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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Potent human broadly SARS-CoV-2–neutralizing IgA and IgG antibodies effective against Omicron BA.1 and BA.2
This article has 134 authors:Reviewed by ScreenIT
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Convergence of immune escape strategies highlights plasticity of SARS-CoV-2 spike
This article has 16 authors:Reviewed by ScreenIT
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Uptake of COVID-19 Vaccines among Pregnant Women: A Systematic Review and Meta-Analysis
This article has 6 authors:Reviewed by ScreenIT
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Long-term psychological consequences of long Covid: a propensity score matching analysis comparing trajectories of depression and anxiety symptoms before and after contracting long Covid vs short Covid
This article has 3 authors:Reviewed by ScreenIT
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Effectiveness of an inactivated Covid-19 vaccine with homologous and heterologous boosters against Omicron in Brazil
This article has 17 authors:Reviewed by ScreenIT
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Use of a Digital Assistant to Report COVID-19 Rapid Antigen Self-test Results to Health Departments in 6 US Communities
This article has 27 authors:Reviewed by ScreenIT
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Emulation of epidemics via Bluetooth-based virtual safe virus spread: Experimental setup, software, and data
This article has 12 authors:Reviewed by ScreenIT
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COVID-19 in people with neurofibromatosis 1, neurofibromatosis 2, or schwannomatosis
This article has 8 authors:Reviewed by ScreenIT
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Effect of vaccination rates on the prevalence and mortality of COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Antibody evolution to SARS-CoV-2 after single-dose Ad26.COV2.S vaccine in humans
This article has 24 authors:Reviewed by ScreenIT