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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Laboratory validation of an RNA/DNA hybrid tagmentation based mNGS workflow on SARS-CoV-2 and other respiratory RNA viruses detection
This article has 11 authors:Reviewed by ScreenIT
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Systemic Corticosteroids and Mortality in Severe and Critical COVID-19 Patients in Wuhan, China
This article has 23 authors:Reviewed by ScreenIT
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Health literacy of inland population in the mitigation phase 3.2. of COVID-19's pandemic in Portugal - a descriptive cohort study
This article has 4 authors:Reviewed by ScreenIT
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Early safety indicators of COVID-19 convalescent plasma in 5000 patients
This article has 36 authors:Reviewed by ScreenIT
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Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: An observational retrospective study
This article has 21 authors:Reviewed by ScreenIT
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Comparative analyses revealed reduced spread of COVID-19 in malaria endemic countries
This article has 6 authors:Reviewed by ScreenIT
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Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection
This article has 9 authors:Reviewed by ScreenIT
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Rapid selection of a human monoclonal antibody that potently neutralizes SARS-CoV-2 in two animal models
This article has 17 authors:Reviewed by ScreenIT
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Patient DNA cross-reactivity of the CDC SARS-CoV-2 extraction control leads to an inherent potential for false negative results
This article has 1 author:Reviewed by ScreenIT
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Propagation, inactivation, and safety testing of SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT