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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Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests
This article has 7 authors:Reviewed by ScreenIT
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Traces of SARS-CoV-2 RNA in Peripheral Blood Cells of Patients with COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Parasites and their protection against COVID-19- Ecology or Immunology?
This article has 9 authors:Reviewed by ScreenIT
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COVID-19 outcomes in MS
This article has 15 authors:Reviewed by ScreenIT
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How well can we forecast the COVID-19 pandemic with curve fitting and recurrent neural networks?
This article has 3 authors:Reviewed by ScreenIT
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Quantitative Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission
This article has 3 authors:Reviewed by ScreenIT
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Prevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study.
This article has 24 authors:Reviewed by ScreenIT
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SARS-CoV-2 Antibody Responses Do Not Predict COVID-19 Disease Severity
This article has 13 authors:Reviewed by ScreenIT
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Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19
This article has 14 authors:Reviewed by ScreenIT
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A pitfall in estimating the effective reproductive number Rt for COVID-19
This article has 2 authors:Reviewed by ScreenIT