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.
7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “Emotion Elicitation”
Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings
<p><strong>Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings</strong></p><p> </p><p>The Masked Emotion FilmClip Dataset (MEFD) stands as an avant-garde assembly of emotion-inducing video clips tailored for a unique niche - the elicitation of emotions in individuals wearing facial masks. This dataset emerges in response to the global need to understand emotional cues and expressions in the backdrop of widespread facial mask usage. Assembled by leveraging the synergies between cinematography and psychological research, MEFD serves as an invaluable trove for researchers, especially those in AI, seeking to decode emotions even when a significant portion of the face is concealed.</p><p><strong>Dataset Highlights</strong>:</p><ul><li><strong>Facial Masks</strong>: All subjects in the video clips are seen wearing facial masks, replicating real-world scenarios and augmenting the dataset's relevance.</li><li><strong>Film Titles</strong>: The title of each selected film enriching the context of the emotional narrative.</li><li><strong>Emotion Label</strong>: Clear emotion classification associated with every clip, ensuring replicability in emotional elicitation.</li><li><strong>Clip Duration</strong>: Precise duration details ensuring standardized exposure and consistent emotion elicitation.</li><li><strong>Curated with Expertise</strong>: Clips have undergone rigorous evaluation by seasoned psychologists and film critics, affirming their effectiveness in eliciting the designated emotion.</li><li><strong>Consent and Ethics</strong>: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li></ul><p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p><p>Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395</p><p>A paramount feature of MEFD is its emphasis on "key moments". These timestamps, a product of collective expertise from psychologists and film critics, guide the researcher to intervals of heightened emotional resonance within the clips, especially challenging to discern with masked faces.</p><p><strong>Key Emotional Moments within Dataset</strong>:</p><p>Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334</p><p> </p><p>-----------------</p><p>DATA STRUCTURE<br>-----------------</p><p>SADNESS_XXX.CSV<br>timestamp emotion<br>1625062890.938222 NEUTRAL --> Initial time start for the neutral video<br>1625062932.567609 SADNESS --> Initial time start for the EMOTION video</p><p><br>Notes:<br>** Subject id 15: FEAR label started to fast; Neutral data very few<br>-----------------</p><p> </p><p><i>The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.</i></p><p>MEFD is more than just a dataset; it is a testament to human resilience and adaptability. As facial masks become ubiquitous, understanding the nuances of masked emotional expressions becomes imperative. MEFD rises to this challenge, bridging gaps and pioneering a new frontier in emotion research.</p>
Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation
<p><strong>Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation</strong></p> <p><strong>Description</strong>:</p> <p>Introducing the Gilman-Adhered FilmClip Emotion Dataset (GAFED) - a cutting-edge compilation of video clips curated explicitly based on the guidelines set by Gilman et al. (2017). This dataset is meticulously structured, leveraging both the realms of film and psychological research. The objective is clear: to induce specific emotional responses with utmost precision and reproducibility. Perfectly tuned for researchers, therapists, and educators, GAFED facilitates an in-depth exploration into the human emotional spectrum using the medium of film.</p> <p><strong>Dataset Highlights</strong>:</p> <ul> <li><strong>Gilman's Guidelines</strong>: GAFED's foundation is built upon the rigorous criteria and insights provided by Gilman et al., ensuring methodological accuracy and relevance in emotional elicitation.</li> <li><strong>Film Titles</strong>: Each selected film's title provides an immersive backdrop to the emotions sought to be evoked.</li> <li><strong>Emotion Label</strong>: A focused emotional response is designated for each clip, reinforcing the consistency in elicitation.</li> <li><strong>Clip Duration</strong>: Standardized duration of every clip ensures a uniform exposure, leading to consistent response measurements.</li> <li><strong>Curated with Precision</strong>: Every film clip in GAFED has been reviewed and handpicked, echoing Gilman et al.'s principles, thereby cementing their efficacy in triggering the intended emotion.</li> </ul> <p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p> Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395 <p>The crowning feature of GAFED is its identification of "key moments". These crucial timestamps serve as a bridge between cinema and emotion, guiding researchers to intervals teeming with emotional potency.</p> <p><strong>Key Emotional Moments within Dataset</strong>:</p> Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334 <p><strong>Based on</strong>: Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6).</p> <p>GAFED isn't merely a dataset; it's an amalgamation of cinema and psychology, encapsulating the vastness of human emotion. Tailored to perfection and adhering to Gilman et al.'s insights, it stands as a beacon for researchers exploring the depths of human emotion through film.</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>
Experiential Training in Eliciting Disclosure & Emotions for Mental Health Trainees
ClinicalTrials.gov study NCT04511754. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Decoding of baby calls: can adult humans identify the eliciting situation from emotional vocalizations of preverbal infants?
Preverbal infants often vocalize in emotionally loaded situations, yet the communicative potential of these vocalizations is not well understood. The aim of our study was to assess how accurately adult listeners extract information about the eliciting situation from infant preverbal vocalizations. Vocalizations of 19 infants aged 5-10 months were recorded in 3 negative (Pain, Isolation, Demand for Food) and 3 positive (Play, Reunion, After Feeding) situations. The recordings were later rated by 333 adult listeners on the scales of emotional valence and intensity. Subsequently, the listeners assigned the eliciting situations in a forced choice task. Listeners were almost perfectly able to discriminate whether a recording came from a negative or a positive situation. Their discrimination may have been based on perceived valence as they consistently assigned higher valence when listening to positive, and lower valence when listening to negative, recordings. Ability to identify the particular situation within the negative or positive realm was substantially weaker, with only three of the six situations being discriminated above chance. The best discriminated situation, Play, was associated with high perceived intensity. The weak qualitative discrimination of negative situations seemed to be based on graded perception of negative recordings, from the most intense and unpleasant (assigned to Pain) to the least intense and least unpleasant (assigned to Demand for Food). Parenthood and younger age, but not gender of listeners, had weak positive effects on the accuracy of judgments. Our results indicate that adults almost flawlessly distinguish positive and negative infant sounds, but are rather inaccurate regarding identification of the specific needs of the infant and may normally employ other sensory channels to gain this information.
Data from: Decoding of baby calls: can adult humans identify the eliciting situation from emotional vocalizations of preverbal infants?
Open the record for dataset details and reuse information.
DEMoS: an Italian emotional speech corpus. Elicitation methods, machine learning, and perception
<p>DEMoS (Database of Elicited Mood in Speech), is a corpus of induced emotional speech in Italian. DEMoS encompasses 9,365 emotional and 332 neutral samples produced by 68 native speakers (23 females, 45 males) in seven emotional states: the 'big six' anger, sadness, happiness, fear, surprise, disgust, and the secondary emotion guilt. To get more realistic productions, instead of acted speech, DEMoS contains emotional speech elicited by combinations of Mood Induction Procedures (MIP). Three elicitation methods are presented, made up by the combination of at least three MIPs, and considering six different MIPs in total. To select samples 'typical' of each emotion, evaluation strategies based on self- and external assessment were applied. The selected part of the corpus encompasses 1,564 prototypical samples produced by 59 speakers (21 females, 38 male). DEMoS has been published in the Journal Language, Resousrces, and Evalaution.</p> <p> </p> <p>Emilia Parada-Cabaleiro, Giovanni Costantini, Anton Batliner, Maximilian Schmitt, and Björn Schuller (2019), <em>DEMoS: An Italian emotional speech corpus. Elicitation methods, machine learning, and perception</em>, Language, Resources, and Evaluation, Feb 2019. <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eCoqdGxOfRWEudjYRrxU-2BI-3D_Ru5N6PJ4ngeR7K-2Fncs2CW1jGAzl4dMvrVh77-2BVH-2B9g5urNss1KItQNXvWL1jiHKvcYDtUVs2c78DX20PMDTauCGehGiQvHdgrAknGggtu7pHINBqVKjp16-2BTn63kNrm22m52e-2FPV-2FidpRe8A-2FplLxPMV-2FjTR-2FLLIK8Wqe7u0-2BLSZ9w-2BWYtrAXRYn2lvPcjGTP1La8yiTxBuJKbHJpnNeFb6LmBIiNMmGRSZPIY0leXhyj4k07rx5cETF6n34aIQHP-2FwcafanNMN4BoA9QKhXGgFxvRgZQidsQ-2BCDbbTBL0PPjM3CgitSGk66qut9E3pd">https://rdcu.be/bn7oI</a></p> <p> </p> <p><strong>How to access DEMoS</strong></p> <p>To get access to the dataset, please send the signed End User License Agreement (EULA) when making the request. The EULA <strong>must be signed by somebody from a university holding a permanent position</strong>, typically a full professor. Note that requests without an EULA appropriately filled out, as well as those performed from a non-institutional e-mail address, will be automatically rejected. Please download the EULA from the following link:</p> <p>https://drive.google.com/file/d/1v6GaCVyNcib5v802t2uXHYOioqIkoBQ-/view?usp=share_link</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.