Climate Fact DB

A structured, queryable climate science knowledge base with denial debunks, prediction tracking, lobbying exposure, and bias flagging.

222 entries | 1,742 key facts | 199 denial responses | 191 tracked predictions | 100 lobbying exposures | 115 bias flags

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Why This Exists

Most climate data online falls into one of these traps:

• Raw datasets (NASA, NOAA) that require scientific literacy to interpret
• Academic papers that are paywalled and written for other academics
• Activist sites that cherry-pick and editorialize
• Wikipedia that's surface-level and avoids "taking sides" even when the science is one-sided

This database is different. It's a structured JSON knowledge base designed for:

• Journalists who need instant, sourced facts on deadline
• Educators who need clear explanations with evidence strength ratings
• Fact-checkers who need denial claims mapped to responses
• Content creators who need accurate science without having to read 100 papers
• Developers who want to build climate tools on top of structured data

What Makes This Unique

1. Denial Response System

Every major climate denial claim mapped to its factual response with evidence sources and strength ratings.

{
  "claim": "CO2 has been higher before, so current levels aren't a problem",
  "response": "During events like the PETM (56 million years ago), high CO2 caused mass disruption. The issue isn't the level alone but the RATE of change. Current release rate is 10x faster than the PETM and unprecedented in 66 million years.",
  "evidence": ["Zeebe et al. 2016 Nature Geoscience", "Gutjahr et al. 2017"],
  "strengthOfEvidence": "overwhelming"
}

2. Prediction Tracking

Climate predictions from scientists and bodies tracked against actual outcomes.

{
  "prediction": "Global temperature will exceed 1.5C above pre-industrial levels",
  "predictedBy": "IPCC AR6",
  "predictedFor": "2030-2035",
  "accuracy": "on-track"
}

3. Lobbying Exposure

Documented fossil fuel industry actions with evidence sources.

{
  "entity": "ExxonMobil",
  "action": "Internal climate research accurately predicted warming while publicly funding denial",
  "evidence": "Supran et al. 2023 Science — Exxon's own models predicted warming with 'ichilling accuracy'",
  "impact": "Delayed public understanding and policy action by decades"
}

4. Bias Flagging

Unlike most climate resources, this database flags potential bias — including from pro-climate sources.

{
  "flag": "doom-exaggeration",
  "note": "Presents worst-case scenarios as certainties rather than possibilities. Scientific content is accurate but framing skews toward hopelessness."
}

Topics Covered

Topic	Coverage	Entries
Climate Fundamentals	CO2 physics, temperature records, ECS, attribution	Strong
Oceans	Sea level, AMOC, coral, acidification, deoxygenation	Strong
Tipping Points	AMOC, Greenland, permafrost, Thwaites, Amazon, cascades	Strong
Fossil Fuel Lobbying	Exxon, Koch network, think tanks, dark money, greenwashing	Strong
Predictions vs Reality	Hansen, IPCC projections, "faster than expected" pattern	Strong
Biodiversity Loss	Extinction rates, insect decline, 6th extinction, habitat fragmentation	Strong
Scientific Methodology	GCMs, CMIP6, IPCC process, attribution science, RCP/SSP scenarios	Strong
Data Sources	Five temperature datasets, Keeling Curve, ice cores, Argo	Strong
Extinction History	Big Five mass extinctions, PETM, Permian deep dive	Strong
Planetary Boundaries	Rockstrom framework, 6 of 9 crossed, novel entities	Strong
Aerosol Masking	Global dimming, IMO 2020, unmasking risk, geoengineering	Strong
Water Crisis	Groundwater depletion, Ogallala, Colorado River, glacial melt	Strong
Methane	80x potency, satellite detection, permafrost, livestock, leaks	Strong

Data Sources

195 entries extracted from curated YouTube science channels (+ 27 research-fill entries):

• potholer54 — Denial debunking, source citation
• Climate Town — Corporate lobbying, greenwashing
• Count Everything — Data-heavy impacts
• Just Have a Think — Science + solutions
• Sabine Hossenfelder — Physicist rigor
• Paul Beckwith — Tipping points, AMOC
• Nate Hagens — Systems thinking, limits
• Nature Bats Last — Extreme scenarios (bias-flagged)
• Simon Clark — Atmospheric physics
• PBS Terra — Journalism

27 research-fill entries compiled from peer-reviewed literature to close coverage gaps: Mass extinctions, PETM, planetary boundaries, Thwaites glacier, temperature datasets, GCMs, ocean acidification, Amazon dieback, Koch dark money, habitat fragmentation, ecosystem services, IPCC process, cascading tipping points, attribution science, RCP/SSP scenarios, aerosol masking, water crisis, methane, nuclear vs fossil death toll, health mortality from pollution, storm intensification, ocean-atmosphere CO2 dynamics, biodiversity economic value, jailed UK climate protesters, corporate profit vs public health.

Schema

Each entry contains some or all of:

Field	Type	Description
source	string	Source title
channel	string	YouTube channel or "Research compilation"
expert	string	Primary expert/presenter
publishDate	string	Source publication date
url	string	Source URL
summary	string	2-4 paragraph overview
keyFacts	string[]	Specific, verifiable facts with numbers
tags	string[]	Searchable topic tags (848 unique)
denialResponses	object[]	Claim + response + evidence + strength
predictions	object[]	Prediction + source + timeline + accuracy
lobbyingExposures	object[]	Entity + action + evidence + impact
biasFlags	object[]	Flag type + explanation

Quick Start

The entire database is a single JSON file: climate-fact-db.json

Load in Python

import json

with open('climate-fact-db.json') as f:
    db = json.load(f)

entries = db['entries']

# Find all denial responses about temperature
for entry in entries:
    for dr in entry.get('denialResponses', []):
        if 'temperature' in dr.get('claim', '').lower():
            print(f"CLAIM: {dr['claim']}")
            print(f"RESPONSE: {dr['response']}")
            print(f"EVIDENCE: {dr['evidence']}")
            print()

Load in JavaScript

const db = require('./climate-fact-db.json');

// Find all predictions that are "on-track"
const onTrack = db.entries
  .flatMap(e => e.predictions || [])
  .filter(p => p.accuracy === 'on-track');

console.log(`${onTrack.length} predictions currently on-track`);

Search by Tag

# Find all entries about AMOC
amoc = [e for e in entries if 'AMOC' in e.get('tags', [])]
print(f"{len(amoc)} entries about AMOC")

Stats at a Glance

• 222 total entries
• 1,742 individual key facts
• 199 denial claim/response pairs
• 191 tracked predictions with outcome status
• 100 documented lobbying exposures
• 115 bias flags (including pro-climate source bias)
• 965 unique searchable tags
• 10 YouTube channels + 27 research-fill entries from peer-reviewed literature

Contributing

Found an error? Have a source to add? Open an issue or PR.

Guidelines:

• All facts must be specific and verifiable (numbers, dates, named sources)
• Include evidence strength ratings
• Flag potential bias — even from sources you agree with
• Use the existing schema format

License

This data is released under CC BY 4.0. You are free to use, share, and adapt it for any purpose with attribution.

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Built with VidBrainz research intelligence + targeted gap-fill research. Data extracted and structured using AI, verified against peer-reviewed sources.