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35 results for “researcher perceptions”
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>
Human auditory ecology : Extending hearing research to the perception of natural soundscapes by humans in rapidly-changing environments
<p>The audiomaterial corresponding to boreal, tropical and temperate forests, desert, savannah, sub-alpine meadow, and the construction site in New York is copyrighted (license from Wild Sanctuary) and cannot be used without explicit agreement of Bernie Krause. Additional audiomaterial (urban park and street traffic in Paris, France; fast street traffic in Marseille, France; English and French speech material) may only be used with the explicit agreement of the following authors: Jérôme Sueur and Sylvain Haupert (Museum National d'Histoire Naturelle in Paris, France); Sabine Meunier (LMA/CNRS in Marseille, France); Franck Ramus (CNRS in Paris, France) (see Figure legends).</p>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
IHTApark. Multi-detailed 3D architectural model for sound perception research in Virtual Reality
<p><strong>IHTApark – Multi-detailed 3D architecture model</strong></p> <p>This dataset describes visual and acoustic 3D architectural models of the park next to the IHTA.</p> <p>Institute of Hearing Technology and Acoustics (IHTA), RWTH Aachen, 52056 Aachen, Germany</p> <p>Files are stored in FBX format for geometry, JPEG format for visual textures, and Unreal Engine for the virtual reality scenes.</p> <p><strong>VERSION 1: Visual photogrammetry + Acoustic model</strong></p> <p>As used in the publication:</p> <p>[1] Llorca-Bofí, J. and Vorländer, M. (2021). Multi-Detailed 3D Architectural Framework for Sound Perception Research in Virtual Reality. Front. Built Environ. 7:687237.doi: https://doi.org/10.3389/fbuil.2021.687237</p> <p>Data is available separately for each definition, and for each visual and acoustic cue. The level of detail for each definition is shown here:</p> <ul> <li>Visual cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> <li>Acoustic cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> </ul> <p>This version of the model includes only the modules used for the description of the referenced paper. The authors reserve the right to complete other levels of detail if future applications require them.</p> <p>An additional data file contains a unique file in [IHTApark_UnrealEngine] Unreal Engine format, with the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_UnrealEgine] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_UnrealEngine]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark</strong> map under the folder <strong>Content > Maps</strong> by double clicking on it.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p><strong>VERSION 2: Object-based visualization in three different weather conditions</strong></p> <p>As used and described in the publication:</p> <p>[2] Submitted to journal.</p> <p>The file [IHTApark_3weath_comp] Unreal Engine format contains the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_3weath_comp] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_3weath_comp]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark_warm</strong>, <strong>IHTApark_wet </strong>or<strong> IHTApark_snowy</strong> maps under the folder <strong>Content > Maps</strong> by double clicking on it to visualize each weather condition.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p>The folder [IHTApark_3weathers_audio] contains the sound signals, as .wav files, in fist order ambisonics format (B-format).</p> <p> </p>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Dataset of article: Investigating Developers' Perception on Success Factors for Research Software Development
<p>This dataset is an addendum to the article "Investigating Developers' Perception on Success Factors for Research Software Development" to provide information regarding the anonymously collected data.</p> <p> </p> <p> </p> <p> </p>
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
<p class="CxSpFirst">Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements. </p>
Löhr-Limpens, M., Göhringer, F., Schenk, T., & Hesse, C. (2019). 'Grasping and perception are both affected by irrelevant information and secondary tasks: new evidence from the Garner paradigm'. Psychological Research. DOI: 10.1007/s00426-019-01151-z
<p>This repository contains 6 data files and one readme file describing the content of the data files.</p> <p> </p>
Research data sharing in Spain: exploring determinants, practices and perceptions
<p>Responses of the Spanish survey for research data, which was carried out within the framework of the project Datasea at the beginning of 2015. The purpose was to identify the habits and current experiences of Spanish researchers in health sciences in relation to the management and sharing of raw research data. The electronic questionnaire composed of 40 questions divided into three blocks that contained questions on the following aspects: A) Personal information; B) Creation and reuse of data; C) Preservation of data.</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>
Pediatric standardized residency training faculty's perceptions of medical education research
<p><strong>Background:</strong> Rigorous medical education research (MER) conducted by faculty in pediatric standardized residency training (SRT) can contribute to the promotion of child health care. </p> <p><strong>Aim:</strong> This study aimed to assess the perceptions of pediatric SRT faculty regarding MER in Guangdong Province, China. </p> <p><strong>Methods:</strong> A questionnaire survey was conducted involving 40 pediatric SRT clinical teachers from 10 hospitals in Guangdong Province.</p> <p><strong>Results:</strong> Among the 40 teachers, 16 (40.00%) stated that they did not participate any MER activities. The main challenges they encountered in conducting MER were the lack of training (72.50%), limited time (80.00%), and lack of grants funding (60.00%). Only 10 (25.00%)teachers were reported to have received grants for MER projects. </p> <p><strong>Conclusion: </strong>The findings highlight the importance of providing support and training to pediatric SRT faculty in Guangdong Province, China, to enhance their engagement in MER. Addressing these challenges can lead to significant improvements in child heal care promotion through evidence-based educational practices. </p>
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
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Pediatric standardized residency training faculty’s perceptions of medical education research
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360° Binaural Room Impulse Response (BRIR) Database for 6DOF spatial perception research
<p>by Applied Psychoacoustics Lab, University of Huddersfield</p> <p> </p> <p>Authors: Bogdan Bacila and Hyunkook Lee</p> <p>bogdan.bacila@hud.ac.uk, h.lee@hud.ac.uk</p> <p> </p> <p><strong>Description</strong></p> <p>An open-access database for 360° binaural room impulse responses (BRIR) captured in a reverberant concert hall. Head-rotated BRIRs were acquired with 3.6° angular resolution for each of 13 different receiver positions, using a custom-made head-rotation system that was automated and integrated with the Huddersfield Acoustical Analysis Research Toolbox. The BRIRs are provided in the SOFA format. The library also contains impulse responses captured using a first-order Ambisonic microphone and an omnidirectional microphone. It is expected that the database would be useful for studying the perception of spatial attributes in a six degrees-of-freedom context.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>The impulse responses are organised into two main folders:</p> <p>* Binaural: Contains the SOFA files and MATLAB files for each position, with a 3.6° angular resolution, recorded with the Neumann KU100 binaural head.</p> <p>* FOA: Contains the First Order Ambisonics audio files in A format and B format for each individual position, recorded with an Sennheiser Ambeo microphone in an end-fire configuration. </p> <p> </p> <p><strong>Naming Convention</strong></p> <p>The files are named after their relative position on the stage and the distance from the stage:</p> <p>* C = Centre</p> <p>* L = Left</p> <p>* LW = Left Wide</p> <p> </p> <p><strong>License</strong></p> <p>This project is licensed under the CC-BY-4.0 License - see the License.txt file for details</p> <p> </p> <p><strong>Publication</strong></p> <p>This database was presented at the Audio Engineering Society 146th International Convention.</p> <p>Download link: http://www.aes.org/e-lib/browse.cfm?elib=20371</p> <p> </p> <p><strong>Referencing</strong></p> <p>If you use the database for your research, please reference it as follows.</p> <p>Bacila, B. I., & Lee, H. (2019). 360° Binaural Room Impulse Response (BRIR) Database for 6DOF Spatial Perception Research. Presented at the Audio Engineering Society Convention 146, Dublin, e-Brief 513</p>
Perceptions of the Brazilian Software Engineering Research Community About Grey Literature
<p>Replication package of the study "Perceptions of the Brazilian Software Engineering Research Community About Grey Literature" conducted with the Brazilian Software Engineering Researchers.</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
Early Childhood Caries and Health Professionals' Perception: a Qualitative Research Protocol to Assess Oral Health Stigma
ClinicalTrials.gov study NCT05284279. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Giant icons and lesser kin: Chinese public perception of panda welfare at Chengdu Research Base of Giant Panda Breeding
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Data from: Perceptions of research bronchoscopy in Malawian adults with pulmonary tuberculosis: a cross-sectional study.
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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.