FemHealth Data Collaborative

Overview

In Assam, the health outcomes for women and girls are influenced by the intricate interplay of maternal health, nutrition, mental wellbeing, climate vulnerability, and public finance. Yet, the data that informs these realities is scattered across systems: reports, surveys, tenders, health facilities, schemes and administrative departments rarely speak to each other. This lack of integration hinders policymakers, researchers, and practitioners from grasping the complete picture. Eventually, holistic empowerment of women continues to remain hindered by systemic biases; critical gaps go unnoticed, resource allocation may be inefficient, and communities with urgent needs often remain overlooked.

The FemHealth Data Collaborative aims to enable changemakers using responsible AI and data technology. The integrated approach of this solution combines technology, institutional reform and collaboration to drive sustainable improvements in women’s health outcomes. Through the FemHealth Analytics we will uncover insights and enable better decision making through consolidated datasets across health, finance, climate and social indicators, AI powered analytics & hotspot detection for vulnerable geographies, multilingual summaries (Assamese/Hindi/English) for inclusive accessibility, spatial visualisation & trend mapping for real time situational awareness, policy linked datasets to understand how funds, services and outcomes connect, collaborative, open platform for researchers, CSOs and government stakeholders

This repository contains datasets, analytics and models used in the FemHealth Data Collaborative.

Directory Structure

• analytics/: Models and Ex;loratory Data Analysis for understanding gender-based development indices in Assam
• data-ecosystem/: Extracted data, ETL pipelines and data dictionaries. Each "sources" folder contains raw data as well as scripts to extract them into machin-readable formats for loading into models
• docs/: Documentation files

Contributing

1. Create a feature branch from the main branch
2. Make your changes
3. Submit a pull request

License

This project is licensed under the MIT License.