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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SARS-CoV-2 mutations altering regulatory properties: deciphering host’s and virus’s perspectives
This article has 2 authors:Reviewed by ScreenIT
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Evolution and spread of SARS-CoV-2 likely to be affected by climate
This article has 2 authors:Reviewed by ScreenIT
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Loss of pH switch unique to SARS-CoV2 supports unfamiliar virus pathology
This article has 3 authors:Reviewed by ScreenIT
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A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Selection, biophysical and structural analysis of synthetic nanobodies that effectively neutralize SARS-CoV-2
This article has 19 authors:Reviewed by ScreenIT
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CD209L/L-SIGN and CD209/DC-SIGN act as receptors for SARS-CoV-2
This article has 18 authors:Reviewed by ScreenIT
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A nanoluciferase SARS-CoV-2 for rapid neutralization testing and screening of anti-infective drugs for COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Antibodies against SARS-CoV-2 among health care workers in a country with low burden of COVID-19
This article has 17 authors:Reviewed by ScreenIT
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SARS-CoV-2 exposure, symptoms and seroprevalence in healthcare workers in Sweden
This article has 24 authors:Reviewed by ScreenIT
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Qualitative assessment of SARS‐CoV‐2‐specific antibody avidity by lateral flow immunochromatographic IgG/IgM antibody assay
This article has 9 authors:Reviewed by ScreenIT