docs(citation): update to ask for citation of paper

Author: amyheather

README.md

@@ -72,9 +72,14 @@ For more information on this project, check out the [STARS project website](http
 
 ## Citation
 
-To cite this work, see the `CITATION.cff` file in this repository or use the "Cite this repository" button on GitHub.
+If you this book supports your work, please **cite our paper**:
 
-You can also cite the archived version of this work on Zenodo: https://doi.org/10.5281/zenodo.17094155.
+> Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
+
+You may choose to also cite the software repository or archived version:
+
+* Repository details are also provided in the `CITATION.cff` file in this repository or via the "Cite this repository" button on GitHub.
+* Archived version of this work on Zenodo: https://doi.org/10.5281/zenodo.17094155.
 
 <br>
 

_quarto.yml

@@ -129,7 +129,7 @@ website:
           <a href="/pages/changelog.qmd">Changelog</a>
     right:
       - text: |
-          Heather, A., Monks, T., Mustafee, N., Harper, A., Alidoost, F., Challen, R., & Slater, T. (2025). DES RAP Book: Reproducible Discrete-Event Simulation in Python and R. https://github.com/pythonhealthdatascience/des_rap_book. https://doi.org/10.5281/zenodo.17094155.
+          Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
 
 format:
   html:

index.qmd

@@ -29,6 +29,10 @@ format:
 
 This open book is a self-paced training resource that teaches you how to design, implement, and share discrete-event simulation (DES) models in Python and R as part of a reproducible analytical pipeline. It combines a **step-by-step guide** with **complete example repositories** that you can adapt for your own projects.
 
+If you this book supports your work, please **cite our paper**:
+
+> Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
+
 The material is designed for analysts, researchers, and students in health and operations research who want to build transparent, trustworthy simulation models. To get the most from this resource, you should be comfortable with basic programming in either Python or R and have some familiarity with probability and basic statistics. **No prior DES experience is required**: short introductions to DES, reproducible analytical pipelines, and free and open source software are provided in the "Intros" section and linked below.
 
 ::: {.pale-blue}
@@ -130,12 +134,7 @@ The book is written by **Amy Heather** [![ORCID](images/orcid.png){fig-alt="ORCI
 * Dr. **Rob Challen** [![ORCID](images/orcid.png){fig-alt="ORCID logo"}](https://orcid.org/0000-0002-5504-7768)
 * **Tom Slater** [![ORCID](images/orcid.png){fig-alt="ORCID logo"}](https://orcid.org/0009-0007-0838-7499)
 
-The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1] from 1st May 2024 to 31st October 2026. The listed researchers are associated with the **University of Exeter** Medical and Business Schools, and the **University of Bristol** School of Engineering, Mathematics and Technology.
-
-You can find out more about our project on the [**STARS project website**](https://pythonhealthdatascience.github.io/stars/){target="_blank"}. If you use this resource, **please cite us:**
-
-> Heather, A., Monks, T., Mustafee, N., Harper, A., Alidoost, F., Challen, R., & Slater, T. (2025). DES RAP Book: Reproducible Discrete-Event Simulation in Python and R. https://github.com/pythonhealthdatascience/des_rap_book. https://doi.org/10.5281/zenodo.17094155.
-
+The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1] from 1st May 2024 to 31st October 2026. The listed researchers are associated with the **University of Exeter** Medical and Business Schools, and the **University of Bristol** School of Engineering, Mathematics and Technology. You can find out more about our project on the [**STARS project website**](https://pythonhealthdatascience.github.io/stars/){target="_blank"}.
 <br>
 
 <i><b>Keywords:</b> discrete-event simulation; reproducible analytical pipelines; health services research; Python; R; simulation modelling; research software engineering; open-source tools; SimPy; simmer.</i>

pages/guide/further_info/conclusion.qmd

@@ -101,7 +101,7 @@ The code in this book is licensed under an **MIT License**, and the text is unde
 
 Suggested citation:
 
-> Heather, A., Monks, T., Mustafee, N., Harper, A., Alidoost, F., Challen, R., & Slater, T. (2025). DES RAP Book: Reproducible Discrete-Event Simulation in Python and R. https://github.com/pythonhealthdatascience/des_rap_book. https://doi.org/10.5281/zenodo.17094155.
+> Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
 
 ## Find out more about STARS
 

pages/guide/sharing/citation.qmd

@@ -111,15 +111,20 @@ To cite this work, see the `CITATION.cff` file in this repository or use the "Ci
 ```
 :::
 
-**2. Provide citation details directly**. Example for DES RAP Book (as of 6th January 2026):
+**2. Provide citation details directly**. Example for DES RAP Book (as of 30th June 2026):
 
 :::{.pale-grey}
 ```{.text}
 ## Citation
 
-To cite this work, use:
+If you this book supports your work, please **cite our paper**:
 
-Heather, A., Monks, T., Mustafee, N., Harper, A., Alidoost, F., Challen, R., & Slater, T. (2025). DES RAP Book: Reproducible Discrete-Event Simulation in Python and R. https://github.com/pythonhealthdatascience/des_rap_book. https://doi.org/10.5281/zenodo.17094155.
+> Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
+
+You may choose to also cite the software repository or archived version:
+
+* Repository details are also provided in the `CITATION.cff` file in this repository or via the "Cite this repository" button on GitHub.
+* Archived version of this work on Zenodo: https://doi.org/10.5281/zenodo.17094155.
 ```
 :::