datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
protein_stability_single_mutation
Protein Data Stability - Single Mutation
This repository contains data on the change in protein stability with a single mutation.
Attribution of Data Sources
Primary Source: Tsuboyama, K., Dauparas, J., Chen, J. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023). Link to the paper
Dataset Link: Zenodo Record
As to where the dataset comes from in this broader work, the relevant dataset (#3) is shown in… See the full description on the dataset page: https://huggingface.co/datasets/Trelis/protein_stability_single_mutation.exp-temporal-stability
Experiment H13: Temporal Stability Across Model Versions
Paper DOI: 10.5281/zenodo.19422427 — R15 (Zharnikov, 2026v)
Dataset DOI: 10.57967/hf/8455
Source Code: spectralbranding/sbt-papers/r15-ai-search-metamerism
Dataset Summary
450 LLM API calls testing whether successive model versions produce significantly different dimensional weight profiles for the same brands. Supplementary to the R15 study on dimensional collapse in AI-mediated brand perception (Zharnikov… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/exp-temporal-stability.autonomous-driving-ethical-stability-accountability-mapping-v0.1
What this dataset tests
Whether a system can evaluatehow a driving decisionaffects overall scene stabilityand who carries responsibilityfor resulting disturbance.
Required outputs
stability impact description
accountability nodes
stability score
accountability score
recovery quality
Use case
Final layer of ethical navigation stack.
Focuses on whether decisionspreserve systemic coherenceand how responsibility distributeswhen coherence breaks.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-ethical-stability-accountability-mapping-v0.1.pg19-stability-bench
PG-19 Stability Evaluation Prompts
Long-context prompts in chat-message format at five bucket sizes (8K, 16K, 32K, 64K, 128K user-message tokens), designed for output-stability evaluation of LLMs under stress: as context grows (and RoPE scaling extends the effective window), do generations stay coherent — or do they degrade into mojibake, token soup, phrase loops, or script drift?
Each prompt is a 2-turn conversation (system + user) ready to send to any OpenAI-compatible… See the full description on the dataset page: https://huggingface.co/datasets/nnilayy/pg19-stability-bench.