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2,359 results for “Online”
Understanding levels of online participation in the UK museum sector
<p>This is the corresponding data set for the article "Understanding levels of online participation in the UK museum sector". </p> <p>This data set uses a representative sample of 315 UK museums to create a much-needed benchmark against which museum practitioners can evaluate and contextualise prior studies and their own experiences. It includes data from museum websites and five social media platforms, and is one of the largest data sets of its kind in the European museum sector and the first of such scale in the UK. </p>
Dataset of paper Privacy Orientation during Online Teaching-Learning Activities: Practices Adopted and Lessons Learned
<p>Dataset of the paper accepted for publication in the Journal on Interactive Systems (JIS).</p> <p>DA SILVA, M.; VITERBO, J.; SALGADO, L. C. C.; MOURÃO, E. Privacy Orientation during Online Teaching-Learning Activities:<br> Practices Adopted and Lessons Learned. Journal on Interactive Systems, Porto Alegre, RS, 2023.</p>
Supplementary data for "WebQUAST: online evaluation of genome assemblies"
<p>Supplementary data for A. Mikheenko, V. Saveliev, P. Hirsch, A. Gurevich. WebQUAST: online evaluation of genome assemblies.</p> <p><br> Reference genomes of two <em>Escherichia coli</em> K-12 substrains (MG1655 and W3110) and genome annotation of MG1655.</p> <p><em>De novo</em> assemblies of the <em>Escherichia coli</em> K-12 MG1655 short-read Illumina dataset (<a href="https://trace.ncbi.nlm.nih.gov/Traces/index.html?view=run_browser&acc=ERR008613&display=metadata">ERR008613</a>) with ABySS, MEGAHIT, SPAdes, and Velvet.</p> <p>WebQUAST reports in three evaluation modes: <br> * Use Case 1: reference-free evaluation (<em>sample_data_no_ref</em>)<br> * Use Case 2: reference-based evaluation (<em>sample_data_true_ref</em>) <br> * Use Case 3: evaluation based on a close reference (<em>sample_data_close_ref</em>)</p> <p>The snapshot (<a href="https://github.com/ablab/quast/commit/2bd50600bcf63ee826965c10f6ca4cdc2aa046e5">commit 2bd5060</a>) of the QUAST command-line version used by WebQUAST for generating the reports.</p>
Online Panel Participation SPSS Dataset
<p>The provided dataset consists of 454 cases collected for the research of participation in non-probability online research panels. The dataset includes the following variables:</p> <p>Demographics variables</p> <p>The following demographic variables are included: participants' age, gender, level of education, and working status.</p> <p>Trust Scale</p> <p>The six-item General Trust Scale (Yamagishi, T., & Yamagishi, M., 1994) is used to examine the general level of trust of the respondents.</p> <p>Big Five Scale</p> <p>BFI-S (15 items) measuring the Big Five personality characteristics of the participants</p> <p>Online panel participation</p> <p>Online panel participation is measured with one categorical variable.</p> <p>Social media use</p> <p>Social media use is measured with one categorical variable exploring the duration of daily time spent on social media.</p>
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
<p>Dataset of optimisation runs performed for a study comparing reinforcement learning and Bayesian optimisation for online continuous tuning at the example of a linear particle accelerator tuning task.</p> <p> </p> <p><strong>Abstract of the Paper on the Study</strong></p> <p>Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study at the example of a routine task on a real particle accelerator, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study’s results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering<br> advancements.</p>
Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election
<p>This dataset contains ~140M tweets related to the 2020 United States Presidential Election, published and collected between October 2, 2020, and December 2, 2020. In addition, we provide nodes and edges of the superspreader user similarity network, as described in the paper below.</p> <p><strong>Tardelli, S., Nizzoli, L., Avvenuti, M., Cresci, S., & Tesconi, M. Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election.</strong></p> <p>In detail, the dataset consists of:</p> <ul> <li><em>tweet-ids.csv.zip</em></li> <li><em>user_similarity_nework_nodes.csv</em>: a CSV file with the columns "id" and "cluster," relating to the nodes of the superspreader user similarity network mentioned in the paper.</li> <li><em>user_similarity_nework_edges.csv</em>: a CSV file with the columns "source," "target," "weight," and "alpha" relating to the edges of the superspreader user similarity network mentioned in the paper.</li> </ul>
Can we collect health-related quality of life information from anticoagulated atrial fibrillation participants who have recently experienced a bleed? An observational feasibility study in primary, and secondary care and through an online forum
<p>The purpose of the study was to evaluate the feasibility of recruiting participants diagnosed with atrial fibrillation (AF) taking oral anticoagulation therapies (OACs) and recently experiencing a bleed to collect health-related quality of life (HRQoL) information.</p> <p><strong>Design</strong></p> <p>Observational feasibility study. The study aimed to determine the feasibility of recruiting participants with minor and major bleeds, the most appropriate route for recruitment and the appropriateness of the Patient Reported Outcome Measures (PROMs) selected for collecting HRQoL information in AF patients, and the preferred format of the surveys.</p> <p><strong>Setting</strong></p> <p>Primary care, secondary care, and via an online patient forum.</p> <p><strong>Participants</strong></p> <p>The study population was adult patients (≥ 18) with Atrial Fibrillation (AF) taking oral anticoagulation therapies (OATs) who had experienced a recent major or minor bleed within the last four weeks.</p> <p><strong>Primary and Secondary outcome measures</strong></p> <p>Primary outcome:</p> <p>Patient reported outcome measures (PROMs): EuroQol 5 dimensions-5 levels (EQ-5D-5L); Perception of anticoagulant treatment questionnaire, part 2 only (PACT-Q, part 2); Atrial fibrillation effect on quality of life (AFEQT)</p> <p>Secondary outcomes:</p> <p>Location of bleed; bleed severity; current treatment; patient perceptions of HRQoLin relation to bleeding events.</p> <p><strong>Results</strong></p> <p>We received initial expressions of interest from 103 participants. We subsequently recruited 32 participants to the study- 14 from primary care and 18 through the AF forum. No participants were recruited through secondary care. Despite 32 participants consenting, only 26 initial surveys were completed. We received follow-up surveys from 11 participants (8 primary care and 3 AF forum). COVID-19 had a major impact on the study. </p> <p><strong>Conclusions</strong></p> <p>Primary care was the most successful route for recruitment. Most participants recruited to the study experienced a minor bleed. Further ways to recruit in secondary care should be explored, especially to capture more serious bleeds. </p> <p><strong>Registration</strong></p> <p>The study was adopted onto the NIHR Portfolio (I.D. #47771) and registered with www.ClinicalTrials.gov (#NCT04921176) in February 2021.</p> <p>Dataset contains all anonymised data for the participants who completed the survey including demographics, details of bleeds, co-morbidities and completed patient reported outcomes</p>
Village sections layer screenshot of the Kunbaja online resource
<p>Information layer "village sections" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Village sections dataset of the Kunbaja online resource
<p>Information layer "village sections" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Road names layer screenshot of the Kunbaja online resource
<p>Information layer "road names" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Plots dataset of the Kunbaja online resource
<p>Information layer "plots" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Features dataset of the Kunbaja online resource
<p>Information layer "features" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Boundaries dataset of the Kunbaja online resource
<p>Information layer "boundaries" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Road names dataset of the Kunbaja online resource
<p>Information layer "road names" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Hydro features layer screenshot of the Kunbaja online resource
<p>Information layer "hydro features" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Roads dataset of the Kunbaja online resource
<p>Information layer "roads" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Roads layer screenshot of the Kunbaja online resource
<p>Information layer "roads" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Plot layer screenshot of the Kunbaja online resource
<p>Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Hydro features dataset of the Kunbaja online resource
<p>Information layer "hydro-features" of the Kunbaja online resource model built in QGIS as dataset in the ESRI shapefile format</p>
Magyar plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</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.