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ShareScore release 0.9.0
Dataset results
33 results for “user interaction”
Usibility Test for User friendly interaction in intelligent digital assistance systems.
<p>Te Interview for usability test.</p>
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>
Data from: Trap nests for bees and wasps to analyse trophic interactions in changing environments - a systematic overview and user guide
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Perception of speaker sincerity in complex social interactions by cochlear implant users
<p><span>Understanding insincere language, for example sarcasm and teasing, is a fundamental element of communication and crucial for maintaining social relationships. This can be a challenging task for cochlear implant users (CIs) who receive degraded suprasegmental information important for perceiving a speaker's attitude. We measured perception of speaker sincerity (literal positive, literal negative, sarcasm, and teasing) in 16 adults with CIs using an established video inventory. Participants were presented with audio-only and audio-visual social interactions between two people with and without supporting verbal context. They were instructed to describe the content of the conversation and answer whether the speakers meant what they said. Results showed that subjects could not always identify speaker sincerity, even when the content of the conversation was perfectly understood. This deficit was greater for perceiving insincere relative to sincere utterances. Performance improved when additional visual cues or verbal context cues were provided. Subjects who were better at perceiving the content of the interactions in the audio-only condition benefited more from having additional visual cues for judging the speaker's sincerity, suggesting that the two modalities compete for cognitive recourses. Percentage scores for understanding the content also did not correlate with that for extracting speaker sincerity, suggesting that what was said vs. how it was said were perceived using unrelated segmental versus suprasegmental cues. Our results further showed that subjects who had access to lower-order resolved harmonic information provided by a hearing aids in the contralateral ear identified speaker sincerity better than those who used implants alone. The results suggest that measuring speech recognition alone in CI users do not fully describe the outcome and our findings stress the importance of measuring social communication functions in people with CIs.</span></p>
Data from: Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface
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Perception of speaker sincerity in complex social interactions by cochlear implant users
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How do users interact with Virtual Geographic Environments? Users' behavior evaluation in urban participatory planning [Dataset]
<p>VGEs used in the experimental study.</p>
User Experience of Multi-mode and Multitasked Extended Reality on Different Mobile Interaction Platforms
<p>Video illustrating the implementation of XR contents and mobile platforms.</p>
Data for: Evaluating motivation attributes and interaction elements in user adoption of voice intelligent assistant
<p class="MsoNormal"><span>Artificial intelligence (AI) has come to human life in various forms. As external activities decrease after COVID-19, people treat voice intelligent assistant (VIA) as a friend or secretary. This study suggests a conceptual model to identify the contributors to the continuance intention of VIA users. Data is collected from 262 users who use VIAs in their daily life for validating the model. The current research conducted partial least square structural equation modeling to validate the proposed model. The findings indicated that cabin fever syndrome significantly affects both utilitarian motivation and hedonic motivation. Loneliness in COVID-19 causes utilitarian motivation. The results verified that utilitarian motivation is the determinant of perceived usefulness, interaction, and voice attractiveness. The findings of the study figured out that hedonic motivation has a significant influence on interaction, parasocial interaction, and voice attractiveness. The analysis uncovered that interaction impacts satisfaction and continuance intention. The findings validated that voice attractiveness is significantly associated with satisfaction. Satisfaction is the determinant of continuance intention.</span></p>
User Study Data for "Proxemic Cursor Interactions for Touchless Widget Control"
<p>User study data from the Proxemic Cursor Interactions for Touchless Widget Control paper, published at the SUI 2023 conference.</p>
Interactive Telehealth for Wheelchair Users
ClinicalTrials.gov study NCT04266808. IPD Sharing: NO. Countries: 1. Publications: 0.
Data for: Evaluating motivation attributes and interaction elements in user adoption of voice intelligent assistant
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Video recordings of "Modular Tangible User Interfaces: Impact of Module Shape and Bonding Strength on Interaction"
<p>We provide the video data recorded during the experiment presented in the publication <a href="https://doi.org/10.1145/3569009.3572731">https://doi.org/10.1145/3569009.3572731</a>.</p> <p>We conducted the first study on how the bonding strength and the shape of modules impact usability during user interaction with modular tangible user interfaces (TUIs). We built six magnetic modular prototypes featuring 6x6x6mm modules with three different levels of bonding strength (low, mid, high) and two different shapes (cubes and rounded cubes). We asked participants to perform eight common tasks found in the HCI literature for (non-)modular TUIs, which are presented in section 3.3 from the publication:</p> <ol> <li>Lift</li> <li>Split one module</li> <li>Split two modules</li> <li>Split a line of modules</li> <li>Split half of the modules</li> <li>Slide parts of the interface</li> <li>Bend the interface</li> <li>Fold the interface</li> </ol> <p><br> Participants performed the manipulations on two types of configuration of modules: a flat configuration (i.e., one layer of 80 modules) or a thick configuration (i.e., two layers on 40 modules each). The video data we provide consists of recordings of the hands of the participants when they perform the tasks with each condition of prototype. The video data we provide consists of recordings of the hands of the participants when they perform the tasks with each condition of prototype.</p> <p>The procedure presented in the video recordings is as follow:<br> <br> The participant sits in front of a table with a delimited manipulation area, with an overhead camera pointing towards the manipulation area. Participants are asked to perform the experiment within the borders of the manipulation area.<br> The experimental software (not shown in the recordings, but in the publication) presents the participants with a picture of a starting State A (e.g., a rectangular block) and a target State B (e.g., the rectangular block split in two halves). Reaching state B only requires the use of one elementary manipulation. There is no indication as to how the UI was grabbed to reach State B, to avoid bias. The experimenter places the first prototype on the manipulation area, mimicking State A. The participant manipulates the prototype to reach State B. The task is repeated three times to allow exploring different strategies. After completion of the task, the participant fills a short questionnaire, which is when a mobile tablet is shown in the recordings. They are then presented with the next task to perform (i.e., new State A and B pictures) while the experimenter presents the next prototype. This procedure is repeated for each manipulation and each condition.</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.