https://www.socictopen.socict.org/files/original/d85c9ef95d089a9727314ee6acaa0e1d.pdf 5d397620b6089f8055cf0a0fd91a9de7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource COVID-19: Worldwide Profiles during the First 250 Days Creator An entity primarily responsible for making the resource Nuno António, Paulo Rita, Pedro Saraiva Description An account of the resource The present COVID-19 pandemic is happening in a strongly interconnected world. This interconnection explains why it became universal in such a short period of time and why it stimulated the creation of a large amount of relevant open data. In this paper, we use data science tools to explore this open data from the moment the pandemic began and across the first 250 days of prevalence before vaccination started. The use of unsupervised machine learning techniques allowed us to identify three clusters of countries and territories with similar profiles of standardized COVID-19 time dynamics. Although countries and territories in the three clusters share some characteristics, their composition is not homogenous. All these clusters contain countries from different geographies and with different development levels. The use of descriptive statistics and data visualization techniques enabled the description and understanding of where and how COVID-19 was impacting. Some interesting extracted features are discussed and suggestions for future research in this area are also presented. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19 pandemic, machine learning, Clustering, data science, unsupervised learning Identifier An unambiguous reference to the resource within a given context 10.3390/app11083400 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Chemistry, Engineering (General). Civil engineering (General), Technology, Physics