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216
datasets available to search
ShareScore release 0.9.0
Dataset results
216 results for “workplace.”
Impact Study of Workplace Mental Health Education on Early Intervention for Healthcare Workers With Mental Health Issues
ClinicalTrials.gov study NCT02158871. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Mindfulness Training for Smoking Cessation in Women in Workplaces
ClinicalTrials.gov study NCT02497339. IPD Sharing: Not stated. Countries: 1. Publications: 2.
MANAGE AT WORK: Addressing the Challenge of Chronic Physical Health Conditions in the Workplace
ClinicalTrials.gov study NCT01978392. IPD Sharing: YES. Countries: 1. Publications: 5.
Implementation of Physical Exercise at the Workplace (IRMA08) - Healthcare Workers
ClinicalTrials.gov study NCT01921764. IPD Sharing: Not stated. Countries: 1. Publications: 4.
AR vs In Person Simulation for Medical Workplace Training
ClinicalTrials.gov study NCT05674188. IPD Sharing: NO. Countries: 1. Publications: 1.
Testing the Efficacy of an Online Alcohol Intervention in a Workplace Setting
ClinicalTrials.gov study NCT01931618. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Prompting Activity in a Workplace Setting
ClinicalTrials.gov study NCT02785640. IPD Sharing: NO. Countries: 1. Publications: 12.
Modifying the Workplace to Decrease Sedentary Behavior and Improve Health
ClinicalTrials.gov study NCT02376504. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Developing Interview Questions to Estimate Workplace Exposure to Electric and Magnetic Fields
ClinicalTrials.gov study NCT00340054. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Lean Leadership Training Program on Head Nurses' Lean Performance and Staff Nurses' Workplace Safety at Mansoura Children Hospital
ClinicalTrials.gov study NCT06989294. IPD Sharing: NO. Countries: 1. Publications: 0.
Internet-delivered Interventions for Stress, Anxiety and Depression in the Workplace
ClinicalTrials.gov study NCT03271645. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Stress and Mental Ill-health in the Workplace: Evaluation of an Intervention for the Prevention of Sick Leave
ClinicalTrials.gov study NCT02563743. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Implementation of Physical Exercise at the Workplace (IRMA06) - Slaughterhouse Workers
ClinicalTrials.gov study NCT01671267. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Soft Active Back Exosuit to Reduce Workplace Back Pain
ClinicalTrials.gov study NCT05802914. IPD Sharing: YES. Countries: 1. Publications: 4.
StandUP UBC: Reducing Workplace Sitting
ClinicalTrials.gov study NCT03375749. IPD Sharing: NO. Countries: 1. Publications: 6.
Combating Obesity in a Workplace
ClinicalTrials.gov study NCT06768892. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Criticism by community people and poor workplace communication as risk factors for the mental health of local welfare workers after the Great East Japan Earthquake: a cross-sectional study
Open the record for dataset details and reuse information.
Data from: Adherence to the Tobacco Control Act, 2007: presence of a workplace policy on tobacco use in bars and restaurants in Nairobi, Kenya
Open the record for dataset details and reuse information.
2011 Census estimates of the workplace population in England and Wales
<p>Estimated number of workers in each 2011 Census output area in England and Wales, processed and saved in an easy to analyse format.</p> <p>Original Source: https://www.nomisweb.co.uk/datasets/1300_1</p> <p>This dataset provides 2011 Census estimates of the workplace population in England and Wales by residence type (household or communal resident), by sex and by age. The estimates are as at census day, 27 March 2011.</p> <p>Statistics about the number and demographic characteristics of people are used to monitor differences and track how these proportions change over time.</p> <p><strong>Statistical Disclosure Control</strong></p> <p>In order to protect against disclosure of personal information from the 2011 Census, there has been swapping of records in the Census database between different geographic areas, and so some counts will be affected. In the main, the greatest effects will be at the lowest geographies, since the record swapping is targeted towards those households with unusual characteristics in small areas.</p> <p>More details on the ONS Census disclosure control strategy may be found on the http://www.ons.gov.uk/ons/guide-method/census/2011/census-data/2011-census-prospectus/new-developments-for-2011-census-results/statistical-disclosure-control/index.html[Statistical Disclosure Control] page on the ONS web site.</p> <p>Distributed under Open Government License v3.0: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/</p>
Data from : Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning
<h2>Description</h2> <p>This repository serves as a complementary resource accompanying the academic paper titled "Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning" published in <em>Applied Acoustics </em>(available at: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apacoust.2024.109955" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apacoust.2024.109955</a>). It comprises a dataset containing the acoustic data, metadata, and perceptual annotations.</p> <p>In addition, we provide the .<em>h5</em> datasets and PyTorch model weights to run the code corresponding to the neural network section discussed in the paper (publicly accessible at: <a href="https://github.com/effajr/predicting_alarm_audibility">https://github.com/effajr/predicting_alarm_audibility</a>).</p> <p>The repository is composed of five .<em>zip</em> files:</p> <ul> <li><strong>source_audio </strong>: contains the source audio files used to generate the alarms and backgrounds present in the <strong>data</strong> file.</li> <li><strong>data</strong> : contains the audio files corresponding to the alarms and backgrounds, along with the perceptual annotations.</li> <li><strong>metadata</strong> : contains the metadata related to the alarms and backgrounds present in the <strong>data</strong> file, and to the source audio files contained in <strong>source_audio</strong>.</li> <li><strong>features </strong>: contains the .<em>h5</em> files representing the development and evaluation subsets (mel-spectrograms and perceptual labels) used in the deep learning approach presented in the paper.</li> <li><strong>trained_models </strong>: contains the PyTorch model weights for the 10 runs of model training mentionned in the paper.</li> </ul> <h2>How to use the data</h2> <p>To run the code present in the GitHub repository, we recommend extracting the files <strong>data</strong>.zip, <strong>features</strong>.zip and <strong>trained_models</strong>.zip in their corresponding folders in the "<strong>application</strong>" folder (see <a href="https://github.com/effajr/predicting_alarm_audibility">https://github.com/effajr/predicting_alarm_audibility</a>).</p> <h2>Content</h2> <p>Content of <strong>data</strong>.zip </p> <pre>annotations/ ├─ dev/ │ ├─ annotation_compilation_dev.csv : Compilation of all the listening conditions and <br>│ │ individual annotator responses for the development data.<br>│ ├─ dev_conditions.csv : Unique listening conditions (extracted from annotation_compilation_dev.csv). │ ├─ dev_labels.csv : All individual annotator responses for each <br>│ │ unique listening condition (extracted from annotation_compilation_dev.csv). │ ├─ dev_train_valid_split.csv : Random 80%/20% training/validation split used for development <br>│ │ in the experiments reported in the paper. ├─ eval/<br>│ ├─ annotation_compilation_eval.csv : Compilation of all the listening conditions and individual annotator <br>│ │ responses for the evaluation data.<br>│ │ The column 'clearly_audible_mean' represents individual annotator <br>│ │ binary responses evaluated for each listening condition.<br>│ │ The column 'clearly_audible_pf' represents individual annotator <br>│ │ psychometric functions evaluated for each listening condition.<br>│ │<br>│ ├─ eval_conditions.csv : Unique listening conditions (extracted from annotation_compilation_eval.csv). │ ├─ eval_labels_apf.csv : All individual annotator psychometric function values for each <br>│ │ unique listening condition (extracted from annotation_compilation_eval.csv). │ ├─ eval_labels_mv.csv : All individual annotator binary responses for each <br>│ │ unique listening condition (extracted from annotation_compilation_eval.csv)<br>│<br>audio/ : <em>.wav</em> files corresponding to the alarms and backgrounds for Development and <br> Evaluation subsets of the dataset. ├─ dev/ │ ├─ alarms/ │ ├─ backgrounds/ ├─ eval/ │ ├─ alarms/ │ ├─ backgrounds/<br><br><br>Content of <strong>metadata</strong>.zip <br><br>├─ audio_metadata.xlsx : Table of the alarms and background files with short descriptions, <br>│ source file names, and temporal information (in seconds). ├─ source_file_metadata.xlsx : Metadata table of the original files used to generate alarms and backgrounds. </pre>
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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)
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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.