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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Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19
This article has 16 authors:Reviewed by ScreenIT
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Comparative Efficacy and Safety of Pharmacological Managements for Hospitalized COVID-19 Patients: Protocol for Systematic Review and Trade-Off Network Meta-Analysis.
This article has 3 authors:Reviewed by ScreenIT
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Meteorological Conditions and Covid-19 in Large U.S. Cities
This article has 7 authors:Reviewed by ScreenIT
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Recombinant SARS-CoV-2 spike proteins for sero-surveillance and epitope mapping
This article has 5 authors:Reviewed by ScreenIT
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Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people
This article has 16 authors:Reviewed by ScreenIT
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Immunogenic profile of SARS-CoV-2 spike in individuals recovered from COVID-19
This article has 20 authors:Reviewed by ScreenIT
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Dynamic changes in anti-SARS-CoV-2 antibodies during SARS-CoV-2 infection and recovery from COVID-19
This article has 20 authors:Reviewed by ScreenIT
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Fuzzy Autocatalytic analysis of Covid-19 outbreak in Malaysia
This article has 7 authors:Reviewed by ScreenIT
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Dynamical model for social distancing in the U.S. during the COVID-19 epidemic
This article has 1 author:Reviewed by ScreenIT
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A Novel Smart City Based Framework on Perspectives for application of Machine Learning in combatting COVID-19
This article has 9 authors:Reviewed by ScreenIT