ScreenIT
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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A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study
This article has 8 authors:Reviewed by ScreenIT
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Safety-Critical Control of Active Interventions for COVID-19 Mitigation
This article has 4 authors:Reviewed by ScreenIT
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Modelling the potential spread of virus during soccer matches
This article has 3 authors:Reviewed by ScreenIT
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Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning
This article has 6 authors:Reviewed by ScreenIT
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Inheritance of a common androgen synthesis variant allele is associated with female COVID susceptibility in UK Biobank
This article has 4 authors:Reviewed by ScreenIT
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Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China
This article has 5 authors:Reviewed by ScreenIT
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Inhibition of SARS-CoV-2 polymerase by nucleotide analogs: a single molecule perspective
This article has 20 authors:This article has been curated by 1 group: -
Estimating the Cumulative Incidence of SARS-CoV-2 Infection and the Infection Fatality Ratio in Light of Waning Antibodies
This article has 9 authors:Reviewed by ScreenIT
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High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing
This article has 3 authors:This article has been curated by 1 group: -
The Potential Role of an Aberrant Mucosal Immune Response to SARS-CoV-2 in the Pathogenesis of IgA Nephropathy
This article has 9 authors:Reviewed by ScreenIT