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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Prime hAd5 Spike + Nucleocapsid Vaccination Induces Ten-Fold Increases in Mean T-Cell Responses in Phase 1 Subjects that are Sustained Against Spike Variants
This article has 29 authors:Reviewed by ScreenIT
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Acute and longer-term psychological distress associated with testing positive for COVID-19: longitudinal evidence from a population-based study of US adults
This article has 2 authors:Reviewed by ScreenIT
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Development of a Real-Time Risk Model (RTRM) for Predicting In-Hospital COVID-19 Mortality
This article has 12 authors:Reviewed by ScreenIT
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A prospective study of long-term outcomes among hospitalized COVID-19 patients with and without neurological complications
This article has 48 authors:Reviewed by ScreenIT
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Impaired activation of transposable elements in SARS‐CoV‐2 infection
This article has 3 authors:Reviewed by ScreenIT
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AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations
This article has 4 authors:Reviewed by ScreenIT
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Economic Benefits of COVID-19 Screening Tests with a Vaccine Rollout
This article has 4 authors:Reviewed by ScreenIT
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Using PARIHS framework to design a community-based COVID-19 intervention in rural Ghana
This article has 3 authors:Reviewed by ScreenIT
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Evaluation of the immunogenicity of prime-boost vaccination with the replication-deficient viral vectored COVID-19 vaccine candidate ChAdOx1 nCoV-19
This article has 42 authors:Reviewed by ScreenIT
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Bacterial expression and purification of functional recombinant SARS-CoV-2 spike receptor binding domain
This article has 9 authors:Reviewed by ScreenIT