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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A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing
This article has 4 authors:Reviewed by ScreenIT
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Intention of healthcare workers to accept COVID-19 vaccination and related factors
This article has 5 authors:Reviewed by ScreenIT
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Stochastic modelling of the effects of human-mobility restriction and viral infection characteristics on the spread of COVID-19
This article has 6 authors:Reviewed by ScreenIT
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SARS-CoV-2 Seroprevalence among Healthcare Workers in General Hospitals and Clinics in Japan
This article has 16 authors:Reviewed by ScreenIT
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Age groups that sustain resurging COVID-19 epidemics in the United States
This article has 28 authors:Reviewed by ScreenIT
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Isolation thresholds for curbing SARS-CoV-2 resurgence
This article has 1 author:Reviewed by ScreenIT
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Predictors and rates of PTSD, depression and anxiety in UK frontline health and social care workers during COVID-19
This article has 8 authors:Reviewed by ScreenIT
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Protection after Quarantine: Insights from a Q-SEIR Model with Nonlinear Incidence Rates Applied to COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset
This article has 8 authors:Reviewed by ScreenIT
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Transmission and Protection against Reinfection in the Ferret Model with the SARS-CoV-2 USA-WA1/2020 Reference Isolate
This article has 9 authors:Reviewed by ScreenIT