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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REACT-2 Round 5: increasing prevalence of SARS-CoV-2 antibodies demonstrate impact of the second wave and of vaccine roll-out in England
This article has 20 authors:Reviewed by ScreenIT
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Heat inactivation of the severe acute respiratory syndrome coronavirus 2
This article has 4 authors:Reviewed by ScreenIT
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Factors Associated with Intention to Receive Vaccination against COVID-19 in Puerto Rico: An Online Survey of Adults
This article has 8 authors:Reviewed by ScreenIT
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Recurrent emergence of SARS-CoV-2 spike deletion H69/V70 and its role in the Alpha variant B.1.1.7
This article has 882 authors:Reviewed by ScreenIT
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Binding Mechanism of Neutralizing Nanobodies Targeting SARS-CoV-2 Spike Glycoprotein
This article has 5 authors:Reviewed by ScreenIT
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A recombinant receptor-binding domain in trimeric form generates protective immunity against SARS-CoV-2 infection in nonhuman primates
This article has 15 authors:Reviewed by ScreenIT
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The BioNTech / Pfizer vaccine BNT162b2 induces class-switched SARS-CoV-2-specific plasma cells and potential memory B cells as well as IgG and IgA serum and IgG saliva antibodies upon the first immunization
This article has 12 authors:Reviewed by ScreenIT
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Prediction of COVID-19 mortality among hospitalized patients in Sudan
This article has 3 authors:Reviewed by ScreenIT
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Increased transmission of SARS-CoV-2 lineage B.1.1.7 (VOC 2020212/01) is not accounted for by a replicative advantage in primary airway cells or antibody escape
This article has 17 authors:Reviewed by ScreenIT
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Mental health of patients with mental illness during the COVID-19 pandemic lockdown: a questionnaire-based survey weighted for attrition
This article has 4 authors:Reviewed by ScreenIT