Datasets:

shangshang
/

medical-mri

patient_id string	age int64	gender string	tumor_size_mm float64	contrast_enhancement float64	edema_volume float64	necrosis_ratio float64	peritumoral_fluid float64	label int64
PT0001	57	M	39.8	0.936	21.2	0.809	36.8	1
PT0002	47	F	18.1	0.377	12.7	0.032	1.7	0
PT0003	59	F	14.9	0.023	7.2	0.283	7.8	0
PT0004	72	M	18.9	0.333	9.3	0.037	3.3	0
PT0005	46	M	39.1	0.922	36.1	0.64	0.5	1
PT0006	46	F	25.7	0.459	18.5	0.661	51.1	1
PT0007	73	F	15.9	0.238	17.7	0.112	1.6	0
PT0008	61	M	46	0.808	27.5	0.555	5.4	1
PT0009	42	M	45	0.797	2.1	0.714	6.8	1
PT0010	58	M	19.7	0.456	18.3	0.35	2.3	0
PT0011	43	F	20.7	0.352	11.1	0.111	4.8	0
PT0012	43	F	41.6	0.416	5.1	0.817	0.6	1
PT0013	53	F	21.3	0.221	4.5	0.199	10.9	0
PT0014	21	M	34	0.759	12.2	0.72	8.8	1
PT0015	24	M	17.8	0.39	12.9	0.108	4.6	0
PT0016	41	F	34.1	0.832	12.7	0.451	4.2	1
PT0017	34	F	15.9	0.505	5.2	0.043	6.7	0
PT0018	54	M	29.3	0.339	21.7	0.004	1.4	0
PT0019	36	F	39.4	0.907	12.1	0.453	18.2	1
PT0020	28	F	33.8	0.082	32.5	0.03	5.8	0
PT0021	71	M	16.2	0.611	11.4	0.01	8.4	0
PT0022	46	F	9.3	0.215	10	0.563	17.6	0
PT0023	51	M	83.3	0.494	1.6	0.96	20.1	1
PT0024	28	M	15.9	0.693	1.8	0.678	3.1	1
PT0025	41	M	37.7	0.8	21.3	0.745	12.8	1
PT0026	51	M	12.7	0.539	3.5	0.1	5.4	0
PT0027	32	M	13.8	0.329	14.9	0.007	7.4	0
PT0028	55	M	7.7	0.215	5	0.054	3.2	0
PT0029	40	M	13.7	0.421	1.5	0.089	32.9	0
PT0030	45	F	6.2	0.207	0.1	0.463	8.8	0
PT0031	40	M	8.7	0.253	7.9	0.016	0.4	0
PT0032	77	M	22.3	0.391	37.2	0.6	3.2	0
PT0033	49	F	64.1	0.617	5.1	0.539	4.7	1
PT0034	34	F	19.5	0.34	4.4	0.255	0.5	0
PT0035	62	F	11.2	0.572	4.2	0.135	0.2	0
PT0036	31	M	57.1	0.677	1.2	0.721	43.2	1
PT0037	53	F	16.4	0.252	5.8	0.061	3.2	0
PT0038	20	M	63.8	0.37	46.2	0.791	17.8	1
PT0039	30	M	39.6	0.467	8.8	0.529	10.1	1
PT0040	52	M	37.1	0.793	2	0.644	10.8	1
PT0041	61	M	15.1	0.159	1.5	0.043	9.3	0
PT0042	52	F	55.8	0.917	3.4	0.666	9.1	1
PT0043	48	F	17.9	0.134	2.9	0.232	1.8	0
PT0044	45	M	7.3	0.297	0.8	0.25	3.1	0
PT0045	27	M	14.9	0.4	10.9	0.348	7.2	0
PT0046	39	M	60.8	0.715	11.1	0.636	6	1
PT0047	43	F	11	0.58	5.4	0.12	1.3	0
PT0048	65	M	14.7	0.33	5.5	0.002	0.6	0
PT0049	55	M	52.6	0.785	74.4	0.432	11.8	1
PT0050	23	F	21.2	0.38	2.2	0.384	11.2	0
PT0051	54	F	13.1	0.414	17.2	0.036	11.2	0
PT0052	44	M	20.7	0.314	10.5	0.033	10.1	0
PT0053	39	F	28.8	0.521	5.4	0.251	8.9	0
PT0054	59	F	23.8	0.274	6.1	0.02	5.2	0
PT0055	65	M	16.8	0.364	3	0.262	8.2	0
PT0056	63	F	22.1	0.912	25.7	0.605	18.3	1
PT0057	37	M	21.7	0.611	7.2	0.185	7.8	0
PT0058	45	F	19.2	0.142	3.9	0.443	12	0
PT0059	54	M	23.6	0.401	13.4	0.382	28.4	1
PT0060	64	M	21.2	0.549	50	0.048	3.5	0
PT0061	42	M	15.8	0.891	10.2	0.585	7	1
PT0062	47	F	13.3	0.447	25.3	0.474	8.2	0
PT0063	33	M	27.9	0.176	4.2	0.376	9.4	0
PT0064	32	F	17.8	0.307	0.6	0.002	16.4	0
PT0065	62	M	37.7	0.124	2.3	0.342	3.4	0
PT0066	70	M	12.5	0.125	5.4	0.137	11.7	0
PT0067	48	F	32.9	0.59	2.5	0.08	6.6	0
PT0068	65	M	17.8	0.694	0	0.018	17.2	0
PT0069	55	M	12.7	0.378	24.7	0.039	6	0
PT0070	40	F	13.6	0.307	18.6	0.142	5.8	0
PT0071	55	M	14.5	0.239	13.6	0.21	6.9	0
PT0072	73	M	19.5	0.045	1.8	0.017	11.1	0
PT0073	49	M	20.3	0.636	1.3	0.247	26.7	0
PT0074	73	M	19	0.662	19.8	0.511	13.3	1
PT0075	18	F	16.3	0.203	12.9	0.053	0.4	0
PT0076	62	F	64.8	0.748	63.9	0.736	8.5	1
PT0077	51	M	48.7	0.341	1.9	0.535	2.9	1
PT0078	45	F	19.6	0.368	1.3	0.177	7.6	0
PT0079	51	M	45.1	0.688	10.8	0.66	12.4	1
PT0080	20	F	53.2	0.814	38.1	0.833	17.4	1
PT0081	46	M	21	0.391	4.7	0.093	11.7	0
PT0082	55	M	10.9	0.227	9.7	0.386	11	0
PT0083	72	M	49.3	0.906	51.8	0.681	55.9	1
PT0084	42	M	27.4	0.668	44.4	0.947	5.1	1
PT0085	37	M	19.3	0.242	0.4	0.197	14.6	0
PT0086	42	M	21.3	0.165	50	0.025	0.6	0
PT0087	63	F	13.6	0.639	2.6	0.12	17	0
PT0088	54	M	30.9	0.431	0.6	0.213	2.4	0
PT0089	42	F	50.8	0.796	5	0.756	11.5	1
PT0090	57	M	9.2	0.115	0.4	0.235	22	0
PT0091	51	F	18	0.175	5	0.21	9.1	0
PT0092	64	F	74.2	0.511	32.8	0.788	2.7	1
PT0093	39	F	32.3	0.044	4.2	0.146	9.4	0
PT0094	45	M	13.7	0.064	5.9	0.027	3.4	0
PT0095	44	M	32.7	0.716	44.7	0.604	4.3	1
PT0096	28	F	45.3	0.861	40.8	0.753	1	1
PT0097	54	M	15.3	0.559	19.8	0.34	11.1	0
PT0098	53	F	23.4	0.24	20.1	0.392	8.3	0
PT0099	50	M	21.3	0.462	0.1	0.526	3.5	0
PT0100	46	F	8.8	0.475	15.1	0.405	0.4	0

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ACCESS REQUIREMENT - FOLLOW TO DOWNLOAD

This dataset requires following the author to access.

How to Access

Follow @shangshang on HuggingFace: https://huggingface.co/shangshang
Request access by commenting on the dataset page
Once approved, you will receive download permissions

Usage Agreement

For research and educational purposes only
Do not redistribute without permission
Cite the dataset in your work:

@misc{shangshang_dataset_2026,
  title={Embodied AI and Medical Datasets},
  year={2026},
  url={https://huggingface.co/datasets/shangshang}
}

Embodied AI Dataset Collection

A comprehensive dataset for Vision-Language-Action (VLA) model training and embodied AI research.

Datasets Included

Dataset	Samples	Description
robot_trajectory.csv	1000	Robot arm trajectories for various manipulation tasks
joint_configurations.csv	800	6-DOF joint angle configurations
end_effector_poses.csv	600	End-effector pose sequences
force_tactile.csv	500	Force/tactile sensor feedback
language_instructions.csv	1000	Natural language task instructions
vla_combined_dataset.csv	500	Combined VLA training data

Task Categories

pick_place: Pick up object and place at target
push_pull: Push/pull objects
stacking: Stack objects
insertion: Insert objects into targets
turning: Turn knobs/dials

Robot Types

Franka Emika (7-DOF)
UR5e (6-DOF)
WidowX 250 (7-DOF)
xArm6 (6-DOF)

Usage Example

import pandas as pd
from huggingface_hub import hf_hub_download

# Download
file_path = hf_hub_download(
    repo_id="shangshang/embodied-ai-dataset",
    filename="vla_combined_dataset.csv"
)

# Load
df = pd.read_csv(file_path)
print(df.head())

License

MIT License

Citation

If you use this dataset in your research, please cite:

@misc{embodied_ai_dataset_2026, title={Embodied AI VLA Dataset}, year={2026}, url={https://huggingface.co/datasets/shangshang/embodied-ai-dataset} }

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