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1,617 results for “user”
Results of user research project to understand data curation practices
<p>Supporting scalable curation is a part of the mission of the Elixir Data Platform.Thus far, we have established infrastructure capable of ingesting and aggregating text-mined outputs from multiple providers and making these available via an API. This public API is used by Europe PMC to display specific entities and relationships on full text articles (via the SciLite application). To ensure that the future development of this infrastructure meets the needs of curators, we first carried out user research to understand and identify common workflow patterns and practices via an observational study. Building on these outcomes, we then devised a curator community survey to more specifically understand which entity types, sections of a paper and tools are of top priority to address. The results of the project is presented here.</p>
Figure 19 in Paleobiology Database User Guide Version 1.0
Figure 19. Typical Systematic Paleontology section of a primary journal reference (Sumrall et al. 2009, PBDB ref. 65844). In this listing, the opinion linking species Cardiocystella prolixora to genus Cardiocystella and that linking Cardiocystella to Cothurnocystidae are "with evidence" whereas the other opinions are "without evidence."
Figure 16 in Paleobiology Database User Guide Version 1.0
Figure 16. Navigation bar at top showing no reference selected and at bottom showing Uhen 2004 as the selected reference.
Figure 27 in Paleobiology Database User Guide Version 1.0
Figure 27. Occurrences entry form example. Yours may look different depending on your data entry preferences.
Figure 7 in Paleobiology Database User Guide Version 1.0
Figure 7. Examples of how the PBDB time rules assign occurrences to time bins. This timescale is abstracted from the New Zealand Geological Timescale (Raine et al. 2015). See the text for a full explanation of these cases.
Figure 3 in Paleobiology Database User Guide Version 1.0
Figure 3. The differing functionality of the Main Menu for General (left) and Contributory (right) Users.
User Experience Evaluation of BRI Smart Billing Mobile Application Using System Usability Scale and Heuristic Evaluation
<p>This research aims to evaluate user perceptions of the design and functionality of the BRI Smart Billing Mobile app. Through a questionnaire distributed to active BRI Smart Billing users, this study will identify factors that influence user satisfaction with the visual appearance, layout of elements, and ease of use of features available on the dashboard. The results of this research are expected to provide input to the application developers regarding efforts to improve the quality of digital application services and provide recommendations for improving the design of the BRI Smart Billing Mobile dashboard to be more user-friendly and meet user needs.</p>
Questionnaires answers and data processing for a mixed-presence user study with two wall-sized displays
<p>Questionnaire answers and data processing tabs that was part of a mixed-presence experiment with two wall-sized displays.<br>Was used for a study in Q4 2023. Accompanies a paper.<br>Complements the protocol for that study that can be found at https://zenodo.org/doi/10.5281/zenodo.12663837 and contains answers for the questionnaires that can be found at https://zenodo.org/doi/10.5281/zenodo.12664007</p>
Using ELN Functionality of Kadi4Mat (KadiWeb) in a Materials Science Case Study of a User Facility
<p>This record contains the dataset belonging to the paper "Using ELN Functionality of Kadi4Mat (KadiWeb) in a Materials Science Case Study of a User Facility"</p>
Exploring multi-camera views from user-generated sports videos
<div> <p><strong>The proliferation of mobile devices</strong> with video recording capabilities has revolutionized the creation, sharing, and consumption of audiovisual content, turning user-generated video (UGV) platforms into major data sources. </p> <div> <div> <p><strong>Despite this growth</strong>, there is a notable gap in the availability of public datasets featuring multi-angle recordings of sports events captured by various mobile cameras. This led to the creation of the <strong>MUVY Dataset</strong>, with the name stemming from <strong>Multiview User-generated Videos from YouTube.</strong></p> <div> <div> <p>The dataset offers a diverse collection of sports videos from multiple perspectives, without restrictions on video size. In its first version, it covers sports like, <strong>American football, artistic gymnastics, athletics, basketball, tennis, and cricket.</strong></p> <div> <div> <div> <p>The dataset addresses common challenges in user-generated videos, such as shaking, occlusions, blurring, and abrupt movements. Each video is accompanied by metadata including camera identification, YouTube URLs, extracted frames, and object annotations.</p> </div> </div> </div> </div> </div> </div> </div> </div>
Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour
<p>This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.</p>
Survey questionnaire and results on user needs for energy models for the European energy transition, related to Süsser et al. (2021)
<p>The online survey was designed and conducted in the framework of the EU H2020 project SENTINEL in collaboration with the project openENTRANCE. The aim of the survey was to identify needs by modellers and model result users across Europe for energy modelling. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s and energy industry. </p> <p>The study by Süsser <em>et al.</em> (2021) investigates the differences between energy model improvements and adjustments as perceived by modellers, and the actual needs of users of model results. If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Süsser, D., Gaschnig, H., Ceglarz, A., Stavrakas, V., Flamos, A. & Lilliestam, J. (under review). Better suited or just more complex? </em><em>On the fit between user needs and modeller-driven improvements of energy system models. Energy.</em></p>
Evaluation notebook and files for FAIR Workbench user evaluation
<p>This archive contains the Jupyter notebook and associated (image) files used in the June 2021 evaluation of the FAIR Workbench.</p>
Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts
<p><strong>Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts</strong></p> <p>This is a research artefact for the paper: <strong>What network simulator questions do users ask? a large-scale study of stack overflow posts</strong>. This artefact is a repository consisting of the collected dataset including 2,322 network-simulator-related Stack Overflow questions. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"
<p>We publicly release the anonymized <em>song_embeddings.parquet user_embeddings.parquet user_features_test.parquet user_features_train.parquet user_features_validation.parquet</em> datasets, with each of the TT-SVD or UT-ALS versions of embeddings, from the music streaming platform Deezer, as described in the article "<em>A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"</em> published in the proceedings of the 27TH ACM SIGKDD conference on knowledge discovery and data mining (<em>KDD 2021</em>). The paper is available <a href="https://arxiv.org/abs/2106.03819">here</a>.</p> <p>These datasets are used in the GitHub repository <a href="https://github.com/deezer/semi_perso_user_cold_start">deezer/semi_perso_user_cold_start</a> to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>
3D Virtual Reality vs. 2D Desktop Registration User Interface Comparison
<p>Thank you for visiting this repository, which contains a ZIP file with the data for our Registration User Interface (RUI) 2D Desktop vs. VR user study.</p>
Exploring Augmented Reality Privacy Icons for Smart Home Devices and their Effect on Users' Privacy Awareness
<p><strong>Exploring Augmented Reality Privacy Icons for Smart Home Devices and their Effect on Users' Privacy Awareness</strong></p> <p><strong>Authors</strong></p> <p>Kathrin Knutzen, Florian Weidner, Wolfgang Broll</p> <p> </p> <p><strong>About</strong></p> <p>This data represents the supplementary material for the conference paper with above title submitted at ISMAR 2021.</p> <p> </p> <p><strong>Contents</strong></p> <p>The supplementary material contains five files:</p> <ol> <li>The abstraction of paraphrases and transcripts after each condition respectively.<br> According to qualitative content analysis procedure, the conducted interviews were transcribed, paraphrased and subsequently abstracted to generate a category system. Every category is described with a definition and some exemplary quotes. Statements of participants are condensed and abstracted. Number of participants who made statements regarding a category, and most prominent valence are taken as basis to generate tree maps in Figure 5 and 6.<br> Please note that the prevalences represent the views or opinions of the participants on the single categories. Also, the mentioned categories have several subcategories and only the most important regarding privacy awareness are mentioned in the article.<br> <br> Transcripts, audio files and paraphrases are available upon request.</li> <li>The experimental task description. It served as exposition for the task that the participants had to complete.</li> <li>The interview guideline. Please note that this study was part of a larger project that also focused on topics such as usability and immersion, however, the article reports only on privacy awareness.</li> <li>The R script file to generate the tree maps in Figures 5 and 6. The dataset is created using data from the abstraction Excel sheet.</li> <li>A demonstration video of the experimental setup.</li> </ol>
Pre-processed Tweets by verified users, Elon Musk, Vitalik Buterin and CZ Binance
<p>This dataset comprises of tweets from verified users, Elon Musk, Vitalik Buterin and CZ Binance that have keywords "bitcoin", "cryptocurrency", "btc" and "crypto"</p>
Global Vkontakte User Dataset
<p>Online Social Networks enable individuals to present a version of themselves to their immediate social circle and beyond. Those presentations express cultural factors such as an individuals gender, location, political, philosophical and religions values. Obtaining such data; however, is often challenging on the aggregate level as it typically involves negotiations with private entities and ownership restrictions. This study presents a dataset of 563,615,517 user accounts from the platform Vkontakte, an online social network collected in June of 2020. Vkontakte is a social media platform similar in nature to Facebook that allows individuals to connect with other users, communicate with them through public and private messages, as well as create public personas of themselves. This dataset can be used to perform cross-national and cross-cultural analyses of online culture from a large proportion of the world.</p>
Automatic extraction of opinions of users of social networks on reproductive behavior issues
<p>The database contains an upload of text comments in Russian from the social network Vkontakte in .tsv format (UTF-8 encoding). Comments are collected from communities, which discuss pregnancy, childhood, motherhood, paternity, etc. Comments are collected from communities, which discuss pregnancy, childhood, motherhood, paternity, etc. <br> The database contains train, test and valid sets for machine learning processing. For the analysis of opinions in the field of reproductive behavior, nine groups of the social network Vkontakte were selected, in the names or descriptions of which the words "childfree" and their variations were clearly present, and 341 groups, in the names or descriptions of which the keywords "mother", " mothers "," children ", etc. and the number of subscribers of which was more than 10,000 people. This distribution of groups depended on the different activity of groups - supporters of a childless lifestyle produced significantly more posts and comments on our topics. Using data from different groups of the social network Vkontakte avoids data homogeneity - one of the weak points of sentiment analysis. Thus, the presented database is suitable for the analysis of specific demographic groups, which we conditionally called “anti-natalists” and “pronatalists”. Note that in the group of pronatalists there are largely representatives of a small child model of reproductive behavior. The sample contains data about stance on 6 topics: "maternity capital / benefits", "abortion", "large families", "childlessness", "parental leave", "individualism".The topics are selected by a set of keywords such as abortion, childfree, rest, no child and so on.</p> <p>Sentences from the collected sample were randomly selected for annotator marking. Each sentence was marked with three annotators. Since each sentence could discuss several issues, the annotator marked each sentence on all seven topics. The proposals were marked up mainly by professional demographers and linguists.</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.