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.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “multimodal interaction”
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Toward multimodal information and AI interaction: a quasi-experiment with ChatGPT
<p>The development of argumentative text and information comprehension (CoI) skills related to the critical reconstruction of meaning (CT) is crucial in undergraduate education. Especially now in the era of social media and AI-mediated information. Generative AI aids in information creation, but its unconscious use can complicate complex information navigation. Argument maps (AM), commonly used for analyzing analog and static texts, can help visualize, understand, and rework multimodal and dynamic arguments and information.</p> <p>Stemming from the Vygotskian idea, our study used a design-based research approach on the use of AMs and ChatGPT as socio-technical artifacts to stimulate and support the understanding of information (CoI) and thus the development of critical thinking (CT). The workshop introduced the multimodal element through a 3-group quasi-experiment. The first group dealt with fully analog texts, the second group used maps with multimodal textual modes, and the third group only interacted with ChatGPT. The research focused on comparing the three groups and focusing on the two experimental groups (experimental macro-focus). </p> <p>The research had three main objectives: 1) to test whether AMs improved students' CoI enhancement and critical processing (CT); 2) to determine whether interaction with ChatGPT supported information reprocessing and critical construction of opinions and assessment tools; and 3) to determine whether interaction with ChatGPT alone, without AMs, still fostered greater integration of information and viewpoints.</p> <p>Our preliminary analysis showed that AMs improved students' CoI and CT, especially when exposed to multimodal information. ChatGPT interaction increased critical reflection and awareness of AI's role in education. Students using only ChatGPT performed well in argumentative reworking, suggesting that interaction with the chatbot can be effective. However, integrating AMs and ChatGPT could provide optimal support for comprehension and critical thinking skills.</p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:</p> <p>1. Comprehension of Text and AMs Results - Arg_Map.xlsx</p> <p>2. Critical Thinking level - CriThink.xlsx</p> <p>3. Descriptive and Inferential Statistics Comprehension and Critical Thinking - Preliminary Analysis.R</p> <p>4. Elaboration and Integration Opinion - Opi_G1.xlsx; Opi_G2.xlsx & Opi_G3.xlsx</p> <p>5. Descriptive and Inferential Statistics Opinion level - Preliminary Analysis_opi.R</p> <p>6. Sentiment Analysis - Sentiment Analysis.R</p> <p>7. Vocabulary Frequent words - Vocabulary.csv</p> <p>8. Codebook qualitative Analysis with Nvivo (Codebook.xlsx)</p> <p>9. Results Nvivo Analysis G1 & G2 - Codebook-ChatGPT_G1&G2.docx</p> <p> </p> <p>Any comments or improvements are welcome!</p>
Light environment interacts with visual displays in a species-specific manner in multimodal signaling wolf spiders
<p>Light availability is highly variable, yet predictable, over various timescales and the light environment is expected to play an important role in the evolution of visual signals. Courtship displays within the wolf spider genus Schizocosa always involve the use of substrate borne vibrations, however, there is substantial variation between species in the use of visual displays. We assessed the impact of light intensity on the courtship of four species of Schizocosa that vary in their use of visual signals during courtship. To examine the effects of the light environment on mating success and courtship effort in each species, we ran behavioral trials at three light intensities (bright, dim and dark). We also examined each species' circadian activity patterns. We found multiple effects of the light environment and these varied between species. Circadian activity patterns also differed between species and sexes. Our results suggest that dim-light might favor more conspicuous visual displays, while less conspicuous displays might be favored in bright light conditions. Additionally, we found evidence for light-dependent changes in selection on male traits, illustrating that short-term changes in light intensity have the potential for strong effects on the dynamics of sexual selection.</p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
HiCube: Interactive visualization of multiscale and multimodal Hi-C and 3D genome data
<p>Test dataset for HiCube.</p> <p>HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and three-dimensional (3D) genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function.</p> <p>HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube).</p>
Light environment interacts with visual displays in a species-specific manner in multimodal signaling wolf spiders
Open the record for dataset details and reuse information.
Material Suplementar - Identifying Requirements for a Multimodal User Experience Evaluation Framework for Interactive Web Systems
<p>Esta planilha contém o detalhamento do processo de codificação realizado para a identificação dos requisitos apresentado no artigo "<em>Identifying Requirements for a Multimodal User Experience Evaluation Framework for Interactive Web Systems</em>". Este artigo está submetido no XXIX Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia).</p> <p>A primeira aba da planilha contém os trechos dos artigos que geraram os códigos e os respectivos requisitos. A segunda aba mostra o agrupamento dos códigos similares para definir o conjunto de requisitos.</p>
Multimodal Imaging Assessment of Chronic Kidney Disease Patients at Different Stages From a Cardio-Renal Interaction Perspective
ClinicalTrials.gov study NCT07107919. IPD Sharing: NO. Countries: 1. Publications: 0.
Multimodal Neurobiological Approaches to Explore the Gene-Environment Interactions in ADHD
ClinicalTrials.gov study NCT04571125. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of Intra- and Interpersonal Multimodal Synchronizations in a Social Interaction in Individuals with a Diagnosis of Schizophrenia.
ClinicalTrials.gov study NCT06484387. IPD Sharing: NO. Countries: 1. Publications: 0.
IndEmoVis: An Indian Multimodal Audio-Visual Emotion Dataset for Conversational Interactions
<p>The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The data collection process involved conversational interactions between pairs of participants within a controlled environment. The recordings captured a combination of spontaneous and guided conversations. The dataset consists of 45 recordings of guided conversations and 77 recordings of natural conversations, resulting in a total of 216 video clips portraying various emotions from the guided interactions and 379 video clips displaying emotions from the natural conversations. To facilitate further analysis and research, the video clips were further processed to extract image frames. From each clip, the frame with the highest intensity, capturing the peak expression of the corresponding emotion, was selected for subsequent analysis. These peak images serve as representative snapshots of the emotional expressions and can be used for facial emotion recognition tasks. IndEmoVis dataset is annotated for six basic emotion categories (Happiness, Sadness, Surprise, Disgust, Anger, Fear), along with complex emotion categories of Awe and Sympathy, and a Neutral emotion class. Additionally, the dataset is annotated for the presence of braces, spectacles, and prominent hand gestures, providing valuable context for emotion analysis. Furthermore, intensity and confidence levels are annotated on a scale of 0 to 5 and 0 to 7, respectively, by the clinical psychologist to further enrich the dataset with nuanced emotional expressions.</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.