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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High‐coverage SARS‐CoV‐2 genome sequences acquired by target capture sequencing
This article has 19 authors:Reviewed by ScreenIT
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Undergraduate medical students in India are underprepared to be the young-taskforce against Covid-19 amid prevalent fears
This article has 6 authors:Reviewed by ScreenIT
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Self-Collected Oral Fluid and Nasal Swabs Demonstrate Comparable Sensitivity to Clinician Collected Nasopharyngeal Swabs for Coronavirus Disease 2019 Detection
This article has 7 authors:Reviewed by ScreenIT
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Effectiveness of N95 Respirator Decontamination and Reuse against SARS-CoV-2 Virus
This article has 14 authors:Reviewed by ScreenIT
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Alltest rapid lateral flow immunoassays is reliable in diagnosing SARS-CoV-2 infection from 14 days after symptom onset: A prospective single-center study
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 outbreak at a large homeless shelter in Boston: Implications for universal testing
This article has 4 authors:Reviewed by ScreenIT
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Pregnancy outcomes, Newborn complications and Maternal-Fetal Transmission of SARS-CoV-2 in women with COVID-19: A systematic review of 441 cases
This article has 3 authors:Reviewed by ScreenIT
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Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: a UK-based focus group study
This article has 4 authors:Reviewed by ScreenIT
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Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study
This article has 16 authors:Reviewed by ScreenIT
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Adapting re‐usable elastomeric respirators to utilise anaesthesia circuit filters using a 3D‐printed adaptor ‐ a potential alternative to address N95 shortages during the COVID‐19 pandemic
This article has 7 authors:Reviewed by ScreenIT