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
-
Analytical and clinical performance of the panbio COVID-19 antigen-detecting rapid diagnostic test
This article has 19 authors:Reviewed by ScreenIT
-
Children hospitalized for COVID-19 during the first winter of the pandemic in Buenos Aires, Argentina
This article has 8 authors:Reviewed by ScreenIT
-
Silicon Nitride Inactivates SARS-CoV-2 in vitro
This article has 6 authors:Reviewed by ScreenIT
-
In Search of the SARS-CoV-2 Protection Correlate: Head-to-Head Comparison of Two Quantitative S1 Assays in Pre-characterized Oligo-/Asymptomatic Patients
This article has 137 authors:Reviewed by ScreenIT
-
Estimate of Covid prevalence using imperfect data
This article has 2 authors:Reviewed by ScreenIT
-
Association of working shifts, inside and outside of healthcare, with severe COVID−19: an observational study
This article has 12 authors:Reviewed by ScreenIT
-
Long, thin transmission chains of Severe Acute Respiratory Syndrome Coronavirus 2 may go undetected for several weeks at low to moderate reproduction numbers: Implications for containment and elimination strategy
This article has 4 authors:Reviewed by ScreenIT
-
Assessment of commercial SARS-CoV-2 antibody assays, Jamaica
This article has 16 authors:Reviewed by ScreenIT
-
Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic
This article has 6 authors:Reviewed by ScreenIT
-
Impact of nonpharmaceutical strategies on trends of COVID-19 in São Paulo State
This article has 6 authors:Reviewed by ScreenIT