COVID-19
Please cite the following paper when using this dataset:
N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049
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1377 Views
The respiratory includes selected files related to a study of physiological changes recorded by wearable devices during physical exercise on a home exercise bike. It is focused on testing the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes.
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337 Views
Please cite the following paper when using this dataset:
N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109
Abstract
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968 Views
Please cite the following paper when using this dataset:
N. Thakur, “MonkeyPox2022Tweets: A large-scale Twitter dataset on the 2022 Monkeypox outbreak, findings from analysis of Tweets, and open research questions,” Infect. Dis. Rep., vol. 14, no. 6, pp. 855–883, 2022, DOI: https://doi.org/10.3390/idr14060087.
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3531 Views
A shortage of beds and cross-infection in hospitals due to patient crowding and overloading during the COVID-19 pandemic necessitate the use of telemedicine over face-to-face treatment. This study used statistical analysis to evaluate the impact of treatment choice among hospitals, patients, and the government to encourage them to employ telemedicine to avoid overload risk in the IoT environment during the pandemic by analyzing data from Tongji Hospital of Wuhan, China from January to September 2020.
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711 Views
StEduCov, a dataset annotated for stances toward online education during the COVID-19 pandemic. StEduCov has 17,097 tweets gathered over 15 months, from March 2020 to May 2021, using Twitter API. The tweets are manually annotated into agree, disagree or neutral classes. We used a set of relevant hashtags and keywords. Specifically, we utilised a combination of hashtags, such as '#COVID 19' or '#Coronavirus' with keywords, such as 'education', 'online learning', 'distance learning' and 'remote learning'.
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777 Views
Artificial database that simulates COVID-19 patients and critical situations to be able to evaluate the BeCalm system performance (https://www.idatis.org/proyecto-becalm/). Generated with https://github.com/BOSCH-UCM/BeCalm
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368 Views
“DCA-IoMT Dataset” belongs to the research article entitled “DCA-IoMT: Knowledge Graph Embedding-enhanced Deep Collaborative Alerts-recommendation against COVID19 (DOI: 10.1109/TII.2022.3159710)” accepted for publication in the Journal of IEEE Transactions on Industrial Informatics.
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1352 Views
Este conjunto de datos es el resultado de un instrumento de medición aplicado para el desarrollo del proyecto "Aplicación de técnicas de minería de datos para la caracterización de estudiantes bajo el efecto de la pandemia de COVID-19".
En dicho instrumento se recolectaron datos sobre de variables sociodemográficas, económicas, condiciones técnicas referentes a la educación a distancia, salud emocional, así como académicas de estudiantes de un programa educativo de la Universidad Autónoma del Estado de Hidalgo.
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232 Views
These datasets are used for epidemilogical modeling using artifical neural network.
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825 Views