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
-
COVID-19 knowledge, attitude, and practices among the Rohingya refugees in Cox's Bazar, Bangladesh
This article has 7 authors:Reviewed by ScreenIT
-
The Achilles’ heel of coronaviruses: targeting the 5’ Polyuridines tract of the antigenome to inhibit Mouse coronavirus-induced cell death
This article has 3 authors:Reviewed by ScreenIT
-
'I don't feel like I'm learning how to be a doctor': early insights regarding the impact of Covid-19 on UK medical student professional identity
This article has 8 authors:Reviewed by ScreenIT
-
TMPRSS2 promotes SARS-CoV-2 evasion from NCOA7-mediated restriction
This article has 12 authors:Reviewed by ScreenIT
-
Correlation of SARS-CoV-2-breakthrough infections to time-from-vaccine
This article has 9 authors:Reviewed by ScreenIT
-
Evolutionary dynamics of indels in SARS-CoV-2 spike glycoprotein
This article has 8 authors:Reviewed by ScreenIT
-
Severity of maternal infection and perinatal outcomes during periods of SARS-CoV-2 wildtype, alpha, and delta variant dominance in the UK: prospective cohort study
This article has 11 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
-
Accuracy verification of low-cost CO 2 concentration measuring devices for general use as a countermeasure against COVID-19
This article has 3 authors:Reviewed by ScreenIT
-
Promoting resilience in healthcare workers during the COVID-19 pandemic with a brief online intervention
This article has 13 authors:Reviewed by ScreenIT
-
Proton-pump inhibitor use is not associated with severe COVID-19-related outcomes: a propensity score-weighted analysis of a national veteran cohort
This article has 16 authors:Reviewed by ScreenIT