datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
localization_multiRNAIf you use this dataset, please cite the paper below:
Citation:
Wang, Jun, Marc Horlacher, Lixin Cheng, and Ole Winther. ‘DeepLocRNA: An Interpretable Deep Learning Model for Predicting RNA Subcellular Localization with Domain-Specific Transfer-Learning’. Edited by Pier Luigi Martelli. Bioinformatics 40, no. 2 (1 February 2024): btae065. https://doi.org/10.1093/bioinformatics/btae065.
localizationuniprot_subcellular_localization
UniProt Subcellular Localization (Vertebrates) — ProVADA
A curated collection of vertebrate UniProt/Swiss‑Prot protein domains labeled for cytosolic and extracellular localization. We remove signal peptides, restrict domain lengths, and provide both the full set and a 30% identity‑clustered representative set with train/test/validation splits (70/20/10). This dataset underpins the subcellular localization oracle in ProVADA (preprint).
See the Files section for exact filenames and… See the full description on the dataset page: https://huggingface.co/datasets/Xiaowei0402/uniprot_subcellular_localization.LoRNA_localizationaviation-vibration-manifold-distortion-and-fault-localization-v0.1What this dataset tests
Whether a system can detect topological distortion
in the vibration mode manifold and localize likely damage.
It must not confuse confounders with damage:
turbulence
engine harmonics
icing
payload shifts
control surface modes.
Required outputs
distortion_pattern_type
likely_fault_location
fault_severity_estimate
localization_confidence
confounder_flags
integrity_percent_of_baseline
Scoring conventions
severity ranges 0 to 1
localization confidence ranges… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-vibration-manifold-distortion-and-fault-localization-v0.1.