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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Point-of-care bulk testing for SARS-CoV-2 by combining hybridization capture with improved colorimetric LAMP
This article has 7 authors:Reviewed by ScreenIT
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COVID-19 vaccine BNT162b1 elicits human antibody and TH1 T cell responses
This article has 42 authors:Reviewed by ScreenIT
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Early Humoral Response Correlates with Disease Severity and Outcomes in COVID-19 Patients
This article has 25 authors:Reviewed by ScreenIT
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Efficacy of Targeting SARS-CoV-2 by CAR-NK Cells
This article has 4 authors:Reviewed by ScreenIT
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Cognitive deficits in people who have recovered from COVID-19
This article has 11 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Sensitivity of nasopharyngeal, oropharyngeal, and nasal wash specimens for SARS-CoV-2 detection in the setting of sampling device shortage
This article has 5 authors:Reviewed by ScreenIT
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Estimation of Effective Reproduction Number for COVID-19 in Bangladesh and its districts
This article has 8 authors:Reviewed by ScreenIT
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Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)
This article has 2 authors:Reviewed by ScreenIT
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Transient dynamics of SARS-CoV-2 as England exited national lockdown
This article has 15 authors:Reviewed by ScreenIT
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Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: Systematic review and meta-analysis
This article has 6 authors:Reviewed by ScreenIT