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 FACTORS FOR CLINICAL COURSE OF PATIENTS WITH COVID-19: PROTOCOL FOR A RAPID LIVING SYSTEMATIC REVIEW
This article has 14 authors:Reviewed by ScreenIT
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Optimal upper respiratory tract sampling time for novel coronavirus pneumonia suspects
This article has 10 authors:Reviewed by ScreenIT
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Diagnostic serial interval as a novel indicator for contact tracing effectiveness exemplified with the SARS-CoV-2/COVID-19 outbreak in South Korea
This article has 3 authors:Reviewed by ScreenIT
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Indian community’s Knowledge, Attitude & Practice towards COVID-19
This article has 7 authors:Reviewed by ScreenIT
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VIRTUS: a pipeline for comprehensive virus analysis from conventional RNA-seq data
This article has 4 authors:Reviewed by ScreenIT
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Intra-genome variability in the dinucleotide composition of SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Knowledge, attitude and practice of secondary school students toward COVID-19 epidemic in Italy: a cross selectional study
This article has 2 authors:Reviewed by ScreenIT
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Worsening of Preexisting Psychiatric Conditions During the COVID-19 Pandemic
This article has 23 authors:Reviewed by ScreenIT
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Knowledge, attitudes, and practices toward the novel coronavirus among Bangladeshis: Implications for mitigation measures
This article has 10 authors:Reviewed by ScreenIT
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A sub-national analysis of the rate of transmission of COVID-19 in Italy
This article has 63 authors:Reviewed by ScreenIT