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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Temporal and geographical variation of COVID-19 in-hospital fatality rate in Brazil
This article has 13 authors:Reviewed by ScreenIT
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Test-adjusted results of mortality for Covid-19 in Germany, USA, UK
This article has 2 authors:Reviewed by ScreenIT
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FnCas9-based CRISPR diagnostic for rapid and accurate detection of major SARS-CoV-2 variants on a paper strip
This article has 15 authors:Reviewed by ScreenIT
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The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak - evidence from China
This article has 9 authors:Reviewed by ScreenIT
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Disease and healthcare burden of COVID-19 in the United States
This article has 4 authors:Reviewed by ScreenIT
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Virus‐Free and Live‐Cell Visualizing SARS‐CoV‐2 Cell Entry for Studies of Neutralizing Antibodies and Compound Inhibitors
This article has 32 authors:Reviewed by ScreenIT
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Epidemiological Characteristics of COVID-19; a Systemic Review and Meta-Analysis
This article has 6 authors:Reviewed by ScreenIT
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SalivaSTAT: Direct-PCR and Pooling of Saliva Samples Collected in Healthcare and Community Setting for SARS-CoV-2 Mass Surveillance
This article has 16 authors:Reviewed by ScreenIT
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ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition—implications for COVID-19
This article has 36 authors:Reviewed by ScreenIT
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Epidemic size of novel coronavirus-infected pneumonia in the Epicenter Wuhan: using data of five-countries’ evacuation action
This article has 4 authors:Reviewed by ScreenIT