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121 results for “Online Training”
Popularity Dataset for Online Stats Training
<p>This is a dataset used for the online stats training website (<a href="https://www.rensvandeschoot.com/tutorials/">https://www.rensvandeschoot.com/tutorials/</a>) and is based on the data used by <a href="https://doi.org/10.1016/j.adolescence.2009.12.004">van de Schoot, van der Velden, Boom, and Brugman (2010)</a>.</p> <p>The dataset is based on a study that investigates an association between popularity status and antisocial behavior from at-risk adolescents (n = 1491), where gender and ethnic background are moderators under the association. The study distinguished subgroups within the popular status group in terms of overt and covert antisocial behavior.For more information on the sample, instruments, methodology, and research context, we refer the interested readers to <a href="https://doi.org/10.1016/j.adolescence.2009.12.004">van de Schoot, van der Velden, Boom, and Brugman (2010)</a>.</p> <p> </p> <p>Variable name Description</p> <p>Respnr = Respondents’ number</p> <p>Dutch = Respondents’ ethnic background (0 = Dutch origin, 1 = non-Dutch origin)</p> <p>gender = Respondents’ gender (0 = boys, 1 = girls)</p> <p>sd = Adolescents’ socially desirable answering patterns</p> <p>covert = Covert antisocial behavior</p> <p>overt = Overt antisocial behavior</p>
Dataset and trained models belonging to the article 'Distant reading patterns of iconicity in 940.000 online circulations of 26 iconic photographs'
<p>Quantifying Iconicity - Zenodo</p> <p><br> ## The Dataset<br> This dataset contains the material collected for the article "Distant reading 940,000 online circulations of 26 iconic photographs" (to be) published in New Media & Society (DOI: 10.1177/14614448211049459). We identified 26 iconic photographs based on earlier work (Van der Hoeven, 2019). The Google Cloud Vision (GCV) API was subsequently used to identify webpages that host a reproduction of the iconic image. The GCV API uses computer vision methods and the Google index to retrieve these reproductions. The code for calling the API and parsing the data can be found on GitHub: https://github.com/rubenros1795/ReACT_GCV.</p> <p>The core dataset consists of .tsv-files with the URLs that refer to the webpages. Other metadata provided by the GCV API is also found in the file and manually generated metadata. This includes:<br> - the URL that refers specifically to the image. This can be an URL that refers to a full match or a partial match<br> - the title of the page<br> - the iteration number. Because the GCV API puts a limit on its output, we had to reupload the identified images to the API to extend our search. We continued these iterations until no more new unique URLs were found<br> - the language found by the ``langid`` Python module [link](https://github.com/saffsd/langid.py), along with the normalized score.<br> - the labels associated with the image by Google<br> - the scrape date</p> <p>Alongside the .tsv-files, there are several other elements in the following folder structure:</p> <p>```<br> ├── data<br> │ ├── embeddings<br> │ └── doc2vec<br> │ └── input-text<br> │ └── metadata<br> │ └── umap<br> │ └── evaluation<br> │ └── results<br> │ └── diachronic-plots<br> │ └── top-words<br> │ └── tsv<br> ```</p> <p>1. The ```/embeddings``` folder contains the doc2vec models, the training input for the models, the metadata (id, URL, date) and the UMAP embeddings used in the GMM clustering. Please note that the date parser was not able to find dates for all webpages and for this reason not all training texts have associated metadata.<br> 2. The ```/evaluation``` folder contains the AIC and BIC scores for GMM clustering with different numbers of clusters.<br> 3. The ```/results``` folder contains the top words associated with the clusters and the diachronic cluster prominence plots.</p> <p>## Data Cleaning and Curation<br> Our pipeline contained several interventions to prevent noise in the data. First, in between the iterations we manually checked the scraped photos for relevance. We did so because reuploading an iconic image that is paired with another, irrelevant, one results in reproductions of the irrelevant one in the next iteration. Because we did not catch all noise, we used Scale Invariant Feature Transform (SIFT), a basic computer vision algorithm, to remove images that did not meet a threshold of ten keypoints. By doing so we removed completely unrelated photographs, but left room for variations of the original (such as painted versions of Che Guevara, or cropped versions of the Napalm Girl image). Another issue was the parsing of webpage texts. After experimenting with different webpage parsers that aim to extract 'relevant' text it proved too difficult to use one solution for all our webpages. Therefore we simply parsed all the text contained in commonly used html-tags, such as ```<p>```, ```<h1>``` etc.</p>
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
Data from: Effectiveness of Online Off-the-Job Training in Attracting Participants and Video-On-Demand Streaming in Improving Work-Life Balance: A Study Focusing on Medical Technologists
<p>The Nara Association of Medical Technologists has introduced online Off-Job Training (Off-JT) starting from FY2020 in response to the COVID-19 pandemic. This study aims to evaluate the online Off-JT, which differs from the traditional face-to-face format. Firstly, we compared the online format's ability to attract participants with the face-to-face format based on the number of training sessions and attendees. Despite having fewer training sessions (40.8% less), the online format had an average attendance of 105.4% higher (39.7 vs. 19.3) than the face-to-face format. To enhance participant convenience, we offered a limited number of live and video-on-demand (VOD) sessions on YouTube, evaluating their usefulness through an online survey focusing on work-life balance (WLB). The survey results showed that 81.9% (458/559) of respondents reported an improvement in WLB. The effect on WLB improvement varied depending on the viewing method, with VOD sessions showing 84.1% (376/447) and live sessions showing 73.2% (82/112). We believe that the increased ability to attract participants in the online Off-JT is mainly due to the elimination of travel burdens through internet-connected devices. The combination of live and VOD sessions on YouTube allowed participants to adjust their viewing time, leading to better allocation of free time and improved WLB. The online Off-JT and VOD delivery have shown to enhance convenience for participants by removing geographical and time constraints, resulting in positive effects.</p>
Online MedEd Intern Bootcamp: Online Training for First Year Residents
ClinicalTrials.gov study NCT06977243. IPD Sharing: YES. Countries: 1. Publications: 2.
Assessing Mental Health Providers' Clinical Knowledge and Skills Via an Online Training on LGBTQ-affirmative Cognitive-behavioral Therapy
ClinicalTrials.gov study NCT04559698. IPD Sharing: NO. Countries: 1. Publications: 5.
Increasing Help-Seeking Behavior Among Transitioning Veterans at Risk for Suicide With Online Gatekeeper Training
ClinicalTrials.gov study NCT04565951. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness of Online Emotional Intelligence Training
ClinicalTrials.gov study NCT06751745. IPD Sharing: NO. Countries: 1. Publications: 6.
Serious Game Versus Online Course to Pre-train Medical Students on the Management of an Adult Cardiac Arrest.
ClinicalTrials.gov study NCT02758119. IPD Sharing: NO. Countries: 1. Publications: 5.
Effectiveness of an Online Parenting Training
ClinicalTrials.gov study NCT05111886. IPD Sharing: YES. Countries: 1. Publications: 5.
Online MedEd Intern Bootcamp: Hybrid (Online+Live) Training for First Year Residents
ClinicalTrials.gov study NCT06970340. IPD Sharing: YES. Countries: 1. Publications: 1.
The Effect of Online Coping Skills Training
ClinicalTrials.gov study NCT05107895. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
Open the record for dataset details and reuse information.
Data for "Improving semantic video retrieval models by training with a relevance-aware online mining strategy"
<p>This repository contains all the data available for the publication:</p> <p><a href="https://doi.org/10.1016/j.cviu.2024.104035">Alex Falcon, Giuseppe Serra, and Oswald Lanz. <em>Improving semantic video retrieval models by training with a relevance-aware online mining strategy</em>. <strong>Computer Vision and Image Understanding</strong>. 2024.</a></p> <p>Code is available at: <a href="https://github.com/aranciokov/ranp/">https://github.com/aranciokov/ranp/</a></p> <p>The data includes:</p> <ul> <li>pre-extracted features (ordered_feature_*.zip files)</li> <li>annotations, such as pre-extracted semantic graphs, glove checkpoints, class annotations, etc (annotations_*.zip files)</li> <li>train/val/test, when available, split information (public_split_*.zip) files</li> <li>pretrained models for HGR and EAO (details in the github repo)</li> </ul>
Online Training for Addressing Perinatal Depression
ClinicalTrials.gov study NCT04919967. IPD Sharing: YES. Countries: 1. Publications: 7.
Online Group-based Dual-task Training to Improve Cognitive Function of Community-dwelling Older Adults
ClinicalTrials.gov study NCT05573646. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness of an Online Training and Support Program (iSupport) for Informal Dementia Caregivers
ClinicalTrials.gov study NCT04104568. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluation of an Online Comprehensive Behavioral Intervention for Tics (CBIT) Therapist Training Program
ClinicalTrials.gov study NCT05547854. IPD Sharing: YES. Countries: 1. Publications: 8.
Investigating Differential Effects of Online Mental Training Interventions on Mental Well-being and Social Cohesion
ClinicalTrials.gov study NCT04889508. IPD Sharing: Not stated. Countries: 1. Publications: 2.
RCT Regarding SDM Online Training and Face-to-face SDM Training
ClinicalTrials.gov study NCT02674360. IPD Sharing: NO. Countries: 1. Publications: 9.
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.