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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Prognostic and Predictive Biomarkers in Patients With Coronavirus Disease 2019 Treated With Tocilizumab in a Randomized Controlled Trial*
This article has 23 authors:Reviewed by ScreenIT
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An Engineered Antibody with Broad Protective Efficacy in Murine Models of SARS and COVID-19
This article has 26 authors:Reviewed by ScreenIT
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Suicide and mental health during the COVID-19 pandemic in Japan
This article has 3 authors:Reviewed by ScreenIT
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The spatio-temporal distribution of COVID-19 infection in England between January and June 2020
This article has 7 authors:Reviewed by ScreenIT
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Economic and social impacts of COVID-19 and public health measures: results from an anonymous online survey in Thailand, Malaysia, the UK, Italy and Slovenia
This article has 23 authors:Reviewed by ScreenIT
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Control of COVID‐19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination
This article has 18 authors:Reviewed by ScreenIT
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Non-applicability of a validated predictive model for intensive care admission and death of COVID-19 patients in a secondary care hospital in Belgium
This article has 8 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 antibodies in Seattle, Washington: October 2019–April 2020
This article has 9 authors:Reviewed by ScreenIT
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Defining high-value information for COVID-19 decision-making
This article has 36 authors:Reviewed by ScreenIT
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Structure-based phylogeny identifies avoralstat as a TMPRSS2 inhibitor that prevents SARS-CoV-2 infection in mice
This article has 9 authors:Reviewed by ScreenIT