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Format Selection Guide

This guide helps you choose the right serialization format for your MARC data based on your use case.

Quick Decision Tree

What's your primary goal?
│
├─ Exchange with other library systems?
│  └─ ISO 2709 (.mrc) - Universal compatibility
│
├─ Build a REST API?
│  └─ JSON or MARCJSON - Human-readable, widely supported
│
├─ Linked data / semantic web?
│  └─ BIBFRAME - RDF graph model for modern linked data
│
├─ Metadata crosswalks?
│  ├─ Simple metadata? → Dublin Core (15-element standard)
│  ├─ Detailed bibliographic? → MODS (bidirectional read/write)
│  └─ Import from MODS? → MODS read (crosswalk import)
│
├─ Spreadsheet export?
│  └─ CSV
│
└─ Long-term archival?
   └─ ISO 2709 or JSON - Wide tool support

Format Comparison

Format Speed Size Compatibility Best For
ISO 2709 Fast Compact Universal Library system interchange
JSON Medium Large Universal Web APIs, debugging
MARCJSON Medium Large LOC Standard Standards-compliant JSON
XML Slow Very Large Universal MARCXML systems
CSV Fast Medium Universal Spreadsheets, simple tools
Dublin Core Fast Medium Universal Simple metadata exchange
MODS Medium Large Good Detailed metadata crosswalks
BIBFRAME Medium Large Growing Linked data, RDF applications

Detailed Format Profiles

ISO 2709 (MARC Binary)

Use when: - Exchanging with traditional library systems (ILS, OCLC, etc.) - Maximum compatibility is required - Working with existing MARC workflows

Avoid when: - Building modern web APIs - Need for human-readable output - Integrating with linked data systems

# Reading
with open("records.mrc", "rb") as f:
    for record in mrrc.MARCReader(f):
        print(record.title())

# Writing
with open("output.mrc", "wb") as f:
    with mrrc.MARCWriter(f) as writer:
        writer.write(record)

JSON / MARCJSON

Use when: - Building web APIs that need human-readable responses - Debugging or inspecting records - JavaScript frontend integration - MARCJSON when LOC standard compliance is required

Avoid when: - Bandwidth is limited - Processing millions of records - Need binary efficiency

# Single record
json_str = record.to_json()
marcjson_str = record.to_marcjson()

# Parse back
restored = mrrc.json_to_record(json_str)

BIBFRAME

Use when: - Building linked data applications - Publishing bibliographic data as RDF - Integrating with SPARQL endpoints or triple stores - Following Library of Congress modernization initiatives

Avoid when: - Simple record exchange between library systems - Need for compact binary format - Systems that only support traditional MARC

from mrrc import marc_to_bibframe, BibframeConfig

config = BibframeConfig()
config.set_base_uri("http://library.example.org/")
graph = marc_to_bibframe(record, config)

# Serialize to Turtle, RDF/XML, JSON-LD, or N-Triples
turtle = graph.serialize("turtle")

See the BIBFRAME Conversion Guide for details.

CSV

Use when: - Exporting to spreadsheets (Excel, Google Sheets) - Simple data analysis tools - Quick tabular view of MARC data

Avoid when: - Need to preserve full MARC structure - Round-trip conversion is required

csv_str = mrrc.records_to_csv(records)

Dublin Core

Use when: - Simple metadata exchange across systems - OAI-PMH harvesting - Need 15-element standard metadata

dc_xml = record.to_dublin_core()

MODS

Use when: - Detailed metadata crosswalks (bidirectional) - Digital library applications - Need richer metadata than Dublin Core - Importing MODS records into MARC workflows

# Convert to MODS XML
mods_xml = record.to_mods()

# Parse from MODS XML
record = mrrc.mods_to_record(mods_xml)

Use Case Recommendations

Library System Integration

Recommended: ISO 2709

Traditional library systems universally support ISO 2709. Use it for: - OCLC WorldCat submissions - ILS data migration - Z39.50 compatible systems

Web Application Backend

Recommended: JSON or MARCJSON

For APIs serving web or mobile clients: - JSON for generic integration - MARCJSON for LOC-compliant systems

Linked Data / Semantic Web

Recommended: BIBFRAME

BIBFRAME enables integration with: - SPARQL endpoints - Triple stores - Linked data ecosystems - Library of Congress initiatives

Metadata Crosswalks

Recommended: Dublin Core or MODS

  • Dublin Core for simple, widely-supported metadata
  • MODS for detailed bibliographic description

Spreadsheet Export

Recommended: CSV

CSV works with Excel, Google Sheets, and simple data tools.

Archival / Preservation

Recommended: ISO 2709 or JSON

  • ISO 2709 for maximum future compatibility with library systems
  • JSON for wide tool support and human readability

Next Steps