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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Mental health indicators in Sweden over a 12-month period during the COVID-19 pandemic – Baseline data of the Omtanke2020 Study
This article has 11 authors:Reviewed by ScreenIT
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Protection provided by vaccination, booster doses and previous infection against covid-19 infection, hospitalisation or death over time in Czechia
This article has 10 authors:Reviewed by ScreenIT
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Cytokine release syndrome-like serum responses after COVID-19 vaccination are frequent and clinically inapparent under cancer immunotherapy
This article has 59 authors:Reviewed by ScreenIT
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Predicting Vaccine Effectiveness for Hospitalization and Symptomatic Disease for Novel SARS-CoV-2 Variants Using Neutralizing Antibody Titers
This article has 2 authors:Reviewed by ScreenIT
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NIH funding of COVID-19 research in 2020: a cross-sectional study
This article has 12 authors:Reviewed by ScreenIT
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CalScope: Monitoring Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence From Vaccination and Prior Infection in Adults and Children in California May 2021–July 2021
This article has 18 authors:Reviewed by ScreenIT
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Reduced Odds of Severe Acute Respiratory Syndrome Coronavirus 2 Reinfection After Vaccination Among New York City Adults, July 2021–November 2021
This article has 10 authors:Reviewed by ScreenIT
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Insights on the mutational landscape of the SARS-CoV-2 Omicron variant
This article has 4 authors:Reviewed by ScreenIT
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Reduced neutralisation of SARS-CoV-2 omicron B.1.1.529 variant by post-immunisation serum
This article has 14 authors:Reviewed by ScreenIT
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Coronavirus (COVID-19) Spike in Georgia: An Epidemiologic Study of Data, Modelling, and Policy Implications to Understand the Gender-and Race- Specific Variations
This article has 1 author:Reviewed by ScreenIT