: the mediating role of trust and emotional exhaustion
<p>SPSS dataset for the paper: "The effect of ethical leadership on organizational outcomes in the hospitality industry: the mediating role of trust and emotional exhaustion".</p>
How accurate are developers' emotions inferred from social media?
<p>Companion DATA of the paper "How accurate are developers' emotions inferred from social media?" submitted to the Journal of Systems and Software, 2021.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Impairment of aversive episodic memories during Covid-19 pandemic: The impact of emotional context on memory processes
<p>The threatening context of the COVID-19 pandemic provided a unique setting to study the effects of negative psychological symptoms on memory processes. Episodic memory is an essential function of the human being related to the ability to store and remember experiences and anticipate possible events in the future. Studying this function in this context is crucial to understand what effects the pandemic will have on the formation of episodic memories. To study this, the formation of episodic memories was evaluated by free recall, recognition, and episode order tasks for an aversive and neutral content. The results indicated that aversive episodic memory is impaired both in the free recall task and in the recognition task. Even the beneficial effect that emotional memory usually has for the episodic order was undermined as there were no differences between the neutral and aversive condition. The present work adds to the evidence that indicates that the level of activation does not modify memory processes in a linear way, which also depends on the type of recall and the characteristics of the content to be encoded.</p>
Reactivation of hedonic but not sensory representations in human emotional learning
<p>This repository contains data used in the following papers</p> <p>Reactivation of hedonic but not sensory representations in human emotional learning<br> Ehlers MR, Kryklywy JH, Beukers AO, Moore SR, Forys BJ, Anderson AK, Todd RM<br> https://doi.org/10.1101/2021.11.25.469891</p>
Learning diary emotions, competence self-assessment and difficulty experience connected with epistemic beliefs
<p>Anonym group level dataset where university students have made emotion, competence and difficulty selections on a set of study related topics and their epistemic beliefs before and after the semester long use of learning diary.</p> <p> </p> <p>Groups are formed based on the overall number of credits the participants studied in their first academic year (master's level). The number of students in each group:</p> <p>ECTS; Number of students</p> <table> <tbody> <tr> <td>0-19</td> <td>2</td> </tr> <tr> <td>20-39</td> <td>11</td> </tr> <tr> <td>40-59</td> <td>17</td> </tr> <tr> <td>60-79</td> <td>26</td> </tr> <tr> <td>>80</td> <td>8</td> </tr> </tbody> </table>
CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos
<p><a href="http://ieeexplore.ieee.org/document/9599346">CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360° Videos</a></p> <p><br> ## General Information<br> We develop the <a href="http://www.dis.cwi.nl/ceap-360vr-dataset/">CEAP-360VR</a> dataset to address the lack of continuously annotated behavioral and physiological datasets for 360 video VR affective computing. Accordingly, this dataset contains a) questionnaires (SSQ, IPQ, NASA-TLX); b) continuous valence-arousal annotations; c) head and eye movements as well as left and right eye pupil diameters while watching videos; d) peripheral physiological responses (ACC, EDA, SKT, BVP, HR, IBI). Our dataset also concludes the data pre-processing, data validating scripts, along with dataset description and key steps in the stage of data acquisition and pre-processing.</p> <p><br> ## Dataset Structure<br> The CEAP-360VR folder contains the following six subfolders</p> <p>1_Stimuli<br> 2_QuestionnaireData<br> 3_AnnotationData<br> 4_BehaviorData<br> 5_PhysioData<br> 6_Scripts<br> The following is a detailed description of each sub-file:</p> <p>1_Stimuli</p> <p> - VideoThumbNails<br> contains the eight thumbNails for each video (.jpg)<br> - VideoInfo.json<br> contains the detailed information for eight videos</p> <p>2_QuestionnaireData</p> <p> - PXX_Questionnaire_Data.json (X = 1, 2, ..., 32)<br> contains questionnaire data for each participant</p> <p>3_AnnotationData</p> <p> - Raw<br> contains the raw annotation data captured from the Joy-Con joystick for each participant<br> - Transformed<br> contains the transformed valence-arousal data generated from the raw data for each participant<br> - Frame<br> contains the re-sampled annotation data from the transformed data for each participant</p> <p>4_BehaviorData</p> <p> - Raw<br> contains the raw behavior data captured from the HTC VIVE Pro Eye Tobii Device for each participant<br> - Transformed<br> contains the transformed heam/eye movement data (pitch/yaw) generated from the raw data, as well as pupil diameter data for each participant<br> - Frame<br> contains the re-sampled behavior data generated from the transformed data for each participant<br> - HM_ScanPath<br> contains the head scanpath data generated from the transformed data for each participant<br> - EM_Fixation<br> contains the eye gaze fixation data generated from the transformed data for each participant</p> <p>5_PhysioData</p> <p> - Raw<br> contains the raw physiological data captured from the Empatica E4 wristband for each participant<br> - Transformed<br> contains the transformed physiological data generated from the raw data for each participant<br> - Frame<br> contains the re-sampled physiological data from the transformed data for each participant</p> <p>6_Scripts</p> <p> - Unity Project<br> contains the complete project of our user-controlled experiment (Unity 2018.4.1f1, HTC VIVE Pro Eye HMD)<br> - Data Processed<br> contains scripts that undertake the pre-processing steps for converting the raw data to the transformed/frame data in the transformed and frame folders.<br> conatins scripts for continuous annotation, behavior and physiological data analysis and visualization.<br> - CEAP-360VR_Baseline<br> contains scripts to generate processed behavioral and physiological data with V-A labels for deep learning experiments and features for machine learning experiments.<br> contains scripts to run ML and DL experiments under both subject-dependent and subject-independent model.</p> <p><br> ## Dataset Description<br> The CEAP-360VR Dataset [Description.pdf](https://github.com/cwi-dis/CEAP-360VR-Dataset/blob/master/CEAP-Dataset%20Description.pdf) introduces the dataset description and key steps in the stage of data acquisition and pre-processing.</p> <p><br> ## Dataset License<br> CEAP-360VR dataset is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license].</p> <p>## Citation</p> <p>Please cite our paper in any published work that uses this dataset as follows:<br> - Plain Text<br> T. Xue, A. El Ali, T. Zhang, G. Ding, and P. Cesar, "CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360° Videos," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3124080.</p> <p>- BibTex<br> @ARTICLE{Xue2021CEAP-360VR,<br> author={Xue, Tong and Ali, Abdallah El and Zhang, Tianyi and Ding, Gangyi and Cesar, Pablo},<br> journal={IEEE Transactions on Multimedia}, <br> title={CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360° Videos}, <br> year={2021},<br> volume={},<br> number={},<br> pages={1-1},<br> doi={https://doi.org/10.1109/TMM.2021.3124080}}</p> <p><br> ## Usage</p> <p> 1. We have performed the time alignment of different types of data and <br> videos for each participant, as well as the proceesing scripts that <br> can be used to generate both the transformed and frame data. <br> Researchers can run their analysis methods on them.<br> <br> 2. For researchers who want to try other data processing methods, you can directly use the raw data.</p> <p><br> ## About <br> The CEAP-360VR Dataset is maintained by Key Laboratory of Digital Performance and Simulation Technology at Beijing Institute of Technology and the Distributed & Interactive Systems (DIS) research group at Centrum Wiskunde & Informatica .</p> <p>Contact the authors<br> - Tong Xue: xuetong@bit.edu.cn, xue.tong@cwi.nl<br> - Abdallah El Ali: abdallah.el.ali@cwi.nl</p>
A Kannada Emotional Speech Dataset
<p>There was no emotional speech dataset available in Kannada. This was a limiting factor for research in the Kannada-speaking world. I introduce a Kannada emotional speech dataset and give details about its design and content. This dataset contains six different sentences, pronounced by thirteen people (four male and nine female), in five basic emotions plus one neutral emotion. They are all Kannada speakers. The dataset has been contributed by volunteers and the recordings were not made in a controlled environment. The dataset contains a total of 468 audio samples, each one in a separate audio file. The file naming convention is as follows: AA-EE-SS.wav where AA is a two-character field that gives the actor number (01 to 13), EE is a two-character field that indicates the emotion number (01 to 06), and SS is a two-character field that gives the sentence number (01 to 06). This dataset is freely available under a Creative Commons license.</p> <p>Gender and age of each of the 13 people who contributed to the dataset</p> <ul> <li>01, F, 45</li> <li>02, F, 20</li> <li>03, F, 21</li> <li>04, M, 47</li> <li>05, F, 48</li> <li>06, M, 20</li> <li>07, F, 20</li> <li>08, F, 45</li> <li>09, F, 21</li> <li>10, F, 12</li> <li>11, F, 12</li> <li>12, M, 17</li> <li>13, M, 26</li> </ul> <p>Identification characters for the emotions in the dataset</p> <ul> <li>01, Anger</li> <li>02, Sadness</li> <li>03, Surprise</li> <li>04, Happiness</li> <li>05, Fear</li> <li>06, Neutral</li> </ul> <p>Identification characters for the sentences in the dataset</p> <ul> <li>01, ರೋಗಿಗಳಿಗೆ ಚಿಕಿತ್ಸೆ ನೀಡಿ ಉಪಚರಿಸುವುದು</li> <li>02, ಈ ಕಾದಂಬರಿಯು ಎರಡು ಪಾತ್ರಗಳನ್ನು ಒಳಗೊಂಡಿದೆ</li> <li>03, ಖಾಸಗಿ ವಿಮಾನಗಳೆಂದೂ ಸಾರ್ವಜನಿಕ ವಿಮಾನಗಳೆಂದೂ ವಿಂಗಡಿಸಿದ್ದಾರೆ</li> <li>04, ನಿಮ್ಮನ್ನು ಬೀಟಿಯಾಗಿ ಬಹಳ ಸಂತೋಶ ಆಯಿತು</li> <li>05, ರಾಮನ ಎಡಬಲ ದಲ್ಲಿ ಸೀತಾ ಲಕ್ಷ್ಮಣ ರಿದ್ದಾರೆ</li> <li>06, ಕನ್ನಡವನ್ನು ಕಲಿಯಬೆಕು</li> </ul>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results</p> <p> Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
MINDFULNESS, REARING STYLES AND EMOTION DYSREGULATION IN A SAMPLE OF ADOLESCENTS WITH BORDERLINE PERSONALITY DISORDER.
<p>Our objective is to understand the influence of rearing styles and mindfulness skills in emotion dysregulation and BPD in adolescents. We tested the idea in this study. Firstly, we compared rearing styles, emotion dysregulation, and mindfulness skills between a clinical BPD group and a non-clinical control group. We sought to determine whether these characteristics were present in different levels in adolescents with and without BPD features or other psychopathologies. Later, we analyzed the effect of the different rearing styles and mindfulness skills in emotion dysregulation and borderline characteristics.</p>
Finnish War Letter Emotion Lexicon
<p>The XLSX file contains (1) a manually constructed list of Finnish emotion words in the context of World War 2, (2) the frequency of each emotion word in the Finnish War Letter Collection, and (3) information on whether the emotion word is included in the filtered FEIL emotion lexicon (emotion intensity score >0.6). The construction of the Finnish War Letter Lexicon is described in: Turunen, R., Taskinen, I., Uusitalo, L. & Kivimäki, V. (2022). “Mining Emotions from the Finnish War Letter Collection, 1939–1944”. <em>Proceeding of the 6th Digital Humanities in the Nordic and Baltic Countries Conference (DHNB 2022)</em>, Uppsala, Sweden, March 15–18, 2022.</p>
Animal eMotion, or the emotional evaluation of moving animals
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., Schürmann, L., & Haberkamp, A. (in press). Animal EMotion, or The Emotional Evaluation of Moving Animals. <em>Cognition & Emotion</em>. <a href="https://doi.org/10.1080/02699931.2022.2087600">https://doi.org/10.1080/02699931.2022.2087600</a></p> <p>Each experiment folder contains the data relative to one study in .csv and .mat format and a text file with comments. The example stimuli folder contains example images and videos that are published under licenses allowing us to share them freely.</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.