datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
diont-cosmology-sphere-core
DIONT — Космология Сферы Ядра (PUB-002)
Открытый исследовательский корпус независимой группы DIONT.
Ревизия PUB-002 — 47 документов (космология, формулы, методы).
👉 Читать удобнее на витрине: https://huggingface.co/spaces/kuym/diont-cosmology
Эта страница — хранилище файлов (технический вид).
Что это
Перед вами не описание мира. Перед вами — одна из возможных проекций. Это модель, которая
может лечь в основу симуляций, игровых сред и исследовательских… See the full description on the dataset page: https://huggingface.co/datasets/kuym/diont-cosmology-sphere-core.cellular-cosmology-evidence
Evidence for Cellular Cosmology
This dataset contains the PDF documents and supporting evidence for the "Cellular Cosmology Hypothesis".
Linked Model:
https://huggingface.co/alis-sila/The-Absolute-Truth-of-Reality
Scientific Context
These papers provide the empirical foundation for replacing the "Dark" mysteries of physics with measurable biological isomorphies. This dataset bridges the gap between Hubble's expansion observations and cellular mitosis mechanics.… See the full description on the dataset page: https://huggingface.co/datasets/alis-sila/cellular-cosmology-evidence.cosmology-agentic-sft
Cosmology Agentic SFT Dataset
This is a reproducible SFT export built from real agentic coding sessions
(Claude Code and Codex transcripts) plus a small tranche of explicitly labeled
reconstructions grounded in saved cosmology run artifacts. It targets
repository-grounded cosmology coding, scientific data work, and multi-step tool
use. The recommended training view is train_turns.jsonl.
The canonical export has 411 trajectories (367 train, 44 validation) and 3,976
turn-level… See the full description on the dataset page: https://huggingface.co/datasets/licongxu/cosmology-agentic-sft.cosmology-arxiv
Cosmology arXiv Papers (astro-ph.CO)
LaTeX source files for cosmology papers from arXiv.
Columns
Column
Description
arxiv_id
arXiv paper ID
title
Extracted paper title
abstract
Extracted abstract
year
Publication year
tex_source
Full LaTeX source (with \input expanded)
Usage
from datasets import load_dataset
ds = load_dataset("adammoss/cosmology-arxiv")
# Access a paper
paper = ds['train'][0]
print(paper['title'])… See the full description on the dataset page: https://huggingface.co/datasets/adammoss/cosmology-arxiv.the-path-cosmologycosmologynotesparagraphscosmologynotes