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 timing of COVID-19 transmission
This article has 15 authors:Reviewed by ScreenIT
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A qualitative study about the mental health and wellbeing of older adults in the UK during the COVID-19 pandemic
This article has 3 authors:Reviewed by ScreenIT
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Longevity of SARS-CoV-2 immune responses in hemodialysis patients and protection against reinfection
This article has 21 authors:Reviewed by ScreenIT
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Exhaled aerosol increases with COVID-19 infection, age, and obesity
This article has 12 authors:Reviewed by ScreenIT
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An epidemiological model to aid decision-making for COVID-19 control in Sri Lanka
This article has 4 authors:Reviewed by ScreenIT
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A Novel SARS-CoV-2 Multitope Protein/Peptide Vaccine Candidate is Highly Immunogenic and Prevents Lung Infection in an AAV hACE2 Mouse Model and non-human primates
This article has 32 authors:Reviewed by ScreenIT
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Convalescent Plasma for Preventing Critical Illness in COVID-19: a Phase 2 Trial and Immune Profile
This article has 19 authors:Reviewed by ScreenIT
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Framework for enhancing the estimation of model parameters for data with a high level of uncertainty
This article has 5 authors:Reviewed by ScreenIT
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Extinction of COVID-19 Clusters in a Lebanese Village: A Quick, Adapted Molecular and Contact tracing
This article has 7 authors:Reviewed by ScreenIT
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Network structural metrics as early warning signals of widespread vaccine refusal in social-epidemiological networks
This article has 2 authors:Reviewed by ScreenIT