fix: mixed data-type fixed for csv files, chore: citation data updated

Author: aritraroy24

README.md

@@ -8,7 +8,6 @@
 
 [![Python Version](https://img.shields.io/badge/python-3.12%20%7C%203.13-blue.svg)](https://www.python.org/downloads/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![PyPI](https://img.shields.io/pypi/v/comproscanner)](https://pypi.org/project/comproscanner/)
 [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://slimeslab.github.io/ComProScanner/)
 
 ## Overview
@@ -172,14 +171,14 @@ eval_visualizer.plot_multiple_radar_charts(
 If you use ComProScanner in your research, please cite:
 
 ```bibtex
-@misc{roy2025comproscanner,
+@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={example},
+      eprint={2510.20362},
       archivePrefix={arXiv},
-      primaryClass={cond-mat.mtrl-sci},
-      url={https://arxiv.org/abs/example},
+      primaryClass={physics.comp-ph},
+      url={https://arxiv.org/abs/2510.20362},
 }
 ```
 

docs/about/citation.md

@@ -3,13 +3,13 @@
 If you use ComProScanner in your research, please cite our related paper:
 
 ```bibtex
-@misc{roy2025comproscanner,
+@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={example},
+      eprint={2510.20362},
       archivePrefix={arXiv},
-      primaryClass={cond-mat.mtrl-sci},
-      url={https://arxiv.org/abs/example},
+      primaryClass={physics.comp-ph},
+      url={https://arxiv.org/abs/2510.20362},
 }
 ```

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/example).
+To read more about the evaluation metrics, please refer the journal article [here](https://arxiv.org/abs/2510.20362).
 
 ## Quick Start
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "comproscanner"
-version = "0.1.1"
+version = "0.1.2"
 description = "Multi-agent system for extracting and processing structured composition-property data from scientific literature"
 readme = "README.md"
 authors = [{ name = "Aritra Roy", email = "contact@aritraroy.live" }]

src/comproscanner/utils/data_preparator.py

@@ -298,7 +298,7 @@ def get_unprocessed_data(self) -> list[Dict]:
         Process materials data extracted from the CSV database and run CrewAI Workflow.
         """
         all_files = glob.glob(self.extracted_folderpath + "/*.csv")
-        dfs = [pd.read_csv(f) for f in all_files]
+        dfs = [pd.read_csv(f, dtype=str) for f in all_files]
         if not dfs:
             logger.error(f"No files found in the folder: {self.extracted_folderpath}")
             raise FileNotFoundErrorHandler(

src/comproscanner/utils/database_manager.py

@@ -158,7 +158,8 @@ def write_to_csv(self, final_df, filepath, keyword, source, csv_batch_size):
             output_file = f"{filepath}/{source}_{keyword}_paragraphs.csv"
 
             if os.path.exists(output_file):
-                existing_df = pd.read_csv(output_file)
+                # Read all columns as strings to avoid mixed type issues
+                existing_df = pd.read_csv(output_file, dtype=str)
                 final_df = final_df[~final_df["doi"].isin(existing_df["doi"])]
                 if not final_df.empty:
                     combined_df = pd.concat([existing_df, final_df], ignore_index=True)