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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SARS-CoV-2 specific T cell responses are lower in children and increase with age and time after infection
This article has 16 authors:Reviewed by ScreenIT
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Transmissibility of 2019 Novel Coronavirus: zoonotic vs. human to human transmission, China, 2019-2020
This article has 3 authors:Reviewed by ScreenIT
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No association between the SARS-CoV-2 variants and mortality rates in the Eastern Mediterranean Region
This article has 3 authors:Reviewed by ScreenIT
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Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
This article has 12 authors:Reviewed by ScreenIT
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Flattening the curve is not enough, we need to squash it: An explainer using a simple model
This article has 3 authors:Reviewed by ScreenIT
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Challenges for Nontechnical Implementation of Digital Proximity Tracing During the COVID-19 Pandemic: Media Analysis of the SwissCovid App
This article has 1 author:Reviewed by ScreenIT
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The presence of SARS-CoV-2 RNA in human sewage in Santa Catarina, Brazil, November 2019
This article has 14 authors:Reviewed by ScreenIT
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Mathematical modeling of the spread of the coronavirus under strict social restrictions
This article has 4 authors:Reviewed by ScreenIT
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Motif Analysis in k-mer Networks: An Approach towards Understanding SARS-CoV-2 Geographical Shifts
This article has 4 authors:Reviewed by ScreenIT
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Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan
This article has 8 authors:Reviewed by ScreenIT