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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Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
This article has 6 authors:Reviewed by ScreenIT
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Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19
This article has 11 authors:Reviewed by ScreenIT
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A Model Based Analysis for COVID-19 Pandemic in India: Implications for Health Systems and Policy for Low- and Middle-Income Countries
This article has 7 authors:Reviewed by ScreenIT
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Increased infections, but not viral burden, with a new SARS-CoV-2 variant
This article has 22 authors:Reviewed by ScreenIT
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The impact of COVID-19 on the lives and mental health of Australian adolescents
This article has 6 authors:Reviewed by ScreenIT
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Fluctuating High Throughput Serological Assay Results in Recurrent Convalescent Plasma Donors
This article has 7 authors:Reviewed by ScreenIT
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High prevalence of SARS-CoV-2 antibodies in pets from COVID-19+ households
This article has 15 authors:Reviewed by ScreenIT
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Susceptibility and Sustainability of India against CoVid19: a multivariate approach
This article has 2 authors:Reviewed by ScreenIT
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Test, track, and trace: How is the NHSX Covid app performing in a hospital setting?
This article has 2 authors:Reviewed by ScreenIT
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Direct visualization of native infectious SARS-CoV-2 and its inactivation forms using high resolution Atomic Force Microscopy
This article has 8 authors:Reviewed by ScreenIT