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1,903 results for “Perceptions”
Testing of Medtronic Percept PC with MEG phantom
<p>The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested.</p> <p>In the present study, we tested the most commonly implanted telemetric stimulator – Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system.</p> <p>The dataset and code herein make it possible to reproduce most of the figures in the paper and examine additional conditions not described in detail in the paper. The data can be useful for developing, testing and benchmarking MEG artefact removal methods.</p>
Public Transit Infrastructure and Heat Perceptions in Hot and Dry Climates (June-July, 2018; Phoenix, Arizona, USA)
Increasing the use of public transit is an important sustainability goal targeted by many cities worldwide. However, cities in hot and warming climates risk to compromise residents’ health and thermal comfort by incentivizing public transit use and, thus, subjecting them to prolonged heat exposure. This dataset contains data collected during a study on the relationships between public transit infrastructures, microclimate and heat perceptions in the hot and dry city of Phoenix, Arizona. A field campaign at six Phoenix bus stops was held between June 6 and July 27, 2018. Filed campaign consisted of surveying bus riders at bus stops and measuring microclimate variables at sun exposed and shaded locations at bus stops. Standard, advertising and art bus stop types along an arterial Phoenix road in South Mountain Village neighborhood were sampled. Standard and advertising bus stop shelters were metal with no landscaping, art stops had a larger polycarbonate canopy, integrated artwork, trees and landscaping features. Eighty-three participants filled out the survey, 241 microclimate measurements and 1003 surface temperatures at bus stops were taken. Data were collected at three intervals: 7:00-9:00am, 12:00-2:00pm, and 3:00-5:00pm. Differences between sun and shade, as well as heat perceptions were analyzed using statistical methods. The research team has found that certain infrastructure types are more effective in reducing particular microclimate variables, for instance, trees were most effective in reducing air temperature by as much as 1.3°C on average, and shade from vertical advertising sign was most effective in reducing mean radiant temperature by an average of 11°C. Many surface temperatures of sun exposed materials sampled at bus stops exceeded skin burn thresholds. Study participants perceived stops with improved infrastructure and landscaping as slightly cooler. Data collected in this study gives a glimpse of current microclimate conditions at Phoenix bus stops
Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy
<p>The uploaded dataset was used for the statistical analysis of the pre-print "Artificial Intelligence for EU Decision-Making: Effects on Citizens’ Perceptions of Input, Throughput and Output Legitimacy" (Permanent identifier: <a href="https://arxiv.org/abs/2003.11320">arXiv:2003.11320</a>)</p> <p>A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.</p>
Dataset: Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions
<p># Dataset for Paper "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions" - Rev 1#</p> <p>This is the dataset for the paper titled "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions". All mapping study data has the prefix *MAP*, while all survey data the prefix *SUR*. It has been updated for a major revision at Springer Empirical Software Engineering (Rev 1).</p> <p>In case of questions, feel free to contact the author, Grischa Liebel, ORCID: https://orcid.org/0000-0002-3884-815X, current affiliation and email: Reykjavik University, Iceland, grischal@ru.is</p> <p>## Systematic Mapping Study ##<br> The mapping study data is mainly contained in the *MAPmappingStudy.xlsx* file. Different tabs are used for the two phases: exclusion by title and abstract (tab *title_abs*), and exclusion by fulltext (tab *fulltextScreening*). The final set of papers is obtained by filtering the *fulltextScreening* tab by included papers (Column V).</p> <p>The tab *fulltextScreening* contains a number of columns named \*range (e.g., *noStudentsRange*). These columns contain the unified/categorised values for the verbatim values listed in the column with the same name without *range*. For instance, *noStudentsRange* contains the range of students in the primary study, while *noStudents* contains the actual number obtained from the studies.</p> <p>The file *MAPvenues.txt* contains the included venues in the mapping study.</p> <p>Finally, the raw search results are provided as BIB/RIS files with the prefix *MAPRAW*.</p> <p>## Survey ##<br> The survey folder contains the survey pages (named *surveyPageN.pdf*), as well as the raw data in *surveyDataAnon.xlsx*. Note that free-text answers have been aggregated, anonymised, and sorted alphabetically in individual tabs. Similarly, countries that only occur once have been changed to Do Not Disclose answers, and all answers have been sorted randomly. All questions are listed by their acronym. The corresponding questions, as well as possible answers, are described in the *QuestionKey* tab.</p>
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
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>
W4RES Needs, perceptions and challenges in RHC landscape for eight regions dataset1 2021/03/30
<p>The main idea behind the survey design was to obtain a clear identification of key factors correlated to the market uptake of RHC solutions through a gender disaggregated analytical approach in eight countries (Greece, Italy, Germany, Belgium, Denmark, Slovakia, Norway, and Bulgaria). To better address different perspectives across the quadruple helix, the survey has been designed to include questions that are specifically targeted to each group. The survey has been divided into broad sections covering the most relevant topics to be addressed.</p><p>As for the interviews, to analyze needs, perceptions and challenges of market actors and stakeholders through a gender lens, the following research questions were defined:</p><p>I. Identify needs, perceptions and challenges of market actors and stakeholders across the quadruple helix in the 8 regions regarding the uptake of RHC solutions.</p><p>II. Identify needs, perceptions and challenges of market actors and stakeholders across the quadruple helix in the 8 regions regarding the role of women in RHC.</p><p>The survey was coded on the EUSurvey software and translated in the following languages: English, German, French, Bulgarian, Danish, Greek, Italian, Dutch and Slovak. As advised by ECWT, the partner based in Norway, it was agreed that disseminating the survey in English to the potential respondents operating in Norway was an appropriate strategy and that a translation in Norwegian was not necessary.</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>
Time perception in suspenseful films
<p>Dataset of an experimental study in which participants (N = 50) watched short video clips from horror films. Participants were asked to estimate perceived suspense levels and to do a time estimation task. </p>
EXIT_Dataset_Survey on key policies and perceptions_2023_Survey data Serbia
<p>Survey data generated in WP2 of the EXIT project in the context of Serbia.The purpose of the survey was to collect data on the perceptions of policies used to address territorial inequalities in Serbia. </p>
Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation - Dataset
<p>Raw data related to the publication:</p> <p>Schülke, R., Schmitter, C. V., & Straube, B. (2023). Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation. <em>Journal of Psychiatry and Neuroscience</em>, <em>48</em>(4), E245–E254. <a href="https://doi.org/10.1503/jpn.220184">https://doi.org/10.1503/jpn.220184</a></p> <p>Variables:</p> <ul> <li>Subject</li> <li>Condition – Stimulation condition; parietal (left parietal cathodal, right parietal anodal [LPC-RPA]), frontoparietal (left frontal cathodal, right parietal anodal [LFC-­RPA]), frontal (left frontal cathodal, right frontal anodal [LFC­-RFA])</li> <li>Timepoint – Before/After (stimulation)</li> <li>Angle – in degrees</li> <li>Angle_scaled – mean-centered and scaled Angle</li> <li>Delay_ms – in milliseconds</li> <li>Delay_ms_scaled – mean-centered and scaled Delayed_ms</li> <li>Causality – causal/non-causal (judgment)</li> <li>RT – reaction time in milliseconds</li> </ul> <p>In the original version of the data, the data had been incorrectly labelled: The data actually corresponding to the LFC-RPA condition had been incorrectly labelled as LPC-RPA, and the data actually corresponding to the LPC-RPA condition had been incorrectly labelled as LFC-RPA. This has been corrected with the 04/2024 version of the dataset.</p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
AUTH-OpenDR Mixed Image Annotated Dataset for Human-centric Perception Tasks
<p>The dataset was generated through a mixed (real and synthetic) image data generation method which utilizes real background images and DL-generated human models. It contains 50000 real images depicting urban scenes, populated by synthetic human models in various positions and poses and is suitable for training/evaluating (a) pose estimation, (b) person detection, (c) identity recognition methods. Annotations for 2D bounding boxes of the depicted humans, their IDs and 2D keypoints etc are provided. The 133 3D human models, required by the method, were generated using the Pixel-aligned Implicit Function (PIFu) and full-body images of people from the Clothing Co-Parsing (CCP) dataset. As background images, a subset of the Cityscapes dataset was used. The Cityscapes license prohibits the distribution of any modified versions of itself. Thus, we provide code that can re-generate the exact same dataset, given that the Cityscapes dataset is downloaded by the website of its authors.</p> <p>Code and instructions for re-generating the dataset are provided <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation/human_dataset_generation">here</a>.</p> <p>The dataset was developed by Aristotle University of Thessaloniki (AUTH) within the H2020 OpenDR Project.</p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 1: Essays, Finland.
<p><strong>Version 1.1.</strong></p> <p><strong>Updated from </strong>https://zenodo.org/record/5517595</p> <p><strong>Changes: </strong>added .csv copy of the dataset. Clarified the README below, and added name of publishing journal. No other changes.</p> <p>Added a FEDORA project README below.</p> <p> </p> <p><strong>Description of dataset:</strong></p> <p>This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article <em>Students’ technological images of the future: implications for science and technology education, </em>accepted to be published in European Journal of Futures Research.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article.</p> <p>385 such excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. The numbering was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A-C).</p> <p>The dataset is intended for providing transparency, but it may also be used for further research. Assistance may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred.</p> <p>The related research article gives a fuller description of the dataset and analysis.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Students_images_of_technological_futures_DATA_Zenodo_csv.csv</p> <p>Students_images_of_technological_futures_DATA_Zenodo_xlsx.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/6397196</em></p>
LMMA perceptions of outcomes data
<p>Data collected in Madagascar in May and June 2017 forming part of a PhD thesis and paper on the perceptions of adopters of Locally Managed Marine Areas (LMMAs). </p>
Perceptions on the utility of community question and answer websites like Stack Overflow to software developers (Replication package)
<p>Interview Questions on the perception of the utility of CQAs like Stack Overflow to software developers. In this study, we focused on the questions highlighted in yellow.</p>
Student's logs and perceptions of an automated assessment tool in a software engineering MOOC specialization
<p>Our dataset contains students' perceptions and usage of an automated assessment tool (MOOCauto) for obtaining formative feedback in software engineering assignments that are part of a MOOC specialization at Universidad Politécnica de Madrid (Spain), delivered by the MiriadaX platform. The dataset has previously been used in a study to evaluate students' perceptions of the tool and to analyze their usage patterns using Growth Mixture Models <a href="https://www.computer.org/csdl/magazine/so/5555/01/10196480/1P9AhkBLYXK">(López-Pernas et al., 2023)</a>. The code of each of the assignments is available on Github: <a href="https://github.com/ging-moocs">https://github.com/ging-moocs</a>.</p> <p>Our dataset contains two files:</p> <h2>MOOCauto usage logs</h2> <p>The first file is called<strong> moocauto_logs.csv </strong>and it contains 9,108 anonymized logs of students' use of the automated assessment tool in the MOOC specialization assignments. The columns of the dataset are as follows:</p> <ul> <li><strong>MOOCid</strong>: Unique numeric identifier for the MOOC (1-4)</li> <li><strong>MOOC: </strong>Name of the MOOC: Frontend Development, Backend Development, Git & Github, Fullstack Development</li> <li><strong>AssignmentName</strong>: Name of the assignment.</li> <li><strong>AssignmentId</strong>: Unique identifier for each assignment (1-17)</li> <li><strong>user: </strong>Unique identifier of the student (it varies per assignment)</li> <li><strong>timestamp: </strong>Time in which the assessment was performed</li> <li><strong>score</strong>: Score obtained (0-10)</li> </ul> <h2>Students' perceptions of MOOCauto</h2> <p>The second file is called <strong>moocauto_questionnaire.csv</strong> and it contains 213 students' responses to the questionnaire conducted at the end of each MOOC in order to evaluate their opinion of the tool and perception on usefulness, ease of use, and other aspects related to the Technology Acceptance Model (TAM). The questions were as follows:</p> <ul> <li><strong>What is your general opinion of MOOCauto?</strong> (1 Horrible - 5 Excellent)</li> <li><strong>Indicate your level of agreement with the following statements </strong>(1 Strongly disagree - 5 Strongly agree) <ul> <li>MOOCauto has been easy to install</li> <li>MOOCauto has been easy to use</li> <li>The feedback provided by MOOCauto was easy to understand</li> <li>The feedback provided by MOOCauto was useful</li> <li>The feedback provided by MOOCauto helped me improve my assignments</li> <li>The documentation Of MOOCauto was useful</li> <li>MOOCauto has increased my motivation to work on the assignments</li> <li>I prefer the feedback from MOOCauto than from peer assessment</li> <li>I would like to have a bot like MOOCauto in other MOOCs</li> </ul> </li> <li><strong>How useful do you perceive the following features of MOOCauto?</strong> (1 Useless - 5 Very useful) <ul> <li>It works locally on my computer</li> <li>It allows to run the test suite as many times as I want</li> <li>It provides instantaneous feedback every time the test suite is executed</li> <li>It has documentation that explains its use and available options</li> </ul> </li> </ul>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Extended data of the project "A survey exploring biomedical editors' perceptions of editorial interventions to improve adherence to reporting guidelines"
<p>Figure S1: Survey questionnaire</p> <p>Table S2: Barriers, facilitators and possible improvements of the interventions included in the survey</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.