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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T cell perturbations persist for at least 6 months following hospitalization for COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Predicting COVID-19 cases using SARS-CoV-2 RNA in air, surface swab and wastewater samples
This article has 22 authors:Reviewed by ScreenIT
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Broad neutralization of SARS-CoV-2 variants by circular mRNA producing VFLIP-X spike in mice
This article has 13 authors:Reviewed by ScreenIT
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The Effect of Waning on Antibody Levels and Memory B Cell Recall following SARS-CoV-2 Infection or Vaccination
This article has 8 authors:Reviewed by ScreenIT
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Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
This article has 60 authors:Reviewed by ScreenIT
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Conditions of Confinement in U.S. Carceral Facilities during COVID-19: Individuals Speak: Incarcerated during the COVID-19 Epidemic (INSIDE)
This article has 8 authors:Reviewed by ScreenIT
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Targeting an evolutionarily conserved “E-L-L” motif in spike protein to identify a small molecule fusion inhibitor against SARS-CoV-2
This article has 18 authors:Reviewed by ScreenIT
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A conserved immune trajectory of recovery in hospitalized COVID-19 patients
This article has 16 authors:Reviewed by ScreenIT
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Life expectancy loss among Native Americans during the COVID-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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Parsing the role of NSP1 in SARS-CoV-2 infection
This article has 18 authors:Reviewed by ScreenIT