M³GIM: Mapping Mobile Musicians

Digital Archive & Research Platform for Music Theater Mobility Studies

Project Status

Current Phase: Production-Ready Prototype

✅ Archive data migrated, validated, and exported as RiC-O conformant JSON-LD (436 records) ✅ Intelligent data pipeline with real archive signatures ✅ Complete visualization system with interactive features ✅ Modular CSS architecture with design tokens ✅ ES6 module-based JavaScript architecture ✅ Export functionality (SVG, PNG, CSV) ✅ Vite build system configured ✅ ESLint code quality enforcement

Project Overview

M³GIM (Mapping Mobile Musicians - Mobilität und Musiktheaterwissen im Graz der Nachkriegszeit am Beispiel der Sängerin Ira Malaniuk) is a digital humanities research project investigating transnational knowledge transfer in postwar music theater (1945-1969) through the lens of Ukrainian-Austrian mezzo-soprano Ira Malaniuk (1919-2009).

Project Team:

Project Lead: Univ.-Prof.in Dr.in Nicole K. Strohmann (KUG Graz)
Technical Implementation: Dr. Christopher Pollin (Digital Humanities Craft OG)

Duration: 01.01.2026 - 31.12.2026 (Phase 1: Feasibility Study)

Data Pipeline

data/archive-export/     →  migrate.py   →  data/processed/
(Original Excel)                            (Normalized Excel)
                                                   ↓
                                            validate.py
                                                   ↓
                                            data/reports/
                                            (Validation Report)
                                                   ↓
                                            create-ric-json.py
                                                   ↓
                               ┌────────────data/export/m3gim.jsonld
                               │            (436 archive records)
                               │                    ↓
                               │            build-views.py
                               │                    ↓
                               │            data/views/
                               │            ├── partitur.json (7.5KB)
                               │            ├── matrix.json (3.1KB)
                               │            ├── kosmos.json (4.9KB)
                               │            └── sankey.json (2.8KB)
                               │                    ↓
                               └──────────→ docs/data/
                                            (Production data)

Visualization Data Generation

The build-views.py script generates optimized JSON files for each visualization:

Weighted Intensities: Document types weighted (Letter=3, Program/Poster=2, Photo=1)
Role Extraction: Automatic detection of opera roles (Brangäne, Amneris, etc.)
Geographic Context: Location tracking per work (Bayreuth, München, Wien)
Real Signatures: All data references actual archive signatures

Current Data

Category	Count
Objects (Hauptbestand, Plakate, Tonträger)	208
Photographs	228
Total Records	436

Data Analysis Results

Generated reports from archive analysis:

data/reports/archive-analysis.md: Top 20 persons, works, locations
data/reports/signature-index.md: Detailed signature reference
data/reports/ZUSAMMENFASSUNG.md: Data enrichment recommendations

Best documented periods:

1950-1954: 68 documents
1995-1999: 58 documents
1955-1959: 49 documents

Visualizations

1. Mobilitäts-Partitur

Interactive timeline showing all biographical dimensions simultaneously:

7 life phases from childhood (1919) to retirement (2009)
Geographic locations (residence vs. performance venues)
Mobility events with classification
Network intensity over time
Repertoire evolution (Wagner, Verdi, Strauss)
Document density per year

Export: SVG, PNG

2. Begegnungs-Matrix

Heatmap showing relationship intensity to persons over time periods:

4 persons from 436 archive records
Weighted intensity based on document types
Click to view linked archive signatures
Category indicators (Conductor, Director, Agent, Colleague)

Export: SVG, PNG, CSV

3. Rollen-Kosmos

Force-directed graph of the artistic universe:

Center: Ira Malaniuk (1919-2009, Mezzo-soprano)
Composers: Wagner (18 docs), Verdi (21 docs), Strauss (9 docs)
Works with roles and locations
Interactive drag, click for details

Export: SVG, PNG, CSV

4. Karriere-Fluss

Sankey diagram showing career flow:

Phases: Anfänge, Aufstieg, Höhepunkt, Spätphase
Repertoire: Wagner, Verdi, Strauss, Gluck/Händel, Beethoven
Locations: Wien, Bayreuth, München, Salzburg, Graz
Flow thickness represents document counts

Export: SVG, PNG

Repository Structure

m3gim/
├── data/
│   ├── archive-export/    # Original archive Excel exports
│   ├── processed/         # Normalized Excel (migrate.py output)
│   ├── export/            # JSON-LD export (create-ric-json.py output)
│   └── reports/           # Validation reports
├── docs/                  # GitHub Pages frontend
│   ├── index.html         # Main HTML with accessibility enhancements
│   ├── css/
│   │   ├── main.css       # CSS orchestrator (imports all modules)
│   │   ├── tokens.css     # Design tokens (colors, spacing, typography)
│   │   ├── base.css       # Reset and base styles
│   │   ├── components/    # UI component styles (8 modules)
│   │   └── visualizations/ # D3.js visualization styles (4 modules)
│   ├── js/
│   │   ├── main.js        # ES6 module entry point
│   │   ├── modules/       # Core modules (config, state, utils)
│   │   ├── services/      # Data and filter services
│   │   ├── components/    # UI components (modal, grid, filters, etc.)
│   │   ├── app.js         # Legacy application code
│   │   └── partitur.js    # Partitur visualization
│   └── data/
│       ├── m3gim.jsonld       # Archive data (436 records)
│       └── synthetic-data.json # Prototype visualization data
├── scripts/
│   ├── migrate.py         # Archive → Normalized Excel
│   ├── validate.py        # Data validation
│   └── create-ric-json.py # Excel → JSON-LD (RiC-O)
├── knowledge/             # Project documentation (13 files)
│   ├── 01-PROJEKT.md      # Project overview
│   ├── 02-DATENMODELL.md  # RiC-O data model
│   ├── 03-DESIGN-SYSTEM.md # UI/UX specifications
│   ├── 04-VISUALISIERUNGEN.md # Visualization concepts
│   ├── 05-ENTSCHEIDUNGEN.md # Architecture decisions
│   ├── 06-ANFORDERUNGEN.md # Requirements
│   ├── 07-PIPELINES.md    # Data pipelines
│   ├── 08-IMPLEMENTIERUNGSPLAN.md # Implementation plan
│   ├── 09-DATENANFORDERUNGEN.md # Real data requirements
│   └── VIZ-*.md           # Individual visualization specs
├── .eslintrc.cjs          # ESLint configuration
├── vite.config.js         # Vite build configuration
├── package.json           # Node.js dependencies
└── README.md

Technical Stack

Data Model: Records in Contexts (RiC-O) 1.1
Serialization: JSON-LD with custom DocumentaryFormTypes
Processing: Python (pandas, openpyxl)
Build System: Vite 6.x (dev server, production bundling)
Frontend: ES6 Modules, CSS Design Tokens
Visualizations: D3.js v7 (Partitur, Matrix, Kosmos, Sankey)
Code Quality: ESLint with custom rules
Icons: Lucide Icons
Authority Data: Wikidata Q-IDs

JSON-LD Namespaces

Prefix	URI	Purpose
`rico:`	https://www.ica.org/standards/RiC/ontology#	RiC-O ontology
`m3gim:`	https://dhcraft.org/m3gim/vocab#	Project vocabulary
`m3gim-dft:`	https://dhcraft.org/m3gim/documentary-form-types#	DocumentaryFormTypes
`wd:`	http://www.wikidata.org/entity/	Wikidata entities

Scripts

migrate.py

Transforms archive Excel exports to normalized format with controlled vocabularies.

python scripts/migrate.py

validate.py

Validates data against schema rules (signatures, vocabularies, dates).

python scripts/validate.py

create-ric-json.py

Exports to RiC-O conformant JSON-LD.

python scripts/create-ric-json.py

build-views.py

Generates pre-aggregated view data for visualizations with intelligent extraction.

python scripts/build-views.py

Features:

Weighted intensity calculation based on document types
Role extraction from titles (Fricka, Brangäne, Amneris, etc.)
Geographic context tracking (Bayreuth, München, Wien)
Composer-work relationships with real archive signatures

Output:

data/views/partitur.json - Timeline view data
data/views/matrix.json - Person-time matrix
data/views/kosmos.json - Composer-work network
data/views/sankey.json - Career flow data

Development

Prerequisites

Node.js 18+
Python 3.10+ (for data processing)

Setup

npm install

Development Server

npm run dev

Opens at http://localhost:5173

Production Build

npm run build
npm run preview

Linting

npm run lint      # Check for issues
npm run lint:fix  # Auto-fix issues

Source Materials

Archive: Ira Malaniuk Estate (UAKUG/NIM) Location: Universitätsarchiv der Kunstuniversität Graz Extent: 182 archival units in 17 archive boxes Period: 1924-1998 (focus: 1945-1969)

License

Code: MIT License
Data & Documentation: CC BY 4.0
Source materials: See individual file rights

Contact

Project Lead: nicole.strohmann@kug.ac.at
Technical Lead: christopher.pollin@gmail.com

Last Updated: 2026-01-18

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Repository files navigation

M³GIM: Mapping Mobile Musicians

Project Status

Project Overview

Data Pipeline

Visualization Data Generation

Current Data

Data Analysis Results

Visualizations

1. Mobilitäts-Partitur

2. Begegnungs-Matrix

3. Rollen-Kosmos

4. Karriere-Fluss

Repository Structure

Technical Stack

JSON-LD Namespaces

Scripts

migrate.py

validate.py

create-ric-json.py

build-views.py

Development

Prerequisites

Setup

Development Server

Production Build

Linting

Source Materials

License

Contact

About

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Name		Name	Last commit message	Last commit date
Latest commit History 34 Commits
.github/workflows		.github/workflows
data		data
docs		docs
knowledge		knowledge
scripts		scripts
.eslintrc.cjs		.eslintrc.cjs
.gitignore		.gitignore
.prettierrc		.prettierrc
IMPLEMENTATION-SUMMARY.md		IMPLEMENTATION-SUMMARY.md
README.md		README.md
analyze_archive.py		analyze_archive.py
analyze_archive_v2.py		analyze_archive_v2.py
create_signature_index.py		create_signature_index.py
package.json		package.json
vite.config.js		vite.config.js

DigitalHumanitiesCraft/m3gim

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M³GIM: Mapping Mobile Musicians

Project Status

Project Overview

Data Pipeline

Visualization Data Generation

Current Data

Data Analysis Results

Visualizations

1. Mobilitäts-Partitur

2. Begegnungs-Matrix

3. Rollen-Kosmos

4. Karriere-Fluss

Repository Structure

Technical Stack

JSON-LD Namespaces

Scripts

migrate.py

validate.py

create-ric-json.py

build-views.py

Development

Prerequisites

Setup

Development Server

Production Build

Linting

Source Materials

License

Contact

About

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Uh oh!

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Releases

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Contributors 3

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