From e4ac20f5ca79c166b5181329ec16ecce9c2da6c9 Mon Sep 17 00:00:00 2001
From: Oksana Kolisnyk <zazera777@gmail.com>
Date: Sat, 16 May 2026 15:37:51 +0300
Subject: [PATCH 1/2] Add research quest system

---
 RESEARCH-QUESTS/_TEMPLATE.md                  | 241 ++++++++++++++++++
 .../research-quest-01-ai-data-pipeline.md     | 213 ++++++++++++++++
 assets/js/quest.js                            |  44 +++-
 3 files changed, 491 insertions(+), 7 deletions(-)
 create mode 100644 RESEARCH-QUESTS/_TEMPLATE.md
 create mode 100644 RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md

diff --git a/RESEARCH-QUESTS/_TEMPLATE.md b/RESEARCH-QUESTS/_TEMPLATE.md
new file mode 100644
index 0000000..66c320b
--- /dev/null
+++ b/RESEARCH-QUESTS/_TEMPLATE.md
@@ -0,0 +1,241 @@
+# 🔬 Research Quest: [Title]
+
+**🎯 Focus:** [Research Area] | **⏱️ Time:** [X hours] | **📊 Difficulty:** [Beginner/Intermediate/Advanced]  
+**📍 Source:** [Repository/Files] | **✨ Status:** 📝 Draft / 👀 Review / ✅ Approved
+
+---
+
+## 🧭 Research Context
+
+**🌍 Background:**
+[Brief description of the research area and why it matters - make it engaging!]
+
+**📚 Source Material:**
+- 📁 Repository: `[Source Repo]`
+- 📂 Specific files: `[path/to/files]`
+- 🔗 Key references: `[links if applicable]`
+
+---
+
+## 🎯 Research Objective
+
+**❓ Main Question:**
+[What specific research question does this quest address?]
+
+**💡 Hypothesis:**
+[What hypothesis are we testing or exploring?]
+
+**🎁 Expected Outcome:**
+[What do we expect to learn or validate?]
+
+---
+
+## 🗺️ Phase 1: 🔍 Exploration (30-60 min)
+
+### 📖 Literature Scan
+- [ ] 📚 Review existing documentation in the source repository
+- [ ] 🔍 Identify key concepts and terminology
+- [ ] 📝 Note gaps or unclear areas
+- [ ] 💡 List questions that arise
+
+### 💻 Code/Data Investigation
+- [ ] 🗂️ Examine the structure of the source code/data
+- [ ] ▶️ Run existing examples (if applicable)
+- [ ] 🐛 Document what works and what doesn't
+- [ ] 📸 Take screenshots of key findings
+
+**📝 Notes:**
+```
+Initial observations:
+- Repository structure: [document what you find]
+- Key files identified: [list files]
+- Documentation quality: [assess with emoji rating 😊/😐/😞]
+- Surprises discovered: [any unexpected findings]
+```
+
+---
+
+## 🛠️ Phase 2: 🔬 Hands-On Investigation (2-4 hours)
+
+### 🎯 Task 1: [Specific Task Name]
+**🎯 Goal:** [What are you trying to achieve?]
+
+**📋 Steps:**
+1. [First step] 📍
+2. [Second step] 📍
+3. [Third step] 📍
+
+**✅ Expected Result:** [What should happen?]
+
+**🔍 Actual Result:** [What actually happened?]
+
+**⚠️ Issues Encountered:** [Any problems?]
+
+---
+
+### 🎯 Task 2: [Specific Task Name]
+**🎯 Goal:** [What are you trying to achieve?]
+
+**📋 Steps:**
+1. [First step] 📍
+2. [Second step] 📍
+
+**✅ Expected Result:** [What should happen?]
+
+**🔍 Actual Result:** [What actually happened?]
+
+**⚠️ Issues Encountered:** [Any problems?]
+
+---
+
+### 🔄 Task 3: Reproducibility Check
+**🎯 Goal:** Can someone else reproduce this investigation?
+
+**📋 Steps:**
+1. 🖥️ Document your environment (OS, Python version, dependencies)
+2. 📦 Create a minimal reproducible example
+3. 🧪 Test on a different setup (if possible)
+4. 📝 Document any environment-specific issues
+
+**🔄 Reproducibility Score:** [1-5 scale] ⭐⭐⭐⭐⭐
+
+**📦 Environment Details:**
+- OS: [your OS] 💻
+- Python version: [version] 🐍
+- Key dependencies: [list] 📦
+
+---
+
+## 📊 Phase 3: 🧠 Analysis & Synthesis (60-90 min)
+
+### 🔑 Key Findings
+1. [Finding 1] 🔍
+2. [Finding 2] 🔍
+3. [Finding 3] 🔍
+
+### 🎉 Unexpected Discoveries
+- [Any surprises or unexpected results] 🎁
+
+### ⚠️ Limitations
+- [What are the limitations of this investigation?] 🚧
+
+### 🔗 Connections to Other Research
+- [How does this connect to other S/E/T research areas?] 🔗
+
+### 📈 Impact Assessment
+**🎯 Impact Level:** [Low/Medium/High] 📊
+**🌍 Cross-sphere relevance:** [S/E/T connections] 🌐
+
+---
+
+## 📝 Phase 4: 📚 Documentation (30-60 min)
+
+### 📄 Research Summary
+[Write a 2-3 paragraph summary of the entire investigation - make it clear and engaging!]
+
+### 💡 Recommendations
+
+**🚀 Immediate Actions:**
+- [ ] [Action 1] ⚡
+- [ ] [Action 2] ⚡
+- [ ] [Action 3] ⚡
+
+**🔮 Medium-term Improvements:**
+- [ ] [Improvement 1] 🎯
+- [ ] [Improvement 2] 🎯
+- [ ] [Improvement 3] 🎯
+
+**🌟 Long-term Vision:**
+- [ ] [Vision 1] 🚀
+- [ ] [Vision 2] 🚀
+- [ ] [Vision 3] 🚀
+
+### 📋 Pull Request Proposal
+If this investigation leads to actionable changes:
+
+**📌 Title:** [PR Title]
+**📝 Description:** [Brief description of proposed changes]
+**📁 Files to modify:** [List of files]
+**🧪 Testing approach:** [How to verify the changes]
+**👥 Reviewers:** [Who should review?]
+
+---
+
+## 🤔 Reflection Questions
+
+- 😊 Did the investigation match your expectations?
+- 😰 What was the most challenging part?
+- 💡 What would you do differently next time?
+- 🌟 How does this contribute to the broader research goals?
+- 🎓 What did you learn that surprised you?
+
+**📝 Your Reflections:**
+```
+[Space for your personal reflections]
+```
+
+---
+
+## ✅ Approval Criteria
+
+This research quest is ready for merge when:
+- [ ] 🎯 All phases completed
+- [ ] 📊 Findings documented clearly
+- [ ] 🔄 Reproducibility verified (score 3+)
+- [ ] 💡 Recommendations actionable
+- [ ] 👥 Peer review completed
+- [ ] 📝 Documentation updated
+
+**🎯 Approval Status:** [Pending Review / Approved / Rejected]
+
+---
+
+## 📊 Metadata
+
+**📅 Created:** [Date] 🗓️
+**🔄 Last Updated:** [Date] 🔄
+**👥 Contributors:** [Names] 👤
+**🔗 Related Issues:** [Links to GitHub issues] 🔗
+**🏷️ Tags:** [research, sphere-specific tags] 🏷️
+**⭐ Difficulty Rating:** [1-5] ⭐⭐⭐⭐⭐
+**🎯 Priority:** [Low/Medium/High] 🎯
+
+---
+
+## 🎉 Quest Completion Checklist
+
+**Phase 1 - Exploration:**
+- [ ] 📖 Literature scan completed
+- [ ] 💻 Code/data investigation done
+- [ ] 📝 Initial notes documented
+
+**Phase 2 - Hands-On:**
+- [ ] 🎯 Task 1 completed
+- [ ] 🎯 Task 2 completed
+- [ ] 🔄 Reproducibility checked
+
+**Phase 3 - Analysis:**
+- [ ] 🔑 Key findings identified
+- [ ] 🎉 Unexpected discoveries noted
+- [ ] ⚠️ Limitations documented
+- [ ] 🔗 Connections mapped
+
+**Phase 4 - Documentation:**
+- [ ] 📄 Summary written
+- [ ] 💡 Recommendations listed
+- [ ] 📋 PR proposal ready
+- [ ] 🤔 Reflections completed
+
+**🎊 Overall Progress:** [0% - 100%] 📊
+
+---
+
+## 💬 Notes & Comments
+
+[Space for additional notes, comments, or collaboration]
+
+---
+
+*🔬 This research quest follows the SET methodology for reproducible computational science research*  
+*🌐 Part of the K-RnD-Lab research ecosystem*  
+*📧 Questions? Open an issue or start a discussion*
diff --git a/RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md b/RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md
new file mode 100644
index 0000000..67aeebe
--- /dev/null
+++ b/RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md
@@ -0,0 +1,213 @@
+# Research Quest 01 — AI & Data Analysis Pipeline Investigation
+
+**Title:** Exploring Data Analysis Pipeline in SPHERE-III-TECHNOLOGY
+**Sphere:** T | **Source:** K-RnD-Lab/SPHERE-III-TECHNOLOGY | **Status:** Draft
+**Time Estimate:** 3-4 hours | **Difficulty:** Intermediate
+
+---
+
+## Research Context
+
+**Background:**
+Data analysis pipelines are critical for reproducible research in computational science. This quest investigates the existing data processing tools in SPHERE-III-TECHNOLOGY to understand their capabilities, limitations, and potential improvements.
+
+**Source Material:**
+- Repository: `K-RnD-Lab/SPHERE-III-TECHNOLOGY`
+- Focus areas: Data processing tools, ML pipelines, analytical engines
+- Related: T1 — Research Tools, ML & Analytical Engines
+
+---
+
+## Research Objective
+
+**Main Question:**
+What data analysis capabilities currently exist in SPHERE-III-TECHNOLOGY, and how can they be improved for reproducible research?
+
+**Hypothesis:**
+The repository contains modular data processing tools that can be composed into reproducible pipelines, but documentation and examples may be incomplete.
+
+**Expected Outcome:**
+- Clear understanding of existing data tools
+- Identification of gaps in documentation
+- Concrete recommendations for improvement
+
+---
+
+## Phase 1: Exploration (60 min)
+
+### Literature Scan
+- [ ] Review README.md in SPHERE-III-TECHNOLOGY
+- [ ] Examine T1 directory structure
+- [ ] Identify key data processing files
+- [ ] Note dependencies and requirements
+
+### Code Investigation
+- [ ] Clone or navigate to SPHERE-III-TECHNOLOGY
+- [ ] List all Python/data processing files
+- [ ] Check for existing examples or notebooks
+- [ ] Identify test files that demonstrate usage
+
+**Notes:**
+```
+Initial observations:
+- Repository structure: [document what you find]
+- Key files identified: [list files]
+- Documentation quality: [assess]
+```
+
+---
+
+## Phase 2: Hands-On Investigation (2-3 hours)
+
+### Task 1: Tool Inventory
+**Goal:** Catalog all data analysis tools in the repository
+
+**Steps:**
+1. Search for files with patterns: `*data*.py`, `*pipeline*.py`, `*analysis*.py`
+2. For each file found, document:
+   - Purpose (from docstrings or comments)
+   - Input/output formats
+   - Dependencies
+3. Create a table of tools with metadata
+
+**Expected Result:** Comprehensive inventory of data tools
+
+**Actual Result:** [Fill after investigation]
+
+---
+
+### Task 2: Reproducibility Test
+**Goal:** Test if existing examples can be run successfully
+
+**Steps:**
+1. Find example scripts or notebooks
+2. Set up a clean environment (virtual environment)
+3. Install dependencies from requirements.txt or setup.py
+4. Run the example
+5. Document any errors or missing dependencies
+
+**Expected Result:** Examples run without errors
+
+**Actual Result:** [Fill after investigation]
+
+**Environment:**
+- OS: [your OS]
+- Python version: [version]
+- Key dependencies: [list]
+
+---
+
+### Task 3: Pipeline Composition
+**Goal:** Attempt to compose tools into a simple pipeline
+
+**Steps:**
+1. Select 2-3 related tools from inventory
+2. Design a simple workflow (e.g., load → process → visualize)
+3. Implement the pipeline
+4. Test with sample data
+5. Document the process
+
+**Expected Result:** Working pipeline with documentation
+
+**Actual Result:** [Fill after investigation]
+
+---
+
+## Phase 3: Analysis & Synthesis (60-90 min)
+
+### Key Findings
+1. [Finding 1 - e.g., "Found 5 data processing tools but only 2 have examples"]
+2. [Finding 2 - e.g., "Tool X requires undocumented dependency Y"]
+3. [Finding 3 - e.g., "Pipeline composition is straightforward but lacks error handling"]
+
+### Unexpected Discoveries
+- [Any surprises - e.g., "Found a hidden utility that simplifies data loading"]
+
+### Limitations
+- Investigation limited to T1 directory
+- Did not test with large datasets
+- Environment-specific issues may affect reproducibility
+
+### Connections to Other Research
+- This connects to S sphere research that needs data processing
+- E sphere could use these tools for market intelligence
+- Potential for cross-sphere standardization
+
+---
+
+## Phase 4: Documentation (60 min)
+
+### Research Summary
+This investigation explored the data analysis capabilities in SPHERE-III-TECHNOLOGY. We identified [number] data processing tools, tested [number] examples, and composed [number] pipelines. Key findings include [brief summary]. The tools show promise for reproducible research but need improved documentation and examples.
+
+### Recommendations
+
+**Immediate Actions:**
+- Add example notebooks for each major tool
+- Create a "Getting Started" guide for pipeline composition
+- Document all dependencies clearly
+
+**Medium-term Improvements:**
+- Implement standard error handling across tools
+- Add unit tests for critical functions
+- Create template pipeline scripts
+
+**Long-term Vision:**
+- Develop a unified pipeline orchestration framework
+- Integrate with S sphere research workflows
+- Create cross-sphere data standards
+
+### Pull Request Proposal
+
+**Title:** Improve data analysis tool documentation and examples
+
+**Description:**
+This PR adds example notebooks, improves documentation, and creates a getting started guide for data analysis tools in T1.
+
+**Files to modify:**
+- `T1/README.md` - add getting started section
+- `T1/examples/` - create new example notebooks
+- `T1/docs/` - add detailed tool documentation
+
+**Testing approach:**
+- Run all new examples in clean environment
+- Verify documentation builds without errors
+- Get feedback from at least one other researcher
+
+---
+
+## Reflection Questions
+
+- Did the investigation match your expectations?
+  - [Your answer]
+
+- What was the most challenging part?
+  - [Your answer - e.g., "Setting up the environment took longer than expected"]
+
+- What would you do differently next time?
+  - [Your answer - e.g., "Start with dependency check before code investigation"]
+
+- How does this contribute to the broader research goals?
+  - [Your answer - e.g., "Better documented tools enable faster research across all spheres"]
+
+---
+
+## Approval Criteria
+
+This research quest is ready for merge when:
+- [ ] All phases completed
+- [ ] Tool inventory table filled
+- [ ] At least one pipeline composed and tested
+- [ ] Findings documented clearly
+- [ ] Recommendations include specific file paths
+- [ ] Peer review completed
+
+---
+
+## Metadata
+
+**Created:** 2026-05-16
+**Last Updated:** [Date when completed]
+**Contributors:** [Your name]
+**Related Issues:** [Link to GitHub issue if created]
+**Tags:** research, data-analysis, reproducibility, T-sphere, pipeline
diff --git a/assets/js/quest.js b/assets/js/quest.js
index f8e4ac7..89065ee 100644
--- a/assets/js/quest.js
+++ b/assets/js/quest.js
@@ -15,27 +15,42 @@ const quests = [
   {id:"T3",sphere:"T",dir:"T3 - Dashboards, Interfaces & Open Infrastructure",label:"🖥️ T3 Dashboards & Infra"}
 ];
 
+// Research quests from RESEARCH-QUESTS folder
+const researchQuests = [
+  {id:"R1",sphere:"R",dir:"research-quest-01-ai-data-pipeline",label:"🔬 R1 AI & Data Pipeline Investigation"},
+  {id:"R2",sphere:"R",dir:"research-quest-02-reproducibility-check",label:"🔬 R2 Reproducibility Check"},
+  {id:"R3",sphere:"R",dir:"research-quest-03-method-validation",label:"🔬 R3 Method Validation"}
+];
+
 const REPO_MAP = {
   S:"K-RnD-Lab/SPHERE-I-SCIENCE",
   E:"K-RnD-Lab/SPHERE-II-ENTREPRENEURSHIP",
-  T:"K-RnD-Lab/SPHERE-III-TECHNOLOGY"
+  T:"K-RnD-Lab/SPHERE-III-TECHNOLOGY",
+  R:"K-RnD-Lab/SPHERE-FRONTIER"
 };
+
 const nav = document.getElementById("questNav");
 const content = document.getElementById("mdContent");
 
 function buildNav(){
-  const groups = {S:[],E:[],T:[]};
+  const groups = {S:[],E:[],T:[],R:[]};
   quests.forEach(q => groups[q.sphere].push(q));
-  const labels = {S:"Science",E:"Entrepreneurship",T:"Technology"};
+  researchQuests.forEach(q => groups[q.sphere].push(q));
+  
+  const labels = {S:"Science",E:"Entrepreneurship",T:"Technology",R:"Research Quests"};
   let html = `<a href="./index.html" class="back-link">← K R&D Lab</a><h2>Quests</h2>`;
+  
   Object.entries(groups).forEach(([sphere,items]) => {
+    if(items.length === 0) return;
     html += `<div class="nav-${sphere}"><h3>${labels[sphere]}</h3>`;
     items.forEach(q => {
       html += `<a href="#${q.id}" data-dir="${encodeURIComponent(q.dir)}" data-sphere="${q.sphere}">${q.label}</a>`;
     });
     html += `</div>`;
   });
+  
   nav.innerHTML = html;
+  
   nav.querySelectorAll("a[data-dir]").forEach(a => {
     a.addEventListener("click", e => {
       e.preventDefault();
@@ -47,16 +62,30 @@ function buildNav(){
 }
 
 async function loadQuest(dir, sphere){
-  const repo = REPO_MAP[sphere];
-  const url = `https://raw.githubusercontent.com/${repo}/main/${dir}/README.md`;
+  let url;
+  if(sphere === "R"){
+    // Research quests are in the same repo (SPHERE-FRONTIER)
+    url = `https://raw.githubusercontent.com/K-RnD-Lab/SPHERE-FRONTIER/TEZv-research/RESEARCH-QUESTS/${dir}.md`;
+  } else {
+    const repo = REPO_MAP[sphere];
+    url = `https://raw.githubusercontent.com/${repo}/main/${dir}/README.md`;
+  }
+  
   content.innerHTML = "<p style='color:var(--muted)'>Loading quest...</p>";
+  
   try {
     const res = await fetch(url);
     if(!res.ok) throw new Error(res.status);
     const md = await res.text();
     content.innerHTML = marked.parse(md);
   } catch(err) {
-    const ghUrl = `https://github.com/${repo}/tree/main/${dir}`;
+    let ghUrl;
+    if(sphere === "R"){
+      ghUrl = `https://github.com/K-RnD-Lab/SPHERE-FRONTIER/tree/TEZv-research/RESEARCH-QUESTS`;
+    } else {
+      const repo = REPO_MAP[sphere];
+      ghUrl = `https://github.com/${repo}/tree/main/${dir}`;
+    }
     content.innerHTML = `<p style='color:var(--muted)'>Could not load quest. <a href="${ghUrl}" target="_blank" rel="noreferrer">Open on GitHub →</a></p>`;
   }
 }
@@ -65,7 +94,8 @@ buildNav();
 
 const hash = location.hash.slice(1);
 if(hash){
-  const q = quests.find(q => q.id === hash);
+  const allQuests = [...quests, ...researchQuests];
+  const q = allQuests.find(q => q.id === hash);
   if(q){
     const a = nav.querySelector(`a[href="#${q.id}"]`);
     if(a){ a.classList.add("active"); loadQuest(q.dir, q.sphere); }

From 4d0bb9002fc1794338fd8717b76d0bf0ddeae679 Mon Sep 17 00:00:00 2001
From: Oksana Kolisnyk <zazera777@gmail.com>
Date: Sat, 16 May 2026 20:10:38 +0300
Subject: [PATCH 2/2] Remove RESEARCH-QUESTS from SPHERE-FRONTIER

---
 RESEARCH-QUESTS/_TEMPLATE.md                  | 241 ------------------
 .../research-quest-01-ai-data-pipeline.md     | 213 ----------------
 2 files changed, 454 deletions(-)
 delete mode 100644 RESEARCH-QUESTS/_TEMPLATE.md
 delete mode 100644 RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md

diff --git a/RESEARCH-QUESTS/_TEMPLATE.md b/RESEARCH-QUESTS/_TEMPLATE.md
deleted file mode 100644
index 66c320b..0000000
--- a/RESEARCH-QUESTS/_TEMPLATE.md
+++ /dev/null
@@ -1,241 +0,0 @@
-# 🔬 Research Quest: [Title]
-
-**🎯 Focus:** [Research Area] | **⏱️ Time:** [X hours] | **📊 Difficulty:** [Beginner/Intermediate/Advanced]  
-**📍 Source:** [Repository/Files] | **✨ Status:** 📝 Draft / 👀 Review / ✅ Approved
-
----
-
-## 🧭 Research Context
-
-**🌍 Background:**
-[Brief description of the research area and why it matters - make it engaging!]
-
-**📚 Source Material:**
-- 📁 Repository: `[Source Repo]`
-- 📂 Specific files: `[path/to/files]`
-- 🔗 Key references: `[links if applicable]`
-
----
-
-## 🎯 Research Objective
-
-**❓ Main Question:**
-[What specific research question does this quest address?]
-
-**💡 Hypothesis:**
-[What hypothesis are we testing or exploring?]
-
-**🎁 Expected Outcome:**
-[What do we expect to learn or validate?]
-
----
-
-## 🗺️ Phase 1: 🔍 Exploration (30-60 min)
-
-### 📖 Literature Scan
-- [ ] 📚 Review existing documentation in the source repository
-- [ ] 🔍 Identify key concepts and terminology
-- [ ] 📝 Note gaps or unclear areas
-- [ ] 💡 List questions that arise
-
-### 💻 Code/Data Investigation
-- [ ] 🗂️ Examine the structure of the source code/data
-- [ ] ▶️ Run existing examples (if applicable)
-- [ ] 🐛 Document what works and what doesn't
-- [ ] 📸 Take screenshots of key findings
-
-**📝 Notes:**
-```
-Initial observations:
-- Repository structure: [document what you find]
-- Key files identified: [list files]
-- Documentation quality: [assess with emoji rating 😊/😐/😞]
-- Surprises discovered: [any unexpected findings]
-```
-
----
-
-## 🛠️ Phase 2: 🔬 Hands-On Investigation (2-4 hours)
-
-### 🎯 Task 1: [Specific Task Name]
-**🎯 Goal:** [What are you trying to achieve?]
-
-**📋 Steps:**
-1. [First step] 📍
-2. [Second step] 📍
-3. [Third step] 📍
-
-**✅ Expected Result:** [What should happen?]
-
-**🔍 Actual Result:** [What actually happened?]
-
-**⚠️ Issues Encountered:** [Any problems?]
-
----
-
-### 🎯 Task 2: [Specific Task Name]
-**🎯 Goal:** [What are you trying to achieve?]
-
-**📋 Steps:**
-1. [First step] 📍
-2. [Second step] 📍
-
-**✅ Expected Result:** [What should happen?]
-
-**🔍 Actual Result:** [What actually happened?]
-
-**⚠️ Issues Encountered:** [Any problems?]
-
----
-
-### 🔄 Task 3: Reproducibility Check
-**🎯 Goal:** Can someone else reproduce this investigation?
-
-**📋 Steps:**
-1. 🖥️ Document your environment (OS, Python version, dependencies)
-2. 📦 Create a minimal reproducible example
-3. 🧪 Test on a different setup (if possible)
-4. 📝 Document any environment-specific issues
-
-**🔄 Reproducibility Score:** [1-5 scale] ⭐⭐⭐⭐⭐
-
-**📦 Environment Details:**
-- OS: [your OS] 💻
-- Python version: [version] 🐍
-- Key dependencies: [list] 📦
-
----
-
-## 📊 Phase 3: 🧠 Analysis & Synthesis (60-90 min)
-
-### 🔑 Key Findings
-1. [Finding 1] 🔍
-2. [Finding 2] 🔍
-3. [Finding 3] 🔍
-
-### 🎉 Unexpected Discoveries
-- [Any surprises or unexpected results] 🎁
-
-### ⚠️ Limitations
-- [What are the limitations of this investigation?] 🚧
-
-### 🔗 Connections to Other Research
-- [How does this connect to other S/E/T research areas?] 🔗
-
-### 📈 Impact Assessment
-**🎯 Impact Level:** [Low/Medium/High] 📊
-**🌍 Cross-sphere relevance:** [S/E/T connections] 🌐
-
----
-
-## 📝 Phase 4: 📚 Documentation (30-60 min)
-
-### 📄 Research Summary
-[Write a 2-3 paragraph summary of the entire investigation - make it clear and engaging!]
-
-### 💡 Recommendations
-
-**🚀 Immediate Actions:**
-- [ ] [Action 1] ⚡
-- [ ] [Action 2] ⚡
-- [ ] [Action 3] ⚡
-
-**🔮 Medium-term Improvements:**
-- [ ] [Improvement 1] 🎯
-- [ ] [Improvement 2] 🎯
-- [ ] [Improvement 3] 🎯
-
-**🌟 Long-term Vision:**
-- [ ] [Vision 1] 🚀
-- [ ] [Vision 2] 🚀
-- [ ] [Vision 3] 🚀
-
-### 📋 Pull Request Proposal
-If this investigation leads to actionable changes:
-
-**📌 Title:** [PR Title]
-**📝 Description:** [Brief description of proposed changes]
-**📁 Files to modify:** [List of files]
-**🧪 Testing approach:** [How to verify the changes]
-**👥 Reviewers:** [Who should review?]
-
----
-
-## 🤔 Reflection Questions
-
-- 😊 Did the investigation match your expectations?
-- 😰 What was the most challenging part?
-- 💡 What would you do differently next time?
-- 🌟 How does this contribute to the broader research goals?
-- 🎓 What did you learn that surprised you?
-
-**📝 Your Reflections:**
-```
-[Space for your personal reflections]
-```
-
----
-
-## ✅ Approval Criteria
-
-This research quest is ready for merge when:
-- [ ] 🎯 All phases completed
-- [ ] 📊 Findings documented clearly
-- [ ] 🔄 Reproducibility verified (score 3+)
-- [ ] 💡 Recommendations actionable
-- [ ] 👥 Peer review completed
-- [ ] 📝 Documentation updated
-
-**🎯 Approval Status:** [Pending Review / Approved / Rejected]
-
----
-
-## 📊 Metadata
-
-**📅 Created:** [Date] 🗓️
-**🔄 Last Updated:** [Date] 🔄
-**👥 Contributors:** [Names] 👤
-**🔗 Related Issues:** [Links to GitHub issues] 🔗
-**🏷️ Tags:** [research, sphere-specific tags] 🏷️
-**⭐ Difficulty Rating:** [1-5] ⭐⭐⭐⭐⭐
-**🎯 Priority:** [Low/Medium/High] 🎯
-
----
-
-## 🎉 Quest Completion Checklist
-
-**Phase 1 - Exploration:**
-- [ ] 📖 Literature scan completed
-- [ ] 💻 Code/data investigation done
-- [ ] 📝 Initial notes documented
-
-**Phase 2 - Hands-On:**
-- [ ] 🎯 Task 1 completed
-- [ ] 🎯 Task 2 completed
-- [ ] 🔄 Reproducibility checked
-
-**Phase 3 - Analysis:**
-- [ ] 🔑 Key findings identified
-- [ ] 🎉 Unexpected discoveries noted
-- [ ] ⚠️ Limitations documented
-- [ ] 🔗 Connections mapped
-
-**Phase 4 - Documentation:**
-- [ ] 📄 Summary written
-- [ ] 💡 Recommendations listed
-- [ ] 📋 PR proposal ready
-- [ ] 🤔 Reflections completed
-
-**🎊 Overall Progress:** [0% - 100%] 📊
-
----
-
-## 💬 Notes & Comments
-
-[Space for additional notes, comments, or collaboration]
-
----
-
-*🔬 This research quest follows the SET methodology for reproducible computational science research*  
-*🌐 Part of the K-RnD-Lab research ecosystem*  
-*📧 Questions? Open an issue or start a discussion*
diff --git a/RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md b/RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md
deleted file mode 100644
index 67aeebe..0000000
--- a/RESEARCH-QUESTS/research-quest-01-ai-data-pipeline.md
+++ /dev/null
@@ -1,213 +0,0 @@
-# Research Quest 01 — AI & Data Analysis Pipeline Investigation
-
-**Title:** Exploring Data Analysis Pipeline in SPHERE-III-TECHNOLOGY
-**Sphere:** T | **Source:** K-RnD-Lab/SPHERE-III-TECHNOLOGY | **Status:** Draft
-**Time Estimate:** 3-4 hours | **Difficulty:** Intermediate
-
----
-
-## Research Context
-
-**Background:**
-Data analysis pipelines are critical for reproducible research in computational science. This quest investigates the existing data processing tools in SPHERE-III-TECHNOLOGY to understand their capabilities, limitations, and potential improvements.
-
-**Source Material:**
-- Repository: `K-RnD-Lab/SPHERE-III-TECHNOLOGY`
-- Focus areas: Data processing tools, ML pipelines, analytical engines
-- Related: T1 — Research Tools, ML & Analytical Engines
-
----
-
-## Research Objective
-
-**Main Question:**
-What data analysis capabilities currently exist in SPHERE-III-TECHNOLOGY, and how can they be improved for reproducible research?
-
-**Hypothesis:**
-The repository contains modular data processing tools that can be composed into reproducible pipelines, but documentation and examples may be incomplete.
-
-**Expected Outcome:**
-- Clear understanding of existing data tools
-- Identification of gaps in documentation
-- Concrete recommendations for improvement
-
----
-
-## Phase 1: Exploration (60 min)
-
-### Literature Scan
-- [ ] Review README.md in SPHERE-III-TECHNOLOGY
-- [ ] Examine T1 directory structure
-- [ ] Identify key data processing files
-- [ ] Note dependencies and requirements
-
-### Code Investigation
-- [ ] Clone or navigate to SPHERE-III-TECHNOLOGY
-- [ ] List all Python/data processing files
-- [ ] Check for existing examples or notebooks
-- [ ] Identify test files that demonstrate usage
-
-**Notes:**
-```
-Initial observations:
-- Repository structure: [document what you find]
-- Key files identified: [list files]
-- Documentation quality: [assess]
-```
-
----
-
-## Phase 2: Hands-On Investigation (2-3 hours)
-
-### Task 1: Tool Inventory
-**Goal:** Catalog all data analysis tools in the repository
-
-**Steps:**
-1. Search for files with patterns: `*data*.py`, `*pipeline*.py`, `*analysis*.py`
-2. For each file found, document:
-   - Purpose (from docstrings or comments)
-   - Input/output formats
-   - Dependencies
-3. Create a table of tools with metadata
-
-**Expected Result:** Comprehensive inventory of data tools
-
-**Actual Result:** [Fill after investigation]
-
----
-
-### Task 2: Reproducibility Test
-**Goal:** Test if existing examples can be run successfully
-
-**Steps:**
-1. Find example scripts or notebooks
-2. Set up a clean environment (virtual environment)
-3. Install dependencies from requirements.txt or setup.py
-4. Run the example
-5. Document any errors or missing dependencies
-
-**Expected Result:** Examples run without errors
-
-**Actual Result:** [Fill after investigation]
-
-**Environment:**
-- OS: [your OS]
-- Python version: [version]
-- Key dependencies: [list]
-
----
-
-### Task 3: Pipeline Composition
-**Goal:** Attempt to compose tools into a simple pipeline
-
-**Steps:**
-1. Select 2-3 related tools from inventory
-2. Design a simple workflow (e.g., load → process → visualize)
-3. Implement the pipeline
-4. Test with sample data
-5. Document the process
-
-**Expected Result:** Working pipeline with documentation
-
-**Actual Result:** [Fill after investigation]
-
----
-
-## Phase 3: Analysis & Synthesis (60-90 min)
-
-### Key Findings
-1. [Finding 1 - e.g., "Found 5 data processing tools but only 2 have examples"]
-2. [Finding 2 - e.g., "Tool X requires undocumented dependency Y"]
-3. [Finding 3 - e.g., "Pipeline composition is straightforward but lacks error handling"]
-
-### Unexpected Discoveries
-- [Any surprises - e.g., "Found a hidden utility that simplifies data loading"]
-
-### Limitations
-- Investigation limited to T1 directory
-- Did not test with large datasets
-- Environment-specific issues may affect reproducibility
-
-### Connections to Other Research
-- This connects to S sphere research that needs data processing
-- E sphere could use these tools for market intelligence
-- Potential for cross-sphere standardization
-
----
-
-## Phase 4: Documentation (60 min)
-
-### Research Summary
-This investigation explored the data analysis capabilities in SPHERE-III-TECHNOLOGY. We identified [number] data processing tools, tested [number] examples, and composed [number] pipelines. Key findings include [brief summary]. The tools show promise for reproducible research but need improved documentation and examples.
-
-### Recommendations
-
-**Immediate Actions:**
-- Add example notebooks for each major tool
-- Create a "Getting Started" guide for pipeline composition
-- Document all dependencies clearly
-
-**Medium-term Improvements:**
-- Implement standard error handling across tools
-- Add unit tests for critical functions
-- Create template pipeline scripts
-
-**Long-term Vision:**
-- Develop a unified pipeline orchestration framework
-- Integrate with S sphere research workflows
-- Create cross-sphere data standards
-
-### Pull Request Proposal
-
-**Title:** Improve data analysis tool documentation and examples
-
-**Description:**
-This PR adds example notebooks, improves documentation, and creates a getting started guide for data analysis tools in T1.
-
-**Files to modify:**
-- `T1/README.md` - add getting started section
-- `T1/examples/` - create new example notebooks
-- `T1/docs/` - add detailed tool documentation
-
-**Testing approach:**
-- Run all new examples in clean environment
-- Verify documentation builds without errors
-- Get feedback from at least one other researcher
-
----
-
-## Reflection Questions
-
-- Did the investigation match your expectations?
-  - [Your answer]
-
-- What was the most challenging part?
-  - [Your answer - e.g., "Setting up the environment took longer than expected"]
-
-- What would you do differently next time?
-  - [Your answer - e.g., "Start with dependency check before code investigation"]
-
-- How does this contribute to the broader research goals?
-  - [Your answer - e.g., "Better documented tools enable faster research across all spheres"]
-
----
-
-## Approval Criteria
-
-This research quest is ready for merge when:
-- [ ] All phases completed
-- [ ] Tool inventory table filled
-- [ ] At least one pipeline composed and tested
-- [ ] Findings documented clearly
-- [ ] Recommendations include specific file paths
-- [ ] Peer review completed
-
----
-
-## Metadata
-
-**Created:** 2026-05-16
-**Last Updated:** [Date when completed]
-**Contributors:** [Your name]
-**Related Issues:** [Link to GitHub issue if created]
-**Tags:** research, data-analysis, reproducibility, T-sphere, pipeline