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7 results for “multimodal information”
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>
Supplementary Information: Multimodal binding and inhibition of bacterial ribosomes by the 2 antimicrobial peptides Api137 and Api88
<div> <div>This dataset contains important data files for the MD simulation that are part of this publication.</div> <div> </div> <div>The "simulations" directory contains Gromacs parameter files (.mdp) and the run input files (.tpr) as well as the final coordinate files (.gro) of each individual production simulation.</div> <div> </div> <div>The directory "figure3" contains the raw data used to create Figure 3 in the manuscript.</div> <div> </div> <div>The subdirectory "a" contains the data for the PCA projection plot in subfigure 3a. It includes projections of the simulation ensembles of Api88 conformation I-III on to the two dominant conformational modes (.xvg) and the respective extreme conformations (.pdb). The projections of the three initial models and the optimized structure set are also included.</div> <div> </div> <div>Subdirectory "b" contains a numpy array (.npy) with the data for the correlation heatmap in subfigure 3b.</div> <div> </div> <div>Subdirectory "c" contains the results of several correlation-optimization searches. Each directory "N#_maps", where # is to be replaced by the number of structures in the set, contains the search results for N correlation-optimized structures in the Api88 trajectories in the form of a pickled python dictionary (state.pkl). The dictionary has the following keys:</div> <ul> <li>used: Already used sets of MD structures (frozenset)</li> <li>selection: Structure set selected in the last iteration (set)</li> <li>weights: weights of each structure in the selected structure set (numpy array)</li> <li>iteration: Counter of the last iteration (int)</li> </ul> <div> </div> <div>The directory "supplentary_figure_correlation_time" contains the data for a plot of the optimized correlation coefficient as a function of simulation time. The results of the optimization algorithms (as pickled python objects) are included in the subdirectories with the associated trajectory length as a name.</div> </div> <p> </p>
Prediction of Coronary Artery Disease Based on Multimodal, Non-contact Information With Artificial Intelligence
ClinicalTrials.gov study NCT06092801. IPD Sharing: NO. Countries: 1. Publications: 1.
Study on Adaptive Radiotherapy and Multimodal Information of Cervical Cancer Assisted by Artificial Intelligence
ClinicalTrials.gov study NCT04022018. IPD Sharing: NO. Countries: 1. Publications: 24.
Multimodal Magnetoencephalography and Electroencephalography Exploration of the Acute Effects of THC Exposure on Neural Noise and Information Transmission Within Working Memory Networks
ClinicalTrials.gov study NCT05641766. IPD Sharing: NO. Countries: 1. Publications: 0.
Multimodal Tongue-Pulse Information Fusion for Syndrome Diagnosis and Cohort Study in Children With Asthma
ClinicalTrials.gov study NCT07383883. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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)
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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.