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MIDAS
Sharing Disease Modeling Software and Data
Part of the Reproducibility and Reusability Project

Contents

Draft - v1.0 2 Apr 2022

Introduction

As part of the MIDAS Coordination Center’s efforts to bring increased reproducibility and FAIR (Findable, Accessible, Reusable, and Interoperable)1 data principles to infectious disease modeling research, we have developed this series of recommendations for sharing of relevant software and data artifacts.



Our hope is that these recommendations will provide greater transparency and clarity to disease modeling efforts, both in terms of community methods and supporting communication with the public and policy-makers. When possible, these efforts should align with accepted and emerging community best-practices, such as the EPIFORGE 2020 guidelines for reporting epidemic forecasting and prediction research2 and emerging information models.3



These proposed guidelines have been developed by the MIDAS Coordination Center. In the interest of improving the clarity and utility of these suggestions, we’re interested in any feedback or comments that you might have. Please use the comment form below to let us know what you think.

References

  1. Wilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15;3(1):160018.
  2. Pollett S, Johansson MA, Reich NG, Brett-Major D, Del Valle SY, et al. Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines. PLOS Medicine 18(10): e1003793. https://doi.org/10.1371/journal.pmed.1003793
  3. Xavier JB, Mong JK, Poudel S, Norsigian CJ, Sastry AV, Liao C, Bento J, Suchard MA, Arrieta-Ortiz ML, Peterson JR, Baliga NS, Stoeger T, Ruffin F, Richardson RAK, Gao CA, Horvath TD, Haag AM, Wu Q, Savidge T, Yeaman MR. Mathematical models to study the biology of pathogens and the infectious diseases they cause iScience 2022 Mar 15; 104079.
  4. List M, Ebert P, Albrecht F. Ten Simple Rules for Developing Usable Software in Computational Biology. PLOS Comput Biol. 2017 Jan 5;13(1):e1005265.
  5. Lamprecht A-L, Garcia L, Kuzak M, Martinez C, Arcila R, Martin Del Pico E, et al. Towards FAIR principles for research software. Data Sci. 2020 Jan 1;3(1):37–59.
  6. Lee BD. Ten simple rules for documenting scientific software. PLOS Comput Biol. 2018 Dec 20;14(12):e1006561.
  7. Garijo D, Kinnings S, Xie L, Xie L, Zhang Y, Bourne PE, et al. Quantifying reproducibility in computational biology: the case of the tuberculosis drugome. PloS One. 2013;8(11):e80278.
  8. Romano JD, Moore JH. Ten simple rules for writing a paper about scientific software. PLOS Comput Biol. 2020 Nov 12;16(11):e1008390.
  9. Jacobsen A, de Miranda Azevedo R, Juty N, Batista D, Coles S, Cornet R, et al. FAIR Principles: Interpretations and Implementation Considerations. Data Intell. 2020 Jan 1;2(1–2):10–29.
  10. Albertoni R, Browning D, Cox S, Gonzalez-Beltran A, Perego A, Winstanley P. Data Catalog Vocabulary (DCAT) – Version 2. [Internet]. [cited 2021 Dec 1].
  11. Gray AJG, Baran J, Marshall MS, Dumontier M. Dataset Descriptions: HCLS Community Profile. [Internet]. World-Wide Web Consortium; 2015 May.
  12. Goodman A, Pepe A, Blocker AW, Borgman CL, Cranmer K, Crosas M, et al. Ten Simple Rules for the Care and Feeding of Scientific Data. PLOS Comput Biol. 2014 Apr 24;10(4):e1003542.
 

Comments

This set of recommendations is a living document.  We will continue to improve it based on community feedback.  If you have any comments, please leave them below.

 

If you’d like to email us instead of posting a comment, please email: questions@midasnetwork.us

 

Note: Your comments will not be visible on this page.  Comments will be sent to a moderator, and you will be contacted about your contribution via email if an address one is provided.

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