open-index/open-library

Open Library

The complete Open Library catalog in clean, analysis-ready Parquet. 150.0M+ records across 11 entity types, from ISBNs and author bios to reading logs and Wikidata links.

What is it?

Open Library is a complete snapshot of the Open Library database, an open project of the Internet Archive with the mission of creating "one web page for every book ever published." The catalog is community-edited and contains bibliographic records for millions of authors, works, and physical editions, along with user-contributed star ratings and reading logs.

This dataset converts the official OpenLibrary Data Dumps from their native TSV+JSON format into clean, columnar Apache Parquet files with Zstd compression. Every field from every record type is fully preserved. Nothing is dropped or filtered.

Dump date: 2026-02 | Total records: 150.0M | License: CC0 1.0 (Public Domain)

Why this dataset?

Open Library publishes monthly bulk dumps, but they arrive as multi-gigabyte gzipped TSV files with embedded JSON. They are awkward to query, impossible to stream into a training pipeline, and painful to join across entity types. This dataset takes care of all that:

• Columnar: every field is a named Parquet column with a concrete type, queryable out of the box with DuckDB, Pandas, Polars, or Spark
• Complete: all 11 entity types and all fields, from ISBNs and Dewey Decimal numbers to Wikidata cross-references and reading shelf timestamps
• Normalized: nested JSON structures (text blocks, language references, author role arrays) are unwrapped into clean strings and flat key lists
• Sharded: roughly 1M rows per file, so you can stream or download only what you need
• Joined: the data model connects authors to works to editions, making it straightforward to build knowledge graphs, recommendation systems, or bibliographic search indexes

What is being released?

The dataset ships with 11 configs, one per entity type. Each config has one or more Parquet shards (roughly 1M rows each). The original .txt.gz dumps are also included under raw/ for full reproducibility.

data/                              raw/
  authors/                       authors/
  works/                       works/
  editions/                       editions/
  ratings/                       ratings/
  reading-log/                       reading-log/
  redirects/                       redirects/
  deletes/                       deletes/
  lists/                       lists/
  other/                       other/
  covers_metadata/                       covers_metadata/
  wikidata/                       wikidata/

Config overview

Config	Records	Description
authors	16.0M	Authors with biographies, birth/death dates, and cross-references to Wikidata/VIAF/ISNI
works	41.0M	Abstract works linking editions together, with subjects and descriptions
editions	56.0M	Physical editions with ISBNs, publishers, page counts, languages, and cover IDs
ratings	1.0M	User star ratings (1-5) for works and editions
reading-log	12.0M	User reading shelves: want-to-read, currently-reading, already-read
redirects	2.0M	Key redirects (merged or moved records)
deletes	4.0M	Deleted record stubs
lists	1.0M	User-created reading lists with seed references
other	1.0M	Miscellaneous types: languages, i18n strings, templates, macros
covers_metadata	15.0M	Cover image dimensions (width, height) for cover IDs
wikidata	1.0M	Wikidata cross-reference records with full JSON

Data model

The three core configs form a hierarchy:

Authors ──┐
           ├──→ Works ──→ Editions
Authors ──┘        │
                   ├──→ Ratings
                   └──→ Reading Log

An author writes one or more works (abstract creations). Each work has one or more editions (physical or digital manifestations with ISBNs, publishers, and page counts). Users attach ratings and reading log entries to works.

The remaining configs provide supporting infrastructure. Redirects resolve merged records. Deletes track removed keys. Lists are user-curated collections. Covers metadata maps cover IDs to image dimensions. Wikidata links records to the Wikidata knowledge graph. Other catches miscellaneous types like language definitions and i18n strings.

Data instance

Here is an example row from the editions config:

{
  "key": "/books/OL1M",
  "title": "Fantastic Mr Fox",
  "subtitle": null,
  "author_keys": "[\"/authors/OL34184A\"]",
  "work_keys": "[\"/works/OL45804W\"]",
  "isbn_10": "[\"0140328726\"]",
  "isbn_13": "[\"9780140328721\"]",
  "publishers": "[\"Puffin\"]",
  "publish_date": "October 1, 1988",
  "languages": "[\"eng\"]",
  "number_of_pages": 96,
  "physical_format": "Paperback",
  "covers": "[6498900]",
  "subjects": "[\"Animals\", \"Foxes\", \"Children's fiction\"]",
  "revision": 14,
  "created": "2008-04-01T03:28:50.625462",
  "last_modified": "2024-01-15T10:22:33.891204"
}

Array and object fields are stored as JSON strings (not native Parquet lists) for maximum flexibility. Parse them with json.loads() in Python or json_extract() in DuckDB.

How to download and use

Using datasets

from datasets import load_dataset

# Load a specific config (editions is the default)
editions = load_dataset("open-index/open-library", "editions", split="train")
authors = load_dataset("open-index/open-library", "authors", split="train")

# Stream large configs without downloading everything
for doc in load_dataset("open-index/open-library", "editions", split="train", streaming=True):
    print(doc["key"], doc["title"], doc["isbn_13"])

Using huggingface_hub

from huggingface_hub import snapshot_download

# Download just one config
snapshot_download(
    "open-index/open-library",
    repo_type="dataset",
    local_dir="./open-library/",
    allow_patterns="data/authors/*",
)

# Download everything
snapshot_download(
    "open-index/open-library",
    repo_type="dataset",
    local_dir="./open-library/",
)

For faster downloads, install pip install huggingface_hub[hf_transfer] and set HF_HUB_ENABLE_HF_TRANSFER=1.

Using DuckDB (zero download, remote query)

DuckDB can query the Parquet files directly from HuggingFace without downloading anything to disk. Here are some queries to get you started:

Find all editions of a specific work. Let's look up every printing of Roald Dahl's "Fantastic Mr Fox":

SELECT e.key, e.title, e.isbn_13, e.publishers, e.publish_date
FROM read_parquet('hf://datasets/open-index/open-library/data/editions/*.parquet') e
WHERE e.work_keys LIKE '%OL45804W%'
ORDER BY e.publish_date
LIMIT 20;

key	title	isbn_13	publishers	publish_date
/books/OL1M	Fantastic Mr Fox	["9780140328721"]	["Puffin"]	Oct 1, 1988
/books/OL2M	Fantastic Mr. Fox	["9780375822063"]	["Knopf"]	2002

Discover the highest-rated books. This query finds works with at least 100 ratings, sorted by average score:

SELECT work_key,
       ROUND(avg(rating), 2) AS avg_rating,
       count(*)              AS num_ratings
FROM read_parquet('hf://datasets/open-index/open-library/data/ratings/*.parquet')
GROUP BY work_key
HAVING count(*) >= 100
ORDER BY avg_rating DESC
LIMIT 5;

work_key	avg_rating	num_ratings
/works/OL82563W	4.72	142
/works/OL17860744W	4.68	109
/works/OL5735363W	4.65	287
/works/OL362427W	4.63	516
/works/OL15413843W	4.62	153

Find the most prolific writers. Join authors with works to see who has the most entries in the catalog:

SELECT a.name,
       count(*) AS num_works
FROM read_parquet('hf://datasets/open-index/open-library/data/works/*.parquet') w,
     read_parquet('hf://datasets/open-index/open-library/data/authors/*.parquet') a
WHERE w.author_keys LIKE '%' || a.key || '%'
GROUP BY a.name
ORDER BY num_works DESC
LIMIT 5;

name	num_works
United States	60,657
Anonymous	33,671
William Shakespeare	11,210
Various	8,435
Charles Dickens	8,206

Explore Wikidata cross-references. Over 177K authors are linked to Wikidata, making it easy to enrich records with external knowledge:

SELECT key, name, wikidata_id, birth_date, death_date
FROM read_parquet('hf://datasets/open-index/open-library/data/authors/*.parquet')
WHERE wikidata_id IS NOT NULL
LIMIT 5;

key	name	wikidata_id	birth_date	death_date
/authors/OL34184A	Roald Dahl	Q25161	13 September 1916	23 November 1990
/authors/OL23919A	J.K. Rowling	Q34660	31 July 1965
/authors/OL2162284A	Stephen King	Q39829	September 21, 1947

See what people want to read. The reading log captures millions of shelf actions from Open Library users:

SELECT work_key, count(*) AS readers
FROM read_parquet('hf://datasets/open-index/open-library/data/reading-log/*.parquet')
WHERE shelf = 'want-to-read'
GROUP BY work_key
ORDER BY readers DESC
LIMIT 5;

work_key	readers
/works/OL45804W	8,432
/works/OL82563W	7,891
/works/OL468516W	6,234
/works/OL15413843W	5,987
/works/OL362427W	5,412

Using the raw dumps

The original .txt.gz files are preserved under raw/ for full reproducibility:

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "open-index/open-library",
    "raw/authors/ol_dump_authors_2026-02-28.txt.gz",
    repo_type="dataset",
)

Dataset card for Open Library

Dataset Structure

Authors

The authors config contains one row per author record. Open Library identifies authors by keys like /authors/OL1A.

Column	Type	Description
key	string	OpenLibrary key, e.g. /authors/OL34184A
name	string	Display name, e.g. "Roald Dahl"
alternate_names	string	JSON array of alternative name forms
bio	string	Biography text (unwrapped from text_block)
birth_date	string	Free-form birth date, e.g. "13 September 1916"
death_date	string	Free-form death date
date	string	General date string (used when birth/death are unclear)
entity_type	string	"person", "org", or "event"
fuller_name	string	Full legal name
personal_name	string	Personal name variant
title	string	Honorific (e.g. "Sir", "OBE")
photos	string	JSON array of cover image IDs
links	string	JSON array of {"title": "...", "url": "..."} objects
remote_ids	string	JSON object with wikidata, viaf, isni, goodreads, and more
wikidata_id	string	Extracted Wikidata Q-ID for easy cross-referencing
viaf_id	string	Extracted VIAF identifier
isni	string	Extracted ISNI identifier
source_records	string	JSON array of import source identifiers
revision	int32	Record revision number
created	string	ISO 8601 creation timestamp
last_modified	string	ISO 8601 last modification timestamp

Works

The works config contains abstract works, the creative unit that links multiple editions together. A "work" is language- and format-independent: "Hamlet" is one work whether it appears as a 1603 quarto or a 2024 Penguin paperback.

Column	Type	Description
key	string	OpenLibrary key, e.g. /works/OL45804W
title	string	Work title
subtitle	string	Subtitle
author_keys	string	JSON array of author keys, e.g. ["/authors/OL34184A"]
covers	string	JSON array of cover image IDs
description	string	Work description (unwrapped from text_block)
notes	string	Editorial notes
subjects	string	JSON array of subject headings, e.g. ["Fiction", "Foxes"]
subject_places	string	JSON array of geographic subjects
subject_people	string	JSON array of people subjects
subject_times	string	JSON array of time period subjects
lc_classifications	string	JSON array of Library of Congress classification numbers
first_publish_date	string	Earliest known publication date
revision	int32	Record revision number
created	string	ISO 8601 creation timestamp
last_modified	string	ISO 8601 last modification timestamp

Editions

The editions config is the largest and richest, representing physical or digital manifestations of works. This is where you will find ISBNs, publishers, page counts, and cover images.

Column	Type	Description
key	string	OpenLibrary key, e.g. /books/OL1M
title	string	Edition title
subtitle	string	Subtitle
author_keys	string	JSON array of author keys
work_keys	string	JSON array of parent work keys
isbn_10	string	JSON array of ISBN-10 numbers
isbn_13	string	JSON array of ISBN-13 numbers
lccn	string	JSON array of Library of Congress Control Numbers
oclc_numbers	string	JSON array of OCLC/WorldCat IDs
ocaid	string	Internet Archive identifier (links to digitized full text)
identifiers	string	JSON object with goodreads, librarything, amazon, and more
local_id	string	JSON array of local URNs
publishers	string	JSON array of publisher names
publish_date	string	Publication date (free-form, e.g. "1988", "October 1, 1988")
publish_places	string	JSON array of publication places
publish_country	string	MARC21 country code
edition_name	string	Edition descriptor (e.g. "1st ed.", "Rev. ed.")
by_statement	string	Authorship statement as printed on the book
contributions	string	JSON array of contributors (illustrators, translators, etc.)
languages	string	JSON array of language codes, e.g. ["eng"]
translated_from	string	JSON array of original language codes
translation_of	string	Original title if this edition is a translation
number_of_pages	int32	Page count
pagination	string	Pagination description (e.g. "xi, 345 p.")
physical_format	string	"Paperback", "Hardcover", "E-book", etc.
physical_dimensions	string	Dimensions string
weight	string	Weight string
covers	string	JSON array of cover image IDs
description	string	Edition-specific description
first_sentence	string	Opening sentence of the book
notes	string	Editorial notes
table_of_contents	string	JSON array of TOC entries
subjects	string	JSON array of subject headings
dewey_decimal_class	string	JSON array of Dewey Decimal numbers
lc_classifications	string	JSON array of LC classification numbers
genres	string	JSON array of genre labels
series	string	JSON array of series names
links	string	JSON array of external links
source_records	string	JSON array of import sources (e.g. MARC records, Amazon)
copyright_date	string	Copyright year
other_titles	string	JSON array of alternative titles
work_titles	string	JSON array of related work titles
classifications	string	JSON object of additional classification schemes
revision	int32	Record revision number
created	string	ISO 8601 creation timestamp
last_modified	string	ISO 8601 last modification timestamp

Ratings

Anonymized user star ratings. Each row records one user's rating of a work or edition. No user identifiers are included.

Column	Type	Description
work_key	string	Work reference, e.g. /works/OL45804W
edition_key	string	Edition reference (may be empty)
rating	int32	Star rating, 1 to 5
date	string	ISO date the rating was submitted

Reading Log

Anonymized reading shelf data. Users on Open Library can mark books as "want to read", "currently reading", or "already read."

Column	Type	Description
work_key	string	Work reference
edition_key	string	Edition reference (may be empty)
shelf	string	One of: want-to-read, currently-reading, already-read
date	string	ISO date of the shelf action

Redirects

When records are merged or moved, Open Library creates a redirect. Use this config to resolve old keys to their current locations.

Column	Type	Description
key	string	Old/redirected key
location	string	Target key being redirected to
type_name	string	Type key (always /type/redirect)
revision	int32	Record revision number
last_modified	string	ISO 8601 timestamp

Deletes

Stub records for keys that have been deleted from the database.

Column	Type	Description
key	string	Deleted record key
type_name	string	Type key (always /type/delete)
revision	int32	Record revision number
last_modified	string	ISO 8601 timestamp

Lists

User-created reading lists. Each list has a name, description, and a set of "seeds" (references to works, editions, authors, or subjects).

Column	Type	Description
key	string	List key, e.g. /people/user123/lists/OL1L
name	string	List name
description	string	List description
seeds	string	JSON array of seed keys (works, editions, subjects)
seed_count	int32	Number of seeds in the list
revision	int32	Record revision number
created	string	ISO 8601 creation timestamp
last_modified	string	ISO 8601 timestamp

Covers Metadata

Dimensions of cover images hosted by Open Library. You can construct cover URLs as https://covers.openlibrary.org/b/id/{id}-L.jpg.

Column	Type	Description
id	int64	Cover image ID
width	int32	Image width in pixels
height	int32	Image height in pixels
created	string	Upload timestamp

Wikidata

Cross-reference records linking Open Library entities to Wikidata. The json_data field contains the full Open Library JSON body of each Wikidata-type record.

Column	Type	Description
wikidata_id	string	Wikidata Q-identifier, e.g. Q36322
json_data	string	Full JSON body of the record

Other

Miscellaneous record types: language definitions, i18n strings, internal templates, macros, and other infrastructure records that do not fit the main entity types.

Column	Type	Description
key	string	Record key
type_name	string	Type key, e.g. /type/language, /type/i18n
json_data	string	Full JSON body
revision	int32	Record revision number
last_modified	string	ISO 8601 timestamp

Data Completeness

Every field is extracted from the source data exactly as provided by OpenLibrary. The population rates below are computed directly during conversion and reflect the natural completeness of the community-edited catalog.

Most author records are auto-imported stubs from library catalogs (just a name and key), so optional fields like bio and birth_date appear in a small fraction of records. Editions are the richest config, with strong coverage of ISBNs, publishers, languages, and page counts. Works sit in the middle, with nearly all having author links and about half having subject headings.

You can verify these numbers yourself with DuckDB. Here are the queries and results for the three core configs:

Authors (15.1M records): most are auto-imported stubs, so rich fields like bio are rare, but 1.9M have birth dates and 177K link to Wikidata.

SELECT COUNT(*) AS total,
       COUNT(bio) AS has_bio,
       ROUND(COUNT(bio) * 100.0 / COUNT(*), 1) AS bio_pct,
       COUNT(birth_date) AS has_birth,
       ROUND(COUNT(birth_date) * 100.0 / COUNT(*), 1) AS birth_pct,
       COUNT(wikidata_id) AS has_wikidata,
       ROUND(COUNT(wikidata_id) * 100.0 / COUNT(*), 1) AS wikidata_pct,
       COUNT(remote_ids) AS has_remote_ids,
       ROUND(COUNT(remote_ids) * 100.0 / COUNT(*), 1) AS remote_pct
FROM read_parquet('hf://datasets/open-index/open-library/data/authors/*.parquet');

total	has_bio	bio_pct	has_birth	birth_pct	has_wikidata	wikidata_pct	has_remote_ids	remote_pct
15,071,242	43,822	0.3%	1,935,260	12.8%	177,233	1.2%	273,801	1.8%

Works (40.7M records): nearly all have author links and about half have subject headings, making this config great for topic-based exploration.

SELECT COUNT(*) AS total,
       COUNT(author_keys) AS has_authors,
       ROUND(COUNT(author_keys) * 100.0 / COUNT(*), 1) AS authors_pct,
       COUNT(subjects) AS has_subjects,
       ROUND(COUNT(subjects) * 100.0 / COUNT(*), 1) AS subjects_pct,
       COUNT(description) AS has_desc,
       ROUND(COUNT(description) * 100.0 / COUNT(*), 1) AS desc_pct,
       COUNT(covers) AS has_covers,
       ROUND(COUNT(covers) * 100.0 / COUNT(*), 1) AS covers_pct
FROM read_parquet('hf://datasets/open-index/open-library/data/works/*.parquet');

total	has_authors	authors_pct	has_subjects	subjects_pct	has_desc	desc_pct	has_covers	covers_pct
40,718,247	38,478,663	94.5%	20,024,694	49.2%	1,828,315	4.5%	9,645,694	23.7%

Editions (55.6M records): the richest config by far, with strong coverage of publishers (94.6%), languages (86.6%), and page counts (62.6%). Over half have ISBN-13 numbers.

SELECT COUNT(*) AS total,
       COUNT(isbn_13) AS has_isbn13,
       ROUND(COUNT(isbn_13) * 100.0 / COUNT(*), 1) AS isbn13_pct,
       COUNT(publishers) AS has_pub,
       ROUND(COUNT(publishers) * 100.0 / COUNT(*), 1) AS pub_pct,
       COUNT(languages) AS has_lang,
       ROUND(COUNT(languages) * 100.0 / COUNT(*), 1) AS lang_pct,
       COUNT(number_of_pages) AS has_pages,
       ROUND(COUNT(number_of_pages) * 100.0 / COUNT(*), 1) AS pages_pct
FROM read_parquet('hf://datasets/open-index/open-library/data/editions/*.parquet');

total	has_isbn13	isbn13_pct	has_pub	pub_pct	has_lang	lang_pct	has_pages	pages_pct
55,615,769	29,558,726	53.1%	52,626,364	94.6%	48,137,077	86.6%	34,802,286	62.6%

Dataset Creation

Curation Rationale

Open Library is the largest open bibliographic database in the world, but its bulk dumps are challenging to work with directly. They are multi-gigabyte gzipped TSV files with JSON embedded in the fifth column, different record types mixed together in the same file, and nested structures that require custom parsing. Researchers and ML practitioners who want to train on book metadata, build recommendation systems, or create knowledge graphs must write significant boilerplate just to read the data.

This dataset eliminates that friction. By converting every dump to typed Parquet with normalized fields, we make the entire catalog immediately queryable with standard tools. The editions config is set as the default because it is the most commonly used for ISBN lookups and bibliographic search.

Source Data

Open Library is an open, editable library catalog maintained by the Internet Archive. It publishes monthly bulk data dumps at openlibrary.org/developers/dumps covering every record in the database: authors, works, editions, ratings, reading logs, redirects, deleted records, user lists, cover metadata, and Wikidata cross-references.

The data originates from multiple sources: MARC records from libraries worldwide, Amazon product data, user contributions, and automated imports from other bibliographic databases. The catalog is continuously edited by volunteers and library professionals.

Data Processing

The processing pipeline converts each dump type from its native format into clean Parquet. The entire pipeline is written in Go and runs as a single streaming pass per dump type:

1. Download the .txt.gz dump from Internet Archive (the _latest URLs redirect to the most recent monthly snapshot)
2. Stream through the gzipped file line-by-line using a 16 MB scanner buffer. No intermediate uncompressed copy is created on disk.
3. Parse each TSV line and extract all JSON fields using gjson for zero-allocation field extraction
4. Normalize nested structures:
   • Text blocks ({"type": "/type/text", "value": "..."}) become plain strings
   • Language references ([{"key": "/languages/eng"}]) become ["eng"]
   • Author role arrays ([{"author": {"key": "/authors/OL1A"}}]) become ["/authors/OL1A"]
   • Identifier objects get extracted into top-level columns where useful (wikidata_id, viaf_id, isni)
5. Write to sharded Parquet files with Zstd compression (roughly 1M rows per shard, 500K rows per row group, 4096-row batch size)

Memory usage stays constant regardless of dump size thanks to the fully streaming architecture. All 11 dump types are downloaded and converted in parallel, with results committed to HuggingFace incrementally as each type completes.

Field Encoding

Array and object fields (e.g. subjects, identifiers, remote_ids) are stored as JSON strings, not native Parquet lists or maps. This is a deliberate design choice:

• Flexibility: DuckDB's json_extract(), Python's json.loads(), and Polars' str.json_decode() all work out of the box
• Schema stability: the underlying JSON can contain arbitrary keys without breaking the Parquet schema
• Query friendliness: LIKE '%search_term%' works for simple substring searches, while json_extract() handles structured access

Frequently-used identifiers (wikidata_id, viaf_id, isni on authors) are also extracted as dedicated top-level columns so you can cross-reference without JSON parsing.

Considerations for Using the Data

Data Quality

Open Library is a community-edited catalog. Keep these characteristics in mind:

• Dates are free-form strings. You will encounter "1899", "March 1980", "c. 1650", "1st millennium BCE", and blank values. There is no single date format.
• Duplicate records exist. The redirects config maps old keys to current ones, which can help you deduplicate.
• Coverage varies by era and region. Modern English-language books are well-represented, while older, non-English, and non-Western works may have sparser records.
• Some fields are sparsely populated. Not every edition has a page count, ISBN, or cover image. See the Data Completeness section above for exact population rates.

Personal Information

Author records contain biographical information (names, birth/death dates, biographies, photos) sourced from public library catalogs and community contributions. This is standard bibliographic data, publicly available from libraries worldwide.

Reading logs and ratings are fully anonymized and contain no user identifiers, usernames, or any information that could identify who submitted them.

Known Limitations

• No full text. This dataset contains metadata only. For actual book content, see the Internet Archive's Open Library Lending program or Project Gutenberg.
• No cover images. The covers and photos fields contain numeric IDs, not images. You can construct URLs like https://covers.openlibrary.org/b/id/{id}-L.jpg to fetch them.
• English-centric. While the dataset contains records in many languages (see the languages field), the catalog has stronger coverage of English-language publications.
• JSON string columns. Array fields require an extra parsing step compared to native Parquet lists. This is the trade-off for schema flexibility.

Social Impact

By converting Open Library's dumps into a format that standard data tools can read without custom parsers, we aim to make the world's largest open book catalog accessible to researchers, educators, and developers who might not otherwise be able to work with it. Potential applications include:

• Recommendation systems using ratings, reading logs, and subject headings
• Knowledge graphs built from author, work, and edition relationships with Wikidata links
• Bibliographic search over titles, descriptions, subjects, and ISBNs
• Digital humanities research into publishing trends, translation patterns, and subject evolution over time
• Library science studies on cataloging practices, record quality, and coverage gaps

Additional Information

Licensing

Open Library data is released under CC0 1.0 (Public Domain Dedication). You are free to use it for any purpose, commercial or non-commercial, without attribution. Attribution to Open Library and the Internet Archive is appreciated but not required.

Citation

If you use this dataset in research, please cite both Open Library and this conversion:

@misc{open-library-parquet,
  title   = {Open Library: Complete Catalog in Parquet},
  author  = {open-index},
  year    = {2026},
  url     = {https://huggingface.co/datasets/open-index/open-library},
  note    = {Converted from OpenLibrary.org data dumps}
}

Contact

Please open a discussion on the Community tab for questions, feedback, or issues.