docs(llm_simpy): update title of llm_simpy repository (+ add line breaks and DOI badge to README)

Author: amyheather

Home.py

@@ -12,10 +12,9 @@
     """
 This application is complementary to:
 
-> Thomas Monks, Alison Harper, and Amy Heather. **Using Large
-Language Models to support researchers reproduce and reuse unpublished health
-care discrete-event simulation computer models: a feasibility and pilot study
-in Python**. https://github.com/pythonhealthdatascience/llm_simpy.
+> Thomas Monks, Alison Harper, and Amy Heather. **Research Compendium:
+Replicating Simulations in Python using Generative AI**.
+https://github.com/pythonhealthdatascience/llm_simpy.
 
 It deploys the streamlit applications generated using Perplexity as a single
 app via stlite and GitHub pages.

README.md

@@ -1,11 +1,12 @@
 [![Licence: MIT](https://img.shields.io/badge/Licence-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Python 3.10+](https://img.shields.io/badge/-Python_≥_3.10-306998?logo=python&logoColor=white)](https://www.python.org/downloads/release/python-360+/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15082494.svg)](https://doi.org/10.5281/zenodo.15082494)
 
 # Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models
 
 This repository is complementary to:
 
-> Thomas Monks, Alison Harper, and Amy Heather. **Using Large Language Models to support researchers reproduce and reuse unpublished health care discrete-event simulation computer models: a feasibility and pilot study in Python**. https://github.com/pythonhealthdatascience/llm_simpy.
+> Thomas Monks, Alison Harper, and Amy Heather. **Research Compendium: Replicating Simulations in Python using Generative AI**. https://github.com/pythonhealthdatascience/llm_simpy.
 
 It contains the final formatted code from each of the SimPy discrete-event simulation models that were generated by Perplexity as part of that project.
 
@@ -24,6 +25,8 @@ The stroke `.py` files combine the seperate model and app .py files from the [ll
 
 For a full record of the generation of these models, please refer to: https://github.com/pythonhealthdatascience/llm_simpy.
 
+<br>
+
 ## 👥 Authors
 
 * Thomas Monks &nbsp;&nbsp; [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481)
@@ -32,6 +35,8 @@ For a full record of the generation of these models, please refer to: https://gi
 
 * Amy Heather &nbsp;&nbsp; [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479)
 
+<br>
+
 ## 🌐 Creating the environment
 
 The project uses `conda` to manage dependencies. Navigate your terminal to the directory containing the code and run:
@@ -48,6 +53,8 @@ conda activate gen_simpy_apps
 
 This environment is a simplified version of that from the [llm_simpy](https://github.com/pythonhealthdatascience/llm_simpy) repository, containing only the dependencies required for running the apps.
 
+<br>
+
 ## 🖥️ Viewing the apps locally
 
 For deployment, we have brought the LLM-generated apps together into a single app, which can be deployed by running:
@@ -68,6 +75,8 @@ To test the stlite app locally, run the following command, and then open <http:/
 python3 -m http.server
 ```
 
+<br>
+
 ## 📝 Citation
 
 Please cite the archived repository:
@@ -80,6 +89,8 @@ You can also cite this GitHub repository as:
 
 A `CITATION.cff` file is also provided.
 
+<br>
+
 ## 💰 Funding
 
 This project was developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible Simulations. It is supported by the Medical Research Council [grant number [MR/Z503915/1](https://gtr.ukri.org/projects?ref=MR%2FZ503915%2F1)].