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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UV-C irradiation is highly effective in inactivating SARS-CoV-2 replication
This article has 16 authors:Reviewed by ScreenIT
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Pandemic danger to the deep: the risk of marine mammals contracting SARS-CoV-2 from wastewater
This article has 4 authors:Reviewed by ScreenIT
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Vaccination and Non-Pharmaceutical Interventions: When can the UK relax about COVID-19?
This article has 5 authors:Reviewed by ScreenIT
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Twitter activity about treatments during the COVID-19 pandemic: case studies of remdesivir, hydroxychloroquine, and convalescent plasma
This article has 2 authors:Reviewed by ScreenIT
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Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population
This article has 1 author:Reviewed by ScreenIT
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An Estimation of Reproduction Number of SARS-CoV-2 by Age Class for Age Classes in Japan
This article has 5 authors:Reviewed by ScreenIT
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The demand for inpatient and ICU beds for COVID-19 in the US: lessons from Chinese cities
This article has 6 authors:Reviewed by ScreenIT
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The antibody response to the glycan α‐Gal correlates with COVID‐19 disease symptoms
This article has 9 authors:Reviewed by ScreenIT
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Catching a resurgence: Increase in SARS-CoV-2 viral RNA identified in wastewater 48 h before COVID-19 clinical tests and 96 h before hospitalizations
This article has 18 authors:Reviewed by ScreenIT
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HLA-A*11:01:01:01, HLA-C*12:02:02:01-HLA-B*52:01:02:02, Age and Sex Are Associated With Severity of Japanese COVID-19 With Respiratory Failure
This article has 13 authors:Reviewed by ScreenIT