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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Association between consumption of vegetables and COVID-19 mortality at a country level in Europe
This article has 12 authors:Reviewed by ScreenIT
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Low-Cost Manually Assembled Open Source Reader for Isothermal Pathogen Detection from Saliva using RT-LAMP: SARS-CoV-2 Use Case
This article has 13 authors:Reviewed by ScreenIT
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The landscape of antibody binding in SARS-CoV-2 infection
This article has 10 authors:Reviewed by ScreenIT
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Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar
This article has 10 authors:Reviewed by ScreenIT
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Association of Convalescent Plasma Therapy With Survival in Patients With Hematologic Cancers and COVID-19
This article has 282 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 specific IgG antibodies in District Srinagar, northern India – a cross-sectional study
This article has 18 authors:Reviewed by ScreenIT
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National age and coresidence patterns shape COVID-19 vulnerability
This article has 4 authors:Reviewed by ScreenIT
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Saliva for diagnosis of SARS‐CoV‐2: First report from India
This article has 16 authors:Reviewed by ScreenIT
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Epidemic waves of COVID-19 in Scotland: a genomic perspective on the impact of the introduction and relaxation of lockdown on SARS-CoV-2
This article has 55 authors:Reviewed by ScreenIT
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Update on a Model to Minimize Population Health Loss in Times of Scarce Surgical Capacity During the COVID-19 Crisis and Beyond
This article has 14 authors:Reviewed by ScreenIT