datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
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: 0.3953125
Action score: 0.446875
Valid samples: 320/320
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
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-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: 0.41328125
Action score: 0.4359375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
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: 0.41953125
Action score: 0.4515625
Valid samples: 320/320
qwen35-4b-filter-s_signal5-200-qwen38-27b-newprompt-4k-epoch4
qwen35-4b-filter-s_signal5-200-qwen38-27b-newprompt-4k-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: 0.3890625
Action score: 0.4359375
Valid samples: 320/320
fi-etf-macro-signal-master-databit-signal-store
Bit Signal Store
A shared, append-only cache of raw time-series model outputs and OHLCV
prices, used by the Backtest Lab
Space.
The one rule
This store holds raw model outputs and prices. It never holds trade
decisions. No entries, no exits, no position sizes, no P&L. Trading rules,
costs, slippage and sizing are applied live, per request, by the Backtest
Lab's engine. That separation is what lets many different strategies be
compared against the same model… See the full description on the dataset page: https://huggingface.co/datasets/The-Bit-Trading-Company/bit-signal-store.world-signals
World Signals — a daily cross-country snapshot of attention
One folder per day under data/YYYY-MM-DD/, and the same files copied to latest/.
Built every morning (JST) by the EmpireOS world model. Nothing is generated by a model; every row is a measurement from a public source.
file
what
source
search_trends.csv
rising searches, 30 countries, with approximate traffic and the headline that drove them
Google Trends daily RSS
podcast_charts.csv
top-100 podcasts, 30… See the full description on the dataset page: https://huggingface.co/datasets/Lilambd/world-signals.SIGNAL
SIGNAL
Dataset for Semantic and Inferred Grammar Neurological Analysis of Language
License: CC BY 4.0
Authors
Anna Komissarenko1,2,*,
Ekaterina Voloshina1,
Anastasia Cheveleva2,
Ilia Semenkov1,2,
Oleg Serikov3,
Alex Ossadtchi1,2,4,*
1 AIRI, Moscow, Russia2 Higher School of Economics, Moscow, Russia3 Center of Excellence for Generative AI, KAUST, KSA4 LIFT, Life Improvement by Future Technologies Institute, Moscow, Russia
*Corresponding authors… See the full description on the dataset page: https://huggingface.co/datasets/ContributorsSIGNAL/SIGNAL.clawhub-security-signals
ClawHub Security Signals
🦀 ClawHub | 📝 OpenClaw Blog | 🤗 Hugging Face Blog | 📄 Paper | 📄 Pre-Print
ClawHub Security Signals is a sanitized, MIT-licensed security-signals dataset for public OpenClaw agent skills. It captures how an agent-skill registry evaluates trust, provenance, bundled code, and scanner evidence at scale.
This dataset was presented in the paper ClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagree.
Paper snapshot: this… See the full description on the dataset page: https://huggingface.co/datasets/OpenClaw/clawhub-security-signals.tradingview-ideas-signals
TradingView Crypto Ideas + Binance 1m OHLCV
51,963 published trading ideas (LONG/SHORT/NEUTRAL) from 2,343 TradingView authors, spanning 2014-06 → 2026-07, paired with 1-minute Binance OHLCV candles (748 symbol-year files, 313 spot symbols, ~40M candles) covering the labeling window around every idea. Built for look-ahead-bias-free backtesting of social trading signals: every idea carries its exact publication timestamp, and popularity counters are snapshotted over time rather… See the full description on the dataset page: https://huggingface.co/datasets/tripolskypetr/tradingview-ideas-signals.clawhub-security-signals-live
ClawHub Security Signals Live
This dataset is the refreshed ClawHub security-signals corpus for scanner testing, prompt regression checks, and operational research against recent public ClawHub skills.
It is a moving dataset, not the fixed paper benchmark. main is expected to change when the ClawHub security dataset snapshot workflow publishes a new sanitized export. Pin a Hugging Face revision or commit when you need reproducibility.
For the frozen research-paper snapshot, use… See the full description on the dataset page: https://huggingface.co/datasets/OpenClaw/clawhub-security-signals-live.signalbench
In-Band Signal Compliance (IBSC) — signalbench leaderboard
One metric for prompt injection and temporal blindness. LLM agents read control
instructions and ordinary data through the same channel, so they must decide whether to obey each
instruction.
The benchmark measures two symmetric failure modes: over-compliance (obeying illegitimate
signals — prompt injection) and under-compliance (ignoring legitimate ones). The
Signal-Response Correctness (SRC) metric ranges 0–1 per item;… See the full description on the dataset page: https://huggingface.co/datasets/thamilvendhan/signalbench.vc-deal-flow-signal
Startup GitHub Engineering Velocity Panel
A longitudinal dataset of public GitHub engineering-activity signals for venture-backed startups. It is published under CC BY 4.0 for reproducible research, data journalism, and analysis of alternative data in venture capital.
219 startup-period observations
55 unique startups
18 sectors
4 quarterly periods: Q3 2025, Q4 2025, Q1 2026, and Q2 2026
No missing values in the primary table
Version: 1.0.0
The 219 rows are startup-period… See the full description on the dataset page: https://huggingface.co/datasets/the-data-nerd/vc-deal-flow-signal.FinRL_BTC_news_signals
Overview
This news dataset is created for FinAI Contest 2025 Task 1 FinRL-DeepSeek for Crypto Trading. We collected BTC news for the training and testing period from different sources [1] [2]. For each news, we use the DeepSeek chat model to extract the sentiment score, risk level, and their correpsonding confidence level and one-sentence reasoning.
Column
Description
date_time
Timestamp of when the news article was published (in UTC).
title
Title of the news article.… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/FinRL_BTC_news_signals.signal_theology
NuBerea signal_theology
An emergent, within-Bible operator kernel and narrated-event control surface. The kernel is a
set of typed doctrine / boundary-line statements that surfaced bottom-up from corpus-intrinsic
signals (composition dating, intra-canon citation, reception weighting, embedding geometry) over
the canonical Bible, with the narrated events that ground them and an intra-canon reception
keystone. No doctrine taxonomy was declared in advance; benchmarks were used only… See the full description on the dataset page: https://huggingface.co/datasets/NuBerea/signal_theology.signal-and-noise
Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
Our work studies the ratio between signal, a benchmark's ability to separate models; and noise, a benchmark's sensitivity to random variability during training steps.
This dataset contains evaluation results. For utilites to use this dataset and to reproduce the findings in our paper, please see our github.
Main Eval Suite (375 models)
import… See the full description on the dataset page: https://huggingface.co/datasets/allenai/signal-and-noise.PAH-ENV-001-serc-soil-signals
Pahzuzu SERC Paired Soil Moisture & Temperature (2016–2022)
More than 10 million paired, 30-minute soil observations from five plots at the Smithsonian Environmental Research Center (SERC), cleaned into two analysis-ready Parquet tables with preserved quality flags and machine-readable provenance.
This free research edition contains the complete moisture and temperature tables. It exists so researchers and engineers can inspect the actual data, limitations, schema and provenance… See the full description on the dataset page: https://huggingface.co/datasets/Pahzuzu/PAH-ENV-001-serc-soil-signals.crypto-prediction-market-signals
Crypto + Prediction Market Cross-Signal Dataset
BTC, ETH, SOL prices + funding rates + open interest + gold + Polymarket crypto probabilities — synced at 15-minute intervals.
The only dataset that combines crypto market microstructure with prediction market sentiment in one place.
What's Inside
Table
Rows
Description
candles
8,800+
15-min OHLCV for BTC, ETH, SOL (Binance Futures)
funding_rates
460+
8-hourly funding rates + mark prices
open_interest
190+… See the full description on the dataset page: https://huggingface.co/datasets/manja316/crypto-prediction-market-signals.signal-500
The Signal 500: a hand-scored catalogue of 495 news and blog sources
Snapshot generated 2026-08-30 by FeedsBar. The live, always-current version of this catalogue is at feeds.bar/signal-500 and the method is published at feeds.bar/signal-500/method.
This is a mirror of the Zenodo deposit, DOI 10.5281/zenodo.22311918 (concept DOI 10.5281/zenodo.22311917 always resolves to the latest version).
What is in this dataset
signal-500.csv: one row per source. Name, domain… See the full description on the dataset page: https://huggingface.co/datasets/graemechard/signal-500.