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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Optimal sample pooling: an efficient tool against SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Results and Impact of Intensive SARS-CoV-2 Testing in a High Volume, Outpatient Radiation Oncology Clinic in a Pandemic Hotspot
This article has 8 authors:Reviewed by ScreenIT
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Preclinical efficacy and safety analysis of gamma-irradiated inactivated SARS-CoV-2 vaccine candidates
This article has 27 authors:Reviewed by ScreenIT
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Effects of COVID-19 lockdown on heart rate variability
This article has 4 authors:Reviewed by ScreenIT
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The immune landscape of SARS-CoV-2-associated Multisystem Inflammatory Syndrome in Children (MIS-C) from acute disease to recovery
This article has 23 authors:Reviewed by ScreenIT
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Prevalence of antibodies against sars-cov-2 in professionals of a public health laboratory at são paulo, sp, brazil
This article has 11 authors:Reviewed by ScreenIT
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Severity of Respiratory Infections due to SARS-CoV-2 in Working Population: Age and Body Mass Index Outweigh ABO Blood Group
This article has 18 authors:Reviewed by ScreenIT
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Insights into household transmission of SARS-CoV-2 from a population-based serological survey
This article has 75 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT, NCRC
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An examination of school reopening strategies during the SARS-CoV-2 pandemic
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 seroprevalence in a high-altitude setting in Peru: adult population-based cross-sectional study
This article has 5 authors:Reviewed by ScreenIT