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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A call for governments to pause Twitter censorship: using Twitter data as social-spatial sensors of COVID-19/SARS-CoV-2 research diffusion
This article has 4 authors:Reviewed by ScreenIT
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A single intranasal dose of chimpanzee adenovirus-vectored vaccine confers sterilizing immunity against SARS-CoV-2 infection
This article has 30 authors:Reviewed by ScreenIT
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Determinants of COVID-19 Case Fatality Rate in the US: Spatial Analysis Over One Year of the Pandemic
This article has 2 authors:Reviewed by ScreenIT
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Duration of viral detection in throat and rectum of a patient with COVID-19
This article has 14 authors:Reviewed by ScreenIT
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Development of a rapid point-of-care test that measures neutralizing antibodies to SARS-CoV-2
This article has 16 authors:Reviewed by ScreenIT
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The kids are not alright: A preliminary report of Post-COVID syndrome in university students
This article has 8 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study
This article has 30 authors:Reviewed by ScreenIT
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Community-level SARS-CoV-2 Seroprevalence Survey in urban slum dwellers of Buenos Aires City, Argentina: a participatory research
This article has 9 authors:Reviewed by ScreenIT
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Scrutiny for Child Abuse and Neglect During the COVID-19 Pandemic
This article has 2 authors:Reviewed by ScreenIT
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Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data
This article has 5 authors:Reviewed by ScreenIT