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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Phylogenetic analysis of SARS-CoV-2 in Boston highlights the impact of superspreading events
This article has 53 authors: -
Effective Heat Inactivation of SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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Rapid prediction of in-hospital mortality among adults with COVID-19 disease
This article has 6 authors:Reviewed by ScreenIT
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Kinetics and Isotype Assessment of Antibodies Targeting the Spike Protein Receptor Binding Domain of SARS-CoV-2 In COVID-19 Patients as a function of Age and Biological Sex
This article has 15 authors:Reviewed by ScreenIT
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Impact of pathogen reduction methods on immunological properties of the COVID‐19 convalescent plasma
This article has 20 authors:Reviewed by ScreenIT
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Effect of ethanol cleaning on the permeability of FFP2 mask
This article has 2 authors:Reviewed by ScreenIT
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Abnormal liver tests in patients with SARS-CoV-2 or influenza – prognostic similarities and temporal disparities
This article has 6 authors:Reviewed by ScreenIT
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The first months of COVID-19 in Madagascar
This article has 1 author:Reviewed by ScreenIT
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Estimation of protection for COVID-19 in children from epidemiological information and estimate effect of policy in Japan
This article has 4 authors:Reviewed by ScreenIT
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Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil
This article has 6 authors:Reviewed by ScreenIT