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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Different Neutralization Sensitivity of SARS-CoV-2 Cell-to-Cell and Cell-Free Modes of Infection to Convalescent Sera
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort
This article has 19 authors:Reviewed by ScreenIT
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Uncovering cryptic pockets in the SARS-CoV-2 spike glycoprotein
This article has 14 authors:Reviewed by ScreenIT
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Suppression of Global Protein Translation in SARS-CoV-2 Infection
This article has 6 authors:Reviewed by ScreenIT
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Heterogeneity in SARS-CoV-2 Positivity by Ethnicity in Los Angeles
This article has 3 authors:Reviewed by ScreenIT
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Cumulative Incidence of SARS-CoV-2 Infections Among Adults in Georgia, United States, August to December 2020
This article has 12 authors:Reviewed by ScreenIT
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SARS-CoV-2 Seroprevalence and Profiles Among Convalescents in Sichuan Province, China
This article has 24 authors:Reviewed by ScreenIT
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Assessing COVID-19 Pandemic Risk Perception and Response Preparedness in Veterinary and Animal Care Workers
This article has 12 authors:Reviewed by ScreenIT
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LENZILUMAB EFFICACY AND SAFETY IN NEWLY HOSPITALIZED COVID-19 SUBJECTS: RESULTS FROM THE LIVE-AIR PHASE 3 RANDOMIZED DOUBLE-BLIND PLACEBO-CONTROLLED TRIAL
This article has 13 authors:Reviewed by ScreenIT
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Identification of risk and protective human leukocyte antigens in COVID-19 using genotyping and structural modeling
This article has 5 authors:Reviewed by ScreenIT