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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Disparities in vulnerability to complications from COVID-19 arising from disparities in preexisting conditions in the United States
This article has 6 authors:Reviewed by ScreenIT
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Investigation of subsequent and co-infections associated with SARS-CoV-2 (COVID-19) in hospitalized patients
This article has 7 authors:Reviewed by ScreenIT
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Digital proximity tracing on empirical contact networks for pandemic control
This article has 9 authors:Reviewed by ScreenIT
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A Randomized Clinical Trial of the Efficacy and Safety of Interferon β-1a in Treatment of Severe COVID-19
This article has 8 authors:Reviewed by ScreenIT
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SARS-CoV-2 PCR and antibody testing for an entire rural community: methods and feasibility of high-throughput testing procedures
This article has 16 authors:Reviewed by ScreenIT
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SARS-CoV-2 transmission chains from genetic data: a Danish case study
This article has 8 authors:Reviewed by ScreenIT
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Plasmin cascade mediates thrombolytic events in SARS-CoV-2 infection via complement and platelet-activating systems
This article has 3 authors:Reviewed by ScreenIT
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Efficacy of a novel SARS-CoV-2 detection kit without RNA extraction and purification
This article has 14 authors:Reviewed by ScreenIT
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Lung cancer models reveal SARS-CoV-2-induced EMT contributes to COVID-19 pathophysiology
This article has 28 authors:Reviewed by ScreenIT
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The representation of women as authors of submissions to ecology journals during the COVID-19 pandemic
This article has 1 author:Reviewed by ScreenIT