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.

About Followers

Latest preprint reviews

  1. Modelling COVID 19 in the Basque Country from introduction to control measure response

    1. Maíra Aguiar
    2. Eduardo Millán Ortuondo
    3. Joseba Bidaurrazaga Van-Dierdonck
    4. Javier Mar
    5. Nico Stollenwerk

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. Allocation of COVID-19 Vaccines Under Limited Supply

    1. Xin Chen
    2. Menglong Li
    3. David Simchi-Levi
    4. Tiancheng Zhao

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. LOCKDOWN AS A PANDEMIC MITIGATING POLICY INTERVENTION IN INDIA

    1. Subhayan Mandal
    2. Manoj Kumar
    3. Debasish Sarkar

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Social Network Analysis of COVID-19 Transmission in Karnataka, India

    1. S. Saraswathi
    2. A. Mukhopadhyay
    3. H. Shah
    4. T.S. Ranganath

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

    1. Terry Gao
    2. Grace Wang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Change in vaccine willingness in Australia: August 2020 to January 2021

    1. Nicholas Biddle
    2. Ben Edwards
    3. Matthew Gray
    4. Kate Sollis

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. Antibody response to SARS-CoV-2 infection in humans: A systematic review

    1. Nathan Post
    2. Danielle Eddy
    3. Catherine Huntley
    4. May C. I. van Schalkwyk
    5. Madhumita Shrotri
    6. David Leeman
    7. Samuel Rigby
    8. Sarah V. Williams
    9. William H. Bermingham
    10. Paul Kellam
    11. John Maher
    12. Adrian M. Shields
    13. Gayatri Amirthalingam
    14. Sharon J. Peacock
    15. Sharif A. Ismail

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction

    1. Chen Dun
    2. Christi M. Walsh
    3. Sunjae Bae
    4. Amesh Adalja
    5. Eric Toner
    6. Timothy A. Lash
    7. Farah Hashim
    8. Joseph Paturzo
    9. Dorry L. Segev
    10. Martin A. Makary

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity

  9. Frailty and comorbidity in predicting community COVID ‐19 mortality in the U.K. Biobank: The effect of sampling

    1. Jonathan K. L. Mak
    2. Ralf Kuja‐Halkola
    3. Yunzhang Wang
    4. Sara Hägg
    5. Juulia Jylhävä

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. Variation in COVID-19 Resource Allocation Protocols and Potential Implementation in the Chicago Metropolitan Area

    1. Rupali Gandhi
    2. Gina M. Piscitello
    3. William F. Parker
    4. Kelly Michelson

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
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