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
-
Regional Difference in Seroprevalence of SARS-CoV-2 in Tokyo: Results from the community point-of-care antibody testing
This article has 7 authors:Reviewed by ScreenIT
-
Pharmacophore-based peptide biologics neutralize SARS-CoV-2 S1 and deter S1-ACE2 interaction in vitro
This article has 3 authors:Reviewed by ScreenIT
-
General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States
This article has 5 authors:Reviewed by ScreenIT
-
The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing
This article has 13 authors:Reviewed by ScreenIT
-
Improving Survival of Critical Care Patients With Coronavirus Disease 2019 in England: A National Cohort Study, March to June 2020*
This article has 4 authors:Reviewed by ScreenIT
-
Practical considerations for measuring the effective reproductive number, Rt
This article has 25 authors:Reviewed by ScreenIT
-
Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Avidity Maturation and Association with Disease Severity
This article has 5 authors:Reviewed by ScreenIT
-
Importance of patient bed pathways and length of stay differences in predicting COVID-19 bed occupancy in England
This article has 11 authors:Reviewed by ScreenIT
-
Investigating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Surface and Air Contamination in an Acute Healthcare Setting During the Peak of the Coronavirus Disease 2019 (COVID-19) Pandemic in London
This article has 11 authors:Reviewed by ScreenIT
-
Experiences of the COVID-19 pandemic: cross-sectional analysis of risk perceptions and mental health in a student cohort
This article has 15 authors:Reviewed by ScreenIT