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
-
Statistical analysis of national & municipal corporation level database of COVID-19 cases In India
This article has 5 authors:Reviewed by ScreenIT
-
Convalescent plasma in the management of moderate covid-19 in adults in India: open label phase II multicentre randomised controlled trial (PLACID Trial)
This article has 6 authors:Reviewed by ScreenIT
-
Syncope at SARS-CoV-2 onset
This article has 16 authors:Reviewed by ScreenIT
-
COVID-19 global pandemic planning: Decontamination and reuse processes for N95 respirators
This article has 12 authors:Reviewed by ScreenIT
-
Social-distancing effectiveness tracking of the COVID-19 hotspot Stockholm
This article has 1 author:Reviewed by ScreenIT
-
A Net Benefit Approach for the Optimal Allocation of a COVID-19 Vaccine
This article has 5 authors:Reviewed by ScreenIT
-
Efficient high-throughput SARS-CoV-2 testing to detect asymptomatic carriers
This article has 18 authors:Reviewed by ScreenIT
-
Safety and effectiveness concerns of lopinavir/ritonavir in COVID-19 affected patients: a retrospective series
This article has 4 authors:Reviewed by ScreenIT
-
Characteristics of Anti-SARS-CoV-2 Antibodies in Recovered COVID-19 Subjects
This article has 16 authors:Reviewed by ScreenIT
-
Statistical Deconvolution for Inference of Infection Time Series
This article has 9 authors:Reviewed by ScreenIT