Portfolio Dataset

World Publishing Houses — Publishing Intelligence Dataset

A structured dataset and product prototype for tracking publishers, translated books, translation paths, market events, and rights opportunities across international publishing markets.

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Problem

International publishing metadata is fragmented across retailers, publishers, libraries, rights catalogs, award announcements, and market news. A reader may see that a book exists, but not whether it is available in English, who translated it, which publisher handled the edition, or whether the attribution is reliable.

For translators, publishers, bookstores, and rights professionals, the same fragmentation makes it difficult to compare national markets, identify translation gaps, track rights movement, and understand where acquisition opportunities may exist.

Product framing

World Publishing Houses uses the dataset as the foundation for a publishing intelligence product. The goal is not only to collect book data, but to make trust, provenance, translation paths, and reader-facing availability visible.

Rows marked curated_needs_check are treated as demonstration or research leads, not as fully verified records.

Dataset Overview

Coverage built for a portfolio-ready data product.

This dataset began as a Denmark/Iceland pilot and is designed to scale across the Nordic publishing ecosystem: Denmark, Iceland, Norway, Sweden, and Finland.

Nordic countries

5Denmark, Iceland, Norway, Sweden, and Finland.

Works

70Books and works represented across the Nordic dataset.

Publishers

44Publisher and imprint records across Nordic markets.

Translation records

70Translation records with attribution and availability fields.

Events

82Release, award, market, and publishing signal events.

Rights-watchlist rows

34Rights and acquisition signals for professional workflows.

Dataset Explorer

WPH Nordic Dataset Explorer

Explore publishing, translation, verification, and ML-readiness signals across Denmark, Iceland, Norway, Sweden, and Finland.

This dataset began as a Denmark/Iceland pilot and is designed to scale across the Nordic publishing ecosystem.

5 Nordic countriesDenmark, Iceland, Norway, Sweden, and Finland.

70 worksBooks represented across the Nordic dataset.

44 publishersPublisher and imprint records.

ML readinessExploratory analysis and QA now; predictive ML after broader coverage and stronger class balance.

Warning

Class imbalance

Reader-bucket distribution should be reviewed before any classifier is framed as reliable.

Prototype

ML readiness

The current dataset is suitable for exploratory analysis, metadata QA, trust/provenance design, and dashboard prototyping. Predictive ML should wait until the dataset has broader country coverage, more negative examples, and stronger class balance.

Country	Status	Work sample	Reader bucket	Verification
Denmark	Nordic dataset	The Employees	Read now in English	verified_public_source
Iceland	Nordic dataset	Moonstone	Read now in English	verified_public_source
Norway	Nordic dataset	Sample row available in dataset package	Review in dataset	See source register
Sweden	Nordic dataset	Sample row available in dataset package	Review in dataset	See source register
Finland	Nordic dataset	Sample row available in dataset package	Review in dataset	See source register

Nordic scope

Nordic Expansion Roadmap

The dataset began with Denmark and Iceland as pilot countries and now frames the next product scope across Denmark, Iceland, Norway, Sweden, and Finland to support broader Nordic publishing intelligence, translation-path analysis, publisher comparison, and rights-signal discovery.

Data Model

A schema designed for discovery, trust, and professional workflows.

CountryPublisherWorkTranslationSourceEventRights Opportunity

Core relationships

A country has many publishers.
A publisher can be associated with many works.
A work can have many translation records.
A translation can have source and verification metadata.

Product signals

Events capture releases, awards, market activity, and publishing signals.
Rights Watchlist rows identify potential opportunities for professional users.
Reader buckets translate metadata into product-facing availability categories.
Source rows preserve provenance for review and trust decisions.

Nordic-ready fields

Trust and verification

The dataset separates evidence levels instead of flattening every row into the same confidence level. This matters because publishing metadata can be incomplete, inconsistent, or source-dependent.

verified_public_sourcecurated_needs_check

Verified public-source rows can support reader-facing confidence. Curated rows are useful for portfolio demonstration, research planning, and rights-watchlist exploration, but they need manual review before being treated as authoritative.

Trust fields

Reader mode

Find books available in English now.
Discover upcoming translations.
Explore books not yet available in English.
Browse by country, publisher, or translator.

Professional mode

Track rights and acquisition signals.
Monitor publisher activity.
Identify translation gaps.
Compare countries and market coverage.
Follow market events and awards.

Dashboard Preview

A calm overview for country coverage and rights signals.

Sample Rows

A small sample that preserves verification status.

This table shows a small portfolio sample only. Rows marked curated_needs_check are research leads and should not be presented as fully verified.

Country	Work Title	Author	Publisher	Original Language	English Availability	Translator	Verification Status	Reader Bucket	Rights Signal
Denmark	The Employees	Olga Ravn	Gyldendal	Danish	Available in English	Martin Aitken	verified_public_source	Read now in English	Translation visibility
Iceland	Moonstone	Sjon	Forlagid	Icelandic	Available in English	Victoria Cribb	verified_public_source	Read now in English	Backlist discovery

Product insight

This dataset can support country pages, publisher pages, translated book discovery, translator attribution pages, rights dashboards, bookstore buying-intelligence views, trust badges, and translation-chain transparency.

For machine learning work, it also creates natural opportunities for entity resolution, classification, metadata conflict detection, embeddings-based search, and recommendation workflows.

My role

I designed this as a product-minded data portfolio asset: product researcher, data modeler, QA reviewer, dashboard designer, and ML/Data Science portfolio builder.

I planned the data structure, researched source-backed examples, separated verified and curated records, and mapped how the dataset could support both reader-facing and professional workflows.

Skills Demonstrated

What this case study is meant to show.

Foundation for a larger publishing intelligence platform.

This dataset is the foundation for a larger publishing intelligence platform that can support readers, publishers, translators, and bookstores without hiding uncertainty or provenance.

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