{"references": ["Worldometers. Covid-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/, 2020.", "Ensheng Dong, Hongru Du, and Lauren Gardner. An interactive web-based dashboard to track covid-19 in real time. The Lancet infectious diseases, 2020.", "Richard Lawson. Web scraping with Python. Packt Publishing Ltd, 2015", "Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina. Coronavirus disease (covid-19)\u2013statistics and research. Our World in Data, 2020.", "Laia Subirats Mate and Mireia Calvo Gonzalez. Web scraping. Technical report, UOC, Barcelona, (sf). PID00256970."]}

This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time. We analyse how a type of data behave along the time in the different countries. In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 05/28). So we have 5 time series by country: one for each kind of data. The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date. We obtained this dataset scrapping Worldometers.

This dataset its updated until 2020/05/04. However, the dataset is up to date in https://github.com/AdrianArnaiz/scrap_uoc, where its updated automatically every day.