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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Social Listening: A Thematic Analysis of COVID-19 Discussion on Social Media
This article has 12 authors:Reviewed by ScreenIT
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Dispersion of a New Coronavirus SARS-CoV-2 by Airlines in 2020: Temporal Estimates of the Outbreak in Mexico
This article has 5 authors:Reviewed by ScreenIT
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Conundrum of re-positive COVID-19 cases: A systematic review of case reports and case series
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 cases and testing in 53 prison systems
This article has 4 authors:Reviewed by ScreenIT
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Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa: a microsimulation modelling study
This article has 19 authors:Reviewed by ScreenIT
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Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis
This article has 13 authors:Reviewed by ScreenIT
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Prevalence and incidence of anti-SARS-CoV-2 antibodies among healthcare workers in Belgian hospitals before vaccination: a prospective cohort study
This article has 12 authors:Reviewed by ScreenIT
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Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab
This article has 18 authors:Reviewed by ScreenIT
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Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales
This article has 3 authors:This article has been curated by 1 group: -
Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave
This article has 17 authors:Reviewed by ScreenIT