datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
total-300-lambda02-s_signal_type6-jh-epoch4
total-300-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4046875
Action score: 0.4140625
Valid samples: 320/320
total-300-lambda00-s_signal_type6-jh-epoch4
total-300-lambda00-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3875
Action score: 0.43125
Valid samples: 320/320
total-300-lambda05-s_signal_type6-jh-epoch4
total-300-lambda05-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.35703125
Action score: 0.4375
Valid samples: 320/320
total-300-lambda08-s_signal_type6-jh-epoch4
total-300-lambda08-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38046875
Action score: 0.4078125
Valid samples: 320/320
total-300-lambda10-s_signal_type6-jh-epoch4
total-300-lambda10-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.41875
Valid samples: 320/320
total-300noapp-lambda02-s_signal_type6-jh-epoch4
total-300noapp-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.409375
Valid samples: 320/320
total-300app-lambda02-s_signal_type6-jh-epoch4
total-300app-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3625
Action score: 0.4015625
Valid samples: 320/320
total-131-lambda02-residual-s_signal_type6-jh-epoch4
total-131-lambda02-residual-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3765625
Action score: 0.4171875
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-retry-epoch4
total-300-lambda02-s_signal_type6-jh-retry-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36953125
Action score: 0.3984375
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4-reeval2
total-300-lambda02-s_signal_type6-jh-epoch4-reeval2
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4125
Action score: 0.4265625
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4-reeval1
total-300-lambda02-s_signal_type6-jh-epoch4-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38828125
Action score: 0.4234375
Valid samples: 320/320
Cobot_Magic_turn_on_the_desk_lamp
Cobot_Magic_turn_on_the_desk_lamp
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: agilex_cobot_decoupled_magic
| Codebase Version: v2.1
End-Effector Type: two_finger_gripper
🏠 Scene Types
This dataset covers the following scene types:
home
🤖 Atomic Actions
This dataset includes the following atomic actions:
pressbutton
📊 Dataset Statistics
Metric
Value… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/Cobot_Magic_turn_on_the_desk_lamp.Cobot_Magic_turn_off_the_desk_lamp
Cobot_Magic_turn_off_the_desk_lamp
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: agilex_cobot_decoupled_magic
| Codebase Version: v2.1
End-Effector Type: two_finger_gripper
🏠 Scene Types
This dataset covers the following scene types:
home
🤖 Atomic Actions
This dataset includes the following atomic actions:
pressbutton
📊 Dataset Statistics
Metric
Value… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/Cobot_Magic_turn_off_the_desk_lamp.ChinaTravel
ChinaTravel Query Dataset
This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
ChinaTravel is an open-ended travel-planning benchmark with compositional
constraint validation for language agents. See the
paper,
Hugging Face paper page,
code, and
bilingual sandbox database
(ModelScope mirror)
for the complete benchmark resources.
Introduction
For a given query, a language agent uses the sandbox tools to collect
information and… See the full description on the dataset page: https://huggingface.co/datasets/LAMDA-NeSy/ChinaTravel.LAMDA
LAMDA: A Longitudinal Android Malware Dataset for Drift Analysis
This dataset contains a longitudinal benchmark for Android malware detection designed to analyze and evaluate concept drift in machine learning models. It includes labeled and feature-engineered Android APK data from 2013 to 2025 (excluding 2015), with over 1 million samples collected from real-world sources.
Dataset Details
LAMDA is the largest and most temporally diverse Android malware dataset to date. It… See the full description on the dataset page: https://huggingface.co/datasets/IQSeC-Lab/LAMDA.LAMDA
LAMDA: A Longitudinal Android Malware Dataset for Drift Analysis
This dataset contains a longitudinal benchmark for Android malware detection designed to analyze and evaluate concept drift in machine learning models. It includes labeled and feature-engineered Android APK data from 2013 to 2025 (excluding 2015), with over 1 million samples collected from real-world sources.
Dataset Details
LAMDA is the largest and most temporally diverse Android malware dataset to date. It… See the full description on the dataset page: https://huggingface.co/datasets/PtaSack/LAMDA.vocalLAMDA
LAMDA: A Longitudinal Android Malware Dataset for Drift Analysis
This dataset contains a longitudinal benchmark for Android malware detection designed to analyze and evaluate concept drift in machine learning models. It includes labeled and feature-engineered Android APK data from 2013 to 2025 (excluding 2015), with over 1 million samples collected from real-world sources.
Dataset Details
LAMDA is the largest and most temporally diverse Android malware dataset to date. It… See the full description on the dataset page: https://huggingface.co/datasets/Yanyi10086/LAMDA.ChinaTravel-Sandbox
ChinaTravel Sandbox Environment Database
This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
English | 简体中文
Release version: 2026.08.2
English
This dataset contains the bilingual static sandbox used by
ChinaTravel. It is a companion to
the ChinaTravel query dataset
and an artifact of the
ChinaTravel paper.
The raw ZIP snapshots preserve the exact directory layout expected by the
ChinaTravel evaluator. Viewer-friendly Parquet… See the full description on the dataset page: https://huggingface.co/datasets/LAMDA-NeSy/ChinaTravel-Sandbox.LAMBDA
Dataset Summary
LAMDBA is a long term ad memorability dataset, featuring data from 1749 participants and 2205 ads across 276 brands.
Dataset Structure
from datasets import load_dataset
ds = load_dataset("behavior-in-the-wild/LAMBDA")
ds
DatasetDict({
train: Dataset({
features: ['video_id', 'recall_score', 'youtube_id', 'ad_details'],
num_rows: 1964
})
test: Dataset({
features: ['video_id', 'recall_score', 'youtube_id', 'ad_details']… See the full description on the dataset page: https://huggingface.co/datasets/behavior-in-the-wild/LAMBDA.ridgelora-cross-sensor-sd302d-f-to-m-20260825
RidgeLoRA-FP: SD302A-F to SD302D-M cross-sensor experiment
This public archive contains the leakage-controlled direct cross-sensor
experiment used to evaluate whether Stage-2 synthetic target-sensor images
help recognition on a physically different real sensor.
Locked protocol
Source/condition sensor: NIST SD302A device F.
Target sensor: NIST SD302D device M.
Identity: subject:finger-position; the same fingers exist across both
collections.
Subject split: 160… See the full description on the dataset page: https://huggingface.co/datasets/LamTNguyen/ridgelora-cross-sensor-sd302d-f-to-m-20260825.tcga-laml-tabular-open
TCGA-LAML — Tabular (Open Access)
Open-access TCGA-LAML data from the NCI Genomic Data Commons, reshaped into one table per GDC data_type. Clinical, biospecimen and every open molecular modality for this cohort, in one place, queryable without downloading a single .tar or parsing a single TSV.
GDC data release: Data Release 46.0 - August 10, 2026
Built: 2026-09-12 04:02:09 UTC
Scope: one TCGA project — see [the family][repo] for the others
from datasets import load_dataset… See the full description on the dataset page: https://huggingface.co/datasets/gabrielaltay/tcga-laml-tabular-open.lambda-chat
lambda-chat
lambda-chat is a Japanese instruction-following dataset for supervised fine-tuning of chat models. It combines openly available datasets into one consistent chat format for easier use.
Purpose
The dataset is intended for training and evaluating Japanese chat and instruction-following models. Each example uses a list of messages with role and content fields.
Source Data
The dataset contains data from the following sources.… See the full description on the dataset page: https://huggingface.co/datasets/KeisukeMiyamoto/lambda-chat.PlugChargerMani-6DThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"shape": [
10
],
"names": [
"delta_pos_x",
"delta_pos_y",
"delta_pos_z",
"rot6d_0",
"rot6d_1",
"rot6d_2",
"rot6d_3"… See the full description on the dataset page: https://huggingface.co/datasets/lambdavi/PlugChargerMani-6D.PlugChargerMani-6D-camThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"shape": [
10
],
"names": [
"delta_pos_x",
"delta_pos_y",
"delta_pos_z",
"rot6d_0",
"rot6d_1",
"rot6d_2",
"rot6d_3"… See the full description on the dataset page: https://huggingface.co/datasets/lambdavi/PlugChargerMani-6D-cam.PlugChargerMani-jointThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"shape": [
8
],
"names": [
"action_0",
"action_1",
"action_2",
"action_3",
"action_4",
"action_5",
"action_6"… See the full description on the dataset page: https://huggingface.co/datasets/lambdavi/PlugChargerMani-joint.clean_deskThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6… See the full description on the dataset page: https://huggingface.co/datasets/lambdavi/clean_desk.imslp-crawling
Top Composers
Composer folders size
size
composer
1017G
/
35G
/(1959)Daniel Léo Simpson
31G
/(1756)Wolfgang Amadeus Mozart
24G
/(1685)Johann Sebastian Bach
21G
/(1770)Ludwig van Beethoven
17G
/(1699)Johann Adolph Hasse
16G
/(1678)Antonio Vivaldi
16G
/(1670)Antonio Caldara
14G
/(1685)George Frideric Handel
14G
/(1683)Christoph Graupner
13G
/(1975)Carlotta Ferrari
13G
/(1732)Joseph Haydn
11G
/(1792)Gioacchino Rossini
10G
/(1948)Michel… See the full description on the dataset page: https://huggingface.co/datasets/k-l-lambda/imslp-crawling.protein-secondary-structure-netsurfp
NetSurfP-3.0 Secondary-Structure Splits
This dataset repo contains NetSurfP-derived protein secondary-structure labels
converted for Protein-I-JEPA probe training and evaluation.
Source page: https://services.healthtech.dtu.dk/services/NetSurfP-3.0/5-Dataset.php
Profile: hhblits
Labels are Q3 per-residue labels:
H: helix
E: beta strand
C: coil/other
.: ignored residue for loss and accuracy
Splits
Split
Rows
JSONL
TSV
train
10348
train.jsonl
tsv/train.tsv… See the full description on the dataset page: https://huggingface.co/datasets/lamm-mit/protein-secondary-structure-netsurfp.RULER-16384-Lamma3-Instruct