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Supporting information of a study for the definition and evaluation of a graphical user interface for housing co-design
<p>This dataset is from a study that intends to define, prototype and test a graphical user interface for a housing co-design system. To define the requirements of the interface, we conducted interviews with professionals of architecture, urbanism and social sciences areas, as well as with housing cooperatives and inhabitants of these institutions. An interface solution was prototyped, tested and refined. Then we conducted a heuristic evaluation and a summative evaluation. Such evaluations involved the testing of a high-fidelity prototype, to receive feedback from UX/UI experts, potential users (inhabitants) and architects.</p> <p>S1_File refers to the interview protocol used with the three groups of interviewees. We share the English and Portuguese versions of the interviews with professionals and the original (Portuguese) and translated versions of the remaining ones since these were conducted in Portuguese.</p> <p>S2_File is a dataset reporting the results of the interviews. Each question includes the answers given and the identification (anonymized) of the interviewees who responded to that question.</p> <p>S3_File describes the usability issues identified by the experts during the heuristic evaluation of the high-fidelity prototype. The first page organizes the issues by severity (left) and priority (right). The remaining pages have a table for each issue, including rows for problem designation, heuristic violated, problem description, solution proposal, severity degree, and an image of the interface pointing to the referred issue.</p> <p>S4_File refers to the results of the heuristic evaluation. It includes the identification of each issue, which expert (anonymized) identified such issue, and the heuristic it violates, with the sum of the times each heuristic was violated at the end of each column. At the right, a table presents the consolidation of issues, organized by priority, with columns identifying the issue, severity level, frequency, and priority.</p> <p>S5_File is the script given to potential users to experiment with the interface during the summative evaluation. This script guides the user through the tasks to perform since the prototype does not have all the features functioning.</p> <p>S6_File refers to the questionnaires applied during the summative evaluation with inhabitants. It includes a preliminary questionnaire, a Single Ease Question (SEQ) questionnaire, a System Usability Scale (SUS) questionnaire, and a Graphical User Interface (GUI) questionnaire.</p> <p>S7_File refers to the results of the summative evaluation with inhabitants (potential users).</p> <ul> <li>Page A refers to the preliminary questionnaire with demographic information such as age, gender, education, relationship with digital technologies, etc. Each field corresponds with each inhabitant (anonymised) and the sum and percentage. In the middle, a table presents a summary of the consolidation. In the right possible relations are presented. </li> <li>Page B presents the results of the SEQ questionnaire, identifying the ratings each inhabitant (anonymized) gave each task. A summary of such values is at the right. </li> <li>On page C, the result of each rating for the SUS questionnaire given by each inhabitant (anonymized) is shown. At the bottom is the calculation of the SUS score.</li> <li>Page D presents the GUI questionnaire results for each inhabitant (anonymized), with the average and SD identified for each question. A summary of such results is on the right.</li> <li>Page E holds the notes taken by the researchers based on their observations regarding task performance. The information is organized in tables for each step of each task and includes the completeness, attempts, and time taken for each inhabitant (anonymized) to complete such task. Also, the sum, percentage, average, and SD are registered. Next to each task is a table identifying how many participants accomplished the task at the first attempt.</li> <li>Page F refers to the strong and weak aspects identified by the inhabitants. Strong and weak aspects are identified, as well as which inhabitant (anonymized) has identified them. The sum and percentage are also given. At the right, there is a table with the consolidation of results by combining similar answers. </li> </ul> <p>S8_File refers to the results of the discussion with architects after experiencing the interface. Such results relate to the positive and negative aspects that the architects identified in the interface and its usefulness for architecture. The left table identifies the strong and weak aspects that architects (anonymized) identified and the sum and percentage associated with them. The table on the right consolidates such results, with similar responses combined.</p>
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data
<p>Supplementary Material to the Paper: <em>Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability</em></p> <p>This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.</p>
The language of sound search: Examining User Queries in Audio Search Engines (supplementary materials)
<h2>Overview</h2> <p>This dataset accompanies the <a href="https://dcase.community/documents/workshop2024/proceedings/DCASE2024Workshop_Weck_54.pdf" target="_blank" rel="noopener">paper</a> titled <strong>"The Language of Sound Search: Examining User Queries in Audio Search Engines."</strong> The study investigates user-generated textual queries within the context of sound search engines, which are commonly used for applications such as foley, sound effects, and general audio retrieval.</p> <p>The paper addresses the gap in current research regarding the real-world needs and behaviors of users when designing text-based audio retrieval systems. By analyzing search queries collected from two sources — a custom survey and Freesound query logs — the study provides insights into user behavior in sound search contexts. Our findings reveal that users tend to formulate longer and more detailed queries when not constrained by existing systems, and that both survey and <a href="https://freesound.org/">Freesound</a> queries are predominantly keyword-based.</p> <p>This dataset contains the raw data collected from the survey and annotations of Freesound query logs.</p> <h2>Files in This Dataset</h2> <p>The dataset includes the following files:</p> <ol> <li> <p><strong><code>participants.csv</code></strong><br>Contains data from the survey participants. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each participant.</li> <li><code>fluency</code>: Self-reported English language proficiency.</li> <li><code>experience</code>: Whether the participant has used online sound libraries before.</li> <li><code>passed_instructions</code>: Boolean value indicating whether the participant advanced past the instructions page in the survey.</li> </ul> </li> <li> <p><strong><code>annotations.csv</code></strong><br>Contains annotations of the survey responses, detailing the participants' interaction with the sound search tasks. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each annotation.</li> <li><code>participant_id</code>: Links to the participant’s ID in <code>participants.csv</code>.</li> <li><code>stimulus_id</code>: Identifier for the stimulus presented to the participant (audio, image, or text description).</li> <li><code>stimulus_type</code>: The type of stimulus (audio, image, text).</li> <li><code>audio_result_id</code>: Identifier for the hypothetical audio result presented during the search task.</li> <li><code>query1</code>: Initial search query submitted based on the stimulus.</li> <li><code>query2</code>: Refined search query after seeing the hypothetical search result.</li> <li><code>aspects1</code>: Aspects considered important when formulating the initial query.</li> <li><code>aspects2</code>: Aspects considered important when refining the query.</li> <li><code>result_relevance</code>: Participant's rating of the hypothetical search result's relevance.</li> <li><code>time</code>: Time taken to complete the search task.</li> </ul> </li> <li> <p><strong><code>freesound_queries_annotated.csv</code></strong><br>Contains annotated Freesound search queries. Columns:</p> <ul> <li><code>query</code>: Text of the search query submitted to Freesound.</li> <li><code>count</code>: The number of times the specific query was submitted.</li> <li><code>topic</code>: Annotated topic of the query, based on an ontology derived from AudioSet, with an additional category, <code>Other</code>, which includes non-English queries and NSFW-related content.</li> </ul> </li> <li> <p><strong><code>survey_stimuli_data.zip</code></strong><br>This ZIP file contains three CSV files corresponding to the three stimulus types used in the survey:</p> <ul> <li><strong>Audio stimuli</strong>: Categorized sound recordings presented to participants.</li> <li><strong>Image stimuli</strong>: Annotated images that prompted sound-related queries.</li> <li><strong>Text stimuli</strong>: Summarized descriptions of sounds provided to participants.</li> </ul> </li> </ol> <p>More details on the stimuli and the survey methodology can be found in the accompanying paper.</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset in your research, please cite the corresponding paper:</p> <div> <pre>B. Weck and F. Font, ‘The Language of Sound Search: Examining User Queries in Audio Search Engines’, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024), Tokyo, Japan, Oct. 2024, pp. 181–185.</pre> <pre><code>@inproceedings{Weck2024, author = "Weck, Benno and Font, Frederic", title = "The Language of Sound Search: Examining User Queries in Audio Search Engines", booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024)", address = "Tokyo, Japan", month = "October", year = "2024", pages = "181--185" }</code></pre> </div>
DUAL-T User Testing Dataset
<p><em><strong>The file</strong></em></p> <p>This file contains keylogging data from 21 professional literary translators, who translated three short stories in three different workflows. Data was collected between October 2023 and January 2024.</p> <p> </p> <p><em><strong>Participants</strong></em></p> <p>Participants have been assigned codes from <strong>P01 to P23</strong>. Originally, there were 24 participants, however logs for P04a, P09, and P20 had to be excluded from analysis due to technical errors, so they are not present in the spreadsheet.</p> <p> </p> <p><em><strong>Workflows</strong></em></p> <p>The workflows were:</p> <p><strong>WF01</strong>: Microsoft Word</p> <p><strong>WF02</strong>: Trados Studio 2022</p> <p><strong>WF03</strong>: Machine-Translation Postediting Platform (proprietary tool, not available on the market)</p> <p>Participants had access to an internet browser (Chrome) for all three conditions.</p> <p> </p> <p><em><strong>Source texts</strong></em></p> <p>The texts used for the experiments were taken from the short story collection <em>One More Thing</em>, by BJ Novak (2014). The three short stories used are:<br><br><strong>T01</strong>: Rome</p> <p><strong>T02</strong>: The Beautiful Girl in the Bookstore</p> <p><strong>T03</strong>: They Kept Driving Faster and They Outrun the Rain</p> <p> </p> <p><strong><em>The keylogging data</em></strong></p> <p>Keylogging data was collected using Inputlog 8.0.0.17 (<a href="https://www.inputlog.net/">https://www.inputlog.net/</a>)</p> <p>The data available in the spreadsheet includes:</p> <ul> <li>Translation time (hh:mm:ss)</li> <li>Translation time (s)</li> <li>Translation time (m)</li> <li>Total <em>n</em> keystrokes</li> <li>Total <em>n</em> mouse actions</li> <li>Total <em>n</em> pauses</li> <li>Pause duration (s)</li> <li>Mean duration of pauses (s)</li> <li>Pause ratio</li> <li>Time spent inside tool (s)</li> <li>Time spent outside tool (s)</li> <li>Source text <em>n</em> words</li> <li>Source text <em>n</em> characters</li> <li>Target text <em>n</em> words</li> <li>Target text <em>n</em> characters</li> <li>Seconds per source text character</li> </ul>
Synthetic dataset of user interactions - postpartum depression.csv
<p>A synthetic data set composed of 200 users' utterances as possible answers to questions related to these topics:</p> <p>(i) Feeling sad or Tearful<br>(ii) Irritable towards baby & partner<br>(iii) Trouble sleeping at night<br>(iv) Problems concentrating or making decision<br>(v) Overeating or loss of appetite<br>(vi) Feeling of guilt<br>(vii) Problems of bonding with baby <br>(viii) Suicide attempt</p>
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Natural Language-Guided Programming User Study
<p>In this dataset you find the user study data that was used in the <strong><em>Natural Language-Guided Programming</em></strong> paper, which is accepted for Onward! 2021. A preprint can be found here <a href="https://arxiv.org/pdf/2108.05198.pdf">https://arxiv.org/pdf/2108.05198.pdf</a>. The dataset consists of the following files:</p> <ul> <li> <p>benchmark.json contains 201 test cases. Each test case consists of context, a natural language intent and target code. The test cases are intended to evaluate a model that can predict code giving a piece of context code and a natural language intent. The test cases were derived from Jupyter notebooks that were crawled from Github projects with permissive licenses. In the project_metadata field you find information about the original project such as its git url and license.</p> </li> <li> <p>predictions-annotated.json contains predictions of the three models used in the paper for 100 test cases in benchmark.json. Each prediction is accompanied with qualitive assesments from three annotators.</p> </li> <li> <p>train-index.jsonl is the list of github projects that were used for training the models.</p> </li> <li> <p>eval-index.jsonl is a list of github projects that we kept separate for evaluation. The benchmark.json was created from a random subset of the projects in this list.</p> </li> </ul> <p>For more details we refer to the paper.</p>
Survey of Electrical & Electronic Equipment (EEE) Sector Stakeholders & End Users of EEE regarding Circular Economy in the EEE Sector
<p>The data is the full results of surveys of stakeholders in the electrical and electronic equipment (EEE) value chain conducted as part of the Horizon 2020 funded <a href="https://c-serveesproject.eu/index.php">C-SERVEES project</a>. </p> <p>The purpose of the survey was to provide guidance for the successful implementation of circularity in the EEE sector, specifically to develop circular economy business plans for four items of equipment: printers (inc. toner cartridges); washing machines; televisions; and network monitoring equipment. The survey aimed to capture data on:</p> <ul> <li>Circular economy awareness;</li> <li>Current practices relating to circular economy; and</li> <li>Circular economy opportunities, barriers and enablers.</li> </ul> <p>Investigation of opportunities, barriers and enablers focused on technical, economic, socio-cultural, regulatory and environmental themes. In addition, background information was also collected to enable some of the attributes of the respondent to be recorded. This included: country; city, years of experience in the industry as well as, in the household users’ survey, age and education level. </p> <p>The stakeholder groups surveyed are noted below, these being the key EEE supply chain actors. A separate dataset is available for each.</p> <ol> <li>Designers of EEE</li> <li>Suppliers to manufacturers of EEE</li> <li>Manufacturers of EEE</li> <li>EEE Retailers</li> <li>Business users of EEE</li> <li>Household users of EEE</li> <li>Waste EEE handlers</li> </ol> <p>A project report analysing the results of the survey is available: <em><a href="https://c-serveesproject.eu/project_activities.php?op=5">Guidelines for circular economic models in the E&E sector</a> . </em>A journal article outlining the subsequent development of the circular economy business models is also available: <a href="https://www.sciencedirect.com/science/article/abs/pii/S095965262101430X"><em>A circular economy business model innovation process for the electrical and electronic equipment sector</em></a>. </p>
IIIF: raising awareness of the user benefits for scholarly editions - usability testing results
<p>Usability testing results of the bachelor's thesis titled <a href="https://doc.rero.ch/record/306498/"><em>"The International Image Interoperability Framework (IIIF): raising awareness of the user benefits for scholarly editions"</em></a><a href="https://doc.rero.ch/record/306498/">.</a></p> <p>Remote and in-person usability tests on the <a href="http://universalviewer.io/">Universal Viewer</a> and <a href="http://projectmirador.org/">Mirador</a>, two IIIF-compliant clients, took place between March and May 2017. The tests were conducted with Loop11 (remote testing) and Morae (in-person testing).</p> <p>The dataset is composed of Excel files, screenshots (tasks and heat maps) as well as videos.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p>Corresponding dataset for the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The structure of the dataset is as follows:</p> <p><strong>Annotations</strong> </p> <p><em>List of annotations made per day for each of the analysed projects.</em></p> <p><code>annotations.csv </code></p> <p><strong>Comments </strong></p> <p><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></p> <p><code>comments.csv </code></p> <p><strong>Rolechanges</strong> </p> <p><em>List of roles per user to determine number of role changes </em></p> <p><code>478_rolechanges.csv</code></p> <p><code>1104_rolechanges.csv</code></p> <p><code>...</code></p> <p><strong>Totalnetworkdata</strong> </p> <p><em>Network data (edge and node sets) for the given projects (without time slices).</em></p> <p>Edges </p> <ul> <li> <p><code>478_edges.csv</code></p> </li> <li> <p><code>1104_edges.csv</code></p> </li> </ul> <p>Nodes </p> <ul> <li> <p><code>478_nodes.csv</code> </p> </li> <li> <p><code>1104_nodes.csv</code> </p> </li> </ul> <p><strong>Trajectories</strong> </p> <p><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></p> <p>478 </p> <ul> <li>Edges <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> </ul> </li> <li> <p>...</p> </li> <li>Nodes <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> <p> </p> <p>1104 </p> <ul> <li> <p>Edges </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p>Nodes </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><code>...</code></p> </li> </ul> <p> </p>
Tagged Twitter timelines for users reporting SARS-CoV-2 infections and related data
<p>Twitter data was collected through the Twitter API v2.0, specifically through the timeline endpoint. Details about the inference of SARS-CoV-2 self-reports and the tagging of the full timeline of each user can be found in the <a href="https://github.com/digitalepidemiologylab/content_changes_paper">GitHub repository</a>. The larger dataset ("preprocessed_data.csv") consists of a total of 8,534,171 tweets posted by 30,856 users from January 1, 2020 to October to September 30, 2021.</p> <p>The raw data from Twitter, including tweet and user IDs, has been removed or anonymized in order to comply with the EPFL guidelines for data sharing.</p> <p>In particular, the date of the tweets was removed, the text of the tweets, URLs and URL domains have been substituted with the "text", "<URL>" and "<URL_DOMAIN>" token respectively.</p> <p>User IDs have been substitued with new IDs in the [0, number of users] range (e.g. U0, U1, ...) .</p> <p>Tweet IDs have been substitued with new IDs in the [0, number of tweets] range (e.g. T0, T1, ...) .</p> <p>In addition to self-explanatory columns about topics, emotions, URL classification and symptoms we tagged, we also share the columns:</p> <ul> <li>pdate: date of the SARS-CoV-2 infection self-report for that user (adjusted with SUTime)</li> <li>effective_date: date of the tweet adjusted with SUTime, when the SUTime columns is available.</li> <li>rel_effective_day(week, month): days (weeks, months) computed with respect to the positivity date (i.e. "pdate" column). Negative numbers refer to tweets posted before the user reported a COVID-19 infection on Twitter.</li> </ul>
IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations
<p>Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations<br> <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a><br> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>).</p> <p>In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations.</p> <p>This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”<strong>.</strong></p>
DUX: A dataset of User Interactions and User Emotions
<p>User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users' emotions based on their interactions alone would not be intrusive to users and could be easily implemented in most applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.</p>
BiodivAR comparative user test (GNSS vs. RTK) dataset
<p>This dataset comprises data used to evaluate and compare the BiodivAR open source web location-based AR application in a presentation submitted to the FOSS4G 2023 conference in Pritzen, Kosovo. The application is available at <https://biodivar.heig-vd.ch> and its source code at <https://github.com/MediaComem/biodivar>.</p>
Mobile Application Privacy Risk Assessments from User-authored Scenarios
<p>Mobile applications (apps) provide users valuable benefits at the risk of exposing users to privacy harms. Improving privacy in mobile apps faces several challenges, in particular, that many apps are developed by low resourced software development teams, such as end-user programmers or in startups. In addition, privacy risks are primarily known to users, which can make it difficult for developers to prioritize privacy for sensitive data. In this paper, we introduce a novel, lightweight method that allows app developers to elicit scenarios and privacy risk scores from users directly using only an app screenshot. The technique relies on named entity recognition (NER) to identify information types in user-authored scenarios, which are then fed in real-time to a privacy risk survey that users complete. The best-performing NER model predicts information types with a weighted average precision of 0.70 and recall of 0.72, after post-processing to remove false positives. The model was trained on a labeled 300-scenario corpus, and evaluated in an end-to-end evaluation using an additional 203 scenarios yielding 2,338 user-provided privacy risk scores. Finally, we discuss how developers can use the risk scores to prioritize, select and apply privacy design strategies in<br> the context of four user-authored scenarios.</p>
Replication Package: Exploring the Relationship Between Personality Traits and User Feedback
<p>This is the replication package for the paper titled 'Automated User Feedback Analysis: Processing the Feedback Quantity and Quality' accepted for the AffectRE23 workshop track at RE 2023.</p> <p>Full abstract:</p> <p>Previous research has studied the impact of developer personality in different software engineering scenarios, such as team dynamics and programming education. However, little is known about how user personality affect software engineering, particularly user-developer collaboration. Along this line, we present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. They also filled out a questionnaire for the Five Factor Model (FFM) personality test. We observed some isolated effects of neuroticism on user feedback: most notably a significant correlation between neuroticism and feedback elaborateness; and between neuroticism and the rating of certain features. The results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users. This and possibly other personality characteristics should be considered when leveraging feedback analytics for software requirements engineering.</p>
Italian TikTok users online behaviour patterns and social attitudes (survey)
<p>Survey of 500 young TikTok users (18-35) in Italy covering online behaviour patterns and social attitudes</p>
Datasets from the RecSys 2023 article "Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect".
<p>We have publicly released the anonymized "new_release_stream.csv" dataset from the music streaming platform Deezer. This dataset is described in detail in the article titled "Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect", which was published in the proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023).</p> <p>Each row in the dataset contains an anonymized user and item identifier, a reference timestamp in seconds (measured from the first consumption in the dataset), and a binary value "y". This "y" value indicates whether a song was listened to for more than 80% of its duration (y = 1) or not (y = 0).</p> <p>You can find this dataset in the GitHub repository <a href="https://github.com/deezer/ex2vec">deezer/ex2vec</a>, where it is used to reproduce experiments discussed in the article.</p> <p>If you plan to use our code or data in your work, please make sure to cite our paper accordingly.</p> <p> </p> <pre><code>@inproceedings{sguerra2023ex2vec, title={Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect}, author={Sguerra, Bruno and Tran, Viet-Anh and Hennequin, Romain}, booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems}, year = {2023} }</code></pre> <p> </p> <p> </p> <p> </p>
Datasets from the paper "Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community"
<p>Datasets from the paper "Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community", presented at the 24th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL).</p> <p>The dataset consists in 2 files:</p> <ul> <li>tweets.csv: This file contains the ids of the tweets and the user that published the tweet.</li> <li>df_users_seeds.csv: This file contains a table were the first field is the user id; the other columns indicate the seed number and if the user follows that seed (1) or not (0)</li> </ul>
User Study Data for Paper "A case study in designing trustworthy interactions: implications for socially assistive robotics"
<p>Experimental data collected for the user study described in Frontiers paper "A case study in designing trustworthy interactions: implications for socially assistive robotics" by Mengyu Zhong et al. Citation: <i>Zhong, Mengyu, et al. "A case study in designing trustworthy interactions: implications for socially assistive robotics." Frontiers in Computer Science 5.1152532 (2023). </i></p>
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.