Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,836
datasets available to search
ShareScore release 0.7.1
Dataset results
1,836 results for “emotion”
Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
Open the record for dataset details and reuse information.
PsPM-EWO: Eye tracker (including pupillometry) measurements from emotional-words tasks
<p>This dataset includes eye tracker (including pupillometry) measurements for 37 healthy unmedicated participants (25 females and 12 males, age range: 18 - 34 years, mean age: 26.2 +/- 4.7 years) participating in an emotional-words task. In each session, participants were presented with 50 neutral and 50 negative five-letter nouns from the Berlin Affective Word List Reloaded (Võ et al., 2009). Also included are task information, keypress responses, keypress response times.</p>
Cross cultural tears: A systematic investigation of the interpersonal effects of emotional crying across different cultural backgrounds
<p>The present project wants to examine the importance of emotional crying as an attachment behaviour and its fundamental role across a number of diverse cultures.</p> <p>Emotional tears are uniquely human and have fascinated scholars across several decades (Vingerhoets, 2013). Some researchers argue that tearful crying played a significant role in the evolution of humankind with regard to social development and solidarity (Walter, 2006). Recent years have seen an increased interest in exploring the interpersonal effects of human tears (see Gračanin, Bylsma, & Vingerhoets, 2018 for a review), with findings that emotional tears foster approach or support behavior (Gračanin, Krahmer, Rinck, & Vingerhoets, 2018) and crying individuals being evaluated as more communal (e.g., Zickfeld, van de Ven, Schubert, & Vingerhoets, 2018). These findings generally fit the hypothesis that emotional tears constitute a social act, promote social bonding and fulfill an attachment function (Nelson, 2005; Bowlby, 1982; Gračanin, Bylsma, et al., 2018; Murube, Murube, & Murube, 1999; Radcliffe-Brown, 1922; Vingerhoets, 2013). The present projects aims to answer the question whether emotional tears present a fundamental form of solidarity and bonding and whether the findings on increased attributions of warmth and higher approach intentions for tearful individuals replicate across a number of diverse contexts.</p> <p><a href="https://osf.io/fj9bd">Published in OSF: https://osf.io/fj9bd</a>.</p> <p>The OPen SCience FRamework also includes:</p> <ul> <li>Data from the pilot study: https://osf.io/txcw3/</li> <li>General information about the translation process (including the Portuguese version https://osf.io/t4cas/),</li> <li>Data management and research protocol (https://osf.io/5bh7m/),</li> <li>Approvals from ethical committees (https://osf.io/v8rqh/),</li> <li>Data and descriptive document of supplementary analyses (https://osf.io/s8ack/),</li> <li>Data and syntax (https://osf.io/x2pks/),</li> <li>Information about stimuli (https://osf.io/x2pks/) </li> </ul>
The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old
Open the record for dataset details and reuse information.
Emotion regulation in the Ageing Brain, University of Reading, BBSRC
Open the record for dataset details and reuse information.
EMOTIVE IDIOMS
Open the record for dataset details and reuse information.
Visual and auditory brain areas share a representational structure that supports emotion perception: fMRI data
Open the record for dataset details and reuse information.
EEG Data for Emotive Response to Robot Facial Expressions
<p>This dataset consists of EEG recorded during visual human-robot interaction from 10 healthy participants to investigate the emotive response in EEG to different robot facial expressions. Participants observed four different facial expressions (angry, happy, sad and surprised along with neutral expression) displayed by the social robot Miko on its digital screen. EEG was recorded from 16 unipolar channels in frontal, central, temporal, parietal, and occipital locations . During each trial, an emotion stimulus was displayed for approximately 4s followed by 4s break during which the Miko robot displayed neutral expression and blinked regularly. Emotions were displayed in random order. Total of 240 EEG trials were recorded from each participant with 60 trials per emotion. The dataset provides raw minimally filtered EEG along with cleaned EEG with artefacts removal using ICA with sampling frequency of 128 Hz, and corresponding stimulus onset markers. Please refer to README file for further details and example code.</p> <p><em>Please cite the original publication:</em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <em>IEEE Internet of Things Journal</em>, DOI: <a href="https://doi.org/10.1109/JIOT.2021.3097592">10.1109/JIOT.2021.3097592</a>.</p> <p><em>Preprint: </em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <a href="https://arxiv.org/abs/2012.04511">arXiv:2012.04511</a></p>
BIRAFFE2: The 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems
<p>This is our 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (<strong>BIRAFFE2</strong>). It is a dataset consisting of <em><strong>electrocardiogram (ECG)</strong></em>, <em><strong>galvanic skin response (GSR)</strong></em>, changes in <em><strong>facial expression</strong></em> signals and <em><strong>hand movements</strong></em> (represented by gamepad's accelerometer and gyroscope) recorded during affect elicitation by means of <em><strong>audio-visual stimuli</strong></em> (from IADS and IAPS databases) and our proof-of-concept three-level <em><strong>emotion evoking game</strong></em>. All the signals were captured using portable and low-cost equipment: BITalino (r)evolution kit for ECG and GSR and Creative Live! web camera for face photos (further analyzed by MS Face API).</p> <p>Besides the signals, the dataset consists also of <em><strong>participants' self-assessment</strong></em> of their affective state after each stimuli (in the <em><strong>valence and arousal dimensions</strong></em>), <em><strong>"Big Five" personality traits</strong></em> assessment (using NEO-FFI inventory), and <em><strong>game involvement</strong></em>-related metrics (using GEQ questionnaire).</p> <p>In 1.1.0 version, RAW questionnaire data was included. The licence was changed from CC BY-NC-ND 4.0 to CC BY 4.0.</p> <p>For detailed description see <a href="https://doi.org/10.1038/s41597-022-01402-6">BIRAFFE2 Data Descriptor in Nature Scientific Data</a>.<br> For preview of the files before downloading the whole dataset see <em>sample-SUB211-[...]</em> files.</p> <p>All documents and papers that report on research that uses the BIRAFFE dataset should acknowledge this by <strong>citing the paper</strong>:<br> Kutt, K., Drążyk, D., Żuchowska, L., Szelążek, M., Bobek, S., & Nalepa, G. J. (2022). <strong>BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments</strong>. <em>Scientific Data</em>, <em>9</em>, 274. <a href="https://doi.org/10.1038/s41597-022-01402-6">https://doi.org/10.1038/s41597-022-01402-6</a></p>
Supplementary Material for Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics
<p>This dataset accompanies the article "Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics" (Lahnakoski & Bennett et al., submitted). </p> <p>It includes the Neo-Assyrian text corpus that is the basis for the word embeddings, a list of the Akkadian emotion and body words of interest for this study, and the scripts, toolboxes, and data used to generate the heat maps of the body.</p> <p>There is an additional folder containing the high resolution figures included in the article.</p> <p>A detailed ReadMe (README.txt) provides an overview of the folders.</p>
Data for the article "Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic"
<p>Dataset from a study of attitudes and perceptions of medicine and nursing students in Peru during the COVID-19 pandemic. Survey was applied from 2020-07-24 to 2021-04-16.</p> <p>This dataset is described in the article: </p> <p>Castagnetto, J.M., Hancco-Monrroy, D.E., Caballero-Apaza, L.M. <em>et al.</em> Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic. <em>Sci Data</em> <strong>12</strong>, 1259 (2025). <a href="https://doi.org/10.1038/s41597-025-05508-5">https://doi.org/10.1038/s41597-025-05508-5</a> (<a href="https://www.nature.com/articles/s41597-025-05508-5">https://www.nature.com/articles/s41597-025-05508-5</a>)</p>
Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses
<p>Dataset used in the publication: Santos, A. C., Arriaga, P., & Simões, C. (2021). Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses. Biological Psychology, 162, 108089. <a href="https://doi.org/10.1016/j.biopsycho.2021.108089" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.biopsycho.2021.108089</a></p> <p>Includes: data in SPSS and CSV and codebook. </p> <p>In the emotion regulation process more than one strategy is often used, though studies continue to rely on the manipulation of one strategy alone. This study compares the effects of Combined Cognitive Reappraisal (CCR: acceptance and reappraise via perspective-taking) and suppression using the Trier Social Stress Test (TSST). One hundred participants were randomly assigned to one of the two groups and subjective, physiological, and behavioural data were recorded. Continuous electrocardiography was recorded to measure heart rate variability (HRV) and stress levels. Affective ratings were provided before and after the TSST. Behavioural expressions were videotaped and analysed independently. Trait social anxiety/fear, age and gender entered as covariates. Although no group differences were found on affective ratings, the CCR group presented less physiological stress, higher HRV, their speech was better perceived, displayed more affiliative smile and hand gestures. Results suggested that CCR is more appropriate than suppression for managing social stress situations.</p>
TURKISH EMOTION DICTIONARY FOR MAXQDA
<p>This is an emotion dictionary in Turkish, compiled and created as a part of MSCA IF Project EMOFORTE funded by the European Commission.</p> <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> <p>Please use with a proper citation: Akkaraca Kose, M. (2024). TURKISH EMOTION DICTIONARY FOR MAXQDA (V1.2.) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13287741</p>
EMOFORTE- Ideological Map of Emotions in Turkey
<p>EMOFORTE Project Deliverable: An ideological map of emotions with a comparative-synchronic approach to the discourses of Turkish political parties on Foreign Policy at the nexus of national identity and Europe as its significant Other. </p> <p> </p> <p>This diagram has been prepared as a part of EMOFORTE 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>
Emotional Mapping of Turkish Foreign Policy towards Europe
<p>EMOFORTE deliverable: A historical-diachronic emotional mapping which interrogate change in the political discourse of Turkish foreign policy towards Europe with a longitudinal study from 2002-2019</p> <p>This digital product has been prepared as a part of EMOFORTE 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>
Art&Emotions Dataset
<p><strong>Art&Emotion experiment description </strong> </p> <p><br> The Art & Emotions dataset was collected in the scope of EU funded research project SPICE (https://cordis.europa.eu/project/id/870811) with the goal of investigating the relationship between art and emotions and collecting written data (User Generated Content) in the domain of arts in all the languages of the SPICE project (fi, en, es, he, it). The data was collected through a set of Google Forms (one for each language) and it was used in the project (along the other datasets collected by museums in the different project use cases) in order to train and test Emotion Detection Models within the project.<br> <br> The experiment consists of 12 artworks, chosen from a group of artworks provided by the GAM Museum of Turin (https://www.gamtorino.it/) one of the project partners. Each artwork is presented in a different section of the form; for each of the artworks, the user is asked to answer 5 open questions: </p> <p>1. What do you see in this picture? Write what strikes you most in this image. </p> <p>2. What does this artwork make you think about? Write the thoughts and memories that the picture evokes.</p> <p>3. How does this painting make you feel? Write the feelings and emotions that the picture evokes in you </p> <p>4. What title would you give to this artwork?</p> <p>5. Now choose one or more emoji to associate with your feelings looking at this artwork. You can also select "other" and insert other emojis by copying them from this link: https://emojipedia.org/</p> <p> <br> For each of the artworks, the user can decide whether to skip to the next artwork, if he does not like the one in front of him or go back to the previous artworks and modify the answers. It is not mandatory to fill all the questions for a given artwork.</p> <p>The question about emotions is left open so as not to force the person to choose emotions from a list of tags which are the tags of a model (e.g. Plutchik), but leaving him free to express the different shades of emotions that can be felt. </p> <p>Before getting to the heart of the experiment, with the artworks sections, the user is asked to leave some personal information (anonymously), to help us getting an idea of the type of users who participated in the experiment. </p> <p>The questions are: </p> <ol> <li>Age (open) </li> <li>Gender (male, female, prefer not to say, other (open)) </li> <li>How would you define your relationship with art? </li> </ol> <ul> <li>My job is related to the art world </li> <li>I am passionate about the art </li> <li>I am a little interested in art </li> <li>I am not interested in art </li> </ul> <p> 4. Do you like going to museums or art exhibitions? </p> <ul> <li>I like to visit museums frequently </li> <li>I go occasionally to museums or art exhibitions </li> <li>I rarely visit museums or art exhibitions </li> </ul> <p>---------------------</p> <p>Dataset structure:</p> <ul> <li>FI.csv: form data (personal data + open questions) in Finnish (UTF-8)</li> <li>EN.csv: form data (personal data + open questions) in English (UTF-8)</li> <li>ES.csv: form data (personal data + open questions) in Spanish (UTF-8)</li> <li>HE.csv: form data (personal data + open questions) in Hebrew (UTF-8)</li> <li>IT.csv: form data (personal data + open questions) in Italian (UTF-8)</li> <li>artworks.csv: the list of artworks including title, author, picture name (the pictures can be found in pictures.zip) and the mapping between the columns in the form data and the questions about that artwork </li> <li>pictures.zip: the jpeg of the artworks</li> </ul>
Temporal Dynamics of Emotional Music
Open the record for dataset details and reuse information.
Investigating the Effects of Embodiment on Emotional Categorization of Faces and Words in Children and Adults
<p>The three data files uploaded here contain the data used for the analyses in experiments 1a, 1b, and 2 as described in the article carrying the same title as this dataset, published in the journal Frontiers in Psychology. All analyses were carried out in SPSS version 22 as described in the published article.</p> <p>Article Abstract:</p> <p>The facial feedback hypothesis (FFH) indicates that besides being involved in the production of facial expressions, the musculature of the face also influences one’s perception of emotional stimuli. Recently, this effect has been the focus of increased scrutiny as efforts to replicate a key study with adult participants supporting this hypothesis, using the so-called “pen-in-the-mouth” task, have not been successful at several labs. Our series of experiments attempted to investigate whether the assumed embodiment effect can be reproduced in a simplified emotional categorization task for emotional faces and words. We also wanted to test whether the embodiment effect can be detected in children because it is assumed that their bodily processes are especially closely linked with their sensory and cognitive processes. Our experiments involved child and adult participants categorizing faces and words as positive or negative as quickly as possible, while inducing a positive or negative facial or bodily state (holding a straw in the mouth such that a smile or a frown was generated, or creating a positive or negative body posture). The positive or negative facial and bodily states could therefore be either congruent or incongruent with the valence of the target face and word stimuli. Our results did not show any significant differences between the congruent and incongruent conditions in either children or adults. This suggests that embodiment effects either do not significantly impact valence-based categorization or are not strong enough to be detected by our approach considering the sample size in the present study.</p>
Identification of emotional facial expressions in a lab and over the internet
<p>This dataset includes the data used for the analyses presented in the paper published (under the same title as this dataset) in the journal Psychology, Journal of the Higher School of Economics. </p> <p>Abstract of the publication:</p> <p>Collecting data over the internet is an approach that allows researchers to vastly expand the possible sample sizes of their studies, and enables the study of populations that may otherwise be difficult to access. However, to ensure that data collected over the internet is of the same level of quality as data collected in a lab, the comparability of internet-collected data with lab-collected data must first be assessed for individual areas of research and experimental approaches. To answer the question of whether internet data collection is suitable for experiments involving facial expressions, we conducted a deliberately difficult facial emotion-identification experiment where participants completed the same task either under supervision in our lab, or at an unsupervised location over the internet. Stimuli consisted of sad faces that participants were asked to identify as resembling either anger, fear, or disgust. Regardless of belonging to either the group tested in the lab or over the internet, participants showed highly similar response distributions, while differences between the groups were non-significant and of very low magnitude. We can therefore conclude from our findings that internet data collection is a viable method for experiments requiring the identification of emotional facial expressions, being able to produce similar results to those which can be obtained in a lab.</p>
Data from: Grand Theft Empathy? Evidence for the absence of effects of violent video games on empathy for pain and emotional reactivity to violence
<p><strong>Abstract:</strong></p><p>Influential accounts claim that violent video games (VVG) decrease players' emotional empathy by desensitizing them to both virtual and real-life violence. However, scientific evidence for this claim is inconclusive and controversially debated. To assess the causal effect of VVGs on the behavioral and neural correlates of empathy and emotional reactivity to violence, we conducted a prospective experimental study using functional magnetic resonance imaging (fMRI). We recruited eighty-nine male participants without prior VVG experience. Over the course of two weeks, participants played either a highly violent video game, or a non-violent version of the same game. Before and after this period, participants completed an fMRI experiment with paradigms measuring their empathy for pain and emotional reactivity to violent images. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VVGs on the behavioral and neural correlates of empathy. Moreover, participants in the VVG group were not desensitized to images of real-world violence. These results imply that short and controlled exposure to VVGs does not numb empathy nor the responses to real-world violence. We discuss the implications of our findings regarding the potential and limitations of experimental research on the causal effects of VVGs. While VVGs might not have a discernible effect on the investigated subpopulation within our carefully controlled experimental setting, our results cannot preclude that effects could be found in special vulnerable subpopulations, or in settings with higher ecological validity.<br> </p><p><strong>Dataset:</strong><br>This dataset contains the fMRI data collected for the study in the BIDS-format (https://bids.neuroimaging.io/)</p><ul><li>functional neuroimaging (*_bold.nii.gz) data of 89 human participants, collected during two tasks:<ul><li>Empathy-for-Pain paradigm (Session 1 & 2)</li><li>Emotional Reactivity paradigm (Session 2)</li></ul></li><li>associated event files (*_events.tsv) containing event onsets, durations, and behavioral covariates</li><li>metadata</li></ul><p>FMRI bold timeseries are fully preprocessed, as described in the manuscript.</p><p>Additional data, such as behavioral data in a simpler format, can be accessed on https://osf.io/yx423/</p><p> </p>
ScienceDex guides
Understand access before you commit
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.