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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Differential occupational risks to healthcare workers from SARS-CoV-2: A prospective observational study
This article has 52 authors:Reviewed by ScreenIT
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Estimating Total Excess Mortality During a Coronavirus Disease 2019 Outbreak in Stockholm, Sweden
This article has 5 authors:Reviewed by ScreenIT
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Modeling for Corona Virus Outbreak in IRAN
This article has 4 authors:Reviewed by ScreenIT
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Loneliness, physical activity, and mental health during COVID-19: a longitudinal analysis of depression and anxiety in adults over the age of 50 between 2015 and 2020
This article has 11 authors:Reviewed by ScreenIT
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Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis
This article has 13 authors:Reviewed by ScreenIT
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Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT
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Tracking the spread of novel coronavirus (2019-nCoV) based on big data
This article has 3 authors:Reviewed by ScreenIT
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Real-Time Conformational Dynamics of SARS-CoV-2 Spikes on Virus Particles
This article has 23 authors:Reviewed by ScreenIT
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Renin–angiotensin–aldosterone system blockers and region-specific variations in COVID-19 outcomes: findings from a systematic review and meta-analysis
This article has 3 authors:Reviewed by ScreenIT
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Demand for self-managed online telemedicine abortion in eight European countries during the COVID-19 pandemic: a regression discontinuity analysis
This article has 5 authors:Reviewed by ScreenIT