Literature DB >> 32842793

Data sharing during COVID-19 pandemic: what to take away.

Rafael S Rios1, Kenneth I Zheng1, Ming-Hua Zheng1,2,3.   

Abstract

INTRODUCTION: In light of the viral outbreak of SARS-CoV-2 that monopolized the focus of the scientific community and general public alike for the past 6 months, one of the greatest contributors in the battle against this pandemic was the international sharing of information. Whether regarding the viral genome, incubation periods, method of transmission, symptoms, dangerous behaviors, age groups at risk, all information was valuable, all data was shared as soon as possible. AREAS COVERED: Considering that the most severely impacted group of patients are already suffering from other conditions, accessing the impact that metabolic associated fatty liver disease (MAFLD), obesity, and diabetes has on patients by sharing information between different healthcare facilities is of vital importance. However, the value behind open information sharing would remain significant even without a viral outbreak and should there be a more efficient infrastructure in place, the global exchange of data can become more practical and less arduous. EXPERT OPINION: Since the sharing of data by individual researchers is often motivated by personal benefits, this observed international collaboration is conditional at best, and the widespread misinformation during this pandemic could be an indication of a certain lack of consensus within the scientific community itself.

Entities:  

Keywords:  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); coronavirus disease 2019 (COVID-19); data sharing; liver disease; metabolic associated fatty liver disease (MAFLD)

Mesh:

Year:  2020        PMID: 32842793     DOI: 10.1080/17474124.2020.1815533

Source DB:  PubMed          Journal:  Expert Rev Gastroenterol Hepatol        ISSN: 1747-4124            Impact factor:   3.869


  5 in total

1.  Publications About COVID-19 Research by the BME Community.

Authors:  Carly Norris
Journal:  Ann Biomed Eng       Date:  2022-09-06       Impact factor: 4.219

2.  Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study.

Authors:  Celia Alvarez-Romero; Alicia Martinez-Garcia; Jara Ternero Vega; Pablo Díaz-Jimènez; Carlos Jimènez-Juan; María Dolores Nieto-Martín; Esther Román Villarán; Tomi Kovacevic; Darijo Bokan; Sanja Hromis; Jelena Djekic Malbasa; Suzana Beslać; Bojan Zaric; Mert Gencturk; A Anil Sinaci; Manuel Ollero Baturone; Carlos Luis Parra Calderón
Journal:  JMIR Med Inform       Date:  2022-06-02

3.  Sharing datasets of the COVID-19 epidemic in the Czech Republic.

Authors:  Martin Komenda; Jiří Jarkovský; Daniel Klimeš; Petr Panoška; Ondřej Šanca; Jakub Gregor; Jan Mužík; Matěj Karolyi; Ondřej Májek; Milan Blaha; Barbora Macková; Jarmila Rážová; Věra Adámková; Vladimír Černý; Jan Blatný; Ladislav Dušek
Journal:  PLoS One       Date:  2022-04-21       Impact factor: 3.240

4.  The sharing of research data facing the COVID-19 pandemic.

Authors:  Rut Lucas-Dominguez; Adolfo Alonso-Arroyo; Antonio Vidal-Infer; Rafael Aleixandre-Benavent
Journal:  Scientometrics       Date:  2021-04-26       Impact factor: 3.801

5.  Jordanian views regarding sharing of medical data for research: A cross-sectional study during COVID-19 pandemic.

Authors:  Moawiah Khatatbeh; Lobna F Gharaibeh; Omar F Khabour; Rana K Abu-Farha; Karem H Alzoubi
Journal:  PLoS One       Date:  2022-03-21       Impact factor: 3.240

  5 in total

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