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1,617 results for “users”
End-user's survey results on needs and expectations for next- generation Energy Performance Certificates (H2020 X-tendo project)
<p>The SPSS data file consists of survey data from the X-tendo project on the end-user needs and expectations from next-generation energy performance certificates.</p>
ACTIVAGE User needs_requirements and services
<ul> <li>AUC: List of Activage Use cases</li> <li>RUC: List of Reference Ucs</li> <li>Needs: list of DS needs</li> <li>DSReq_list: List of All DS Requirements</li> <li>SLEawRq_list: List of Smart Living Environment for Ageing Well Requirements</li> <li>SLEaw-DS req map: Mapping DSs requirements to ACTIVAGE SLEaw requirements</li> <li>Initial DSs Service list: list provided by DS in Jun 2018 with services, partial description</li> <li>DSs SUBservice list: Decomposition of DS Services in atomic components</li> <li>DSReq_Cl_descr: Desciption of attributes (columns) of DS requirments</li> <li>SLEawReq_Cl_descr: Desciption of attributes (columns) of SLEaw requirments</li> <li>Legenda: This sheet. Include description of sheets and change proposals</li> </ul>
Social Network Online Activity of 100+ Users Over Two Years
<p>This dataset contains a precise (error margin is within 5 seconds) activity log of 138 users recorded over a period of approximately two years. It includes users' log in/log off timestamps as well as a device id which was used during the session. An activity heat map is also provided which can be used to determine the online time (in seconds) in a given hour for a given user. The dataset is completely anonymized and is not linked to real peoples' accounts. Russian social network VK was used to record the data.</p> <p>The database is provided in SQLite3 format. The data format is the following:</p> <p><strong>'sessions' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>platform</td> <td>INTEGER</td> <td>Device identifier for the session (refer to the table below).</td> </tr> <tr> <td>time_from</td> <td>DATE</td> <td>Timestamp of the session's start.</td> </tr> <tr> <td>time_to</td> <td>DATE</td> <td>Timestamp of the session's end.</td> </tr> </tbody> </table> <p><strong>'map' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>hour</td> <td>INTEGER</td> <td>Hour from the 1st Jan 1970 (Unix Epoch / 3600).</td> </tr> <tr> <td>time</td> <td>INTEGER</td> <td>Accumulated online time in the hour (in seconds).</td> </tr> </tbody> </table> <p>Device identifiers:</p> <table> <tbody> <tr> <td>0</td> <td>Unknown</td> </tr> <tr> <td>1</td> <td>Web on Mobile </td> </tr> <tr> <td>2</td> <td>iPhone App</td> </tr> <tr> <td>3</td> <td>iPad App</td> </tr> <tr> <td>4</td> <td>Android App</td> </tr> <tr> <td>5</td> <td>Windows Phone App</td> </tr> <tr> <td>6</td> <td>Windows App</td> </tr> <tr> <td>7</td> <td>Web on Desktop</td> </tr> </tbody> </table> <p> </p> <p>This dataset is associated with the VKWatcher independent research project. The code used to gather the information can be found <a href="https://github.com/Azarattum/VKWatcher-Backend">on GitHub</a>.</p>
Anonymized Graph Data with Friend Connections of 189505 VKontakte Users
<p>The dataset contains anonymized graph data with friend connections of 189505 VKontakte users. The dataset was used in <a href="http://github.com/filipp134/vk_bot_detection">this</a> Github project on the detection of social bots on VKontakte. The script which was used for the collection of the dataset is <a href="https://github.com/filipp134/vk_bot_detection/blob/main/Collecting%20datasets%20and%20merging%20them%20into%20one/collect_graph_data.py">here</a>.</p> <p>The dataset was collected in the following 2 steps by using the official <a href="https://dev.vk.com/api/getting-started">VKontakte API</a>:</p> <p>1. Friend connections of 11766 VKontakte users, who had 177739 unique friends, were collected.</p> <p>2. Friend connections of these 177739 users were collected. </p> <p>The dataset is in JSON format and is quite heavy: 424.5 MB.</p> <p> </p>
Supplementary materials for the paper "Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach"
<p>These are materials necessary to replicate the study discussed in <em>Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach</em>, accepted for publication at VEHITS 2022.</p>
SciKGTeX Scientific Contribution Metadata LaTeX Package User Evaluation Results & Analysis
<p>The responses and measured variables from 26 participants of the first user test of the SciKGTeX package.</p> <p><a href="https://github.com/Christof93/SciKGTeX">https://github.com/Christof93/SciKGTeX</a></p> <p>Also the raw text source for the evaluation tasks and the result analysis notebook with the results saved as tsv file.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments
<p><strong>Supplemental data for the manuscript </strong></p> <p><strong>"Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments".</strong></p> <p>Content</p> <ul> <li>functional_annotations.tar.gz <ul> <li> functional annotations for 10 different functional annotation systems, for 5090 species</li> </ul> </li> <li> genome_info_and_statistics.tar.gz <ul> <li>basic genome info, annotation system statistics, user query statistics</li> </ul> </li> <li>example_user_queries.tar.gz <ul> <li>three example files for user query inputs used in the analysis </li> </ul> </li> <li>README.txt <ul> <li>details about the files and file formats</li> </ul> </li> </ul>
Desired properties of recycling-derived fertilisers from an end-user perspective
<p>This data refers to responses to questions asked to farmers and farm advisors in seven different North-West European countries, relating to the desired properties of the mineral fertiliser substitutes, recycling derived fertilisers (RDFs). In total, 1225 participants responded from Belgium, France, Germany, Ireland, Luxembourg, the Netherlands and the United Kingdom. The types of questions asked included the participants' demographics and farming activities, and the parameters, properties and qualities the respondents were looking for, to determine the desired properties of RDFs.</p>
Dataset from "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.
<p>Dataset used in publication "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.</p> <p><br>This repository contains images from annual pollen trap samples mounted on slides and scanned under light microscopy; image annotation metadata; and the weights of the trained models from the YOLOv5 algorithm, saved after the last training epoch.</p> <p>More details can be found in the README file.</p>
Changes in the users of the social-ecological system around a reciprocal human-dolphin relationship
<p>This package contains the deidentified data and R code to replicate the quantitative analyses and figures of the scientific article accepted for publication at People & Nature:</p> <p><span>Cantor M</span><strong><span>,</span></strong><span> Santos-Silva B, Daura-Jorge FG, Machado AMS, Peterson D, da Rosa DX, Simões-Lopes PC, Valle-Pereira JVS, Zank S, Hanazaki N. Accepted. Changes in the users of the social-ecological system around a reciprocal human-dolphin relationship. People & Nature.</span> </p> <p> </p> <p><span>Abstract</span></p> <p>1. In contrast to many contemporary negative human-nature relationships, Indigenous Peoples and Local Communities have stewarded nature through cultural practices that include reciprocal contributions for both humans and nature. A rare example is the century-old artisanal fishery in which net-casting fishers and wild dolphins benefit by working together, but little is known about the persistence of the social-ecological system formed around this cultural practice.</p> <p>2. Here, we frame the human-dolphin cooperative fishery in southern Brazil as a social-ecological system based on secondary data from the scientific and grey literature. To investigate the dynamics of this system, we survey the local and traditional ecological knowledge and examine potential changes in its main component—the artisanal fishers—over time and space.</p> <p>3. Over 16 years, we conducted four interview campaigns with 188 fishers in fishing sites that are more open (accessible) or closed (restricted) to external influence. We investigated their experience, engagement, and economic dependence on dolphin-assisted fishing, as well as the learning processes and transmission of the traditional knowledge required to cooperate with dolphins.</p> <p>4. Our qualitative data suggest that fishers using accessible and restricted fishing sites have equivalent fishing experience, but those in more restrictive sites tend to be more economically dependent on dolphins, relying on them for fishing year-round. The traditional knowledge on how to cooperate with dolphins is mostly acquired via social learning, with a tendency for vertical learning to be frequent among fishers using sites more restrictive for outsiders. Experience, economic dependence, and reliance on vertical learning seem to decrease recently, especially in the accessible site. Our quantitative analyses, however, suggest that some of these fluctuations were not significant.</p> <p>5. Our study outlines the key components of this social-ecological system and identifies changes in the attributes of a main component, the users. These changes, when coupled with changes on other components such as governance and resource units (fish and dolphins), can have implications for the persistence of this cultural practice and the livelihoods of Local Communities. We suggest that continuous monitoring of this system can help to safeguard the reciprocal contributions of this human-nature relationship in years to come.</p>
[DATA_SCIENCE] Interviews PomBase Users, January-February 2016
<p>Here you find the transcripts of interviews collected by Sabina Leonelli as part of the ERC project "The Epistemology of Data-Intensive Science". You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu. One paper that specifically makes use of these interviews was published by Sabina Leonelli in the journal Philosophy of Science in 2018, under the title "Data in Time: Time-Scales of Data Use in the Life Sciences." The transcripts document yeast researchers' attitudes to data curation and the use of databases in their field. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter.</p>
User Study Data from "Point-and-Shake: Selecting from Levitating Object Displays"
<p>This dataset contains anonymous user study data from the two experiments described in the corresponding CHI 2018 publication.</p>
Simulated Self-user Shadowing for Mobile Phone Antennas at 28 GHz and at 60 GHz
<p>The purpose of this dataset is to supplement the data presented in our conference publication "Self-user shadowing effects of millimeter-wave mobile phone antennas in a browsing mode" at EuCAP 2019 (see <a href="https://ieeexplore.ieee.org/document/8739947">https://ieeexplore.ieee.org/document/8739947</a>).</p> <p>This dataset contains the 3-D surface meshes of the two numeric human body models used in the above publication. One body model holds the mobile phone with one hand (vertically, "OneHand") and the other body model with both hands (horizontally, "TwoHand"). The body models were initially exported from MakeHuman (<a href="http://www.makehumancommunity.org">http://www.makehumancommunity.org</a>), the actual body postures were then created with Blender 3D Creation Suite (<a href="https://www.blender.org">https://www.blender.org</a>), and these final body models were exported in OBJ format (a generic geometry definition file format). Then these models were imported into CST Studio Suite (<a href="http://www.cst.com">http://www.cst.com</a>) in order to simulate the 3-D realised-gain patterns of the antenna. The material properties of the human body model used in the simlations are described in detail in the above publication. Also the dual-polarised mobile-phone antenna design with one vertical feed port and one horizontal feed port is described in detail within the above publication (see Fig. 3) and is not part of this dataset. (Note that "port #1" in Fig. 3 of the publication denotes the vertical antenna port for the <em>one-hand</em> case, while "port #1" denotes the horizontally antenna port in the <em>two-hand</em> case.)</p> <p>This dataset also contains the simulated 3-D polarimetric, directional, complex-valued (real, imaginary) realised-gain patterns, seperately for 28 GHz and for 60 GHz, in 1-degree resolution in both phi and theta directions. The patterns are seperately given for the vertical ("VPolPatch")and the horizontal feed port ("HPolPatch"). The 2-D pattern cuts presented in the above publication (in Figs. 5-11) are subsets of the 3-D patterns in this dataset.</p> <p>The format of the eight ascii files {xxGHzStandingyyHandzzPolPatch.txt} is a follows:<br> 1st column: Theta angle in degrees<br> 2nd column: Phi angle in degrees<br> 3rd column: real part of Gain, theta component, in dBi<br> 4th column: imaginary part of Gain, theta component, in dBi<br> 5th column: real part of Gain, phi component, in dBi<br> 6th column: imaginary part of Gain, phi component, in dBi<br> where xx is "28" or "60" (GHz), yy is "One" or "Two" (-hand grip), and zz is "H" or "V" (-pol. antenna port), as described above.</p> <p>The spherical coordinate system is used in accordance to the IEEE-standard spherical coordinate system. The underlying Cartesian coordinate system is shown in the two attached preview (PNG) image files for both human body models, where the z-axis (theta=0 degrees) points to the directions of the head of the human, the x-axis (phi=0 degrees) towards the left side of the human, and the y-axis toward the back of the human.<br> </p>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
Wikimedia Commons photos by prominent users and their usage across the web
<p>Extract from the Wikimedia Commons database containing a list of users selected by the community for having uploaded high quality photos; list of 310k photos of theirs and of the subset of 59k photos sent to Infringement.Report for matching; list of domains whose matches were ignored as not useful for copyleft license enforcement. Domains were then matched for their rank in the Tranco list and the number of image usages found, and ranked by a mix of the two criteria.</p>
Experimental result to investigate the influence of user's tweets and diversification on serendipitous research paper recommendations
<p>This is a raw dataset of the experiment result to investigate the influence of user's tweets and diversification on serendipitous research paper recommendations.</p> <p> </p>
LFM User Groups
<p>This is a subset of the LFM-1b LastFM dataset (http://www.cp.jku.at/datasets/LFM-1b/), which consists of the listening events (user_events.txt) for three user groups: 1,000 low-mainstream users (low_main_users.txt), 1,000 medium-mainstream users (medium_main_users.txt) and 1,000 high-mainstream users (high_main_users.txt). The mainstreaminess definition used here is the "M_global_R_APC" one from this paper: https://arxiv.org/ftp/arxiv/papers/1912/1912.06933.pdf</p> <p>The format of the three user files is "user , mainstreaminess_value"</p> <p>The format of the user-events file is "user \t artist \t album \t track \t timestamp"</p> <p>Example Python-code for analyzing this dataset as well as more information on the user groups based on mainstreaminess can be found here: https://github.com/domkowald/LFM_processing</p>
Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide
<p>Example Dataset for npstat to test the program and the different options.</p> <p>The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of <em>Drosophila melanogaster </em>(<span>doi: 10.1038/nature10811</span>). The dataset also contains the sequence reference of the 2L chromosome in fasta format, an outgroup sequence in fasta format of <em>D. yakuba</em> (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.</p>
User survey data of learning environment eTKMY3, Turku School of Economics, Finland.
<p>User survey data of learning environment (eTKMY3) of an introduction to statistics course, Turku School of Economics. </p> <p>Variables</p> <p>question_11_row_1 Starting eTKMY3 was difficult</p> <p>question_11_row_3 eTKMY3 is a successful system</p> <p>question_11_row_4 I can manage my studying and exercises easily</p> <p>question_11_row_5 Navigation was easy</p> <p>question_11_row_7 eTKMY3 was complex</p> <p>question_12_row_8 individual starting values of most exercises as a good way to promote independent working</p> <p>Observations: Students of Turku School of Economics taking the course "TKMY3 Introduction of Statistics", Spring 2024.</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.