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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Epidemiology and precision of SARS‐CoV‐2 detection following lockdown and relaxation measures
This article has 21 authors:Reviewed by ScreenIT
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A multicenter study investigating SARS-CoV-2 in tertiary-care hospital wastewater. viral burden correlates with increasing hospitalized cases as well as hospital-associated transmissions and outbreaks
This article has 24 authors:Reviewed by ScreenIT
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A Prospective Evaluation of the Analytical Performance of GENECUBE® HQ SARS-CoV-2 and GENECUBE® FLU A/B
This article has 9 authors:Reviewed by ScreenIT
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Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks
This article has 5 authors:Reviewed by ScreenIT
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Projecting hospital resource utilization during a surge using parametric bootstrapping
This article has 17 authors:Reviewed by ScreenIT
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Take-home dosing experiences among persons receiving methadone maintenance treatment during COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Nationwide study on SARS-CoV-2 transmission within households from lockdown to reopening, Denmark, 27 February 2020 to 1 August 2020
This article has 8 authors:Reviewed by ScreenIT
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Lessons from applied large-scale pooling of 133,816 SARS-CoV-2 RT-PCR tests
This article has 95 authors:Reviewed by ScreenIT
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Transmission of SARS-CoV-2 in domestic cats imposes a narrow bottleneck
This article has 15 authors:Reviewed by ScreenIT
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Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’
This article has 30 authors:Reviewed by ScreenIT