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UncensoredAI123/SciDrawAI

published by @UncensoredAI123

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
117
Last 30 days
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4 KB
Created on HF
2026-04-06
Age
6 months ago

Description

Generative artificial intelligence (AI) has created new possibilities for producing scientific figures, graphical abstracts, and conceptual diagrams at substantially lower time and skill cost. At the same time, publishers and journals have introduced heterogeneous policies governing the disclosure and acceptability of AI-generated imagery, leaving researchers with limited operational guidance. In this paper, we conduct a structured review of editorial policies from 12 major publishers and… See the full description on the dataset page: https://huggingface.co/datasets/UncensoredAI123/SciDrawAI.

Tags

license:apache-2.0arxiv:2603.16159region:us

README current version from Hugging Face


license: apache-2.0

Generative artificial intelligence (AI) has created new possibilities for producing scientific figures, graphical abstracts, and conceptual diagrams at substantially lower time and skill cost. At the same time, publishers and journals have introduced heterogeneous policies governing the disclosure and acceptability of AI-generated imagery, leaving researchers with limited operational guidance. In this paper, we conduct a structured review of editorial policies from 12 major publishers and journals current to January 2026, analyze the principal concerns motivating these policies, and compare representative figure-generation tools for academic use. As an illustrative case, we examine SciDraw, a domain-specific platform for scientific illustration available at https://sci-draw.com. Our analysis indicates that publisher guidance converges on three requirements: transparent disclosure, retained human accountability, and heightened scrutiny for figures that could be mistaken for primary data. Related preprint: https://arxiv.org/abs/2603.16159

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

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  2. 2026-04-06initial commitddf405131 B
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Metadata

License
apache-2.0

Repository

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("UncensoredAI123/SciDrawAI")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="UncensoredAI123/SciDrawAI", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/UncensoredAI123/SciDrawAI
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