317

Codes

Themes

473

Relationships

4,533

Evidence

45%

Cross-Pillar

Overview

Pillars

Relationships

CoE Scores

AMRP

Pillar Distribution

NSS (95)

OR (75)

MLE (60)

IK (87)

Near-perfect pillar balance (Shannon evenness: 0.99). 317 unique codes distributed across 4 pillars from 44 respondents in Gosaba and Patharpratima blocks, Indian Sundarbans.

Key Metrics

Metric	Value	Interpretation
Code Density	7.20	317 codes / 44 respondents
Relationship Density	1.49	473 relationships / 317 codes
Evidence Depth	9.58	4,533 evidence records / 473 relationships
Modularity (Q)	0.770	Strong community structure (Louvain)
Cross-Pillar Rate	45%	213 of 473 relationships cross pillars
Shannon Evenness	0.99	Near-perfect pillar balance across 4 pillars

Nutrition as a Socioecological System (NSS)

95 codes encompassing ecological, agricultural, and environmental determinants of nutritional adequacy.

Theme	Codes	Relationships
T01: Land Erosion, Market Dependence and Settlement Precarity	24	33
T02: Ration Infrastructure, Fuel Scarcity and Livestock Economy	16	20
T03: Dietary Patterns, Health Transformation and Panta Bhat Subsistence	16	21
T04: Pond Aquaculture, Fish Diet and Bangladesh Migration Memory	14	21
T05: Famine Memory, Wild Food Knowledge and Dietary Displacement	12	22
T06: Home Garden, Foraging and Micro-Enterprise Livelihood	11	13

Observational Resilience (OR)

75 codes capturing community-level adaptive capacities, coping strategies, and livelihood diversification.

Theme	Codes	Relationships
T07: Fishing Economy, Marine Resource Depletion and Coastal Livelihoods	28	49
T08: Cyclone-Salinity-Agriculture Collapse and Water Scarcity	20	30
T09: Livelihood Transition, Technology Shift and Generational Change	14	20
T10: Cyclone Shelter, Embankment Politics and Flood Endurance	11	20
T11: Island Isolation, Transport Barriers and Off-Grid Infrastructure	11	16
T12: Paan Boroj Homestead Economy, Cyclone Destruction and Pesticide Dependency	10	15

Multi-Lens Equity (MLE)

60 codes addressing structural inequities in food access across gender, age, caste, and economic position.

Theme	Codes	Relationships
T13: Migration, Island Extinction and Cultural-Religious Transformation	20	30
T14: Gendered Labour, Welfare Gaps, Female Agency and Livelihood Division	22	27

Inclusive Knowledge (IK)

87 codes encompassing traditional ecological knowledge, intergenerational food practices, and indigenous preservation techniques.

Theme	Codes	Relationships
T15: Dietary Identity, Religious Calendar and Household Food Authority	20	39
T16: Place Attachment, Land Loss and Cultural Identity	19	24
T17: Forest-River Ecology, Pioneer Settlement and Panta Bhat Heritage	18	23
T18: Seasonal Taste Knowledge, Gendered Care and Intergenerational Transmission	17	28
T19: Community Solidarity, Disaster Preparedness and Communal Harmony	14	22

Cross-Pillar Relationships (213 of 473, 6 types)

Relationships classified across 6 NOMI types: Leads To, Supports, Is Cause Of, Is Associated With, Impacts On, and Barrier.

From	To	Count	Direction
NSS	OR	52	Bidirectional
NSS	IK	50	Bidirectional
NSS	MLE	39	Bidirectional
OR	MLE	31	Bidirectional
OR	IK	29	Bidirectional
MLE	IK	12	Bidirectional

Relationship Type Distribution (473 total)

Type	Count	Share
Leads To	206	43.6%
Supports	69	14.6%
Is Cause Of	65	13.7%
Is Associated With	54	11.4%
Impacts On	42	8.9%
Barrier	37	7.8%

Convergence of Evidence (CoE) Distribution

Strong

448

Moderate

Weak

CoE computed using Shi et al. (2025) methodology adapted for qualitative CQT framework. 4,533 evidence records from peer-reviewed literature validate 473 relationships.

AMRP (Adaptive Metric Recalibration Protocol) compares a new site's NOMI analysis against the Sundarbans benchmark to identify methodological strengths and gaps.

Benchmark Radar Profile

Each axis represents a normalized AMRP dimension. The Sundarbans benchmark scores near the outer ring across all 7 dimensions.

Benchmark Values

Dimension	Sundarbans Value	Normalized	Interpretation
Code Density	7.20	96%	High saturation
Cross-Pillar Rate	0.45	90%	Strong integration
Pillar Balance	0.99	99%	Near-perfect evenness
Relationship Density	1.49	80%	Rich connectivity
Evidence Depth	9.58	96%	Thorough triangulation
Modularity (Q)	0.770	77%	Strong community structure
CoE Strong Rate	0.053	53%	Developing evidence tier

How to Use AMRP

Run nomi-apply amrp-diagnose on your project to compare your site's metrics against this benchmark. The diagnostic radar chart highlights dimensions where your analysis may need strengthening.

Reading the radar: Dimensions near the outer ring indicate strong alignment with the benchmark. The CoE Strong Rate (orange point) shows room for growth; this is expected for novel study contexts where existing literature may not yet validate all relationships.

Apply NOMI to Your Data

Transform raw qualitative data into a structured NOMI framework analysis for program design and policy translation

Launch Full Pipeline on HuggingFace

🔬

For Researchers

Analyze qualitative field data through the four NOMI pillars. Discover thematic communities, map cross-pillar relationships, and validate findings with literature.

🎯

For Program Designers

Turn NOMI analysis outputs into actionable program recommendations. Use AMRP benchmarking to identify gaps and the One Health pathway for policy translation.

What you need to start: Interview transcripts (plain text, .txt/.csv/.xlsx), a coding framework (or use NOMI's 317-code codebook), and access to the NOMI Dashboard on HuggingFace Spaces.

7-Step Pipeline: Raw Data to NOMI Output

Progress: 1 2 3 4 5 6 7 Step 1 of 7

Upload Transcripts

Upload interview transcripts in .txt, .csv, or .xlsx format. Bengali text is auto-detected. Supports 3 formats: one-per-file, tabular, or multi-column.

Input: Raw interview transcripts from your field site

Output: Parsed, cleaned text segments ready for coding

If this fails: Check file encoding (use UTF-8). For Bengali text, ensure the file is not saved as ANSI. If .xlsx fails, try exporting as .csv first.

Four-Pillar Coding

Auto-code text segments against the NOMI 317-code codebook using TF-IDF similarity. Each segment is assigned to NSS, OR, MLE, or IK pillars.

Input: Parsed text segments from Step 1

Output: Coded segments with pillar assignments and confidence scores

If this fails: Low confidence scores (<0.3) mean your data may not align with the existing codebook. Try adding custom codes specific to your study context, or lower the threshold to capture broader matches.

Community Detection

Run Louvain/Leiden algorithm on the code co-occurrence network to discover thematic communities. Target: Q > 0.4 for meaningful structure.

Input: Coded segments with co-occurrence matrix

Output: Thematic communities (themes) with modularity score

If this fails: Low modularity (Q < 0.3) suggests insufficient data. Collect more interviews (aim for 30+ respondents) or refine coding to increase code diversity. If Q is very high (>0.9), your pillars may be too siloed; check for cross-pillar codes.

Map Cross-Pillar Relationships

Identify directional relationships between codes using 6 NOMI types: Leads To, Supports, Is Cause Of, Is Associated With, Impacts On, and Barrier.

Input: Coded data with thematic communities

Output: Relationship network with cross-pillar rate (target: >30%)

If this fails: Cross-pillar rate below 20% suggests your interview protocol may not capture inter-pillar dynamics. Add probing questions that bridge pillars (e.g., "How does water quality affect your market access?"). Review the 6 relationship type definitions.

CQT Literature Validation

Validate relationships against published literature using the Convergence-Qualification-Triangulation protocol (Shi et al. 2025 weights).

Input: Relationship network from Step 4

Output: CoE scores (Strong/Moderate/Weak) for each relationship

If this fails: Many "Weak" scores are normal for novel contexts. Focus on relationships with Moderate+ scores for program design. For weak relationships, search for grey literature or regional studies that may provide supporting evidence.

AMRP Benchmarking

Compare your site's 7 AMRP dimensions against the Sundarbans benchmark to identify where your analysis is strong and where it needs attention.

Input: Your site's metrics (code density, cross-pillar rate, modularity, etc.)

Output: Radar chart comparing your site vs. benchmark across 7 dimensions

If this fails: Dimensions below 50% of benchmark suggest methodological gaps. Low Code Density: collect more data or refine coding. Low Evidence Depth: expand literature search. Low Pillar Balance: ensure interview protocol covers all 4 NOMI pillars equally.

One Health Policy Translation

Generate program design recommendations through the AMRP pathway, connecting human-animal-environment dimensions for actionable policy outputs.

Input: Validated NOMI analysis with AMRP benchmarking

Output: Policy briefs, program design recommendations, publication-ready figures

If this fails: Weak One Health connections may mean your study context is more sector-specific. Focus on the strongest cross-pillar pathways for program design. Consider which NOMI pillars are most relevant to your local policy context.

Ready to Apply NOMI?

The full interactive pipeline runs on HuggingFace Spaces with step-by-step guidance, file upload, and real-time results.

Launch NOMI Pipeline

Sundarbans Benchmark Dataset