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. Current State and Predicting Future Scenario of COVID-19 Pandemic for Highly Infected Nations

    1. Nandan L. Patil

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity

  2. Psychological resilience, coping behaviours and social support among health care workers during the COVID‐19 pandemic: A systematic review of quantitative studies

    1. Leodoro J. Labrague

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. Using epidemic simulators for monitoring an ongoing epidemic

    1. Mohan Raghavan
    2. Kousik Sarathy Sridharan
    3. Yashaswini Mandayam Rangayyan

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. A Framework for a Statistical Characterization of Epidemic Cycles: COVID-19 Case Study

    1. Eduardo Atem De Carvalho
    2. Rogerio Atem De Carvalho

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact

    1. Hannah E. Davis
    2. Gina S. Assaf
    3. Lisa McCorkell
    4. Hannah Wei
    5. Ryan J. Low
    6. Yochai Re'em
    7. Signe Redfield
    8. Jared P. Austin
    9. Athena Akrami

    Reviewed by ScreenIT, NCRC

    3 evaluationsAppears in 3 listsLatest version Latest activity

  6. Dynamics of COVID-19 pandemic at constant and time-dependent contact rates

    1. Marek Kochańczyk
    2. Frederic Grabowski
    3. Tomasz Lipniacki

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity

  7. Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

    1. Stephan Katzenschlager
    2. Alexandra J. Zimmer
    3. Claudius Gottschalk
    4. Jürgen Grafeneder
    5. Stephani Schmitz
    6. Sara Kraker
    7. Marlene Ganslmeier
    8. Amelie Muth
    9. Alexander Seitel
    10. Lena Maier-Hein
    11. Andrea Benedetti
    12. Jan Larmann
    13. Markus A. Weigand
    14. Sean McGrath
    15. Claudia M. Denkinger

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Bromelain Inhibits SARS-CoV-2 Infection in VeroE6 Cells

    1. Satish Sagar
    2. Ashok Kumar Rathinavel
    3. William E. Lutz
    4. Lucas R. Struble
    5. Surender Khurana
    6. Andy T Schnaubelt
    7. Nitish Kumar Mishra
    8. Chittibabu Guda
    9. Mara J. Broadhurst
    10. St. Patrick M. Reid
    11. Kenneth W. Bayles
    12. Gloria E. O. Borgstahl
    13. Prakash Radhakrishnan

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Ultrarapid detection of SARS-CoV-2 RNA using a reverse transcription–free exponential amplification reaction, RTF-EXPAR

    1. Jake G. Carter
    2. Lorea Orueta Iturbe
    3. Jean-Louis H. A. Duprey
    4. Ian R. Carter
    5. Craig D. Southern
    6. Marium Rana
    7. Celina M. Whalley
    8. Andrew Bosworth
    9. Andrew D. Beggs
    10. Matthew R. Hicks
    11. James H. R. Tucker
    12. Timothy R. Dafforn

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. A HEURISTIC MODEL FOR SPREAD OF COVID-19 INFECTION CASES IN INDIA

    1. Bishwajit Bhattacharjee

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
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