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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Assessment of knowledge, attitude and practices among Accredited Social Health Activists (ASHAs) towards COVID-19: a descriptive cross-sectional study in Tripura, India
This article has 6 authors:Reviewed by ScreenIT
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Controlling the first wave of the COVID–19 pandemic in Malawi: results from a panel study
This article has 7 authors:Reviewed by ScreenIT
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Parental perceptions of COVID-19–like illness in their children
This article has 4 authors:Reviewed by ScreenIT
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Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test
This article has 24 authors:Reviewed by ScreenIT
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The N-terminal domain of spike glycoprotein mediates SARS-CoV-2 infection by associating with L-SIGN and DC-SIGN
This article has 9 authors:Reviewed by ScreenIT
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Was the risk of death among the population of teachers and other school workers in England and Wales due to COVID-19 and all causes higher than other occupations during the pandemic in 2020? An ecological study using routinely collected data on deaths from the Office for National Statistics
This article has 5 authors:Reviewed by ScreenIT
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Description of symptom course in a telemedicine monitoring clinic for acute symptomatic COVID-19: a retrospective cohort study
This article has 4 authors:Reviewed by ScreenIT
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Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility
This article has 34 authors:Reviewed by ScreenIT
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Performing point-of-care molecular testing for SARS-CoV-2 with RNA extraction and isothermal amplification
This article has 12 authors:Reviewed by ScreenIT
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A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects
This article has 7 authors:Reviewed by ScreenIT