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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COVID-19 Pandemic and Prevalence of Self-Care Practices among the Future Physicians: A Bangladesh Study
This article has 8 authors:Reviewed by ScreenIT
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Early cross-coronavirus reactive signatures of protective humoral immunity against COVID-19
This article has 24 authors:Reviewed by ScreenIT
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Quantifying the potential dominance of immune-evading SARS-CoV-2 variants in the United States
This article has 8 authors:Reviewed by ScreenIT
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Dynamic Interactions of Fully Glycosylated SARS-CoV-2 Spike Protein with Various Antibodies
This article has 8 authors:Reviewed by ScreenIT
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Comparison of Mental Health Symptoms Prior to and During COVID-19: Evidence from a Systematic Review and Meta-analysis of 134 Cohorts
This article has 26 authors:Reviewed by ScreenIT
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Evolution of Coronavirus Disease 2019 (COVID-19) Symptoms During the First 12 Months After Illness Onset
This article has 36 authors:Reviewed by ScreenIT
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Reinfection by the SARS-CoV-2 Gamma variant in blood donors in Manaus, Brazil
This article has 18 authors:Reviewed by ScreenIT
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Incidence and Epidemiological study of COVID-19 in Nagpur urban region (India) using Molecular testing
This article has 9 authors:Reviewed by ScreenIT
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COVID-19: A comparative study of severity of patients hospitalized during the first and the second wave in South Africa
This article has 7 authors:Reviewed by ScreenIT
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Meta-analysis of rapid direct-to-PCR assays for the qualitative detection of SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT