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36 results for “material perception”
Thermal Comfort and Perception of Different Materials for Tabletops: Datasets, Processing Code, and Supplementary Tables.
<p>In this data repository, data related to the study <em>Thermal Comfort and Perception of Different Materials for Tabletops </em> is deposited, and includes datasets, processing code, and supplementary tables.</p> <p> </p>
Supplementary Material for the paper "Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells"
<p>Supplementary material for the paper "Daniel Oliveira; Wesley K. G. Assunção; Alessandro Garcia; Baldoino Fonseca; and Márcio Ribeiro. Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells. In: Empirical Software Engineering. 2022. Springer."</p>
Supplementary Material for Work Practices and Perceptions from Women Core Developers in OSS Communities
<p>Supplementary Material for Work Practices and Perceptions from Women Core Developers in OSS Communities which allows to reproduce and extend the results presented in the paper study.</p>
The Influence of Optical Material Properties on the Perception of Liquids
<p>Dataset relative to the following publication:</p> <p>Jan Jaap R. van Assen, Roland W. Fleming (2016). Influence of optical material properties on the perception of liquids. Journal of Vision, 16(15):12, 1–20. doi: 10.1167/16.15.12.</p> <p>One zip file contains the datasets of the various experiments.</p> <p>The second zip file contains the liquid stimuli used during the experiments.</p>
Supplementary Material for ICT Practitioners' Perception of Working from Home During the Covid-19 Pandemic: Exploring Gender Differences
<p>Supplementary Material for the paper <em>ICT Practitioners' Perception of Working from Home During the Covid-19 Pandemic: Exploring Gender Differences. </em></p> <p>The .<em>pdf</em> file contains the form questions used to conduct the survey and the other files contain responses from survey participants.</p>
Supplementary material related to the thesis "Perception of airborne sounds and vibrations in crocodiles"
<p>This repository contains all datasets, statistical codes and videos examples for the 4 different studies conducted during my thesis. </p>
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
Supplementary Material for the Paper "Characterizing Software Developers by Perceptions of Productivity"
<p>Contains the survey questions and preprint for the paper "Characterizing Software Developers by Perceptions of Productivity" submitted to ESEM'17. </p> <p>Please contact Thomas Zimmermann (tzimmer@microsoft.com) in case you have any questions.</p> <p> </p> <p><strong>Paper Abstract:</strong></p> <p>Understanding developer productivity is important to deliver software on time and at reasonable cost. Yet, there are numerous definitions of productivity and, as previous research found, productivity means different things to different developers. In this paper, we analyze the variation in productivity perceptions based on an online survey with 413 professional software developers at Microsoft. Through a cluster analysis, we identify and describe six groups of developers with similar perceptions of productivity: social, lone, focused, balanced, leading, and goal-oriented developers. We discuss design implications of these clusters for tools to support developers’ productivity.</p>
Empirical research in software architecture - perceptions of the community (supplementary material)
<p>This repository contains supplementary material for the manuscript "Empirical research in software architecture - perceptions of the community" published in the Journal of Systems and Software (<a href="https://doi.org/10.1016/j.jss.2023.111684">https://doi.org/10.1016/j.jss.2023.111684</a>):</p> <ul> <li>ICSA_papers.xlsx: This file contains the papers published at ICSA 2017-2021.</li> <li>invited_PC_members.xlsx: This file contains all names of PC members of ECSA, ICSA, QoSA, CBSE and WICSA for all instances of these conferences. This information was collected in June 2017 from the publicly available websites of these conferences as well as the publicly available online profiles of PC members.</li> <li>protocol.pdf: This files contains a summary of the survey protocol.</li> <li>responses.xls: This file contains all the responses of the survey from all respondents for all questions.</li> </ul>
Supplementary Material for Men's Perceptions of Gender Inequality in Software Engineering
<p><strong>README</strong></p> <p>This repository contains supplementary materials for the paper titled "<em>Do you see what happens around you? Men's Perceptions of Gender Inequality in Software Engineering".</em></p> <p>The data in this repository originates from a survey conducted with men technology practitioners, focusing on their experiences within the industry and their perceptions of gender inequality within their teams and workplaces.</p> <p>The survey was administered using Microsoft Forms, allowing practitioners to respond to the questionnaire asynchronously, anonymously, and without supervision.</p> <p>The organization of this repository is as follows:</p> <ol> <li><em><code>charts.Rmd</code>:</em> This file contains R scripts used to generate the figures that present quantitative data in the manuscript.</li> <li><em><code>Q<strong>x</strong>.png</code></em>: These images are the figures generated by the <code>charts.Rmd</code> script.</li> <li><em><code>responses.csv</code></em>: This file contains complete and detailed responses to the questionnaire submitted by participants.</li> <li><em><code>responses_coding.xlsx</code></em>: Within this sheet, you will find the results of the coding process conducted using the Grounded Theory methodology.</li> <li><em><code>survey_questions_english.pdf</code></em>: This document contains a faithful translation of the questionnaire sent to the participants.</li> </ol> <p> </p>
Learner Perceptions on Gamifying Active Video Watching Platforms (supplementary material)
<p>This repository contains supplementary material for "Learner Perceptions on Gamifying Active Video Watching Platforms":</p> <ul> <li>Questionnaire.pdf: PDF file containing the survey questions on motivation, experience and perception on gamification.</li> <li>Perception_on_gamification.xlsx: Raw dataset of the survey.</li> </ul>
Video Editing Materials for Human Perceptions of a Curious Robot that Performs Off-Task Actions
<p>Video footage, editing timelines and compositing resources for the user study described in the HRI 2020 paper "Human Perceptions of a Curious Robot that Performs Off-Task Actions." Adobe Premiere 14 (CC 2019) or greater and a matched release of Adobe After Effects are required to render the clips.</p>
Expectations affect the perception of material properties
<p>Complete stimulus set: Expectations affect the perception of material properties</p>
SELECTIVELY MANIPULATING SOFTNESS PERCEPTION OF MATERIALS THROUGH SOUND SYMBOLISM
<p>Cross-modal interactions between auditory and haptic perception manifest themselves in language, such as sound symbolic words: crunch, splash, and creak. Several studies have shown strong associations between sound symbolic words, shapes (e.g., Bouba/Kiki effect), and materials. Here, we identified these material associations in Turkish sound symbolic words and then tested for their effect on softness perception. First, we used a rating task in a semantic differentiation method to extract the perceived softness dimensions from words and materials. We then tested whether Turkish onomatopoeic words can be used to manipulate the perceived softness of everyday materials such as honey, silk, or sand across different dimensions of softness. In the first preliminary study, we used 40 material videos and 29 adjectives in a rating task with a semantic differentiation method to extract the main softness dimensions. A principal component analysis revealed 7 softness components, including Deformability, Viscosity, Surface Softness, and Granularity, in line with the literature. The second preliminary study used 47 Turkish onomatopoeic words and 31 adjectives in the same rating task. Again, the findings aligned with the literature, revealing dimensions such as Fluidity, Granularity, and Surface Softness. However, no factors related to Deformability were found due to the absence of sound symbolic words in this category. Next, we paired the onomatopoeic words and material videos based on their associations with each softness dimension. We conducted a new rating task, synchronously presenting material videos and spoken onomatopoeic words. We hypothesized that congruent word-video pairs would produce significantly higher ratings for dimension-related adjectives, while incongruent word-video pairs would decrease these ratings, and the ratings of unrelated adjectives would remain the same. Our results revealed that onomatopoeic words selectively alter the perceived material qualities, providing evidence and insight into the cross-modality of perceived softness.</p>
Supplementary materials for "The values of public libraries: a systematic review of empirical studies of stakeholder perceptions"
<p>Supplementary materials for "The values of public libraries: a systematic review of empirical studies of stakeholder perceptions"</p> <p>https://www.emerald.com/insight/0022-0418.htm</p>
Data to "Distinguishing mirror from glass: A 'big data' approach to material perception"
<p>This record contains images, models, and analysis scripts written in MATLAB.</p> <p>Tamura, Prokott, Fleming. "Distinguishing mirror from glass: A ‘big data’ approach to material perception." in preparation.</p>
Supplementary materials for the paper "Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward"
<p>This is supplementary materials for the paper "Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward". Please refer to README.txt for more information.</p>
Supplementary material 1 from: Ngoute CO, Hunter D, Lecoq M (2021) Perception and knowledge of grasshoppers among indigenous communities in tropical forest areas of southern Cameroon: Ecosystem conservation, food security, and health. Journal of Orthoptera Research 30(2): 117-130. https://doi.org/10.3897/jor.30.64266
Supplementary material 1 from: Ngoute CO, Hunter D, Lecoq M (2021) Perception and knowledge of grasshoppers among indigenous communities in tropical forest areas of southern Cameroon: Ecosystem conservation, food security, and health. Journal of Orthoptera Research 30(2): 117-130. https://doi.org/10.3897/jor.30.64266
Supplementary material 2 from: Höbart R, Schindler S, Essl F (2020) Perceptions of alien plants and animals and acceptance of control methods among different societal groups. NeoBiota 58: 33-54. https://doi.org/10.3897/neobiota.58.51522
Table S1. Overview on demographic data of survey respondents
Supplementary material 1 from: Höbart R, Schindler S, Essl F (2020) Perceptions of alien plants and animals and acceptance of control methods among different societal groups. NeoBiota 58: 33-54. https://doi.org/10.3897/neobiota.58.51522
Text S1, S2
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)
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.