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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Sero-prevalence of anti-SARS-CoV-2 Antibodies in Addis Ababa, Ethiopia
This article has 19 authors:Reviewed by ScreenIT
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Impact of COVID-19 restrictions on preschool children’s eating, activity and sleep behaviours: a qualitative study
This article has 9 authors:Reviewed by ScreenIT
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Finding the real COVID-19 case-fatality rates for SAARC countries
This article has 5 authors:Reviewed by ScreenIT
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SARS‐CoV‐2 superspreading in cities vs the countryside
This article has 2 authors:Reviewed by ScreenIT
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Paediatric primary care in Germany during the early COVID-19 pandemic: the calm before the storm
This article has 8 authors:Reviewed by ScreenIT
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The Impact of the COVID-19 Pandemic on College Students’ Health and Financial Stability in New York City: Findings from a Population-Based Sample of City University of New York (CUNY) Students
This article has 5 authors:Reviewed by ScreenIT
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Cell-free prediction of protein expression costs for growing cells
This article has 6 authors:Reviewed by ScreenIT
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Privacy-protecting, reliable response data discovery using COVID-19 patient observations
This article has 62 authors:Reviewed by ScreenIT
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Effect of a mobile‐based intervention on mental health in frontline healthcare workers against COVID‐19: Protocol for a randomized controlled trial
This article has 17 authors:Reviewed by ScreenIT
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Knowledge, practice and associated factors towards the prevention of COVID-19 among high-risk groups: A cross-sectional study in Addis Ababa, Ethiopia
This article has 25 authors:Reviewed by ScreenIT