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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Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the growth rate
This article has 1 author:Reviewed by ScreenIT
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Low plasma 25(OH) vitamin D level is associated with increased risk of COVID‐19 infection: an Israeli population‐based study
This article has 7 authors:Reviewed by ScreenIT
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Cold atmospheric plasma for SARS-CoV-2 inactivation
This article has 4 authors:Reviewed by ScreenIT
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Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning
This article has 19 authors:Reviewed by ScreenIT
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Functional SARS-CoV-2-Specific Immune Memory Persists after Mild COVID-19
This article has 24 authors:Reviewed by ScreenIT
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Incidence, risk factors and outcome of acute kidney injury (AKI) in patients with COVID-19
This article has 23 authors:Reviewed by ScreenIT
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Gender and trust in government modify the association between mental health and stringency of social distancing related public health measures to reduce COVID-19: a global online survey
This article has 3 authors:Reviewed by ScreenIT
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Mental health and well-being of healthcare workers during the COVID-19 pandemic in the UK: contrasting guidelines with experiences in practice
This article has 11 authors:Reviewed by ScreenIT
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A comparison of emergency department presentations for medically unexplained symptoms in frequent attenders during COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Chest drain aerosol generation in COVID-19 and emission reduction using a simple anti-viral filter
This article has 11 authors:Reviewed by ScreenIT