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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Pre-clinical studies of a recombinant adenoviral mucosal vaccine to prevent SARS-CoV-2 infection
This article has 7 authors:Reviewed by ScreenIT
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Genome-wide variations of SARS-CoV-2 infer evolution relationship and transmission route
This article has 7 authors:Reviewed by ScreenIT
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Systematic profiling of SARS-CoV-2-specific IgG epitopes at amino acid resolution
This article has 19 authors:Reviewed by ScreenIT
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Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation
This article has 1 author:Reviewed by ScreenIT
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SARS-CoV-2 uses CD4 to infect T helper lymphocytes
This article has 76 authors:This article has been curated by 1 group: -
Cerebral venous sinus thrombosis associated with SARS-CoV-2; a multinational case series
This article has 16 authors:Reviewed by ScreenIT
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SARS-CoV-2 Seroprevalence in a Cohort of Asymptomatic RT-PCR Negative Croatian First League Football Players
This article has 17 authors:Reviewed by ScreenIT
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High‐resolution serum proteome trajectories in COVID‐19 reveal patient‐specific seroconversion
This article has 14 authors:Reviewed by ScreenIT
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Diverse Humoral Immune Responses in Younger and Older Adult COVID-19 Patients
This article has 15 authors:Reviewed by ScreenIT
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Development of a smartphone-based quantum dot lateral flow immunoassay strip for ultrasensitive detection of anti-SARS-CoV-2 IgG and neutralizing antibodies
This article has 13 authors:Reviewed by ScreenIT