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1,617 results for “users”
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
Competence Centres and User Support Centres Landscaping Results
<p>The dataset contains the data collected from the landscaping activity related to the Competence Centre and user support network for the D7.1 Report on Competence Centres landscape and user support activities.</p>
L3Pilot Global User Acceptance Survey, Second Phase Data
<p>The research leading to these results received funding from the European Commission Horizon 2020 programme under the project L3Pilot (L3Pilot.eu), grant agreement number 723051. The L3Pilot Global User Acceptance Survey investigated the acceptance of SAE Level 3 (L3) conditionally automated cars. Survey data was collected in two phases. This dataset contains the data from the second phase of the survey with responses collected from 9 countries on five continents. This document contains information about the survey methodology and coding of the variables. For a detailed description of the first and second phase survey methodology, please consult L3Pilot deliverable D7.1 ‘Annual quantitative survey about user acceptance towards ADAS and vehicle automation’ by Nordhoff et al. (2021).</p> <p>If you use the dataset, please cite it as: L3Pilot (2023). L3Pilot Global User Acceptance Survey, Second Phase Data. <a href="https://doi.org/10.5281/zenodo.8389718">https://doi.org/10.5281/zenodo.8389718</a></p> <p>For further information, please contact: <a href="mailto:user-survey@eict.de">user-survey@eict.de</a></p>
The User Interface and Functionality Charts of Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This spreadsheet includes data related to user interface and functionality charts of Erkki Kurenniemi's electronic musical instruments. Data covers only musical instruments; not studio equipment. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by the author. The data is visualized with a video published in https://vimeo.com/375784663</p> <p>PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p> <p>The outlining of charts is based on previous research on DMIs, e.g. by</p> <p>Birnbaum, D., Fiebrink, R., Malloch, J., & Wanderley, M. M. Towards a dimension space for musical devices. <em>Proceedings of the 2005 Conference on New Interfaces for Musical Expression, </em>192-195.</p> <p>Magnusson, T. An Epistemic Dimension Space for Musical Devices. <em>Proceedings of the 2010 Conference on New Interfaces for Musical Expression, </em>43-46.</p> <p>Wanderley, Mortensen M. 2002. Evaluation of input devices for musical expression: Borrowing tools from HCI.<em> Computer Music Journal, </em><em>26</em>(3), 62-76.</p>
Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project
<p>This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files: </p> <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul> <p> </p> <p> </p>
User Experience Optimization Experiment Simulations
<p># The `uxo_sim` Package</p> <p>A package for simulations of data matching industry UX optimization experiments, as discussed in:</p> <p>```<br> @article{van_adelsberg_modeling_2019,<br> title = {Modeling {A}/{B} {Test} {Data} is {Hard}: {Effects} of {Overdispersion}, {RandomWalks}, and {Cointegration}},<br> language = {en},<br> journal = {NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy},<br> author = {van Adelsberg, Matthew and Sweeney, Mackenzie},<br> month = dec,<br> year = {2019}<br> }<br> ```</p> <p>The code for running the simulations is included, along with figures and CSV files for each of three specific simulation runs that are used in a publication currently under review for ICML 2020.</p> <p>## Packages:</p> <p>1. `data`: code for running the simulations to produce datasets<br> 2. `viz`: code for visualizing the simulation outputs</p> <p>## Scripts:</p> <p>1. `save_datasets`: save CSV for each simulated dataset in the `inventory`<br> 2. `save_figs`: save PNG figure for each simulated dataset in `plots`</p> <p>## Simulation Datasets:</p> <p>### `fixed_effects_od_20_21_seed27`</p> <p>Data is simulated from a beta-binomial distribution with overdispersion parameter `gamma=0.01` for each of the two treatments and rates `theta=0.20` and `theta=0.21`. This corresponds to beta distribution parameters `alpha, beta = 19.8, 79.2` and `alpha, beta = 20.79, 78.21`.</p> <p>### `drift_down_then_up`</p> <p>Data is simulated from a beta-binomial distribution with overdispersion parameter `gamma=0.01` for each of the two treatments. The rates start at `theta=0.20` and `theta=0.21` and then:</p> <p>1. decrease by 0.005 each day for 20 days<br> 2. increase by 0.005 each day for 30 days<br> 3. stay constant for 10 days</p> <p>The corresponding beta distribution parameters on each day are a function of `theta, gamma` and can be obtained via this function (implemented in `uxo_sims.data.simulations`:<br> ```python<br> def alpha_beta_from_gamma_theta(gamma, theta):<br> virtual_sample_size = 1 / gamma - 1<br> alpha = theta * virtual_sample_size<br> beta = virtual_sample_size - alpha<br> return alpha, beta<br> ```</p> <p><br> ### `arm_addition`</p> <p>Data is simulated from a beta-binomial distribution with overdispersion parameter `gamma=0.01` for each of the two treatments. The rates start at `theta=0.10` and `theta=0.11` and increase by 0.005 each day for 40 days. The corresponding beta distribution parameters can be obtained with the same function as indicated in `drift_down_then_up`.</p>
Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures (Dataset)
<p>The accompanying dataset and code for the ICRA 2020 publication:</p> <p>B. Gromov, J. Guzzi, L. Gambardella, and A. Giusti, "Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures," in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020.</p> <p>The dataset contains a log of user actions and states of the system collected during the user study. The subjects had to fly a nano quadcopter (Bitcraze Crazyflie 2.0) between three targets placed at different heights by using a conventional joystick interface (Logitech F710) and pointing. The pointing is reconstructed using an inertial sensor (mbientlab MetaWearR+) placed on the user's wrist.</p>
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
<p>This data entry contains <strong>annotated mouse data from the <a href="https://www.nature.com/articles/s41593-018-0209-y">DeepLabCut Nature Neuroscience paper</a></strong>.</p> <p>This data entry contains a public release of annotated mouse data from the DeepLabCut paper. The trail-tracking behavior is part of an investigation into odor guided navigation, where one or multiple wildtype (C57BL/6J) mice are running on a paper spool and following odor trails. These experiments were carried out by Alexander Mathis & Mackenzie Mathis in the Murthy lab at Harvard University. </p> <p>Data was recorded by two different cameras (640×480 pixels with Point Grey Firefly (FMVU-03MTM-CS), and at approximately 1,700×1,200 pixels with Grasshopper 3 4.1MP Mono USB3 Vision (CMOSIS CMV4000-3E12)) at 30 Hz. The latter images were cropped around mice to generate images that are approximately 800×800. </p> <p>Here we share 1066, frames from multiple experimental sessions observing 7 different mice. Pranav Mamidanna labeled the snout, the tip of the left and right ear as well as the base of the tail in the example images. The data is organized in <a href="https://www.nature.com/articles/s41596-019-0176-0">DeepLabCut 2.0 project structure</a> with images and annotations in the labeled-data folder. The names are pseudocodes indicating mouse id and session id, e.g. m4s1 = mouse 4 session 1.</p> <p>Code for loading, visualizing & training deep neural networks available at <a href="http://https://github.com/DeepLabCut/DeepLabCut"> https://github.com/DeepLabCut/DeepLabCut</a>.</p>
User requirements of Big Earth Data - Survey 2019
<p>The survey was conducted between November 2018 and May 2019 with the aim to find out how users working with large volumes of environmental data interact with data, what challenges they face and how they would like to use cloud-based data services in the future.</p> <p>The term Big Earth Data in this context refers to digital information about Earth, including observations, imagery, derived higher-level products, forecasts and analyses produced by computer models.</p> <p>The survey was conducted in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF) and as part of a PhD thesis on "Big Data technologies for environmental and climate data" at University of Marburg, Germany.</p> <p>The results are published in form of two articles:</p> <ul> <li>Wagemann, J., Siemen, S., Seeger, B. and J. Bendix (2021): Users of open Big Earth data - An analysis of the current state. Computers and Geosciences 2021. <a href="https://doi.org/10.1016/j.cageo.2021.104916">doi:10.1016/j.cageo.2021.104916</a></li> <li>Wagemann, J. Siemen, S., Seeger, B. and J. Bendix (2021): A user perspective on future cloud-based services for Big Earth data. International Journal of Digital Earth 2021. doi: <a href="http://doi.org/10.1080/17538947.2021.1982031">10.1080/17538947.2021.1982031</a></li> </ul> <p> </p>
UnityMol demo movie showing custom user-added menus
<p>This video provides more detailed supportive information about using Unitymol.</p> <p> </p> <p>1) start up UnityMol</p> <p>2) activate the functionality to remote control Unitymol</p> <p>3) edit the provided example script menu-spike1.py to include the right filepath</p> <p>4) execute menu-spike1.py with python</p> <p>5) first there is only one button, allowing you to load the scene</p> <p>6) once loaded, several customized views are available through dedicated buttons</p>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 671,304 tweet-URL pairs (<em>tweets_2014_sample_6271_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,271 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul>
Social sensing of urban land use based on analysis of Twitter users' mobility patterns
<p>A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file. </p>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 605,080 tweet-URL pairs (<em>tweets_2014_sample_6694_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,694 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul> <p>In addition, the following rankings (based on the odds ratio) of domains, hosts, and URLs that appear in both the researcher dataset and the sample are included:</p> <ul> <li><em>domains_by_odds_ratio.tsv.bz2</em> - a ranking of 61,860 domains,</li> <li><em>hosts_by_odds_ratio.tsv.bz2</em> - a ranking of 80,384 hosts,</li> <li><em>publisher_domains_by_odds_ratio.tsv.bz2</em> - a ranking of 924 publisher domains,</li> <li><em>publisher_urls_by_odds_ratio.tsv.bz2</em> - a ranking of 4,227 publisher URLs.</li> </ul>
Audio Commons WP2 - Responses to user survey (Deliverable 2.1)
<p>These are the responses gathered from the user survey described in Deliverable 2.1 of the Audio Commons project.</p> <p>The survey contained 24 questions asking creatives working in music industry about various subjects like demographics, workflows they use and metadata they would like to use when searching for new audio content on the Web.</p> <p>The file formResponses.csv contains the actual responses, column names in the first row, fields separated by commas and enclosed in double quotes if needed (e.g., a comma was in the value).</p> <p>The Timestamp column contains the response timestamp, in DD/MM/YYYY hh:mm:ss format. </p> <p>Each other column is named as one of the survey questions and contain the corresponding responses.</p> <p>For the relevant context, please check the Audio Commons web page (http://www.audiocommons.org/) and specifically Deliverable 2.1.</p>
162 Human Error Descriptions and Categorizations from a User Study
<p><i><strong>Software Engineers' Human Errors</strong></i></p><p>This dataset contains descriptions of 162 human errors experienced by software engineering students during a user study described in the following publication:</p><ul><li>Benjamin S. Meyers and Andrew Meneely. Taxonomy-Based Human Error Assessment for Senior Software Engineering Students. Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium. Forthcoming in 2024.</li></ul><p><i><strong>Included Files</strong></i></p><p>The "experienced_human_errors.csv" file contains a dataset of 162 human errors experienced during our user study. Participants documented their human errors in a Google Form with 8 questions.</p><p><i><strong>CSV Fields</strong></i></p><ul><li><strong>PARTICIPANT</strong>: Anonymous participant ID.</li><li><strong>INTERVIEW_DATE</strong>: Date of interview discussing human error.</li><li><strong>ID</strong>: Unique ID for experienced human error. Prefixed with "P1" for Phase 1 or "P2" for Phase 2.</li><li><strong>FINAL_CATEGORIZATION</strong>: Agreed upon T.H.E.S.E. categorization following discussion with interview facilitator.</li><li><strong>QUESTION_1</strong>: Anonymized participant answer to Question 1.</li><li><strong>QUESTION_2</strong>: Anonymized participant answer to Question 2.</li><li><strong>QUESTION_3</strong>: Anonymized participant answer to Question 3.</li><li><strong>QUESTION_4</strong>: Anonymized participant answer to Question 4.</li><li><strong>QUESTION_5</strong>: Anonymized participant answer to Question 5.</li><li><strong>QUESTION_6</strong>: Anonymized participant answer to Question 6.</li><li><strong>QUESTION_7</strong>: Anonymized participant answer to Question 7.</li><li><strong>QUESTION_8</strong>: Anonymized participant answer to Question 8.</li></ul><p><i><strong>Interview Questions</strong></i></p><ol><li>Please briefly describe the human error that you experienced.</li><li>If the human error you experienced resulted in a defect that was committed, please provide a link (or Git commit hash) to the commit below.</li><li>Is your human error a slip, lapse, or mistake?</li><li>Now, please examine the Taxonomy of Human Errors in Software Engineering (T.H.E.S.E.) and choose the specific human error that most accurately describes the human error you experienced. If you experienced multiple human errors, please submit this form once for each human error.</li><li>If there are other categories of human error that also describe the human error that you experienced, please note them here.</li><li>If you chose a 'General' or 'Other' category in Question (4), this question is required. Do you believe there is a missing human error category that better describes the human error that you experienced? If yes, please describe it below.</li><li>On a scale of 1 (not at all confident) to 5 (completely confident), how confident are you in your classification in the previous question?</li><li>Do you have any additional comments about this human error?</li></ol><p><i><strong>Anonymity</strong></i></p><p>Institutional Review Board approval for this research involving human subjects was granted by the Human Subjects Research Office at RIT on March 18, 2022. Participants signed an informed consent form acknowledging that (1) their participation was entirely voluntary and had no impact on their grades, and (2) their survey responses would be published in an anonymized format. All data released with this publication has been anonymized by replacing any personally identifiable information with participant identifiers.</p><p><i><strong>Contact</strong></i></p><p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p><p><i><strong>Acknowledgments</strong></i></p><p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
User Feedback Dataset from the Top 15 Downloaded Mobile Applications
<p>This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. <i><strong>The dataset is particularly suitable for Natural Language Processing (NLP) applications</strong></i>, such as text classification and topic modeling.</p><p><strong>List of Included Applications:</strong></p><ul><li>TikTok</li><li>Instagram</li><li>Facebook</li><li>WhatsApp</li><li>Telegram</li><li>Zoom</li><li>Snapchat</li><li>Facebook Messenger</li><li>Capcut</li><li>Spotify</li><li>YouTube</li><li>HBO Max</li><li>Cash App</li><li>Subway Surfers</li><li>Roblox</li><li>Data Columns and Descriptions:</li></ul><p><strong>Data Columns and Descriptions:</strong></p><ul><li>review_id: Unique identifiers for each user feedback/application review.</li><li>content: User-generated feedback/review in text format.</li><li>score: Rating or star given by the user.</li><li>TU_count: Number of likes/thumbs up (TU) received for the review.</li><li>app_id: Unique identifier for each application.</li><li>app_name: Name of the application.</li><li>RC_ver: Version of the app when the review was created (RC).</li></ul><p><strong>Terms of Use:</strong></p><p>This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:</p><p>M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: <a href="https://doi.org/10.1109/ACCESS.2023.3332644">10.1109/ACCESS.2023.3332644</a>.</p><blockquote><p>Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.</p></blockquote>
User study data: Nudges to Mitigate Confirmation Bias during Web Search for Opinion Formation, automatic vs. reflective study
<p>Data of two user studies (282 and 307 participants), investigating the risks and benefits of warning labels with and without obfuscations to mitigate confirmation bias during web search on debated topics.</p> <p> </p> <p>Study Variables (study 1 and study 2)</p> <p> </p> <p> display_con: Search result display<br> - Study 1<br> - 1: targeted warning label with obfuscation<br> - 2: random warning label with obfuscation<br> - 3: regular (no intervention)<br> - Study 2<br> - 1: targeted warning label with obfuscation<br> - 2: targeted warning label without obfuscation<br> - 3: random warning label with obfuscation<br> - 4: random warning label without obfuscation<br> - 5: regular (no intervention)<br>- CRT_cat: Cognitive reflection<br> - 1: intuitive<br> - 2: analytic<br>- topic: Assigned debated topic<br> - 1: Is drinking milk healthy for humans? <br> - 2: Is homework beneficial?<br> - 3: Should people become vegetarian?<br> - 4: Should students have to wear school uniforms?<br>- clicksup_prop: Clicks on attitude-confirming (AC) search results (proportion of all clicks)<br>- clickwarn_prop: Clicks on warning label (WL) search results (proportion of all clicks)<br>- show_clicked: Clicks on show-button (number of clicks, only in conditions with obfuscation)<br>- accuracy_bias: Accuracy bias estimation (Difference between a) observed bias (as the proportion of attitude-confirming clicks) and b) perceived bias (reported in the post-interaction questionnaire and re-coded into values from 0 to 1), positive values indicate an overestimation of bias)<br>- att_change: Attitude change (Difference between attitude reported in the pre-interaction questionnaire and the post-interaction questionnaire. Negative values indicate an attitude change in the attitude-opposing direction, while positive values indicate an attitude strengthening in the attitude-supporting direction.)<br>- knowledge_1: Self-reported prior knowledge (Reported on a seven-point Likert scale ranging from non-existent to excellent as a response to how they would describe their knowledge on the topic they were assigned to)<br>- N_clicks: Cumulative clicks (Number of all clicks on search results)<br>- NFC: Need for Cognition (Mean response to 4-item subset of the NFC questionnaire)<br>- UX_usability: Usability (Mean of responses on a seven-point Likert scale to the module "usability"from the meCUE 2.0 questionnaire)<br>- UX_usefulness: Usefulness (Mean of responses on a seven-point Likert scale to the module "usefulness"from the meCUE 2.0 questionnaire)</p>
User study Data: Boosting Intellectual Humility During Search on Debated Topics
<pre><strong>User study data </strong> The following column headers correspond to the following study variables: Intervention = Intervention (CONTROL= control, DUMMYCONTROL = ATI control, PRIME = prime, QUESTIONNAIRE = remind, FULL = reinforce) DV1_AC_Clicks = Attitude confirming clicks DV2_Lowest_Rank = Lowest rank clicked DV3_Dwell_Time = Dwell time DV4_Task_Completion = Task completion time DV5_Cumulative_Clicks = Cumulative clicks IH = Intellectual Humility Ranking = Ranking Topic = Topic rationale = Rationale for behavior (free text) rationale_category = Rationale for behavior (category, one of IH_driven = driven by IH, ranking_driven = ranking, bias_driven = confirmation bias, content/form_driven = content/form, task_driven/unclear = task/unclear)) Att_change = Attitude change Knowledge = Knowledge gain (1 = no knowledge gain, 5 = substantial knowledge gain) NASA.Mental = Reflection on search task, mental demand NASA.Temporal = Reflection on search task, temporal demand NASA.Performance = Reflection on search task, performance NASA.Effort = Reflection on search task, effort NASA.Frustration = Reflection on search task, frustration</pre>
CREATTIVE3D multimodal dataset of user behavior in virtual reality
<p>In the context of the <a href="https://project.inria.fr/creattive3d/">ANR CREATTIVE3D</a> project, we join the expertise of computer science, neuroscience, and clinical practitioners, with the aim to analyze the impact that a simulated low-vision condition has on user navigation behavior in complex road crossing scenes: a common daily situation where the difficulty to access and process visual information (e.g., traffic lights, approaching cars) in a timely fashion can lead to serious consequences on a person's safety and well-being. As a secondary objective, we also aim to investigate the potential role virtual reality could play in rehabilitation and training protocols for low-vision patients.</p> <p>This dataset contains the data as part of the study described in <a href="https://hal.science/hal-04102737">An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality</a>.</p> <p>The dataset is metadata for the pre-print <a href="https://inria.hal.science/hal-04429351">Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset</a></p> <p>To use this dataset, please cite:</p> <blockquote> <pre>@unpublished{wu:hal-04429351, TITLE = {{Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset}}, AUTHOR = {Wu, Hui-Yin and Robert, Florent Alain Sauveur and Gallo, Franz Franco and <br> Pirkovets, Kateryna and Quere, Cl{\'e}ment and Delachambre, Johanna and <br> Ramano{\"e}l, Stephen and Gros, Auriane and Winckler, Marco and Sassatelli, Lucile and <br> Hayotte, Meggy and Menin, Aline and Kornprobst, Pierre}, URL = {https://inria.hal.science/hal-04429351}, NOTE = {working paper or preprint}, YEAR = {2023}, MONTH = Dec, KEYWORDS = {Virtual reality ; Dataset ; Context ; Low vision ; 3D environments ; User study}, PDF = {https://inria.hal.science/hal-04429351/file/2023_CREATTIVE3D_dataset_arxiv_.pdf}, HAL_ID = {hal-04429351}, HAL_VERSION = {v1}, }<br><br>@inproceedings{robert2023integrated, title={An integrated framework for understanding multimodal embodied experiences in interactive virtual reality}, author={Robert, Florent and Wu, Hui-Yin and Sassatelli, Lucile and Ramanoel, Stephen and <br> Gros, Auriane and Winckler, Marco}, booktitle={Proceedings of the 2023 ACM International Conference on Interactive Media Experiences}, pages={14--26}, year={2023} }</pre> </blockquote> <h3> </h3> <h3>Versions</h3> <p>2024-12-18: Updated readme with description of labels, columns, and suggestions on how to start exploring the dataset. We also provide the questionnaire responses and observation notes in English (questionnaire_translation_EN.csv).</p>
Data and Results of eELib Simulations for the User-Based Multi-Use of Battery Storage Systems
<p>The dataset contains the configuration for the eElib models (model_data.json) and computed simulation results (.hdf5-files). The following scenarios were computed:</p> <ul> <li>ave_A_static-eq</li> <li>ave_A_static</li> <li>ave_A_dynamic_charging</li> <li>ave_A_fully_dynamic</li> <li>ave_B_static-eq_bss</li> <li>ave_B_static_bss</li> <li>ave_B_dynamic_charging_bss</li> <li>ave_B_fully_dynamic_bss</li> <li>MELANI_static</li> <li>MELANI_static_equal</li> <li>MELANI_dynamic_charging</li> <li>MELANI_fully_dynamic</li> </ul> <p><strong>Description of syntax of simulation results:</strong></p> <ul> <li>average (ave_B is half the size of the BSS of ave_A) and MELANI describe the considered multi-family house</li> <li>static/ static / dynamic_charging / fully_dynamic are the three developed operating strategies for the user-based multi-use</li> <li>static-equal: the allocation keys are equally, i.e., the PVS and BSS capabilities are equally distributed among the households of the multi-family house</li> </ul> <div> <div><strong>As part of the publication:</strong></div> <div>Henrik Wagner, Constantin von Lützow, Marcel Lüdecke, Michel Meinert, Bernd Engel "Empowering Collective Self-Consumption in Multi-Family Houses: User-Based Multi-Use of Residential Battery Storage Systems", 23rd Wind & Solar Integration Workshop 2024, Helsinki, Finland, doi: 10.1049/icp.2024.3901</div> <div> </div> <div><strong>Changelog:</strong></div> <div>v2: Added doi for WIW 2024 conference paper to improve citation possibilities</div> </div>
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.