datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DepthQA
Dataset Card for DepthQA
This dataset card is the official description of the DepthQA dataset used for Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization.
Dataset Details
Dataset Description
Language(s) (NLP): EnglishLicense: Creative Commons Attribution 4.0Point of Contact: Sue Hyun Park
Dataset Summary
DepthQA is a novel question-answering dataset designed to evaluate graph-based reasoning… See the full description on the dataset page: https://huggingface.co/datasets/kaist-ai/DepthQA.PARARULE-Plus-Depth-2
PARARULE-Plus-Depth-2
This is a branch which includes the dataset from PARARULE-Plus Depth=2. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-2.PARARULE-Plus-Depth-5
PARARULE-Plus-Depth-5
This is a branch which includes the dataset from PARARULE-Plus Depth=5. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-5.PARARULE-Plus-Depth-3
PARARULE-Plus-Depth-3
This is a branch which includes the dataset from PARARULE-Plus Depth=3. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-3.nanochat-depo-composition-depth2-w4-retry-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.
PARARULE-Plus-Depth-4
PARARULE-Plus-Depth-4
This is a branch which includes the dataset from PARARULE-Plus Depth=4. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-4.depth-psychology-ontology-and-ai
Elena Ferrara – Depth Psychology, Ontology & AI
A curated dataset of peer-reviewed academic articles by Elena Ferrara, Swiss author and depth psychological consultant. The articles explore the intersection of depth psychology, ontology, systemics, and artificial intelligence.
Author
Elena Ferrara (ORCID: 0009-0004-2494-8936) is a Swiss author and depth psychological consultant based in Stadel, Canton of Zurich. She holds a five-year degree in depth psychology and has over… See the full description on the dataset page: https://huggingface.co/datasets/ElenaFerrara/depth-psychology-ontology-and-ai.nanochat-depo-l0-depth1-curriculum-20260715
Nanochat Depo-L0: symbolic
This is a diagnostic, separately versioned Depo source. Each row contains one
16-node cycle and eight queries under the depth1_only schedule. Only the eight
single-letter answers and terminal token are supervised. It is designed for a
one-document-per-sequence training protocol and must not be treated as public
Depo v3 data.
nanochat-depo-composition-depth2-w4-pubfix-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.
openspaces-depth-aware-32-samples
OpenSpaces Depth-Aware Visual QA Dataset
This is a 32-sample visual question answering (VQA) dataset that includes:
RGB images from the OpenSpaces dataset
Predicted depth maps generated using Depth Anything
3 depth-aware QA pairs per image:
Yes/No question (e.g., “Is there a person near the door?”)
Short answer question (e.g., “What color is the man’s coat?”)
Spatial sorting question (e.g., “Sort the objects from closest to farthest”)
Intended Use
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/srimoyee12/openspaces-depth-aware-32-samples.nanochat-depo-composition-depth2-w4-transport-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.
nanochat-depo-composition-depth2-w4-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.