ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Initial Assessment of the Impact of the Emergency State Lockdown Measures on the 1st Wave of the COVID-19 Epidemic in Portugal
This article has 6 authors:Reviewed by ScreenIT
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Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network
This article has 48 authors:Reviewed by ScreenIT
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Frailty and Mortality in Hospitalized Older Adults With COVID-19: Retrospective Observational Study
This article has 12 authors:Reviewed by ScreenIT
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For-profit long-term care homes and the risk of COVID-19 outbreaks and resident deaths
This article has 5 authors:Reviewed by ScreenIT
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Knowledge, attitude, and practice regarding COVID-19 outbreak in Bangladesh: An online-based cross-sectional study
This article has 6 authors:Reviewed by ScreenIT
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Association of the COVID‐19 lockdown with smoking, drinking and attempts to quit in England: an analysis of 2019–20 data
This article has 5 authors:Reviewed by ScreenIT
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Assessment of ACE2, CXCL10 and Their co-expressed Genes: An In-silico Approach to Evaluate the Susceptibility and Fatality of Lung Cancer Patients towards COVID-19 Infection
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 envelope protein topology in eukaryotic membranes
This article has 6 authors:Reviewed by ScreenIT
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Scrutinising the COVID-19 Data on 10.676.000 Cases. A Novel Method using Retrospective, Population-based Descriptive Study for Data Quality Surveillance and a Review at 181.426.000 Cases
This article has 1 author:Reviewed by ScreenIT
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Impact of Comorbidities on SARS-CoV-2 Viral Entry-Related Genes
This article has 8 authors:Reviewed by ScreenIT