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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SARS-CoV-2 detection by nasal strips: A superior tool for surveillance of paediatric population
This article has 11 authors:Reviewed by ScreenIT
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Anti-SARS-CoV-2 IgM and IgG antibodies in health workers in Sergipe, Brazil
This article has 13 authors:Reviewed by ScreenIT
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Age-structured SIR model and resource growth dynamics: a COVID-19 study
This article has 2 authors:Reviewed by ScreenIT
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Saliva or Nasopharyngeal Swab Specimens for Detection of SARS-CoV-2
This article has 51 authors:Reviewed by ScreenIT
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Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities
This article has 10 authors:Reviewed by ScreenIT
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Type I Interferon Limits Viral Dissemination-Driven Clinical Heterogeneity in a Native Murine Betacoronavirus Model of COVID-19
This article has 25 authors:Reviewed by ScreenIT
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Platycodin D prevents both lysosome- and TMPRSS2-driven SARS-CoV-2 infection in vitro by hindering membrane fusion
This article has 14 authors:Reviewed by ScreenIT
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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies at Delivery in Women, Partners, and Newborns
This article has 27 authors:Reviewed by ScreenIT
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Financial hardship and social assistance as determinants of mental health and food and housing insecurity during the COVID-19 pandemic in the United States
This article has 1 author:Reviewed by ScreenIT
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Automated chest radiograph diagnosis: A Twofer for Tuberculosis and Covid-19
This article has 9 authors:Reviewed by ScreenIT