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 transmission in a university setting: a rapid review of modelling studies
This article has 11 authors:Reviewed by ScreenIT
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Antibody response using six different serological assays in a completely PCR-tested community after a coronavirus disease 2019 outbreak—the CoNAN study
This article has 47 authors:Reviewed by ScreenIT
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Digital droplet PCR accurately quantifies SARS-CoV-2 viral load from crude lysate without nucleic acid purification
This article has 8 authors:Reviewed by ScreenIT
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Ketogenesis restrains aging-induced exacerbation of COVID in a mouse model
This article has 17 authors:Reviewed by ScreenIT
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Impact of university re-opening on total community COVID-19 burden
This article has 4 authors:Reviewed by ScreenIT
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Seroprevalence of Anti-SARS-CoV-2 Antibodies in Blood Donors from Nuevo Leon State, Mexico, during 2020: A Retrospective Cross-Sectional Evaluation
This article has 21 authors:Reviewed by ScreenIT
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Rapid single-molecule detection of COVID-19 and MERS antigens via nanobody-functionalized organic electrochemical transistors
This article has 15 authors:Reviewed by ScreenIT
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High Community SARS-CoV-2 Antibody Seroprevalence in a Ski Resort Community, Blaine County, Idaho, US. Preliminary Results
This article has 13 authors:Reviewed by ScreenIT
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RT-PCR testing to detect a COVID-19 outbreak in Austria: rapid, accurate and early diagnosis in primary care (The REAP study)
This article has 11 authors:Reviewed by ScreenIT
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Predicting the second wave of COVID-19 in Washtenaw County, MI
This article has 3 authors:Reviewed by ScreenIT