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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Human mobility and poverty as key drivers of COVID-19 transmission and control
This article has 6 authors:Reviewed by ScreenIT
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A simple ecological model captures the transmission pattern of the coronavirus COVID-19 outbreak in China
This article has 4 authors:Reviewed by ScreenIT
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Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development
This article has 6 authors:Reviewed by ScreenIT
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Inflight transmission of COVID-19 based on experimental aerosol dispersion data
This article has 5 authors:Reviewed by ScreenIT
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Long SARS-CoV-2 nucleocapsid sequences in blood monocytes collected soon after hospital admission
This article has 7 authors:Reviewed by ScreenIT
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SARS‐CoV‐2 RNAemia with a higher nasopharyngeal viral load is strongly associated with disease severity and mortality in patients with COVID‐19
This article has 15 authors:Reviewed by ScreenIT
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Impact of the first COVID-19 shelter-in-place order in the United States on emergency department utilization, Marin County, California
This article has 7 authors:Reviewed by ScreenIT
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Heart Disease Deaths during the Covid-19 Pandemic
This article has 3 authors:Reviewed by ScreenIT
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Making cotton face masks extra-protective by use of impervious cloth as the front layer to restrict flow of aerosols and droplets
This article has 6 authors:Reviewed by ScreenIT
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Meta-analysis of the robustness of COVID-19 diagnostic kit performance during the early pandemic
This article has 7 authors:Reviewed by ScreenIT