Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,617
datasets available to search
ShareScore release 0.9.0
Dataset results
1,617 results for “user”
The Olympic gold medalists and Instagram - A longitudinal study on user characteristics
<p>This dataset includes Instagram user characteristics of those Olympic athletes who won gold medals in the individual events of Rio2016. The name of all these gold medalists of individual events are in the dataset (226 athletes), however only 144 athletes (83 men and 61 women) had a publicly available Instagram account in all of the observations during the 4 months period of data gathering. The first round of data gathering (first observation, i.e. OlympicAthletesData_1) took place 9-Aug-2019 to 12-Aug-2019, the second round of data gathering (second observation, i.e. OlympicAthletesData_2) took place 9-Sep-2019 to 12-Sep-2019, the third round of data gathering (third observation, i.e. OlympicAthletesData_3) took place 9-Oct-2019 to 12-Oct-2019, the fourth round of data gathering (fourth observation, i.e. OlympicAthletesData_4) took place 9-Nov-2019 to 12-Nov-2019. The data gathered for each user (in each observation) consists of:</p> <p>1- Name of the individual event </p> <p>2- Country</p> <p>3- Name</p> <p>4- Gender</p> <p>5- Instagram ID</p> <p>6- Number of Posts</p> <p>7- Number of followers</p> <p>8- Number of followings</p> <p>9- Maximum Number of likes (in the last 10 photo posts)</p> <p>10- Number of comments for the post with Maximum Number of likes (in the last 10 photo posts)</p> <p>11- Number of self-presenting posts in the last 10 photo posts (those posts in which the athlete is present)</p> <p>12- Number of pure self-presenting posts in the last 10 photo posts (those posts in which the athlete is the only person who is present)</p> <p>13- Age</p> <p>14- Date of data crawling</p>
Outcomes over output: a user-centric approach to building successful systems
<p><strong>Opening Plenary and keynote: Jeff Gothelf: </strong>Outcomes over output: a user-centric approach to building successful systems<br> Chair: <strong>Jessica Lindholm</strong>, Chalmers University of Technology</p> <p> </p>
Data for Say it aloud: Measuring change talk and user perceptions in an automated, technology-delivered adaptation of motivational interviewing delivered by video-counsellor
<p>Two of three datasets from study where participants engaged in a spoken 'dialogue' with a pre-recorded video counsellor asking open questions to promote behaviour change. </p> <p><strong>interview length and change talk results.csv</strong> reports duration of dialogue and number of instances of change talk and sustain talk detected by human coders in each participant's speech. Transcripts of participants' speech are not uploaded because we did not request participants' consent for sharing. When designing the study, we did not know if people would feel comfortable speaking aloud to the computer; we did not want to make them feel more self-conscious by knowing their speech would shared. The data are from 16 of 18 participants; two were excluded for not talking about a goal to increase physical activity, which was a requirement of the study.</p> <p>Version 1.0 contains ratings of the number of instances of change and sustain talk, which required the coder to judge when a complex statement should be scored as containing more than one instance. Version 2.0 was created in response to a reviewer's suggestion that coders should simply score the presence or absence of change and sustain talk. The new dataset presents the agreed scoring of two independent coders who scored whether change or sustain talk were present in each participant's response to each question posed by the video-counsellor. </p> <p><strong>Questionnaire Units of Analysis.docx</strong> contains participants' written responses to an evaluation questionnaire completed one week after the interview. These responses were the raw material for thematic analysis. This dataset contains responses from 18 participants, including participants 7 and 11 who did not talk about a goal of physical activity when interacting with the video-counsellor.</p> <p> </p>
Last-fm User and Artist Gender Repository
<p>Dataset consisting of Last.fm listening events data with annotated gender labels for both users and artists. The dataset is generted to acompony the paper '<em>Exploring Artist Gender Bias in Music Recommendation</em>' submitted to the <strong>2nd</strong> <strong>Workshop on the Impact of Recommender Systems with ACM RecSys 2020.</strong></p> <p>The dataset is formed from two Last.fm datasets:</p> <ul> <li><strong>Schedl's Lfm-1b</strong> - LFM-1b-Le75.csv, LFM1b-MB-artists.txt</li> <li><strong>Celma's Lfm-360k</strong> - LastFM360k-Le75.txt, LastFM360k-MB-artists.txt</li> </ul> <p>Artist gender data is recovered via a datawrangler configured to retrieve data from a locally configured version of the music ensicolopedia, MusicBrainz. Code repositories are made openly availible at the following link to elicit reproducibility: <a href="https://github.com/dshakes90/LFM-1b-MusicBrainz-Gender-Wrangler">https://github.com/dshakes90/LFM-1b-MusicBrainz-Gender-Wrangler</a></p> <p> </p> <p> </p>
Teleoperation with Baxter robot and haptic device: testing with experts user.
<p>In this video you can see the experiment with expert users developed by Robotics Group of the Universidad de León, as part of a research project that aims to demonstrate the effectiveness of the use of haptic devices in teleoperation environments.</p>
Supporting live data sessions in multi-user virtual reality using CAVA360VR
<p>A dense composite 2D video clip showing the different screens and audio chat of three analysts using <em>CAVA360VR </em>(Collaborate, Annotate, Visualise, Analysis 360 video in Virtual Reality) to hold a live virtual data session at a national research conference in 2019. The three co-analysts (M, J and P) in VR were collaborating even though they were in three different physical locations across two countries. The video data that they analysed together involved multicam recordings of six participants in a two-team competition to solve a Lego puzzle when only one member in each group could view the hidden Lego construction in order to instruct the others in their team how to rebuild it from a pile of blocks. The video clip shows the viewport chosen by each of the three analysts. There are three possible viewports that can be selected: a) what they each see in their VR HMD (head-mounted display), b) an over-the-shoulder view (with an outline showing their avatar), and c) a static third-person view of the three analysts. A fourth screen shows the view of an observer in non-VR mode.</p>
Raw data: Effect of the Relative Timing between Same-Polarity Pulses on Thresholds and Loudness in Cochlear Implant Users
<p>Raw values in dB re. 1 µV of the thresholds and loudness-balanced levels at MCL from:</p> <p>Guérit, F., Marozeau, J., Epp, B., & Carlyon, R. P. (2020). Effect of the Relative Timing between Same-Polarity Pulses on Thresholds and Loudness in Cochlear Implant Users. <em>Journal of the Association for Research in Otolaryngology</em>, 1–14. doi:10.1007/s10162-020-00767-y</p>
Check Mate: Prioritizing User Generated Multi-Media Content for Fact-Checking
<p>Given volume of content and misinformation on social media, there is a need for systems that can support fact checkers by prioritizing content that needs to be fact checked. Prior research on prioritizing content for fact-checking has focused on news media articles, predominantly in English language. But there is an increasing amount of misinformation in user-generated content. Furthermore, misinformation is generated through information across modalities. In this paper we present a novel dataset that can be used to prioritize check-worthy posts from multi-media content in Hindi. It is unique in its 1) focus on user generated content, 2) multi-modality and 3) Hindi as the primary language of content. In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation. </p>
Dataset for the paper "The skipping behavior of users of music streaming services and its relation to musical structure"
<p>Dataset for the paper "The skipping behavior of users of music streaming services and its relation to musical structure" by Nicola Montecchio, Pierre Roy, François Pachet - 10.1371/journal.pone.0239418</p>
Grey literature strategies survey results and questionnaires: producers, users and collecting services
<p>Survey results from three online surveys conducted in 2013 of research producing organisations,research users and collecting services involved in research for public policy and practice. Most respondents were from Australia. </p> <p>Questionnaires and SPSS versions also provided.</p> <p>Results have been published in various publications available in this collection.</p> <p> </p> <p> </p>
Supporting user preferences in search-based product line architecture design using Machine Learning
<p>The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA design requires intensive human effort as it involves several conflicting factors. In order to support this task, an interactive search-based approach, automated by a tool named OPLA-Tool, was proposed in a previous work. Through this tool the software architect evaluates the generated solutions during the optimization process. Considering that evaluating PLA is a complex task and search-based algorithms demand a high number of generations, the evaluation of all solutions in all generations cause human fatigue. In this work, we incorporated in OPLA-Tool a Machine Learning (ML) model to represent the architect in some moments during the optimization process aiming to decrease the architect's effort. Through the execution of a quanti-qualitative exploratory study it was possible to demonstrate the reduction of the fatigue problem and that the solutions produced at the end of the process, in most cases, met the architect’s needs.</p>
User Involvement in Smart Home Learning
<p>KI-basierte Smart Homes sind in der Regel darauf ausgelegt, vollständig autonom zu arbeiten. Aufgrund dessen berichten viele Nutzer:innen doch jedoch von einem Gefühl des Kontrollverlusts in Verbindung mit mangelndem Verständnis für die Funktionsweise des Systems.</p> <p>In einer ersten Onlinestudie untersuchen wir die Möglichkeit einer besseren User Experience durch Beteiligung der User in der Lernphase eines Smart Homes.</p> <p>In diesem animierten Poster wird kurz in die Thematik und die Studie eingeführt.</p> <p>Detaillierte Informationen befinden sich unter:</p> <p>https://osf.io/hg56x</p>
Adapting ADCI Windows Desktop Graphical User Interface to ADCI-HT on IBM BG/Q.
<p>Supplementary Figure 1. <strong>Adapting ADCI Windows Desktop Graphical User Interface to ADCI-HT on IBM BG/Q. </strong>Data flow diagram illustrating the steps required to perform metaphase image processing tasks using A) Windows ADCI, and B) BG/Q ADCI (ADCI-HT) software platforms.</p>
Paper Prototype - Example of LearnIn's Personal Learning Record Store as User Story
<p>This artefact illustrates a possible user journey and very simplified, the support that LearnIn's digital ecosystem can provide. <br> The steps and stages show - in a paper prototype style - MIZO's learning pathway. </p>
DSpace user group video
<p>This is a video file of Michele Kimpton presenting at a DSpace user group meeting for COAR attendees.</p>
Thaumatin / Diamond Light Source I04 user training
<p>Data recorded on Diamond Beamline I04 as part of user training (block allocation group training; 2013-07-03) from thaumatin crystal prepared following standard methods. Data collection strategy based on recording three images and running EDNA MXv1, parameters recorded in the image header as follows at end of message.</p> <p> </p> <p>Objective of publishing data: (i) testing system (ii) making tutorial data available for DIALS project (http://dials.sourceforge.net) (iii) illustrating methods for publishing raw data for MX community.</p> <p> </p> <p># Detector: PILATUS 6M Prosport+, S/N 60-0100 Diamond</p> <p># 2013-07-03T09:49:57.347</p> <p># Pixel_size 172e-6 m x 172e-6 m</p> <p># Silicon sensor, thickness 0.000320 m</p> <p># Exposure_time 0.0645000 s</p> <p># Exposure_period 0.0670000 s</p> <p># Tau = 199.1e-09 s</p> <p># Count_cutoff 161977 counts</p> <p># Threshold_setting: 6329 eV</p> <p># Gain_setting: mid gain (vrf = -0.200)</p> <p># N_excluded_pixels = 1629</p> <p># Excluded_pixels: badpix_mask.tif</p> <p># Flat_field: (nil)</p> <p># Trim_file: p6m0100_E12658_T6329_vrf_m0p20.bin</p> <p># Image_path: /ramdisk/2013/mx4014-3/20130703/thaumatin/</p> <p># Comment: Mini kappa in use</p> <p># Wavelength 0.97625 A</p> <p># Energy_range (0, 0) eV</p> <p># Detector_distance 0.26527 m</p> <p># Detector_Voffset 0.00000 m</p> <p># Beam_xy (1225.35, 1193.47) pixels</p> <p># Filter_transmission 0.2498</p> <p># Start_angle 82.0000 deg.</p> <p># Angle_increment 0.1500 deg.</p> <p># Detector_2theta 0.0000 deg.</p> <p># Polarization 0.990</p> <p># Alpha 0.0000 deg.</p> <p># Kappa 0.0000 deg.</p> <p># Phi 0.0000 deg.</p> <p># Phi_increment 0.0000 deg.</p> <p># Omega 82.0000 deg.</p> <p># Omega_increment 0.1500 deg.</p> <p># Chi 0.0000 deg.</p> <p># Chi_increment 0.0000 deg.</p> <p># Oscillation_axis X, CW</p> <p># N_oscillations 1</p> <p> </p>
Interactive Tagging Networks (Following/Followers and Tags on 1 million Twitter Users)
<p><strong>Abstract</strong> (our paper)</p> <p>How do users behave if they can tag each other in social networks? In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems.</p> <p><strong>Data</strong></p> <p>twitter.seed.users:<br> The first column is the user id, and the second column is the json of the user objects on Twitter. This is the set of 1 million seed users to collect the following data.</p> <p>twitter.tagging.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id.</p> <p>twitter.tagging-out-going-from-seed-users.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id. This is only the out-going edges from the seed users, <em>i.e.</em>, this is a subset of twitter.tagging.network.</p> <p>twitter.following.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id).</p> <p>twitter.following-closed-seed-users.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id). This is not used in the following publication paper, but will be useful in other studies.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Patterns in Interactive Tagging Networks. <em>Proceedings of the Ninth International AAAI Conference on Web and Social Media (ICWSM-15)</em>. pp.513-522, 2015.<br> http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10556</p> <p><strong>Code</strong></p> <p>Our code outputting experiment results made available at:<br> https://github.com/yamaguchiyuto/icwsm15</p>
ASSOCIATION BETWEEN DIABETIC NEUROPATHY AND CLINICAL VARIABLES IN USERS OF A HEALTH CARE CENTER IN BRAZIL
<p>Database of 207 patients with clinical and sociodemographic information for statistical analysis. No identification of participants.</p> <p> </p>
Sensor solutions for an energy-efficient and user-centered heating system
<p>Corresponding dataset for the article "Sensor solutions for an energy-efficient and user-centered heating system" published in the Journal of Sensors and Sensor Systems Special Issue "Sensors and Measurement Systems 2016".</p>
Dataset - Overcoming Barriers for Ubiquitous User-Centric Healthcare Services
<p>Datasets used for experimental results (Figure 5) for different virtualization configurations: (a) Average TCP latency; (b) Average TCP throughput w.r.t. message size; (c) Request service response time for variable number of concurrent connections; (d) Request throughput per second for variable number of concurrent connections.</p> <p>Intel Xeon E5-2650 Haswell 2.60 GHz, 64 GB RAM, bare-metal OS: Centos 7</p> <p>OpenStack, Linux KVM, Ubuntu 16.04, para-virtualized VirtIO drivers (network card, disk)</p> <p>Data format: CSV</p> <p>Source: Experiments</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.