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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The relation between COVID-19 vaccinations and public governance to improve preparedness of next pandemic impacts and crisis management: a global study
This article has 2 authors:Reviewed by ScreenIT
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Broadly neutralizing antibodies target the coronavirus fusion peptide
This article has 44 authors:Reviewed by ScreenIT
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Incidence of Guillain–Barré syndrome following SARS‐CoV ‐2 immunization: Analysis of a nationwide registry of recipients of 81 million doses of seven vaccines
This article has 23 authors:Reviewed by ScreenIT
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COVID-19 vaccine for people who live and work in prisons worldwide: A scoping review
This article has 5 authors:Reviewed by ScreenIT
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Immune Correlates Analysis of a Single Ad26.COV2.S Dose in the ENSEMBLE COVID-19 Vaccine Efficacy Clinical Trial
This article has 61 authors:Reviewed by ScreenIT
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Genomic epidemiology of Delta SARS-CoV-2 during transition from elimination to suppression in Aotearoa New Zealand
This article has 11 authors:Reviewed by ScreenIT
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Inequities in COVID-19 vaccine and booster coverage across Massachusetts ZIP codes: large gaps persist after the 2021/22 Omicron wave
This article has 7 authors:Reviewed by ScreenIT
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Independent acquisition of short insertions at the RIR1 site in the spike N‐terminal domain of the SARS‐CoV‐2 BA.2 lineage
This article has 2 authors:Reviewed by ScreenIT
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The effect of the COVID-19 lockdown on mental health care use in South Africa: an interrupted time series analysis
This article has 13 authors:Reviewed by ScreenIT
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Quantifying the Relationship between SARS-CoV-2 Wastewater Concentrations and Building-Level COVID-19 Prevalence at an Isolation Residence: A Passive Sampling Approach
This article has 4 authors:Reviewed by ScreenIT