Revise README for OpenMind Wrangler project

Updated the project title and expanded the README to provide a detailed overview of the OpenMind Wrangler project, including its aims, vision, work plan, and evaluation metrics.

README.md

@@ -1,2 +1,96 @@
-# AI-assisted-Neuroimaging-harmonization
-Harmonize Neuroimaging open datasets using AI agents
+# OpenMind Wrangler: AI Agents for Data Preparation in the AI 3.0 Era
+
+This is the repository for the exploratory project OpenMind Wranglers, inspired by the accelerating progress of AI 3.0 models and their potential to automate complex data engineering workflows for multi-study neuroimaging and clinical datasets.
+
+## Project Aim
+
+The goal of this project is to explore whether AI agents (primarily LLM-based) can meaningfully assist in dataset wrangling tasks that precede model inference or training — tasks that are time-consuming yet essential to reproducible, large-scale neuroimaging research.
+
+While the BIDS standard ensures interoperability on the metadata level, many preprocessing steps — such as volume normalization, quality control, outlier detection, or label encoding — remain manual or semi-automated. These steps form a bottleneck in data-driven science, especially when aggregating datasets across multiple studies.
+
+In this proof-of-concept, we aim to determine whether a coordinated system of AI agents can reliably execute these operations and produce AI-ready dataset collections, similar to those hosted on platforms like HuggingFace Datasets: OpenMind, with minimal human intervention.
+
+## The Vision
+
+If successful, OpenMind Wrangler will serve as a foundation for a general-purpose AI data engineering assistant, capable of producing multi-study datasets that are immediately usable for machine learning and statistical analysis — without manual wrangling.
+
+## Rough Work Plan
+
+The project will proceed along four parallel tracks: agent design, dataset exploration, wrangling task automation, and evaluation.
+
+### 1. Agent Design
+
+We will prototype a multi-agent system where each agent handles a specific wrangling subtask (e.g., normalization, QC, encoding). Agents will communicate via a shared memory and task queue.
+
+Goals:
+
+Design a modular agentic framework (e.g., LangChain, CrewAI, or AutoGen)
+
+Define prompt templates and tools for each subtask
+
+Implement logging and reasoning traceability
+
+Resources:
+
+LangChain Agent Docs
+
+OpenAI Function Calling Guide
+
+### 2. Dataset Exploration
+
+OpenNeuro
+
+Tasks:
+
+Retrieve metadata and BIDS structures
+
+Generate schema summaries and compatibility maps
+
+Identify data types for downstream analysis
+
+### 3. Wrangling Task Automation
+
+The central track: enabling LLM-driven automation of the following operations:
+
+Volume normalization (using NiBabel / ANTsPy)
+
+Quality control report generation
+
+Outlier detection (statistical and visual)
+
+Label harmonization and encoding
+
+Data documentation generation (Markdown / JSON-LD)
+
+The focus is not on achieving perfection, but on evaluating how well an AI agent can assist or autonomously perform these tasks, given contextual metadata and goals.
+
+### 4. Evaluation and Benchmarking
+
+The evaluation phase will assess the efficiency, accuracy, and robustness of agentic wrangling workflows compared to traditional, human-coded pipelines.
+
+Metrics will include:
+
+Time saved per dataset compared to manual pipelines
+
+Accuracy of normalization / encoding
+
+Error detection rate (false positives / negatives in QC)
+
+Consistency across heterogeneous datasets
+
+We will also benchmark against a simple scripted baseline (e.g., manual nipype or pandas pipeline).
+
+Milestones
+
+✅ Literature & tooling review on AI-assisted data wrangling
+
+⚙️ Prototype LLM agent framework for dataset preprocessing
+
+🧠 Evaluation on 2–3 public BIDS datasets
+
+📊 Quantitative + qualitative benchmarking report
+
+
+Generative augmentation: Can the agent propose synthetic data to fill gaps or balance classes?
+
+Teams and Participants