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mazieres
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majinbook

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
id: int64
work_id: int64
title: string
authors: list<item: int64>
  child 0, item: int64
pub_year: int64
lang: string
lang_iso: string
publisher: string
isbn: string
asin: string
rating: double
n_ratings: int64
works: list<item: int64>
  child 0, item: int64
birth_place: string
death_year: int64
full_name: string
birth_year: int64
to
{'id': Value('int64'), 'full_name': Value('string'), 'birth_place': Value('string'), 'birth_year': Value('int64'), 'death_year': Value('int64'), 'works': List(Value('int64'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: int64
              work_id: int64
              title: string
              authors: list<item: int64>
                child 0, item: int64
              pub_year: int64
              lang: string
              lang_iso: string
              publisher: string
              isbn: string
              asin: string
              rating: double
              n_ratings: int64
              works: list<item: int64>
                child 0, item: int64
              birth_place: string
              death_year: int64
              full_name: string
              birth_year: int64
              to
              {'id': Value('int64'), 'full_name': Value('string'), 'birth_place': Value('string'), 'birth_year': Value('int64'), 'death_year': Value('int64'), 'works': List(Value('int64'))}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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id int64	full_name string	birth_place string	birth_year int64	death_year int64	works list
19,791,165	Lloyd Davis	Kingston, Jamaica	null	null	[ 86234624, 74485118 ]
8,141,067	Alita Bruce	null	null	null	[ 43060576 ]
619,946	John Driscoll	null	null	null	[ 19386029, 45343410, 69853719, 1279960, 2947999 ]
15,006,842	Silvio Arturo Vazquez	null	null	null	[ 45993023 ]
21,491,901	Casbelieves	null	null	null	[ 90956864 ]
26,576,108	Zwolfter Band Georg Kupke	null	null	null	[ 69099291 ]
2,119,542	Walter Sturdivant	null	null	null	[ 5188327 ]
7,276,537	Roberta Cottam	null	null	null	[ 45128008, 26215274, 42915376 ]
723,419	Mark Gordon	null	null	null	[ 7888608, 38781921, 5930596, 2768101, 39869201, 6427666, 39844409, 21823066 ]
7,236,422	Maria Vargas	null	null	null	[ 224504954, 25989286 ]
6,420,132	Annelija Rufusa	null	null	null	[ 55038 ]
16,960,938	Lorelle Semley	null	null	null	[ 65180673, 57213229 ]
24,850,566	catherine-gourley	null	null	null	[ 1842856 ]
16,343,532	Wiliam Gibson	null	null	null	[ 1634531 ]
49,102	Carol Strickland	New Orleans LA, The United States	null	null	[ 230112, 690369, 690373, 26728070, 23979687, 18423125, 1581467 ]
49,289,811	한용택	null	null	null	[ 9563368 ]
7,362,083	Emilia Hahn	null	null	null	[ 1491906, 2254058 ]
18,735,433	Tish Alexander	null	null	null	[ 67454178, 92538373 ]
3,898,858	Kimberly Stevens	null	null	null	[ 60337650, 74812678 ]
18,718,376	Zef Shoshi	null	null	null	[ 96496578, 21476358, 4682347, 25897585, 24988785, 15930355 ]
18,173,293	Karen Lamb	null	null	null	[ 44603631 ]
7,713,414	Robert Dominguez	null	null	null	[ 69452819, 50142247 ]
1,112,745	Ivana Mollo	null	null	null	[ 6444457, 1175879 ]
14,440,623	Janne Schmidt	null	null	null	[ 66816078 ]
11,909,945	Е. Ластовцева	null	null	null	[ 3732607, 2519210, 13732807 ]
20,915,007	Leni Hanson	null	null	null	[ 87319502 ]
487,378	Mario Klarer	null	null	null	[ 4423844, 4423849, 26074059, 53850699, 91003150, 25464593, 47374586 ]
2,110,065	Deb Thompson	null	null	null	[ 32815113, 67584546, 57702534, 8636042 ]
40,174	Rick Strauss	null	null	null	[ 52687926, 56982479 ]
13,393,079	Melina Revuelta	null	null	null	[ 26725162, 16265327 ]
17,555,470	Yoshinobu Kadoi	null	null	null	[ 55352969, 51262422, 63737494, 63737531, 59414431 ]
5,413,012	KAOS MOON	null	null	null	[ 18528648, 58063169 ]
14,213,690	Nalan Tümay	null	null	null	[ 24258336, 21933444, 65563238, 87084816, 19174545, 25095506 ]
332,264	Duncan Searl	null	null	null	[ 600096, 16880513, 12448396, 3230349, 3230352, 1170430 ]
42,763,918	史蒂芬·约翰逊	null	null	null	[ 26955894 ]
52,784,597	Odille Ousley and David H. Russell	null	null	null	[ 7387224, 41371552 ]
44,225,294	Eugenie; Ellwand David Bird	null	null	null	[ 992717 ]
2,404,183	Brigitte Brix	null	null	null	[ 3275376 ]
23,065,490	Tamara Vuković	null	null	null	[ 2484648, 1777803 ]
15,614,074	Martina Flor	Buenos Aires, Argentina	null	null	[ 50248294, 223002762, 73987982, 52158104, 74511644 ]
7,160,107	Chiara Tana	null	null	null	[ 13462732, 19243557 ]
1,353,866	Jasmine Aimaq	null	null	null	[ 84682555 ]
6,525,220	Marian Tiu	null	null	null	[ 21401264, 2593316 ]
1,944,427	Natalia Lusin	null	null	null	[ 1840715, 4719719 ]
4,200,769	Daniel Gallagher	null	null	null	[ 2617009 ]
4,617,967	P. Elías	null	null	null	[ 487844, 1389477, 2274514, 277716, 69870269 ]
6,559,160	Jun Asuka	null	null	null	[ 12445746, 24327917 ]
22,327,718	Michelle Hurd	null	null	null	[ 94956382 ]
34,204,770	W. J Alexander Worster	null	null	null	[ 6223290 ]
29,762,791	Dóra Hrabovszky	null	null	null	[ 84226744, 43127713, 58060835, 73365782 ]
753,125	Amber Ault	null	null	null	[ 40304146, 47454844 ]
17,655,086	כריסטה וולף	null	null	null	[ 21925003 ]
1,050,082	Theodore Ward	null	null	null	[ 2345962, 52941579, 52921189 ]
46,961,458	Thyrze Van Onna	null	null	null	[ 91823553 ]
38,423,714	Aleksandra Juda	null	null	null	[ 171375728 ]
2,974,481	Sahbi Thabet	null	null	null	[ 16445944 ]
6,576,692	Vitalie Coroban	null	null	null	[ 764641, 51602597, 25984583 ]
5,825,168	Phi Phi	null	null	null	[ 6446328 ]
399,955	Selby Worthington	null	null	null	[ 742807, 4713202, 6466055 ]
42,262,387	William Maz	null	null	null	[ 91094497, 98390570 ]
4,251,406	Carla Wenckebach	null	null	null	[ 926450, 6226709, 4338014, 4061279 ]
15,523,882	Rejane Janowitzer e Regiane Winarski	null	null	null	[ 1888038 ]
653,081	Stanlee Phelps	null	null	null	[ 44916285, 1367159 ]
5,785,046	K.T. Bryan	The United States	1,959	null	[ 41712979, 19139667, 47471455 ]
15,684,131	Eva Bloch	null	null	null	[ 2776625 ]
561,065	Lawrence S. Lerner	null	null	null	[ 103077, 3657006 ]
1,425,765	Dave Ward	null	null	null	[ 48059875, 69826029, 9853426, 73520050, 790036, 26084917, 25205847 ]
2,867,518	Janice Lyndon	null	null	null	[ 6499268, 6866503 ]
14,778,630	П.Л. Травърз	null	null	null	[ 43111943 ]
14,296,482	Douglas Abraham	null	null	null	[ 87660069 ]
14,434,666	Ράντοβαν Μπρκοβιτς	null	null	null	[ 13352440, 2512311, 3593335 ]
17,795,688	Ayumi (歩)	null	null	null	[ 60670352, 60670460 ]
19,804,187	V. Sveshnikov	null	null	null	[ 51043115, 238074, 238075, 238077 ]
14,718,482	Kazimierz Frieske	null	null	null	[ 59145418, 1079861 ]
41,127,913	Mr. MacLeod	null	null	null	[ 95517863 ]
22,042,807	Tomas G. Carkeek	null	null	null	[ 94121685 ]
41,401,450	Marcus Lake	null	null	null	[ 216802724 ]
16,513,578	Wayne K. Hocking	null	null	null	[ 50996446, 3422470 ]
23,441,311	Macmillan Publishing	null	null	null	[ 1750156 ]
14,751,464	Златомира Иванова	null	null	null	[ 6979554, 6742479 ]
8,425,248	Paola Tacchino	null	null	null	[ 3054427 ]
7,246,924	Editors of Seventeen Magazine	null	null	null	[ 26041181, 15248086 ]
7,223,372	Ρενέ Ψυρούκη	null	null	null	[ 3036838, 2373324, 284719, 318160, 1579061, 284664, 2113212, 2688152 ]
19,421,359	Amber Beebe	null	null	null	[ 72578945 ]
4,782,249	Matt Williamson	null	null	null	[ 23580865, 15997154, 23678180, 40570756, 13843176, 18312939, 88703212, 35928952 ]
267,239	Jasvinder Sanghera	null	null	null	[ 55459728, 466010, 3411421, 79939767 ]
16,408,937	Emanuel Stoakes	null	null	null	[ 55219570 ]
18,880,830	Ayça Kamacıoğlu	null	null	null	[ 58414707 ]
520,614	Eamonn McCann	null	null	null	[ 1623811, 1623812, 1623813, 26232327, 1083924, 5206712, 1031966 ]
18,840,950	ربيع صالح	null	null	null	[ 110690 ]
18,915,920	Pascal Orts	null	null	null	[ 54900291 ]
20,187,367	Orlando Gili	null	null	null	[ 79459815 ]
22,072,213	Веслі Чу	null	null	null	[ 50775338, 50775355 ]
20,559,196	Dominic Da Tinio	null	null	null	[ 85393675 ]
22,030,917	Ian Bertrem	null	null	null	[ 50593795 ]
22,950,629	Robert Bain	null	null	null	[ 2294917, 48714630 ]
17,128,381	Petra Švecová	null	null	null	[ 235008, 50115686 ]
203,379	Richard D. Bank	null	null	null	[ 40808205, 563348, 6724153, 17465658, 2208959 ]
34,276,649	Ulverscroft large print series	null	null	null	[ 149984014 ]
3,442,304	Rick Kistner	null	null	null	[ 3164921, 6328892 ]

End of preview.

MajinBook

This document outlines the structure and metadata schemas of the datasets released with MajinBook: An open catalogue of digitally mediated world literature by Antoine Mazières and Thierry Poibeau.

Abstract: This data paper introduces MajinBook, an open catalogue designed to facilitate the use of shadow libraries, such as Library Genesis and Z-Library, for computational social science and cultural analytics. By linking metadata from these vast, crowd-sourced archives with structured bibliographic data from Goodreads, we create a high-precision corpus of over 539,000 references to digitally mediated English-language books. Spanning three centuries and reflecting a contemporary selection bias, these entries are enriched with first publication dates, genres, and popularity metrics such as ratings and reviews. Our methodology prioritises natively digital EPUB files to ensure machine-readable quality, while addressing biases in traditional corpora such as HathiTrust, and includes secondary datasets for French-, German-, and Spanish-language works. We evaluate the linkage strategy for accuracy, release all underlying data openly, and discuss the project’s legal permissibility under EU and U.S. frameworks for text and data mining in research.

The data is available on Zenodo and HuggingFace.

The paper is on ArXiv.

All files are in the JSON Lines text file format.

1. The MajinBook's Catalogue

This section describes the primary high-precision English catalogue introduced in the paper. The secondary datasets in French, German and Spanish follow the same model.

Files

Primary dataset: majinbook_eng.jsonl (English) — 539,530 lines.
Secondary datasets:
- majinbook_fra.jsonl (French) — 47,960 lines.
- majinbook_deu.jsonl (German) — 35,559 lines.
- majinbook_spa.jsonl (Spanish) — 30,169 lines.

JSON Record Example

{
  "first_pub_year": 1913,
  "authors": [
    [
      233619,
      "Marcel Proust"
    ]
  ],
  "genres": [
    "Classics",
    "Literature",
    "Philosophy",
    "20th Century",
    "Novels",
    "Fiction",
    "Literary Fiction",
    "Classic Literature",
    "France",
    "French Literature"
  ],
  "n_reviews": 356,
  "n_ratings": 3635,
  "rating": 4.28,
  "title": "Remembrance of Things Past: Volume I - Swann's Way & Within a Budding Grove",
  "work_id": 45683795,
  "zlibrary_ids": [
    11588490
  ],
  "libgen_ids": null
}

Schema Description

Field	Type	Coverage¹	Description
`first_pub_year`	Integer	100%	The work's first publication year; Range: 1456-2024
`authors`	List[int, str]	100%	A list of the work's authors; each entry contains `[Goodreads Author ID (int), Author's Full Name (str)]`²
`genres`	List[str]	84%	A list of genres (`str`) associated with the work; Max length is 10
`n_reviews`	Integer	99%	Count of reviews on Goodreads
`n_ratings`	Integer	100%	Count of ratings on Goodreads
`rating`	Float	100%	Average rating (aggregated across all editions); Range: 0.00–5.00
`title`	String	100%	The work's title in the catalogue's language
`work_id`	Integer	100%	Unique Goodreads Work ID³
`zlibrary_ids`	List[Integer]	93%⁴	List of Z-Library IDs (`int`) corresponding to this work
`libgen_ids`	List[str]	60%⁴	List of LibGen IDs (`str`) corresponding to this work

Notes:

Coverage is for the primary dataset only (English). Coverage varies for secondary datasets regarding genres and n_reviews, see the paper.
Goodreads Author ID (int) corresponds to the ID of the Author found in the URL of their profile, as in goodreads.com/author/show/233619
Goodreads Work ID, as in goodreads.com/work/editions/45683795
zlibrary_ids and libgen_ids cannot both be null.

2. Underlying datasets

This section describes the metadata datasets collected and processed to construct the MajinBook catalogue.

2.1 Shadow Library metadata

Z-Library

File: zlibrary.jsonl — 8,097,488 lines

JSON Record Example

{
  "id": 11588490,
  "title": "Remembrance of Things Past, Volume I",
  "authors": "Marcel Proust",
  "pub_year": 2011,
  "lang_iso": "eng",
  "publishers": "Knopf Doubleday Publishing Group",
  "isbns": [
    "9780307808554"
  ]
}

Schema Description

Field	Type	Coverage	Description
`id`	Integer	100%	Unique Z-Library ID
`title`	String	100%	The title of the book
`authors`	String	99%	Unformatted string of author names
`pub_year`	Integer	81%	Publication year
`lang_iso`	String	66%	ISO 639-3 code of the book's language
`publishers`	String	65%	Unformatted string of publishers
`isbns`	List[str]	29%	List of ISBN-13 identifiers

Library Genesis (LibGen)

File: libgen.jsonl — 3,032,730 lines

JSON Record Example

{
  "id": "bc0d6411ddd67b2c2d43b39bc8472cfc",
  "collection": "fiction",
  "title": "Alice's Adventures in Wonderland and What the Tortoise Said to Achilles and Other Riddles",
  "lang_iso": "eng",
  "authors": [
    "Lewis Carroll"
  ],
  "pub_year": 2012,
  "publisher": "West Margin Press",
  "isbns": [
    "9780882408712"
  ],
  "asin": "B007HOO22Y"
}

Schema Description

Field	Type	Coverage	Description
`id`	String	100%	Unique Library Genesis ID; an MD5 hash
`collection`	String	100%	LibGen's collection of origin, either `fiction` or `nonfiction`
`lang_iso`	String	99%	ISO 639-3 code of the book's language
`title`	String	99%	The title of the book
`authors`	List[str]	99%	List of author names
`pub_year`	Integer	83%	Publication year
`publisher`	String	78%	The book's publisher
`isbns`	List[str]	57%	List of ISBN-13 identifiers
`asin`	String	18%	Amazon Standard Identification Number (starts with 'B')

Goodreads

Goodreads Works

File: goodreads_works.jsonl — 4,778,124 lines

JSON Record Example

{
  "id": 55548884,
  "suggestions": [
    2998,
    5326,
    5659,
    ...
  ],
  "first_pub_year": 1865,
  "n_ratings": 418248,
  "rating": 3.99,
  "main_authors": [
    8164
  ]
}

Schema Description

Field	Type	Coverage	Description
`id`	Integer	100%	Unique Goodreads Work ID (as in `goodreads.com/work/editions/55548884`)
`suggestions`	List[int]	39%	List of suggested Goodreads Edition IDs (found in `goodreads.com/book/similar/55548884`)
`first_pub_year`	Integer	61%	The work's first publication year
`n_ratings`	Integer	99%	Aggregated count of ratings across all editions of the work
`rating`	Float	99%	Aggregated average rating across all editions of the work
`main_authors`	List[int]	100%	List of Author IDs appearing most frequently across all editions

Goodreads Editions

File: goodreads_editions.jsonl — 28,105,913 lines

JSON Record Example

{
  "id": 60671823,
  "work_id": 55548884,
  "title": "Alice's Adventures in Wonderland (Hardcover)",
  "authors": [
    8164,
    59749
  ],
  "pub_year": null,
  "lang": "English",
  "lang_iso": "eng",
  "publisher": "Pan Macmillan",
  "isbn": "9781529002461",
  "asin": null,
  "rating": 4.08,
  "n_ratings": 71699
}

Schema Description

Field	Type	Coverage	Description
`id`	Integer	100%	Unique Goodreads Edition ID (as in `goodreads.com/book/show/60671823`)
`work_id`	Integer	100%	The Work ID to which the edition belongs
`title`	String	99%	The edition's title
`authors`	List[int]	99%	List of Goodreads Author IDs
`pub_year`	Integer	89%	The edition's publication year
`lang`	String	86%	The edition's language name
`lang_iso`	String	86%	ISO 639-3 language code
`publisher`	String	92%	The publisher's name
`isbn`	String	62%	The edition's ISBN-13 identifier
`asin`	String	49%	The edition's ASIN (starts with 'B')
`rating`	Float	46%	Average rating for this specific edition
`n_ratings`	Integer	46%	Count of ratings for this specific edition

Goodreads Authors

File: goodreads_authors.jsonl — 2,150,522 lines

JSON Record Example

{
  "id": 8164,
  "full_name": "Lewis Carroll",
  "birth_place": "Daresbury, Cheshire, England, The United Kingdom",
  "birth_year": 1832,
  "death_year": 1898,
  "works": [
    87631877,
    164806665,
    155893772,
    ...
  ]
}

Schema Description

Field	Type	Coverage	Description
`id`	Integer	100%	Unique Goodreads Author ID (as in `goodreads.com/author/show/8164`
`full_name`	String	100%	The author's full name
`birth_place`	String	6%	The author's place of birth
`birth_year`	Integer	4%	The author's year of birth
`death_year`	Integer	2%	The author's year of death
`works`	List[int]	99%	List of Work IDs authored by this person

Minhash signatures

File: minhash_signatures.jsonl — 11,130,005 lines

Example

{
  "source": "libgen",
  "id": "0af71d22806865ed567bc0fe2bc9be2a",
  "minhash_signature": [
    87053,
    62778,
    73327,
    ...
  ]
}

Fields

Field	Type	Coverage	Description
`source`	String	100%	Source dataset (`libgen` or `zlibrary`)
`id`	String / Integer	100%	Unique ID in the source dataset (MD5 hash string for `libgen`, integer for `zlibrary`)
`minhash_signature`	List[int]	100%	A list of 128 integers representing the document's MinHash signature (see Section 3.3 of the paper)

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Paper for mazieres/majinbook

MajinBook: An open catalogue of digital world literature with likes

Paper • 2511.11412 • Published Nov 14, 2025