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
-
Comparative pathogenesis of COVID-19, MERS, and SARS in a nonhuman primate model
This article has 24 authors:Reviewed by ScreenIT
-
Modelling the epidemic 2019-nCoV event in Italy: a preliminary note
This article has 3 authors:Reviewed by ScreenIT
-
A high efficient hospital emergency responsive mode is the key of successful treatment of 100 COVID-19 patients in Zhuhai
This article has 9 authors:Reviewed by ScreenIT
-
Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China
This article has 2 authors:Reviewed by ScreenIT
-
Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19
This article has 17 authors:Reviewed by ScreenIT
-
International Expansion of a Novel SARS-CoV-2 Mutant
This article has 7 authors:Reviewed by ScreenIT
-
Serological immunochromatographic approach in diagnosis with SARS-CoV-2 infected COVID-19 patients
This article has 13 authors:Reviewed by ScreenIT
-
Impact of city and residential unit lockdowns on prevention and control of COVID-19
This article has 1 author:Reviewed by ScreenIT
-
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
This article has 8 authors:Reviewed by ScreenIT
-
An emergent clade of SARS-CoV-2 linked to returned travellers from Iran
This article has 22 authors:Reviewed by ScreenIT