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Number sequences for self-supervised learning

Education & Learning Analytics

Tags and Keywords

Computer

Science

Text

Nlp

Deep

Learning

Time

Series

Analysis

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Number sequences for self-supervised learning Dataset on Opendatabay data marketplace

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About

This dataset contains the complete set of number sequences from the Online Encyclopedia of Integer Sequences (OEIS), a renowned database featuring number sequences with unique and intriguing mathematical properties. It includes easily recognisable sequences like the Fibonacci or Catalan numbers, as well as more abstract ones requiring complex validation formulas. This resource is particularly valuable for applications in self-supervised learning.

Columns

The dataset is provided in two main files:
  • sequences.csv: Each row begins with a sequence's A-number, followed by the comma-separated sequence values.
  • metadata.txt: This file maps each sequence's A-number to its definition or description.

Distribution

The dataset consists of two files: sequences.csv and metadata.txt. The format is CSV for sequences and plain text for metadata. Specific numbers of rows or records are not detailed in the available information, but it is stated to be the complete collection of sequences from the OEIS database.

Usage

This dataset is ideal for various applications, including:
  • Developing and evaluating self-supervised learning models.
  • Research in computer science, particularly in areas like natural language processing (NLP) and deep learning.
  • Performing time series analysis on unique numerical patterns.
  • Educational purposes and exploring diverse mathematical properties of integer sequences.

Coverage

The data originates from the Online Encyclopedia of Integer Sequences, which is a globally accessible and continuously updated resource. The dataset reflects the state of the OEIS database as of its last modification on January 20, 2023. It provides a static snapshot of the entire collection of integer sequences.

License

CC-BY-NC

Who Can Use It

This dataset is particularly suited for:
  • Data Scientists and Machine Learning Engineers: For training and testing algorithms in self-supervised learning and deep learning.
  • Researchers in Computer Science and Mathematics: To explore properties of number sequences and their applications.
  • Educators and Students: As a valuable resource for teaching and learning about integer sequences, algorithms, and data analysis.
  • Developers: Interested in building applications that leverage unique numerical patterns.

Dataset Name Suggestions

  • OEIS Integer Sequences for Self-Supervised Learning
  • Online Encyclopedia of Integer Sequences (OEIS) Dataset
  • Mathematical Number Sequences Collection
  • OEIS Data for Machine Learning Research
  • Self-Supervised Number Sequence Library

Attributes

Listing Stats

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0

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0

LISTED

26/06/2025

REGION

GLOBAL

Universal Data Quality Score Logo UDQSQUALITY

5 / 5

VERSION

1.0

Free

Download Dataset in ZIP Format