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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School Teaching Posture Correlates with COVID-19 Disease Outcomes in Ohio
This article has 11 authors:Reviewed by ScreenIT
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Uptake of infant and preschool immunisations in Scotland and England during the COVID-19 pandemic: An observational study of routinely collected data
This article has 17 authors:Reviewed by ScreenIT
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Estimation of the Timing and Intensity of Reemergence of Respiratory Syncytial Virus Following the COVID-19 Pandemic in the US
This article has 5 authors:Reviewed by ScreenIT
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Discovery of nanobodies against SARS-CoV-2 and an uncommon neutralizing mechanism
This article has 13 authors:Reviewed by ScreenIT
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Impact of human airway epithelial cellular composition on SARS-CoV-2 infection biology
This article has 12 authors:Reviewed by ScreenIT
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Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative
This article has 21 authors:Reviewed by ScreenIT
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Accuracy and real life performance of a novel interferon-γ release assay for the detection of SARS-CoV2 specific T cell response
This article has 7 authors:Reviewed by ScreenIT
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State-dependent patterns and coupling between the vaccination schedule, population mobility and the COVID epidemic outline, in the US states
This article has 4 authors:Reviewed by ScreenIT
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Role of Multi-resolution Vulnerability Indices in COVID-19 spread: A Case Study in India
This article has 10 authors:Reviewed by ScreenIT
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The role of mouthwash sampling in SARS-CoV-2 diagnosis
This article has 8 authors:Reviewed by ScreenIT