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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S glycoprotein diversity of the Omicron variant
This article has 5 authors:Reviewed by ScreenIT
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Critical Negatively Charged Residues Are Important for the Activity of SARS-CoV-1 and SARS-CoV-2 Fusion Peptides
This article has 2 authors:Reviewed by ScreenIT
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Pandemic-scale phylogenetics
This article has 7 authors:Reviewed by ScreenIT
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Single Immunization with Recombinant ACAM2000 Vaccinia Viruses Expressing the Spike and the Nucleocapsid Proteins Protects Hamsters against SARS-CoV-2-Caused Clinical Disease
This article has 20 authors:Reviewed by ScreenIT
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This article has 4 authors:
Reviewed by ScreenIT
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Safety and Immunogenicity of the Third Booster Dose with Inactivated, Viral Vector, and mRNA COVID-19 Vaccines in Fully Immunized Healthy Adults with Inactivated Vaccine
This article has 15 authors:Reviewed by ScreenIT
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Underlying Co-Morbidity Reveals Unique Immune Signatures in Type II Diabetes Patients Infected With SARS-CoV2
This article has 13 authors:Reviewed by ScreenIT
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Characteristics of patients referred to a cardiovascular disease clinic for post-acute sequelae of SARS-CoV-2 infection
This article has 6 authors:Reviewed by ScreenIT
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Unconventional secretion of unglycosylated ORF8 is critical for the cytokine storm during SARS-CoV-2 infection
This article has 11 authors:Reviewed by ScreenIT
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Health inequities in SARS-CoV-2 infection, seroprevalence, and COVID-19 vaccination: Results from the East Bay COVID-19 study
This article has 25 authors:Reviewed by ScreenIT