docs: modified readme and other pages with DD links instead of arXiv

Author: aritraroy24

README.md

@@ -2,7 +2,7 @@
   <img src="https://raw.githubusercontent.com/aritraroy24/ComProScanner/refs/heads/main/assets/comproscanner_logo.png" alt="ComProScanner Logo" width="500"/>
 </p>
 
-[![Python Version](https://img.shields.io/badge/python-3.12%20%7C%203.13-blue.svg?logo=python&logoColor=white)](https://www.python.org/downloads/) [![License: MIT](https://custom-icon-badges.demolab.com/badge/license-MIT-yellow.svg?logo=law&logoColor=white)](https://opensource.org/licenses/MIT) [![PyPI](https://img.shields.io/pypi/v/comproscanner?logo=pypi&logoColor=white)](https://pypi.org/project/comproscanner/) [![Documentation](https://custom-icon-badges.demolab.com/badge/docs-latest-brightgreen.svg?logo=materialformkdocs&logoColor=white)](https://slimeslab.github.io/ComProScanner/) [![Coverage](https://img.shields.io/codecov/c/github/aritraroy24/ComProScanner?logo=codecov&logoColor=white&label=coverage&color=e62277)](https://codecov.io/gh/aritraroy24/ComProScanner) [![PyPI - Downloads](https://custom-icon-badges.demolab.com/pypi/dm/comproscanner?logo=download&logoColor=white&color=purple)](https://pypistats.org/packages/comproscanner) [![Ask DeepWiki](https://custom-icon-badges.demolab.com/badge/Ask%20DeepWiki-brightgreen.svg?logo=deepwikidevin&logoColor=white&labelColor=grey&color=5ab998)](https://deepwiki.com/slimeslab/ComProScanner) [![arXiv Preprint](https://custom-icon-badges.demolab.com/badge/arXiv-2510.20362-brightgreen.svg?logo=arxiv&logoColor=white&color=b22929)](https://arxiv.org/abs/2510.20362)
+[![Python Version](https://img.shields.io/badge/python-3.12%20%7C%203.13-blue.svg?logo=python&logoColor=white)](https://www.python.org/downloads/) [![License: MIT](https://custom-icon-badges.demolab.com/badge/license-MIT-yellow.svg?logo=law&logoColor=white)](https://opensource.org/licenses/MIT) [![PyPI](https://img.shields.io/pypi/v/comproscanner?logo=pypi&logoColor=white)](https://pypi.org/project/comproscanner/) [![Documentation](https://custom-icon-badges.demolab.com/badge/docs-latest-brightgreen.svg?logo=materialformkdocs&logoColor=white)](https://slimeslab.github.io/ComProScanner/) [![Coverage](https://img.shields.io/codecov/c/github/aritraroy24/ComProScanner?logo=codecov&logoColor=white&label=coverage&color=e62277)](https://codecov.io/gh/aritraroy24/ComProScanner) [![PyPI - Downloads](https://custom-icon-badges.demolab.com/pypi/dm/comproscanner?logo=download&logoColor=white&color=purple)](https://pypistats.org/packages/comproscanner) [![Ask DeepWiki](https://custom-icon-badges.demolab.com/badge/Ask%20DeepWiki-brightgreen.svg?logo=deepwikidevin&logoColor=white&labelColor=grey&color=5ab998)](https://deepwiki.com/slimeslab/ComProScanner) [![Digital Discovery](https://custom-icon-badges.demolab.com/badge/Digital_Discovery-10.1039/D5DD00521C-brightgreen.svg?logo=rsc&logoColor=white&color=c8c300)](https://doi.org/10.1039/D5DD00521C)
 
 # ComProScanner
 

docs/index.md

@@ -143,21 +143,22 @@ graph LR
 
 ## Paper
 
-Read the details of ComProScanner in the following preprint: [arXiv:2510.20362](https://arxiv.org/abs/2510.20362)
+Read the details of ComProScanner in the following Digital Discovery paper: [10.1039/D5DD00521C](https://doi.org/10.1039/D5DD00521C)
 
 ## Citation
 
 If you use ComProScanner in your research, please cite:
 
 ```bibtex
-@misc{roy2025comproscannermultiagentbasedframework,
-      title={ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature},
-      author={Aritra Roy and Enrico Grisan and John Buckeridge and Chiara Gattinoni},
-      year={2025},
-      eprint={2510.20362},
-      archivePrefix={arXiv},
-      primaryClass={physics.comp-ph},
-      url={https://arxiv.org/abs/2510.20362},
+@Article{roy2026comproscannermultiagentbasedframework,
+author ="Roy, Aritra and Grisan, Enrico and Buckeridge, John and Gattinoni, Chiara",
+title  ="ComProScanner: a multi-agent based framework for composition-property structured data extraction from scientific literature",
+journal  ="Digital Discovery",
+year  ="2026",
+pages  ="Accepted",
+publisher  ="RSC",
+doi  ="10.1039/D5DD00521C",
+url  ="https://doi.org/10.1039/D5DD00521C"
 }
 ```
 

docs/usage/evaluation/overview.md

@@ -56,7 +56,7 @@ Both methods provide:
 - **Classification Metrics**: Standard Precision/Recall/F1 metrics for detailed performance analysis
 - **Normalized Classification Metrics**: Classification metrics normalized to ensure an equitable comparison between articles with significant disparities in the quantity of extractable information.
 
-To read more about the evaluation metrics, please refer the journal article [here](https://arxiv.org/abs/2510.20362).
+To read more about the evaluation metrics, please refer the journal article [here](https://doi.org/10.1039/D5DD00521C).
 
 ## Quick Start