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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Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images
This article has 4 authors:Reviewed by ScreenIT
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Transmission potential and severity of COVID-19 in South Korea
This article has 5 authors:Reviewed by ScreenIT
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Outcomes of persons with coronavirus disease 2019 in hospitals with and without standard treatment with (hydroxy)chloroquine
This article has 21 authors:Reviewed by ScreenIT
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A diagnostic decision-making protocol combines a new-generation of serological assay and PCR to fully resolve ambiguity in COVID-19 diagnosis
This article has 20 authors:Reviewed by ScreenIT
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Cost Benefit Analysis of Limited Reopening Relative to a Herd Immunity Strategy or Shelter in Place for SARS-CoV-2 in the United States
This article has 5 authors:Reviewed by ScreenIT
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Temporal Analysis of Serial Donations Reveals Decrease in Neutralizing Capacity and Justifies Revised Qualifying Criteria for Coronavirus Disease 2019 Convalescent Plasma
This article has 7 authors:Reviewed by ScreenIT
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Optimal allocation of limited test resources for the quantification of COVID-19 infections
This article has 8 authors:Reviewed by ScreenIT
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Digital plasmonic nanobubble detection for rapid and ultrasensitive virus diagnostics
This article has 7 authors:Reviewed by ScreenIT
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Cov-MS: A Community-Based Template Assay for Mass-Spectrometry-Based Protein Detection in SARS-CoV-2 Patients
This article has 55 authors:Reviewed by ScreenIT
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Universities and COVID-19 Growth at the Start of the 2020 Academic Year
This article has 5 authors:Reviewed by ScreenIT