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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Anti-SARS-CoV-2 receptor-binding domain antibody evolution after mRNA vaccination
This article has 27 authors:Reviewed by ScreenIT
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N-glycosylation profiles of the SARS-CoV-2 spike D614G mutant and its ancestral protein characterized by advanced mass spectrometry
This article has 15 authors:Reviewed by ScreenIT
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Improving RT-LAMP detection of SARS-CoV-2 RNA through primer set selection and combination
This article has 2 authors:Reviewed by ScreenIT
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Gender differences in housework and childcare among Japanese workers during the COVID-19 pandemic
This article has 10 authors:Reviewed by ScreenIT
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Multivariate mining of an alpaca immune repertoire identifies potent cross-neutralizing SARS-CoV-2 nanobodies
This article has 13 authors:Reviewed by ScreenIT
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Main COVID-19 information sources in a culturally and linguistically diverse community in Sydney, Australia: A cross-sectional survey
This article has 15 authors:Reviewed by ScreenIT
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Identification and Characterization of Novel Mutants of Nsp13 Protein among Indian SARS-CoV-2 Isolates
This article has 7 authors:Reviewed by ScreenIT
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The vaccination threshold for SARS-CoV-2 depends on the indoor setting and room ventilation
This article has 4 authors:Reviewed by ScreenIT
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Risk factors for Coronavirus disease-associated mucormycosis
This article has 34 authors:Reviewed by ScreenIT
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Proteomic and Metabolomic Signatures Associated With the Immune Response in Healthy Individuals Immunized With an Inactivated SARS-CoV-2 Vaccine
This article has 14 authors:Reviewed by ScreenIT