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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The impact of COVID-19 in diabetic kidney disease and chronic kidney disease: A population-based study
This article has 5 authors:Reviewed by ScreenIT
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The role of population structure when measuring COVID-19 impact across countries
This article has 5 authors:Reviewed by ScreenIT
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Muscle strength is associated with COVID‐19 hospitalization in adults 50 years of age or older
This article has 8 authors:Reviewed by ScreenIT
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Age-dependent regulation of SARS-CoV-2 cell entry genes and cell death programs correlates with COVID-19 severity
This article has 31 authors:Reviewed by ScreenIT
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Characterising proteolysis during SARS-CoV-2 infection identifies viral cleavage sites and cellular targets with therapeutic potential
This article has 19 authors:Reviewed by ScreenIT
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Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction–Based SARS-CoV-2 Tests by Time Since Exposure
This article has 5 authors:Reviewed by ScreenIT
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Spike Glycoprotein and Host Cell Determinants of SARS-CoV-2 Entry and Cytopathic Effects
This article has 8 authors:Reviewed by ScreenIT
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Disparities in case frequency and mortality of coronavirus disease 2019 (COVID-19) among various states in the United States
This article has 7 authors:Reviewed by ScreenIT
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Impact of tiered restrictions on human activities and the epidemiology of the second wave of COVID-19 in Italy
This article has 20 authors:Reviewed by ScreenIT
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Global access to handwashing: implications for COVID-19 control in low-income countries
This article has 4 authors:Reviewed by ScreenIT