Discovering temporal scientometric knowledge in COVID-19 scholarly production.

The mapping and analysis of scientific knowledge makes it possible to identify the dynamics and/or growth of a particular field of research or to support strategic decisions related to different research entities, based on bibliometric and/or scientometric indicators. However, with the exponential growth of scientific production, a systematic and data-oriented approach to the analysis of this large set of productions becomes increasingly essential. Thus, in this work, a data-oriented methodology was proposed, combining Data Analysis, Machine Learning and Complex Network Analysis techniques, and Data Version Control (DVC) tool, for the extraction of implicit knowledge in scientific production bases. In addition, the approach was validated through a case study in a COVID-19 manuscripts dataset, which had 199,895 articles published on arXiv, bioRxiv, medRxiv, PubMed and Scopus databases. The results suggest the feasibility of the proposed methodology, indicating the most active countries and the most explored themes in each period of the pandemic. Therefore, this study has the potential to instrument and expand strategic decisions by the scientific community, aiming at extracting knowledge that supports the fight against the COVID-19 pandemic.

COVID-19: A scholarly production dataset report for research analysis.

COVID-2019 has been recognized as a global threat, and several studies are being conducted in order to contribute to the fight and prevention of this pandemic. This work presents a scholarly production dataset focused on COVID-19, providing an overview of scientific research activities, making it possible to identify countries, scientists and research groups most active in this task force to combat the coronavirus disease. The dataset is composed of 40,212 records of articles' metadata collected from Scopus, PubMed, arXiv and bioRxiv databases from January 2019 to July 2020. Those data were extracted by using the techniques of Python Web Scraping and preprocessed with Pandas Data Wrangling. In addition, the pipeline to preprocess and generate the dataset are versioned with the Data Version Control tool (DVC) and are thus easily reproducible and auditable.

#StayHome: Monitoring and benchmarking social isolation trends in Caruaru and the Região Metropolitana do Recife during the COVID-19 pandemic.

This technical report presents information related to the Social Isolation Index (SII) of the city of Caruaru, Pernambuco, Brazil. The data was provided by In Loco, a technology startup that has collected the movement of around 60 million Brazilians through cell phone location.

Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic.

Understanding the COVID-19 pandemic is a multidisciplinary effort that requires a significant number of variables. This dataset comprises (i) sociodemographic characteristics, compiled from 35 datasets obtained at UN Data; (ii) mobility metrics that can assist the analysis of social distancing, from Google Community Mobility Reports and; (iii) daily counts of cases and deaths by COVID-19, from the European Centre for Disease Prevention and Control and the Johns Hopkins University Center for Systems Science and Engineering. This unified dataset ranges from February 15, 2020 to May 7, 2020, a total of 83 days, and is provided as a collection of time series for 131 countries with 192 variables. The pipeline to preprocess and generate the dataset, along with the dataset itself, are versioned with the Data Version Control tool (DVC) and are thus easily reproducible.