3 datasets found
  1. c

    COVID-19 Impact Dataset: Great British Intelligence Test, 2020

    • datacatalogue.cessda.eu
    Updated Mar 25, 2025
    + more versions
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    Hampshire, A (2025). COVID-19 Impact Dataset: Great British Intelligence Test, 2020 [Dataset]. http://doi.org/10.5255/UKDA-SN-854451
    Explore at:
    Dataset updated
    Mar 25, 2025
    Dataset provided by
    Imperial College London
    Authors
    Hampshire, A
    Time period covered
    Jan 1, 2020 - Oct 1, 2020
    Area covered
    United Kingdom
    Variables measured
    Individual
    Measurement technique
    RecruitmentStarting from December 26th 2019, participants were recruited to the study website, where they completed cognitive tests and a detailed questionnaire. Articles describing the study were placed on the BBC2 Horizon, BBC Home page, BBC News Home page and circulated on mobile news meta-apps from January 1st 2020. To maximise representativeness of the sample there were no inclusion/exclusion criteria. Analyses here exclude data from participants under 16 years old, as they completed a briefer questionnaire, and those who responded to the questionnaire unfeasibly fast (<4 minutes). Cognitive test data will be reported separately. The study was approved by the Imperial College Research Ethics Committee (17IC4009). Data collectionData were collected via our custom server system, which produces study-specific websites (https://gbws.cognitron.co.uk) on the Amazon EC2. Questionnaires and tests were programmed in Javascript and HTML5. They were deliverable via personal computers, tablets and smartphones. The questionnaire included scales quantifying sociodemographic, lifestyle, online technology use, personality, and mental health (Supplement 1). Participants could enrol for longitudinal follow up, scheduled for 3, 6 and 12 months. People returning to the site outside of these timepoints were navigated to a different URL. On May 2nd 2020, the questionnaire was augmented - in light of the Covid-19 pandemic - with an extended mood scale, and an instrument comprising 47 items quantifying self-perceived effects on mood, behaviour and outlook (Pandemic General Impact Scale PD-GIS-11). Questions regarding pre-existing psychiatric and neurological conditions, lockdown context, having the virus, and free text fields were added. This coincided with further promotion via BBC2 Horizon and BBC Homepage.
    Description

    There is an urgent need to understand the factors that mediate and mitigate the impact of the Covid-19 pandemic on behaviour and wellbeing. However, the onset of the outbreak was unexpected and the rate of acceleration so rapid as to preclude the planning of studies that can address these critical issues. Coincidentally, in January 2020, just prior to the outbreak in the UK, my team launched a study that collected detailed (~50 minute) cognitive and questionnaire assessments from >200,000 members of the UK public as part of a collaboration with the BBC. This placed us in a unique position to examine how aspects of mental health subsequently changed as the pandemic arrived in the UK. Therefore, we collected data from a further ~120,000 people in May, including additional detailed measures of self-perceived pandemic impact and free text descriptions of the main positives, negatives and pragmatic measures that people found helped them maintain their wellbeing.

    In this data archive, we include the survey data from January and May 2020 examining impact of Covid-19 on mood, wellbeing and behaviour in the UK population. This data is reported in a preprint article, where we apply a novel fusion of psychometric, multivariate and machine learning analyses to this unique dataset, in order to address some of the most pressing questions regarding wellbeing during the pandemic in a data-driven manner. The preprint is available on this URL. https://www.medrxiv.org/content/10.1101/2020.06.18.20134635v1

  2. BioPropaPhenKG Towards Monkeypox and COVID-19 Case Tracing and Analysing

    • zenodo.org
    • data.niaid.nih.gov
    bin, xml
    Updated Apr 17, 2024
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    Gabriel H. A. Medeiros; Gabriel H. A. Medeiros (2024). BioPropaPhenKG Towards Monkeypox and COVID-19 Case Tracing and Analysing [Dataset]. http://doi.org/10.5281/zenodo.10987743
    Explore at:
    xml, binAvailable download formats
    Dataset updated
    Apr 17, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Gabriel H. A. Medeiros; Gabriel H. A. Medeiros
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This repository contains:

    • The BioPropaPhen ontology created from PropaPhen, being specialized with UMLS and World Knowledge Graph ontologies;
    • A neo4j 4.4.3 dump file of the BioPropaPhenKG knowledge graph with WHO ground truth data about COVID-19 and Monkeypox, and enhanced presence edges between UMLS entities to World KG entities for evaluating the Description-Detection-Prediction Framework

    The datasets used for enhancing the KG are:

    PhenomenonDatasetPeriodDocumentsSourceLink
    COVID-19AylienNov-20198Online Newsttps://aylien.com/resources/datasets/coronavirus-dataset
    COVID-19CORD-19Dec-2019720Medical Articleshttps://allenai.org/data/cord-19
    COVID-19RedditCOVIDFeb-20204,980Social Mediahttps://paperswithcode.com/dataset/the-reddit-covid-dataset
    MonkeypoxMined from BBCMay-202227Online News
    MonkeypoxMined from PubmedJune-202236Medical Articles
    MonkeypoxMonkeyPox2022May-202233,826Social Mediahttps://doi.org/10.3390/idr14060087
  3. o

    Covid Chronicles: Home Schooling: "Don't Stress Too Much"

    • ordo.open.ac.uk
    docx
    Updated Nov 16, 2022
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    Thanuja Hettiarachchi; Ahmad Al-rashid (2022). Covid Chronicles: Home Schooling: "Don't Stress Too Much" [Dataset]. http://doi.org/10.21954/ou.rd.13733053.v1
    Explore at:
    docxAvailable download formats
    Dataset updated
    Nov 16, 2022
    Dataset provided by
    The Open University
    Authors
    Thanuja Hettiarachchi; Ahmad Al-rashid
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Ahmad Al Rashid and Thanuja Hettiarachchi (both parents of young children) report on how home schooling is causing high levels of anxiety for many migrant parents they spoke to. This blog raises awareness of the issues and gives some helpful tips to parents.

    This material is part of the Covid Chronicles from the Margins project, funded by The Open University and The Hague. The project aims to highlight the impact of the pandemic on refugees, asylum seekers & undocumented migrants.

    This item can also be found on our website

  4. Not seeing a result you expected?
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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Hampshire, A (2025). COVID-19 Impact Dataset: Great British Intelligence Test, 2020 [Dataset]. http://doi.org/10.5255/UKDA-SN-854451

COVID-19 Impact Dataset: Great British Intelligence Test, 2020

Explore at:
7 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Mar 25, 2025
Dataset provided by
Imperial College London
Authors
Hampshire, A
Time period covered
Jan 1, 2020 - Oct 1, 2020
Area covered
United Kingdom
Variables measured
Individual
Measurement technique
RecruitmentStarting from December 26th 2019, participants were recruited to the study website, where they completed cognitive tests and a detailed questionnaire. Articles describing the study were placed on the BBC2 Horizon, BBC Home page, BBC News Home page and circulated on mobile news meta-apps from January 1st 2020. To maximise representativeness of the sample there were no inclusion/exclusion criteria. Analyses here exclude data from participants under 16 years old, as they completed a briefer questionnaire, and those who responded to the questionnaire unfeasibly fast (<4 minutes). Cognitive test data will be reported separately. The study was approved by the Imperial College Research Ethics Committee (17IC4009). Data collectionData were collected via our custom server system, which produces study-specific websites (https://gbws.cognitron.co.uk) on the Amazon EC2. Questionnaires and tests were programmed in Javascript and HTML5. They were deliverable via personal computers, tablets and smartphones. The questionnaire included scales quantifying sociodemographic, lifestyle, online technology use, personality, and mental health (Supplement 1). Participants could enrol for longitudinal follow up, scheduled for 3, 6 and 12 months. People returning to the site outside of these timepoints were navigated to a different URL. On May 2nd 2020, the questionnaire was augmented - in light of the Covid-19 pandemic - with an extended mood scale, and an instrument comprising 47 items quantifying self-perceived effects on mood, behaviour and outlook (Pandemic General Impact Scale PD-GIS-11). Questions regarding pre-existing psychiatric and neurological conditions, lockdown context, having the virus, and free text fields were added. This coincided with further promotion via BBC2 Horizon and BBC Homepage.
Description

There is an urgent need to understand the factors that mediate and mitigate the impact of the Covid-19 pandemic on behaviour and wellbeing. However, the onset of the outbreak was unexpected and the rate of acceleration so rapid as to preclude the planning of studies that can address these critical issues. Coincidentally, in January 2020, just prior to the outbreak in the UK, my team launched a study that collected detailed (~50 minute) cognitive and questionnaire assessments from >200,000 members of the UK public as part of a collaboration with the BBC. This placed us in a unique position to examine how aspects of mental health subsequently changed as the pandemic arrived in the UK. Therefore, we collected data from a further ~120,000 people in May, including additional detailed measures of self-perceived pandemic impact and free text descriptions of the main positives, negatives and pragmatic measures that people found helped them maintain their wellbeing.

In this data archive, we include the survey data from January and May 2020 examining impact of Covid-19 on mood, wellbeing and behaviour in the UK population. This data is reported in a preprint article, where we apply a novel fusion of psychometric, multivariate and machine learning analyses to this unique dataset, in order to address some of the most pressing questions regarding wellbeing during the pandemic in a data-driven manner. The preprint is available on this URL. https://www.medrxiv.org/content/10.1101/2020.06.18.20134635v1

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