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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Modifiable lifestyle factors and severe COVID-19 risk: a Mendelian randomisation study
This article has 2 authors:Reviewed by ScreenIT
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LamPORE: rapid, accurate and highly scalable molecular screening for SARS-CoV-2 infection, based on nanopore sequencing
This article has 8 authors:Reviewed by ScreenIT
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An analysis of clinical and geographical metadata of over 75,000 records in the GISAID COVID-19 database
This article has 4 authors:Reviewed by ScreenIT
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Rapid and accurate nucleobase detection using FnCas9 and its application in COVID-19 diagnosis
This article has 26 authors:Reviewed by ScreenIT
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Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate
This article has 8 authors:Reviewed by ScreenIT
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Risk of adverse coronavirus disease 2019 outcomes for people living with HIV
This article has 9 authors:Reviewed by ScreenIT
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Impact of the COVID-19 pandemic on emergency and elective hip surgeries in Norway
This article has 4 authors:Reviewed by ScreenIT
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Effectiveness of COCOA, a COVID-19 contact notification application, in Japan
This article has 3 authors:Reviewed by ScreenIT
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Pandemic-associated mobility restrictions could cause increases in dengue virus transmission
This article has 5 authors:Reviewed by ScreenIT
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Duration of SARS-CoV-2 sero-positivity in a large longitudinal sero-surveillance cohort: the COVID-19 Community Research Partnership
This article has 20 authors:Reviewed by ScreenIT