datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ccnews_www.skornorth_scmscm-regression-mini-trialsc_MATLABsc-matlab-validated
SC MATLAB Validated
Validated MATLAB/Octave code–pseudocode pairs for program comprehension and synthesis research.
Each sample was filtered from semran1/yulan-code-MNBVC-matlab,
converted to pseudocode with Gemini, regenerated back to MATLAB, and kept only when Octave execution output matched the original.
Fields
Column
Description
sample_id
Numeric sample index
code
Original MATLAB/Octave source
pseudocode
LLM-generated pseudocode from the… See the full description on the dataset page: https://huggingface.co/datasets/philip120/sc-matlab-validated.SCM3K
SCM3K
Benchmark dataset for the paper:
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Shu Wan, Abhinav Gorantla, Huan Liu, K. Selçuk Candan
3,450 tabular prediction tasks sampled from random structural causal
models (SCMs), totalling 3.45M records (1,000 samples per task).
Each task ships with the ground-truth Markov boundary of the target
node, so you can evaluate feature selection and prediction under
known causal structure. Nine feature-count… See the full description on the dataset page: https://huggingface.co/datasets/CSE472-blanket-challenge/SCM3K.ccnews_www.kalb_scmResume_Screening_Data_Classificationccnews_www.fox10phoenix_scmccnews_www.hometownstations_scmsdxl-base-1-scm-corpus
Shamima/sdxl-base-1-scm-corpus
Synthetic image corpus generated with Stable Diffusion XL for studying the
Stereotype Content Model (SCM) structure of text-to-image latent space.
Images: 6,600
Categories: 66 occupation/identity groups
Prompt template: "A portrait of a [group], high quality."
Generator: SDXL base 1.0, DPM++ 2M Karras, 30 steps, CFG 7.0
Resolution: see image features
Fields
field
description
image
RGB JPEG
category
Group/occupation label… See the full description on the dataset page: https://huggingface.co/datasets/Shamima/sdxl-base-1-scm-corpus.ccnews_www.expressnews_scmccnews_bismarcktribune_scmccnews_www.wbay_scmlogistics-disruption-archive
Logistics Disruption Archive
Supply chain resilience metrics across 1,000 simulated logistics scenarios, covering five industry sectors under various disruption conditions.
Useful for studying how supplier diversity, delivery reliability, and inventory buffers interact to determine overall chain performance under stress.
Usage
from datasets import load_dataset
dataset = load_dataset("scm-resilience-data/logistics-disruption-archive")
df = dataset["train"].to_pandas()
Or… See the full description on the dataset page: https://huggingface.co/datasets/scm-resilience-data/logistics-disruption-archive.ccnews_newstalk1290_scmccnews_wcfcourier_scmResume_Screening_Data_Generationccnews_www.cantonrep_scmccnews_www.wymt_scmccnews_www.wibw_scmccnews_www.currentargus_scmccnews_pantagraph_scmccnews_www.kxlf_scmsc_Mathematicaccnews_www.myarklamiss_scmccnews_www.cbs42_scmccnews_www.mcall_scmccnews_www.sfgate_scmccnews_www.gadsdentimes_scmccnews_www.chicoer_scm