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 seroprevalence worldwide: a systematic review and meta-analysis
This article has 9 authors:Reviewed by ScreenIT
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Factors influencing nursing students' intention to accept COVID-19 vaccination: A pooled analysis of seven European countries
This article has 20 authors:Reviewed by ScreenIT
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Cytoplasmic short linear motifs in ACE2 and integrin β 3 link SARS-CoV-2 host cell receptors to mediators of endocytosis and autophagy
This article has 4 authors:Reviewed by ScreenIT
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A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes’ interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19
This article has 13 authors:Reviewed by ScreenIT
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Performance Assessment of SARS-CoV-2 PCR Assays Developed by WHO Referral Laboratories
This article has 14 authors:Reviewed by ScreenIT
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A predictive internet-based model for COVID-19 hospitalization census
This article has 4 authors:Reviewed by ScreenIT
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Genetic correlations between COVID-19 and a variety of traits and diseases
This article has 8 authors:Reviewed by ScreenIT
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Estimate of the Maximum Limit of Total Cases of Infected Patients COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Paradoxical Case Fatality Rate dichotomy of Covid-19 among rich and poor nations points to the “hygiene hypothesis”
This article has 3 authors:Reviewed by ScreenIT
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Persisting adaptive immunity to SARS-CoV-2 in Lower Austria
This article has 5 authors:Reviewed by ScreenIT