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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Determinants of COVID-19 outcomes: A systematic review
This article has 3 authors:Reviewed by ScreenIT
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Age specific COVID-19 undercount in Spain from the regional breakdown of 52–week accumulated mortality rates
This article has 1 author:Reviewed by ScreenIT
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Feasibility and Diagnostic Accuracy of Saliva-Based SARS-CoV-2 Screening in Educational Settings and Children Aged <12 Years
This article has 15 authors:Reviewed by ScreenIT
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Primary care clinical management following self-harm during the first wave of COVID-19 in the UK: population-based cohort study
This article has 8 authors:Reviewed by ScreenIT
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A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19
This article has 18 authors:Reviewed by ScreenIT
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Clinical characteristics and outcomes of adult patients admitted with COVID-19 in East London: a retrospective cohort analysis
This article has 4 authors:Reviewed by ScreenIT
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Exploring Integrated Environmental Viral Surveillance of Indoor Environments: A comparison of surface and bioaerosol environmental sampling in hospital rooms with COVID-19 patients
This article has 10 authors:Reviewed by ScreenIT
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Resolving the Dynamic Motions of SARS-CoV-2 nsp7 and nsp8 Proteins Using Structural Proteomics
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 transmission in a theme-park
This article has 12 authors:Reviewed by ScreenIT
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Quantitative SARS-CoV-2 anti-spike responses to Pfizer–BioNTech and Oxford–AstraZeneca vaccines by previous infection status
This article has 23 authors:Reviewed by ScreenIT