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15 results for “User Factors”
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
Supplementary materials for User Acceptance Factors of Usage-Based Insurance
<p><strong>Overview</strong></p> <p>This is supplementary material for the paper <em>User Acceptance Factors of Usage-Based Insurance</em>.</p> <p>What is included:</p> <ul> <li>01-survey-questionnaire.pdf - questionnaire used in the user study.</li> <li>02-ubci-video-explanations.mp4 - a video with an explanation about Usage-Based Car Insurance, which we provided to participants in our user study.</li> <li>03-SEM_Iteration-1-standardized.pdf - SEM analysis of the theoretical model, including the standardized coefficient.</li> <li>04-SEM_Iteration-1-unstandardized.pdf - SEM analysis of the theoretical model, including the unstandardized coefficient.</li> <li>05-SEM_Iteration-22-standardized.pdf - SEM analysis of the refined model, including the standardized coefficient.</li> <li>06-SEM_Iteration-22-unstandardized.pdf - SEM analysis of the refined model, including the unstandardized coefficient.</li> <li>07-linear-regression.pdf - interactors / moderators analysis.</li> </ul>
Supplementary materials for User Acceptance Factors of Usage-Based Insurance
<p><strong>Overview</strong></p> <p>This is supplementary material for the paper <em>User Acceptance Factors of Usage-Based Insurance</em>. Considering that the term UBI might not be well known to the general public, we added the word “car” to the original term, referring to it in survey, video, and data analysis as Usage-Based Car Insurance (UBCI).</p> <p>What is included:</p> <ul> <li>01-online-survey.pdf - questionnaire used in the user study.</li> <li>02-ubci-video-explanations.mp4 - a video with an explanation about Usage-Based Car Insurance, which we provided to participants in our user study.</li> <li>03-SEM_Iteration-1-standardized.pdf - SEM analysis of the theoretical model, including the standardized coefficient.</li> <li>04-SEM_Iteration-1-unstandardized.pdf - SEM analysis of the theoretical model, including the unstandardized coefficient.</li> <li>05-SEM_Iteration-22-standardized.pdf - SEM analysis of the refined model, including the standardized coefficient.</li> <li>06-SEM_Iteration-22-unstandardized.pdf - SEM analysis of the refined model, including the unstandardized coefficient.</li> <li>07-linear-regression.pdf - moderators analysis.</li> </ul>
Safety and Effectiveness of Live Zoster Vaccine in Anti-Tumor Necrosis Factor (TNF) Users (VERVE Trial)
ClinicalTrials.gov study NCT02538341. IPD Sharing: YES. Countries: 1. Publications: 1.
Assessing Factors of User Participation in Social Media During Crisis Events: An Analysis of Features and Practices
<p>This data is analyzed and presented in the publication: Assessing Factors of User Participation in Social Media During Crisis Events: An Analysis of Features and Practices</p><p><strong>Abstract</strong></p><p>The relevance of employing social media in crisis scenarios has aroused interest for researchers and teams responsible for crisis management. The aim of this study is to explore the experience and abilities of social media users related to such events. For this, we conducted an analysis of the functionalities of social media used in this context, followed by a survey with users of social media. The research included the participation of 159 individuals and queried whether the participants had previously witnessed crisis events, whether they habitually share information about these events, and whether they perceived the importance of this practice. The results suggest that participants actively use social media and are inclined to share information about crisis events, provided improvements are made to the existing features. This study offers contributions toward a more profound understanding of social media tools, user interaction, and information sharing while considering the challenges found during such events.</p>
Assessing Factors of User Participation in Social Media During Crisis Events: An Analysis of Features and Practices
<p>This data is analyzed and presented in the publication: Assessing Factors of User Participation in Social Media During Crisis Events: An Analysis of Features and Practices</p> <p><strong>Abstract</strong></p> <p>The relevance of employing social media in crisis scenarios has aroused interest for researchers and teams responsible for crisis management.<br> The aim of this study is to explore the experience and abilities of social media users related to such events.<br> For this, we conducted an analysis of the functionalities of social media used in this context, followed by a survey with users of social media.<br> The research included the participation of 159 individuals and queried whether the participants had previously witnessed crisis events, whether they habitually share information about these events, and whether they perceived the importance of this practice.<br> The results suggest that participants actively use social media and are inclined to share information about crisis events, provided improvements are made to the existing features.<br> This study offers contributions toward a more profound understanding of social media tools, user interaction, and information sharing while considering the challenges found during such events.</p>
Human Factors Study to Validate the User Interface of TOBI Podhaler Using Placebo Capsules
ClinicalTrials.gov study NCT03502070. IPD Sharing: Not stated. Countries: 1. Publications: 0.
User Groups for Robustness of Meta Matrix Factorization Against Decreasing Privacy Budgets
<p>This dataset comprises a subset of rating data from five different datasets, i.e., Douban [1], Hetrec-MovieLens [2], MovieLens 1M [3], Ciao [4] and Jester [5]. Each subset represents rating data from three distinct user groups: users with few ratings (low), users with a medium amount of ratings (med) and users with lots of ratings (high). Each row in the user files includes a user's id and her number of ratings. The rows of the ratings files are in the format (user_id, item_id, rating). For more details, we refer to our publication in <a href="https://rd.springer.com/chapter/10.1007/978-3-030-72240-1_8">https://rd.springer.com/chapter/10.1007/978-3-030-72240-1_8</a>.</p> <p>Douban<br> * 375 users (i.e., 125 users per user group)<br> * 32,191 items<br> * 266,517 ratings</p> <p>Hetrec-MovieLens <br> * 318 users (i.e., 106 users per user group)<br> * 9,553 items<br> * 207,943 ratings</p> <p>MovieLens 1M<br> * 906 users (i.e., 302 users per user group)<br> * 3,613 items<br> * 275,119 ratings</p> <p>Ciao<br> * 1,107 users (i.e., 369 users per user group)<br> * 60,132 items<br> * 107,807 ratings</p> <p>Jester<br> * 11,013 users (i.e., 3,671 per user group)<br> * 100 items<br> * 618768 ratings</p> <p>The python code for generating and utilizing this dataset can be found in https://github.com/pmuellner/RobustnessOfMetaMF.</p> <p>This work is supported by the H2020 project TRUSTS (GA: 871481) and the "DDAI'' COMET Module within the COMET – Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Programme is managed by FFG.</p> <p>[1] Hu, L., Sun, A., Liu, Y.: Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In: SIGIR’14 (2014)<br> [2] Cantador, I., Brusilovsky, P., Kuflik, T.: Second international workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In: RecSys’11(2011)<br> [3] Harper, F. M., Konstan, J. A.: The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS) 5(4), 1–19 (2015)<br> [4] Guo, G., Zhang, J., Thalmann, D., Yorke-Smith, N.: Etaf: An extended trust antecedents framework for trust prediction. In: ASONAM’14 (2014)<br> [5] Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)</p>
Assessing Factors of User Participation in Social Media During Crisis Events: An Analysis of Features and Practices
<p>This data is analyzed and presented in the publication: Assessing Factors of User Participation in Social Media During Crisis Events: An Analysis of Features and Practices</p><p><strong>Abstract</strong></p><p>The relevance of employing social media in crisis scenarios has aroused interest for researchers and teams responsible for crisis management. The aim of this study is to explore the experience and abilities of social media users related to such events. For this, we conducted an analysis of the functionalities of social media used in this context, followed by a survey with users of social media. The research included the participation of 159 individuals and queried whether the participants had previously witnessed crisis events, whether they habitually share information about these events, and whether they perceived the importance of this practice. The results suggest that participants actively use social media and are inclined to share information about crisis events, provided improvements are made to the existing features. This study offers contributions toward a more profound understanding of social media tools, user interaction, and information sharing while considering the challenges found during such events.</p>
Dataset Artikel Ilmiah "ANALYZING THE FACTORS OF USER INTENTION IN ADOPTING SAVINGS MANAGEMENT WITHIN THE DIGITAL BANK"
<p>This dataset served for research puprosed with title "ANALYZING THE FACTORS OF USER INTENTION IN ADOPTING SAVINGS MANAGEMENT WITHIN THE DIGITAL BANK"</p>
Non-vaccination Factors Against Hepatitis B Virus in Drug Users
ClinicalTrials.gov study NCT03817697. IPD Sharing: NO. Countries: 1. Publications: 0.
An Exploratory Study of Genetic and Clinical Factors for Serious Skin Reactions Among Users of Eslicarbazepine Acetate
ClinicalTrials.gov study NCT02520557. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Factors to Affect the Provision of Health Data of Users Through PHR App
ClinicalTrials.gov study NCT05036382. IPD Sharing: NO. Countries: 1. Publications: 0.
Back Pain Prevalence in Wheelchair Users and Associated Risk Factors
ClinicalTrials.gov study NCT00477568. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Observational Study of Risk Factors for Peripheral Artery Disease in Bathroom Scale Withings Body Cardio Users
ClinicalTrials.gov study NCT07082517. IPD Sharing: NO. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.