Update README.md

Author: ramtinz

README.md

@@ -1,24 +1,66 @@
-# DfMD
-Datasheets for Medical Datasets app.
-
-Requirements for Data and Data Dictionary files:
-- .csv or .xlsx files
-- no value are case sensitive
-- List of NaN values:
-  -   ["#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "None", "n/a", "nan", "null", "na", "-"]
-
-Data Dictionary:
-- Requiered Columns:
-  - "variable type"
-    - only values permitted:
-      -"continuous", "categorical", "date and time" 
-  - "role"
-    - only values permitted:
-      - "outcome", "feature", "identifier", "other"
-      - variables labeled as "other" are not evaluated in the app.
-      - If multiple variables are labeled as "identifier" only first is checked
-      
-Data:
-  - List of allowed characters:
-    -   '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ \t\n\r\x0b\x0c'
-  
+# DAIMS: Datasheets for AI and Medical Datasets  
+
+![GitHub license](https://img.shields.io/github/license/PERSIMUNE/DAIMS)  
+![GitHub last commit](https://img.shields.io/github/last-commit/PERSIMUNE/DAIMS)  
+![GitHub issues](https://img.shields.io/github/issues/PERSIMUNE/DAIMS)  
+![GitHub stars](https://img.shields.io/github/stars/PERSIMUNE/DAIMS?style=social)  
+![Streamlit App](https://img.shields.io/badge/Streamlit-App-brightgreen)  
+
+Despite progress in data engineering, inconsistencies in data validation and documentation procedures continue to cause confusion and technical challenges in research involving machine learning (ML). While frameworks like **Datasheets for Datasets** have made strides in addressing these challenges, there is room for improvement to better prepare datasets for ML pipelines.  
+
+To bridge this gap, we introduce **DAIMS**: Datasheets for AI and Medical Datasets. DAIMS extends the foundational framework with additional tools and guidance tailored specifically for medical datasets and AI applications.  
+
+## Key Features  
+
+1. **Comprehensive Checklist**  
+   A 24-point checklist that covers common data standardization requirements. A subset of these checks is automated by the DAIMS software tool to validate dataset readiness.  
+
+2. **Data Documentation Form**  
+   An extended documentation form designed to capture essential metadata, pose relevant research questions, and ensure datasets are well-prepared for ML analysis.  
+
+3. **Data Dictionary Table**  
+   A tabular format to document variable descriptions, data types, units, and any relevant details about the dataset.  
+
+4. **Flowchart for ML Analyses**  
+   A guided flowchart that maps research questions to suggested ML methods, providing researchers with a clear pathway to address their objectives.  
+
+5. **Software Tool**  
+   A publicly available tool to assist in dataset preparation by automating key aspects of the checklist validation process.  
+
+6. **Online App**  
+   DAIMS is available as an easy-to-use online app hosted at [https://daims-app.streamlit.app/](https://daims-app.streamlit.app/), enabling efficient dataset evaluation and preparation.  
+
+## Benefits of DAIMS  
+
+- **Standardization**: Promotes consistent practices for preparing datasets in medical research.  
+- **Guidance**: Offers actionable insights through the flowchart and checklist, helping researchers align datasets with their ML objectives.  
+- **Automation**: Saves time by automating key validation processes.  
+- **Documentation**: Enhances transparency and reproducibility through detailed data documentation.  
+
+## Getting Started  
+
+1. **Access the Repository**  
+   Clone or download the DAIMS repository from GitHub:  
+   [https://github.com/PERSIMUNE/DAIMS](https://github.com/PERSIMUNE/DAIMS)  
+
+2. **Explore the Online App**  
+   Use the online app for streamlined dataset evaluation:  
+   [https://daims-app.streamlit.app/](https://daims-app.streamlit.app/)  
+
+3. **Follow the Checklist**  
+   Refer to the provided checklist to ensure datasets meet the 24 common data standardization requirements.  
+
+4. **Document Your Dataset**  
+   Use the extended form and data dictionary table to comprehensively document your dataset.  
+
+5. **Use the Flowchart**  
+   Map your research questions to suggested ML methods for clearer analytical direction.  
+
+## Contributing  
+
+We welcome contributions to improve DAIMS! Feel free to open issues, submit pull requests, or provide feedback on the GitHub repository.  
+
+## License  
+
+This project is licensed under the [MIT License](LICENSE).  
+