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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COVATOR: A Software for Chimeric Coronavirus Identification
This article has 1 author:Reviewed by ScreenIT
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An epidemic model for economical impact predicting and spatiotemporal spreading of COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Determinants of burnout and other aspects of psychological well-being in healthcare workers during the Covid-19 pandemic: A multinational cross-sectional study
This article has 31 authors:Reviewed by ScreenIT
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Biomarkers of endothelial dysfunction and outcomes in coronavirus disease 2019 (COVID-19) patients: A systematic review and meta-analysis
This article has 5 authors:Reviewed by ScreenIT
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Alzheimer’s and Parkinson’s Diseases Predict Different COVID-19 Outcomes: A UK Biobank Study
This article has 5 authors:Reviewed by ScreenIT
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The landscape of SARS-CoV-2 RNA modifications
This article has 13 authors:Reviewed by ScreenIT
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Transition to multitype mixing in d -dimensional spreading dynamics
This article has 1 author:Reviewed by ScreenIT
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COVID-19 epidemic curve in Brazil: a sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological study
This article has 8 authors:Reviewed by ScreenIT
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Social distancing causally impacts the spread of SARS-CoV-2: a U.S. nationwide event study
This article has 3 authors:Reviewed by ScreenIT
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Physiological effect of prone positioning in mechanically ventilated SARS-CoV-2- infected patients with severe ARDS: An observational study
This article has 10 authors:Reviewed by ScreenIT