Kepler: The Pioneer of Data Science and AI

2025 · doi:10.32388/0eeg86
preprint OA: closed CC-BY-4.0
🔓 Open OA copy Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-06, 2026-06-24 · read from full text ⓘ

The provided text contains only generic metadata about a publication titled “Kepler: The Pioneer of Data Science and AI,” including licensing and templated fields, without any description of study aims, methods, results, or conclusions. Because the actual research content is not present, no population, experimental approach, or key finding can be extracted from the excerpt. No explicit limitations are stated in the provided material beyond the absence of substantive paper content. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

This article examines Johannes Kepler’s pioneering contributions to data-driven scientific discovery and draws parallels with modern advancements in Artificial Intelligence (AI). Kepler’s rigorous analysis of Tycho Brahe’s astronomical data led to the formulation of the fundamental laws of planetary motion, exemplifying early principles of data science. Contemporary AI techniques—such as symbolic regression, neural networks, and explainable AI—can now rediscover physical laws from large datasets, mirroring Kepler’s methodology but at an unprecedented scale. Despite technological progress, challenges persist, including data quality, interpretability, and validation. The synergy between human intuition and machine intelligence holds promise for accelerating scientific breakthroughs across disciplines, extending Kepler’s legacy into the era of big data and AI-driven discovery.
Full text 621 characters · extracted from oa-doi-fallback · click to expand
There is a newer version available for this {{ publicationType }}. View latest version {{ publication.field_name }} {{ publication.subfield_name }} Copyright: © {{ publicationYear }} {{ publication.presentation_authors[0].full_name + (publication.presentation_authors.length > 1 ? ' et al' : '') }}. This is an open access publication distributed under the terms of the CC BY 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Check the {{ publicationType | capitalize }} Source for copyright and license information. Listen on

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-22T02:00:06.705733+00:00
License: CC-BY-4.0