BibleData Dataset

The BibleData dataset provides a comprehensive collection of structured biblical texts and metadata, covering various translations, alongside historical texts.

The whole dataset contains 18 CSV files. To maintain readability of the inferred Schema Category and overall clarity we have worked with these 9 files:

AlamoPolyglot.csv with plain-text data as well as metadata entries for the Bible manuscripts
Book.csv with books of the Bible and their details
Commandments.csv with basic information on the biblical commandments
Event.csv with specifics of the events described
HitchcocksBibleNamesDictionary.csv with Roswell D. Hitchcock’s Bible Names Dictionary
Person.csv with information about each named individual in the Bible
PersonLabel.csv with detailed information about individual’s labels
PersonRelationship.csv with details on the relationships among individuals
Reference.csv - with unique identifiers for each book, chapter and verse in the Bible

BibleData dataset

Initial Dataset Specifications

Entity	Data Link	Mapping
Alamo Polyglot	Data Link	Mapping `_: { id: 95, book_name: 97, chapter: 98, verse: 99, world_english_bible_web: 100, king_james_bible_kjv: 101, leningrad_codex: 102, jewish_publication_society_jps: 103, codex_alexandrinus: 104, brenton: 105, samaritan_pentateuch: 106, samaritan_pentateuch_english: 107, onkelos_aramaic: 108, onkelos_english: 109, book_id: 117.18 }`
Book	Data Link	Mapping `_: { book_name: 9, hebrew_name: 10, hebrew_transliteration: 11, hebrew_meaning: 12, greek_name: 13, greek_transliteration: 14, greek_meaning: 15, chapter_count: 16, verse_count: 17, book_id: 18, christian_sequence: 19, hebrew_sequence: 20, short_name: 21, usx_code: 22, writer_id: 23, written_start_date: 24, written_end_date: 25, written_location_id: 26 }`
Commandments	Data Link	Mapping `_: { commandment_number: 82, commandment_concept: 83, commandment_polarity: 84, scripture_english: 86, scripture_hebrew: 87, scripture_greek: 88, scripture_parashah: 89, sefer_hachinuch_number: 90, mishneh_torah_book_number: 91, mishneh_torah_book_name: 92, mishneh_torah_category: 93, p119f_category: 94, reference_id: 116.68 }`
Event	Data Link	Mapping `_: { event_id: 50, event_label: 51, event_description: 52, event_type: 53, event_year_ah: 55, person_age_at_event: 56, event_year_offset: 57, event_reference_id: 58, event_year_calculation: 59, event_location: 60, event_location_reference_id: 61, person_id: 111.0, event_notes: -62 { _index: 63, _value: 64 } }`
Hitchcock's Dictionary	Data Link	Mapping `_: { Meaning: 67, english_label: 112.29 }`
Person	Data Link	Mapping `_: { person_id: 0, person_name: 1, surname: 2, unique_attribute: 3, sex: 4, tribe: 5, person_notes: 6, name_instance: 7, person_sequence: 8 }`
Person Label	Data Link	Mapping `_: { person_label_id: 27, english_label: 29, hebrew_label_transliterated: 33, hebrew_strongs_number: 37, greek_label_transliterated: 41, greek_label_meaning: 42, greek_strongs_number: 43, label_reference_id: 44, label_type: 45, label-given_by_god: 46, label_notes: 47, person_label_count: 48, label_sequence: 49, person_id: 110.0, greek_label: -38 { _index: 39, _value: 40 }, hebrew_label_meaning: -34 { _index: 35, _value: 36 }, hebrew_label: -30 { _index: 31, _value: 32 } }`
Person Relationship	Data Link	Mapping `_: { person_relationship_id: 74, person_relationship_sequence: 75, relationship_type: 77, person_id_2: 78, relationship_category: 79, relationship_notes: 81, person_id: 114.0, reference_id: 115.68 }`
Reference	Data Link	Mapping `_: { reference_id: 68, usx_code: 70, chapter: 71, verse: 72, verse_sequence: 73, book_id: 113.18 }`

Generated Dataset Specifications

Case A: Transforming Person Relationships and Labels into Neo4j

The Person, PersonLabel, and PersonRelationship data are transformed into a graph structure and stored in Neo4j, a graph database. The original data contains relational information about biblical figures, and their relationships with one another. This inherent graph-like structure makes Neo4j an ideal choice for storing and analyzing this data.

The relationships in the dataset form a natural graph structure, with people as nodes and their connections as edges. Neo4j is specifically designed to store, query, and analyze such graph data efficiently.

Entity Output Mapping

Person

Entity	Output Mapping
Person	Output Mapping `_: { person_id: 0, person_name: 1, surname: 2, unique_attribute: 3, sex: 4, tribe: 5, person_notes: 6, name_instance: 7, person_sequence: 8 }`
PersonLabel	Output Mapping `_: { person_label_id: 9, english_label: 11, label_type: 27, label_notes: 29, person_label_count: 30, person_id: 40.0 }`
PersonRelationship	Output Mapping `_: { person_relationship_id: 32, person_relationship_sequence: 33, relationship_type: 35, person_id_2: 36, relationship_category: 37, relationship_notes: 39, person_id: 41.0 }`

Output Mapping

_: {
    person_id: 0,
    person_name: 1,
    surname: 2,
    unique_attribute: 3,
    sex: 4,
    tribe: 5,
    person_notes: 6,
    name_instance: 7,
    person_sequence: 8
}

PersonLabel

Output Mapping

_: {
    person_label_id: 9,
    english_label: 11,
    label_type: 27,
    label_notes: 29,
    person_label_count: 30,
    person_id: 40.0
}

PersonRelationship

Output Mapping

_: {
    person_relationship_id: 32,
    person_relationship_sequence: 33,
    relationship_type: 35,
    person_id_2: 36,
    relationship_category: 37,
    relationship_notes: 39,
    person_id: 41.0
}

Generated Data Manipulation Language (DML) Commands:

Commands Link