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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Do Some Super-Spreaders Spread Better? Effects of individual heterogeneity in epidemiological traits
This article has 7 authors:Reviewed by ScreenIT
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Estimation of the Ascertainment Bias in Covid Case Detection During the Omicron Wave
This article has 1 author:Reviewed by ScreenIT
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Disruption of long-term psychological distress trajectories during the COVID-19 pandemic: evidence from three British birth cohorts
This article has 8 authors:Reviewed by ScreenIT
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Association between household composition and severe COVID-19 outcomes in older people by ethnicity: an observational cohort study using the OpenSAFELY platform
This article has 26 authors:Reviewed by ScreenIT
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How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics
This article has 4 authors:Reviewed by ScreenIT
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A Quantum Dot Biomimetic for SARS-CoV-2 to Interrogate Dysregulation of the Neurovascular Unit Relevant to Brain Inflammation
This article has 7 authors:Reviewed by ScreenIT
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A within-host model of SARS-CoV-2 infection
This article has 4 authors:Reviewed by ScreenIT
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Immune and pathophysiologic profiling of antenatal coronavirus disease 2019 in the GIFT cohort: A Singaporean case-control study
This article has 14 authors:Reviewed by ScreenIT
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A Platform for Data-centric, Continuous Epidemiological Analyses
This article has 12 authors:Reviewed by ScreenIT
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An efficient approach for SARS-CoV-2 monoclonal antibody production via modified mRNA-LNP immunization
This article has 8 authors:Reviewed by ScreenIT