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
-
Dynamics of SARS-CoV-2 mutations reveals regional-specificity and similar trends of N501 and high-frequency mutation N501Y in different levels of control measures
This article has 9 authors:Reviewed by ScreenIT
-
Policy and Effectiveness of Covid-19 Response
This article has 1 author:Reviewed by ScreenIT
-
CorCast: A Distributed Architecture for Bayesian Epidemic Nowcasting and its Application to District-Level SARS-CoV-2 Infection Numbers in Germany
This article has 7 authors:Reviewed by ScreenIT
-
Forecasting COVID-19 Number of Cases by Implementing ARIMA and SARIMA with Grid Search in United States
This article has 2 authors:Reviewed by ScreenIT
-
Assessment of Changes in US Veterans Health Administration Care Delivery Methods During the COVID-19 Pandemic
This article has 4 authors:Reviewed by ScreenIT
-
Quantitation of tizoxanide in multiple matrices to support cell culture, animal and human research
This article has 12 authors:Reviewed by ScreenIT
-
Vaccine Hesitancy and Anti-Vaccination Attitudes during the Start of COVID-19 Vaccination Program: A Content Analysis on Twitter Data
This article has 5 authors:Reviewed by ScreenIT
-
UV and violet light can Neutralize SARS-CoV-2 Infectivity
This article has 13 authors:Reviewed by ScreenIT
-
The legacy of maternal SARS-CoV-2 infection on the immunology of the neonate
This article has 10 authors:Reviewed by ScreenIT
-
A suitable murine model for studying respiratory coronavirus infection and therapeutic countermeasures in BSL-2 laboratories
This article has 33 authors:Reviewed by ScreenIT