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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SARS-CoV-2-specific T cell memory is sustained in COVID-19 convalescent patients for 10 months with successful development of stem cell-like memory T cells
This article has 11 authors:Reviewed by ScreenIT
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A Scalable, Easy-to-Deploy Protocol for Cas13-Based Detection of SARS-CoV-2 Genetic Material
This article has 13 authors:Reviewed by ScreenIT
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Self-amplifying RNA SARS-CoV-2 lipid nanoparticle vaccine induces equivalent preclinical antibody titers and viral neutralization to recovered COVID-19 patients
This article has 11 authors:Reviewed by ScreenIT
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Social Distancing with Movement Restrictions and the Effective Replication Number of COVID-19: Multi-Country Analysis Based on Phone Mobility Data
This article has 2 authors:Reviewed by ScreenIT
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Antibody response after first and second-dose of ChAdOx1-nCOV (CovishieldTM®) and BBV-152 (CovaxinTM®) among health care workers in India: The final results of cross-sectional coronavirus vaccine-induced antibody titre (COVAT) study
This article has 7 authors:Reviewed by ScreenIT
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Using High Effective Risk of Adult–Senior Duo in Multigenerational Homes to Prioritize COVID-19 Vaccination
This article has 3 authors:Reviewed by ScreenIT
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Massively multiplexed affinity characterization of therapeutic antibodies against SARS-CoV-2 variants
This article has 8 authors:Reviewed by ScreenIT
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Catching SARS-CoV-2 by Sequence Hybridization: a Comparative Analysis
This article has 6 authors:Reviewed by ScreenIT
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Declines in life expectancy following the COVID-19 pandemic in provinces of Spain
This article has 5 authors:Reviewed by ScreenIT
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Comparing the mental health trajectories of four different types of keyworkers with non-keyworkers: 12-month follow-up observational study of 21 874 adults in England during the COVID-19 pandemic
This article has 4 authors:Reviewed by ScreenIT