datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.SIGNAL-Dataset-Hiddens-meta-llama_Meta-Llama-3-8B-Instructlouisiana-ai-signals-public-ledger
Louisiana AI Signals — Public Ledger
Official public dataset distribution of the Louisiana AI Signals — Public Ledger, published by Louisiana AI Hub, LLC.
Canonical current system of record: https://louisianaaihub.com/ledger
This Hugging Face dataset is an official public distribution snapshot. It is not an independent system of record. If this distribution differs from the current Public Ledger at LouisianaAIHub.com, the current Public Ledger controls.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/LouisianaAIHub/louisiana-ai-signals-public-ledger.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-Dataset-Hiddens-RefalMachine-RuadaptQwen2.5-7B-InstructDataset-Signal-Peptides
Description
This dataset contains 25693 amino acid sequences and labels on each amino acid.
Protein Format: AA sequence
Splits
traing: 20490
valid: 2569
test: 2634
Related paper
The dataset is from SignalP 6.0 predicts all five types of signal
peptides using protein language models.
Label
Each amino acid has 7 classes:
S (0): Sec/SPI signal peptide | T (1): Tat/SPI or Tat/SPII signal peptide | L (2): Sec/SPII signal peptide |
P (3): Sec/SPIII signal… See the full description on the dataset page: https://huggingface.co/datasets/SaProtHub/Dataset-Signal-Peptides.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.SIGNAL-Dataset-Hiddens-RefalMachine-RuadaptQwen2.5-14B-InstructSIGNAL-Dataset-Hiddens-Qwen-Qwen3-4B-Instruct-FP8This dataset contains hidden states of Qwen3-4B-Instruct model generated using SIGNAL Dataset.
Sentence tokenization
from transformers import AutoTokenizer
from datasets import load_dataset
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
# TBD
SIGNAL-Dataset-Hiddens-meta-llama_Meta-Llama-3-8Bringdown-damping-signals
Ring-Down Damping Signals: 12K Labelled Decay Waveforms
How this dataset was created
This is original data created programmatically — it was not collected, recorded, scraped, or
derived from any external source. Each of the 12,000 signals was generated from scratch by a
deterministic, seeded Python program:
Draw the label theta uniformly at random from [1, 5], plus a random overall base-decay rate.
Pick a random number of tones ("modes", 30–55), each with a… See the full description on the dataset page: https://huggingface.co/datasets/botfx/ringdown-damping-signals.SIGNAL-Dataset-Hiddens-Qwen-Qwen2.5-7BSIGNAL-Dataset-Hiddens-RefalMachine-RuadaptQwen3-4B-Instructclinical-quad-signal-detection-drift-ae-coding-variance-unblinding-risk-dsmb-decision-delay-v0.1
Clinical Quad: Signal Detection Drift × AE Coding Variance × Unblinding Risk × DSMB Decision Delay
This dataset targets safety governance collapse.
Signals weaken or shift.AE coding diverges across sites.Unblinding pressure rises.The DSMB response slows.
The quad can turn a manageable safety issue into a governance failure.
Variables
signal_detection_drift (low | medium | high)
ae_coding_variance (low | medium | high)
unblinding_risk (low | medium | high)… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-signal-detection-drift-ae-coding-variance-unblinding-risk-dsmb-decision-delay-v0.1.clinical-quad-site-performance-signal-drift-oversight-lag-v0.1Clarus Clinical Quad Coupling Site Performance Signal Drift Oversight Lag v0.1
What this dataset isThis dataset tests whether a model can detect site-level performance drift driven by four interacting nodes.
Quad coupling nodes
Enrollment or reporting signal shift
Data capture or documentation gaps
Operational staffing or monitoring lag
Governance pressure such as reviews, incentives, or interim analyses
Input
One site vignette
OutputReturn strict JSON only.
Required output JSON… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-site-performance-signal-drift-oversight-lag-v0.1.clinical-quad-safety-signal-latency-reporting-lag-conmed-confound-v0.1Clarus Clinical Quad Coupling Safety Signal Latency Reporting Lag Conmed Confound v0.1
What this dataset isThis dataset tests whether a model can detect latent safety signals when four interacting nodes create uncertainty.
Quad coupling nodes
Emerging safety event pattern
Reporting or entry latency
Concomitant medication or behavior confound
Governance decision timing such as DSMB, batch release, or safety review
Input
One vignette
OutputReturn strict JSON only.
Required output… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-safety-signal-latency-reporting-lag-conmed-confound-v0.1.climate-cross-scale-signal-decoupling-detection-v0.1What this dataset tests
Whether fast and slow climate variablesare losing their ability to coordinate across scales.
Required outputs
cross-scale coherence index
lag structure shift
decoupling onset time
affected scales
early warning confidence
Use case
First layer of Tipping Point Early-Warning Signatures.
clinical-quad-early-safety-signal-detection-suite-v0.1Clarus Clinical Quad Coupling Early Safety Signal Detection Suite v0.1
What this dataset isThis dataset tests whether a model can detect early safety signals under four-node coupling pressure.
Quad coupling nodes
Observed biological signal pattern
Concomitant medication confounding
Operational measurement and reporting conditions
Governance constraints that force holds, pauses, or timing rules
Input
One vignette in prompt
OutputReturn strict JSON only.
Required output JSON keys… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-early-safety-signal-detection-suite-v0.1.clinical_early_safety_signal_detection_v0.1Clinical Early Safety Signal Detection v0.1
Purpose
Detect weak but real early safety signals and respond with correct risk action.
Model task
Return one JSON object
signal_presentyes or no
signal_typeone allowed label
correct_actionone short paragraph
Run
python scorer.py --predictions predictions.jsonl --test_csv data/test.csv
clinical-quad-target-engagement-biomarker-shift-adaptive-resistance-signal-fade-v0.1Clinical Quad Target Engagement Biomarker Shift Adaptive Resistance Signal Fade v0.1
Each row is a patient snapshot.
Core quad
Target engagementBiomarker driftAdaptive resistanceSignal fade
Target
label_loss_of_response_next_60d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
titan-signal-b2b-ai-leads
Titan Signal - B2B AI Company Lead Intelligence
Verified contact records for decision-makers at AI, ML, and enterprise software companies.
Built by Titan Signal's 230+ autonomous harvesting agents. Continuously refreshed, MX-verified, 90-day auto-purge.
Fields
Company name, contact title, email
Industry vertical, headcount band, revenue band
Tech stack tags, engagement score, verification date
Full Dataset
This is a 50-record sample. Full datasets (10K-1M+… See the full description on the dataset page: https://huggingface.co/datasets/SophieTitan/titan-signal-b2b-ai-leads.cross-signal-data
cross-signal-data
The labeled Polymarket crash-recovery dataset behind a 80.2% win-rate live trading bot.
308 closed trades. Real Polymarket markets. Real entry triggers. Real outcomes. Public for anyone who wants to build their own mean-reversion bot, replicate our results, or prove us wrong.
What's in here
A single CSV (data/crashes_v1.csv) with one row per closed trade on Polymarket where the crash-recovery bot entered. Each row has:
The market (public Polymarket… See the full description on the dataset page: https://huggingface.co/datasets/LuciferForge/cross-signal-data.SIGNAL-Dataset-Hiddens-Qwen-Qwen2.5-7B-Instructunnat-alpha-signals-india
🚀 Unnat Alpha Signals India (UASI)
Institutional Multi-Factor Quantitative Intelligence & Alpha Signals for Indian Equities (NSE / BSE)
📌 Executive Summary
Unnat Alpha Signals India (UASI) is an institutional-grade quantitative equities intelligence dataset curated and maintained by Unnat Stock AI (a flagship sovereign fintech division of Unnat AI Solutions and Unnat Tuition Centre).
This dataset provides researchers, algorithmic traders, quantitative… See the full description on the dataset page: https://huggingface.co/datasets/unnattuition/unnat-alpha-signals-india.clinical-quad-evidence-drift-endpoint-signal-claim-language-certainty-narrative-break-v0.1What this repo does
This dataset models narrative continuity break in clinical trial summaries. It predicts when the interaction between evidence consistency, endpoint signal strength, claim strength, and certainty language indicates that the written narrative has drifted away from the underlying trial results.
Core quad
evidence_consistency_index
endpoint_signal_strength_index
claim_strength_index
certainty_language_index
Prediction target
label_narrative_break
Row structure
Each row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-evidence-drift-endpoint-signal-claim-language-certainty-narrative-break-v0.1.clinical-rtt-early-basin-shift-signal-detection-v0.1What this dataset tests
Whether early recovery signalspredict the later recovery topology.
It focuses on the first days, not weeks.
Early signals include
mobility trend
sleep fragmentation
HRV shifts
fatigue volatility
mood direction
Outputs
predicted recovery topology
basin-shift confidence
key early signals
Typical failures
ignoring early crashes
mistaking flat early signals for steady ascent
missing volatility that predicts oscillation
Suggested prompt wrapper… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-rtt-early-basin-shift-signal-detection-v0.1.clinical-quad-trial-pop-variance-realworld-subgroup-signal-generalization-claim-drift-v0.1What this repo does
This dataset models population mismatch narrative drift in clinical trial reporting. It predicts when the interaction between trial population variance, real-world variance, subgroup signal strength, and generalization claim intensity indicates that narrative claims extend beyond what the data supports.
Core quad
trial_population_variance_index
real_world_variance_index
subgroup_signal_strength_index
generalization_claim_index
Prediction target
label_claim_drift
Row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-trial-pop-variance-realworld-subgroup-signal-generalization-claim-drift-v0.1.aikyatansinha_s-and-p-500-institutional-signals-dataset
S&P 500 Institutional Signals Dataset
S&P 500 Institutional Derivatives: Vectorized Black-Scholes & Z-Score Analysis
Dataset Info
Source: Kaggle
Original Size: 28.60 MB
Kaggle Downloads: 8
Files: 1
Files
SP500_Institutional_Signals.csv
Mirrored from Kaggle