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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How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov
This article has 6 authors:Reviewed by ScreenIT
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Feasibility of Establishing a Return-To-Work Protocol Based on COVID-19 Antibodies Testing
This article has 3 authors:Reviewed by ScreenIT
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Serological prevalence of antibodies to SARS CoV-2 amongst cancer centre staff
This article has 3 authors:Reviewed by ScreenIT
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Face masks release water vapour but where does it go? An early observational study
This article has 5 authors:Reviewed by ScreenIT
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Efficient Deep Network Architecture for COVID-19 Detection Using Computed Tomography Images
This article has 4 authors:Reviewed by ScreenIT
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Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes
This article has 6 authors:Reviewed by ScreenIT
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The Ugandan Severe Acute Respiratory Syndrome -Coronavirus 2 (SARS-CoV-2) Model: A Data Driven Approach to Estimate Risk
This article has 13 authors:Reviewed by ScreenIT
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Aerosol tracer testing in Boeing 767 and 777 aircraft to simulate exposure potential of infectious aerosol such as SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT
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An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy
This article has 1 author:Reviewed by ScreenIT
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Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand
This article has 21 authors:Reviewed by ScreenIT