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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Clinical Validation and Performance Evaluation of the Automated Vitros Total Anti–SARS-CoV-2 Antibodies Assay for Screening of Serostatus in COVID-19
This article has 11 authors:Reviewed by ScreenIT
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Prediction of Non-canonical Routes for SARS-CoV-2 Infection in Human Placenta Cells
This article has 5 authors:Reviewed by ScreenIT
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Longitudinal Isolation of Potent Near-Germline SARS-CoV-2-Neutralizing Antibodies from COVID-19 Patients
This article has 28 authors:Reviewed by ScreenIT
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Validation and performance of a quantitative IgG assay for the screening of SARS-CoV-2 antibodies
This article has 3 authors:Reviewed by ScreenIT
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Room Temperature Isothermal Colorimetric Padlock Probe Rolling Circle Amplification for Viral DNA and RNA Detection
This article has 16 authors:Reviewed by ScreenIT
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Structural basis for potent neutralization of SARS-CoV-2 and role of antibody affinity maturation
This article has 10 authors:Reviewed by ScreenIT
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Highly Sensitive and Specific Multiplex Antibody Assays To Quantify Immunoglobulins M, A, and G against SARS-CoV-2 Antigens
This article has 20 authors:Reviewed by ScreenIT
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Hidden in plain sight: The effects of BCG vaccination in COVID-19 pandemic
This article has 8 authors:Reviewed by ScreenIT
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Machine Learning Maps Research Needs in COVID-19 Literature
This article has 6 authors:Reviewed by ScreenIT
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Specific viral RNA drives the SARS CoV-2 nucleocapsid to phase separate
This article has 16 authors:Reviewed by ScreenIT