Foundation Tools for Data Management & Reproducible Research

This master bundle includes five fully built, reusable modules designed to support field research, MEL systems, and reproducible workflows across public interest data work in India.

📁 Included Modules

1. data_validation/

Reusable scripts for validating raw or cleaned datasets:

  • Duplicate ID checks (Python, Stata, SPSS)
  • Variable name validation
  • Value range scans
  • Code label checks

2. survey_to_codebook/

Converts an XLSForm to a clean Markdown codebook:

  • Input: XLSForm with survey and choices sheets
  • Output: Human-readable .md codebook
  • Language: Python

3. data_flow.md

A Markdown template to document how your dataset moved from:

  • Raw form → cleaned version → analysis-ready file
  • Includes transformation logs, tool tracking, and derived variable mapping

4. label_variables/

Templates to assign meaningful labels to variables in:

  • Python (DataFrame metadata)
  • SPSS syntax
  • Stata .do format

5. replication/

Basic structure to replicate your core analysis:

  • Python regression example
  • requirements.txt
  • README.md for clear execution instructions

🔧 How to Use

Each folder is independent and reusable. Drop into your project directory, adapt paths, and run based on your toolchain (SPSS/Stata/Python).

🔍 Why This Matters

These tools promote better:

  • Research reproducibility
  • Field-level data cleaning workflows
  • Open knowledge practices
  • Institutional memory in long-term programs

This bundle aligns with open standards like DIME (World Bank) and MEL frameworks used by IHCRF, Udaiti, and public health researchers.