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
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Novel Investigation of SARS-CoV-2 in COVID-19 Survivors’ Semen in Surabaya, Indonesia
This article has 14 authors:Reviewed by ScreenIT
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Effect of Heterologous Vaccination Regimen with Ad5-nCoV CanSinoBio and BNT162b2 Pfizer in SARS-CoV-2 IgG Antibodies Titers
This article has 10 authors:Reviewed by ScreenIT
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Decline in Prenatal Buprenorphine/Naloxone Fills during the COVID-19 Pandemic in the United States
This article has 6 authors:Reviewed by ScreenIT
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Monitoring and forecasting the COVID-19 epidemic in Moscow: model selection by balanced identification technology - version: September 2021
This article has 2 authors:Reviewed by ScreenIT
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Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality
This article has 19 authors:Reviewed by ScreenIT
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The effect of mandatory COVID-19 certificates on vaccine uptake: synthetic-control modelling of six countries
This article has 2 authors:Reviewed by ScreenIT
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Cardiovascular Mortality During the COVID-19 Pandemics in a Large Brazilian City: A Comprehensive Analysis
This article has 9 authors:Reviewed by ScreenIT
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The military as a neglected pathogen transmitter, from the nineteenth century to COVID-19: a systematic review
This article has 5 authors:Reviewed by ScreenIT
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Characterizing Anchoring Bias in Vaccine Comparator Selection Due to Health Care Utilization With COVID-19 and Influenza: Observational Cohort Study
This article has 4 authors:Reviewed by ScreenIT
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Safely return to schools and offices: early and frequent screening with high sensitivity antigen tests effectively identifies COVID-19 patients
This article has 6 authors:Reviewed by ScreenIT