Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review

Overview

The materials and data in this repository support: Harper and Monks (2023). Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review. All materials are published under an MIT permissive license.

Author ORCIDs

Write up of study

Methods, results, and code are available in our online Jupyter Book https://tommonks.github.io/des_sharing_lit_review

A full write-up of the work is available open access in the Journal of Simulation. If you use this work please cite the paper.

Monks, T., & Harper, A. (2023). Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review. Journal of Simulation, 1–16. https://doi.org/10.1080/17477778.2023.2260772

Bibtex citation:

@article{monks_harper_2023,
author = {Thomas Monks and Alison Harper},
title = {Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review},
journal = {Journal of Simulation},
pages = {1--16},
year = {2023},
publisher = {Taylor \& Francis},
doi = {10.1080/17477778.2023.2260772},
URL = {https://doi.org/10.1080/17477778.2023.2260772},
eprint = {https://doi.org/10.1080/17477778.2023.2260772}
}

Aim and research questions:

The overarching research aim is determine to what extent authors of DES health studies share models and where models are shared how is this done.

Primary research questions

1. What proportion of DES healthcare studies share code?
2. How is sharing affected by FOSS, Covid-19, publication type and year of publication?
3. What proportion of studies make use of a reporting guideline?
4. What methods, tools, and resources did authors use to share their computer models and code?
5. To what extent do the DES health community follow best practice for open science when sharing computer models?
6. To what extent can the healthcare DES community improve its sharing of computer models?

Dependencies

All dependencies can be found in binder/environment.yml and are pulled from conda-forge. To run the code locally, we recommend install mini-conda; navigating your terminal (or cmd prompt) to the directory containing the repo and issuing the following command:

conda env create -f binder/environment.yml

Online Alternatives:

• Visit our [jupyter book]((https://tommonks.github.io/des_sharing_lit_review) for interactive code and explanatory text
• Run out Jupyter notebooks in binder

Repo overview

.
├── binder
│   └── environment.yml
├── CITATION.cff
├── content
│   ├── 01_intro
│   ├── 02_methods
│   ├── 03_results
│   ├── 04_discussion
│   └── 04_prisma
├── data
├── LICENSE
├── README.md
├── _config.yml
└── _toc.yml

• binder - contains the environment.yml file (des_review) and all dependencies managed via conda.
• CITATION.cff - citation information for GitHub repository.
• content - the analysis notebooks and markdown arranged by introductory, methods, results, and PRISMA reporting checklist chapters.
• data - directory containing data files used by analysis notebooks.
• LICENSE - details of the MIT permissive license of this work.
• README - what you are reading now!
• _config.yml - configuration of our Jupyter Book.
• _toc.yml - the table of contents for our Jupyter Book.

Study data

All study data is contained within this repository. It can be found in the data sub-directory.

Main data files:

• share_sim_data_extract.zip: main study data stored as a CSV. It includes all publications carried forward to the data extraction phase.
• bp_audit.zip: Contains the studies and additional data extraction used within the best practice audit of shared computer models.

Testing

If any updates to the data are made we recommend re-running the Data source testing notebook. This will perform a set of tests on the main and best practice audit datasets to check that data is in the correct place and format.