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README

Awesome Data Journalism

An open source, open data and just open Data Journalism repository to learn and understand practical data journalism.

What is Data Journalism?
Handbooks & guides
Education & learning
Data sources
Data collection tools
Data cleaning
Data analysis
Data visualization
Interactive storytelling
Fact-checking & verification
Newsrooms & publications
Community & professional networks
Related resources
Quick reference: Data journalism workflow

What is Data Journalism?

This part is for humans who are new to Data Journalism.

Data journalism is the practice of using data to find, create, and tell news stories—through the systematic collection, analysis, and visualization of structured information to inform the public. Unlike conventional reporting that relies primarily on interviews and observation, data journalism integrates statistical reasoning, programming, and design into the storytelling process.

The practice rests on three interconnected pillars:

| Pillar | Focus | |--------|--------| | Data acquisition | Finding and extracting relevant datasets from diverse sources | | Data processing | Cleaning, transforming, and analyzing information | | Data presentation | Visualizing and contextualizing findings for audiences |

What is Data Journalism? — Data Journalism Handbook
Wikipedia: Data-driven journalism

Handbooks & guides

Foundational handbooks

The Data Journalism Handbook — Open-access guide with global case studies
The Investigative Reporter's Handbook — Data methods with traditional investigative techniques
CIJ Data Journalism Book — Centre for Investigative Journalism
The Functional Art — Alberto Cairo's foundational visualization theory
Data + Design — Introduction to data and design

Specialized manuals

Finding Stories in Spreadsheets — Paul Bradshaw's guide to spreadsheet analysis
Scraping for Journalists — Web extraction techniques with legal guidance
Data Journalism Heist — Leanpub
How Charts Lie — Critical guide to visualization pitfalls
Knowledge is Beautiful
The Information Capital
Organising an Online Investigation Team

Investigative methods

GIJN Guide to Data Journalism — Global investigative journalism resources
Verification Handbook — Data verification protocols and authentication
Facts are Sacred — The Guardian Datablog

Education & learning

Formal education

North America

Specialization in data @ Columbia Journalism School
Stanford Journalism Program — Data journalism and storytelling
Computational Journalism @ Georgia Tech — Technical depth with computer science

Europe

Data journalism @ City, University of London
Data Journalism MA @ Tilburg University
Data journalism @ Sciences Po Paris — Political/economic reporting

Russia & Asia

Data journalism, magister program @ HSE — High School of Economics, Russia

MOOCs and online learning

Doing Journalism with Data — European Journalism Centre MOOC (datajournalism.com)
Python for Journalists — Programming for data cleaning, analysis, and visualization
Learno.net data courses
Codecademy — Web API courses

Data sources

Government and international open data

World Bank Open Data — 200+ countries, 1,400+ indicators
UN Data — Multiple UN agencies, aggregated portal
HDX — Humanitarian data, real-time updates
US Government open data — data.gov
UK Government open data — data.gov.uk
Data.europa.eu — EU institutional and open data

Specialized databases and APIs

OpenCorporates — Company registries across 140+ jurisdictions
OpenSecrets — U.S. campaign finance and lobbying
NASA Earthdata — Satellite imagery and climate variables
Global Forest Watch — Near-real-time forest change data
IPUMS — Harmonized census microdata across countries
Pew Research Center — Download datasets — Public opinion and social trends
Epstein Exposed — Searchable database of Jeffrey Epstein DOJ case files (full-text search, network graph, REST API)

Data collection tools

Guides and tutorials

Scraping for Journalism: A Guide for Collecting Data — ProPublica
Making data on the web useful: scraping — School of Data
HTML Scraping Python Guide with lxml
Beginner's guide to Web Scraping in Python using BeautifulSoup
A Guide to Web Scraping Tools

Browser-based and no-code scrapers

Web Scraper — Chrome extension, point-and-click, pagination, CSV/JSON export
Data Miner — Chrome extension and cloud, recipes, scheduling
ParseHub — Turn dynamic websites into APIs
Diggernaut — Turn website content into datasets
Chrome Scraper extension — Simple browser scraper

Programming libraries

Python: Beautiful Soup + Requests, Scrapy, Selenium, Playwright
R: rvest, RSelenium

PDF and document parsers

Tabula — Extract tables from PDFs
Camelot — Python PDF table extraction
Amazon Textract — ML-based OCR and form recognition

Data cleaning

OpenRefine — Dedicated data cleaning; faceted browsing, clustering, GREL, reconciliation
CSV Lint — Validate CSV against standards
GoodTables — Data quality validation, type inference, range checking
Spreadsheet tools: Microsoft Excel (Power Query, pivot tables), Google Sheets (collaboration, API), LibreOffice Calc, Trifacta Wrangler — Cloud-based transformation suggestions

Data analysis

Python ecosystem

pandas — Data manipulation with DataFrames
NumPy — Numerical computing
matplotlib / seaborn — Statistical visualization
scikit-learn — Machine learning
statsmodels — Statistical modeling and hypothesis testing
NLTK / spaCy — Natural language processing

R ecosystem

tidyverse — dplyr, tidyr, readr, purrr
ggplot2 — Grammar of Graphics visualization
shiny — Interactive web apps
sf — Spatial data
tidytext — Text mining

Notebooks and interactive computing

Jupyter / JupyterLab
Quarto — Multi-format publishing (documents, presentations, sites)
Google Colab — Free GPU/TPU, Google Drive
Kaggle Notebooks — Competitions and datasets
Observable — JavaScript-native reactive notebooks
Posit (RStudio) — R environment with Quarto and Shiny

Data visualization

Online chart and graph builders

Datawrapper — Accessibility, responsive design, journalistic defaults
Flourish — Animation, storytelling, 3D, templates
RAWGraphs — Complex chart types (alluvial, voronoi, sunburst), SVG export
Tableau Public
Google Looker Studio — Dashboards, 500+ connectors
Canva, Piktochart, Venngage, Infogram
Plotly, Charted, Data Illustrator

Timelines

Timeline JS — Knight Lab
Preceden, Tiki-Toki, Hstry

Code-based visualization libraries

D3.js — Data-Driven Documents; maximum flexibility
Vega-Lite — Declarative grammar
Observable Plot — Grammar-based, concise
Chart.js — Lightweight, responsive
ECharts — Apache; performant, extensive options
ggplot2 (R), Matplotlib (Python), Bokeh (Python)
Highcharts, amCharts, r2d3
RAW — RAWGraphs predecessor; export SVG
Opendata-tools visualization list
Chartmaker — comparison of data visualisation tools
Periodic table of Visualization

Interactive storytelling

Scrollytelling and narrative platforms

Scrollama — JavaScript library for scroll-driven narratives
Idyll — Reactive markup for narrative development
Shorthand — Hosted platform for longform and team collaboration
ArcGIS StoryMaps — Map-centric narratives
The Data Board — Semantic CSV-to-narrative discovery

Immersive and 3D

Three.js — WebGL-based 3D for globes and scenes
A-Frame — WebXR/VR with HTML-like markup

Audio and video

Whisper (OpenAI) — Transcription, multilingual, local deployment
Descript — Text-based audio/video editing, Overdub, collaboration
Remotion — Programmatic video with React

Annotation and diagramming

Excalidraw — Hand-drawn style diagrams, collaborative
Figma — Design systems, newsroom workflows
Miro / Mural — Collaborative whiteboards
FigJam — Figma-integrated whiteboarding

Fact-checking & verification

Image and media verification

Google Images, TinEye, Yandex Images — Reverse image search
ExifTool — Metadata extraction (camera, GPS, editing)
Forensically — Error level analysis, clone detection
Microsoft Video Authenticator, Sensity — Deepfake detection (human judgment and source verification remain essential)

Claim and source verification

Google Fact Check Tools — Aggregated fact-checks, API
Duke Reporters' Lab — Fact-checking database
Bellingcat Toolkit — Open-source investigation techniques
Check (Meedan) — Collaborative verification, claim documentation

Data integrity and provenance

DocumentCloud — Document upload, OCR, annotation, publication
SecureDrop — Secure anonymous source communication
OpenTimestamps / OriginStamp — Timestamping for tamper-evident documentation

Newsrooms & publications

Data journalism desks and outlets

The New York Times — The Upshot, Graphics Desk, R&D Lab
The Guardian — Datablog, Visuals team
ProPublica — Data and Research; open methodology
NPR Visuals — Audio-centric innovation, accessible design
Vox — Storytelling Studio, explainers
The Pudding — Visual essays, experimental formats

Industry and academic publications

Data Driven Journalism
Source (OpenNews) — Newsroom technology practice
Nieman Lab — Journalism innovation
Columbia Journalism Review
Digital Journalism, Journalism Practice — Peer-reviewed
A short list of online articles and references on data journalism
How to get started with GitHub for Dummies Journalists
Journalism and New media — CARTO

Research centers and institutes

Tow Center for Digital Journalism (Columbia), Knight Lab (Northwestern), Stanford Computational Journalism Lab
OpenNews — SRCCON, fellowships, Source
GIJN — Global Investigative Journalism Network
ICIJ — International Consortium of Investigative Journalists

Community & professional networks

Social media and hashtags

Twitter/X: #datajournalism, #ddj, #infovis — Guardian Data, Data Journalism Blog, Simon Rogers, Paul Bradshaw, Daten Journalist
Facebook: Data Driven Journalism, Data journalism blog
LinkedIn: Data Journalism and Investigative Journalists groups

Discussion platforms and associations

IRE — Investigative Reporters and Editors — NICAR conference, training, resource library
SND — Society for News Design — Design and visualization awards
ONA — Online News Association
Data Visualization Society — datavisualizationsociety.com
Hacks/Hackers — hackshackers.com — Journalist–technologist meetups
News Nerdery (Slack), Data Visualization Society (Slack) — Invitation or membership-based
Global Data Journalists Directory
MaryJo Webster's training materials

Conferences and events

NICAR (IRE) — Premier data journalism conference, U.S.
Dataharvest — European investigative journalism, Belgium
International Journalism Festival — April, Perugia, Italy
CIJ Summer School — July, London, UK
Malofiej — Infographics and visualization, Spain
Hacks/Hackers chapters — 80+ cities; DVS regional events; national journalism association tracks

Related resources

Other awesome lists

awesome-awesomeness
awesome-datascience — Data science resources
awesome-machine-learning
awesome-dataviz
awesome-d3 — D3.js resources
awesome-python
awesome-R
awesome-public-datasets
awesome-opendata-rus — Open data in Russian
lists
Data Science IPython Notebooks

Curated datasets and tool directories

ProPublica Data Store
FiveThirtyEight Data
BuzzFeed News GitHub — Investigative data and replication
Kaggle Datasets
Google Dataset Search
Dateno — Dataset search engine; 22+ million open datasets across 5,000+ catalogs worldwide
Journalism Tools, Data Journalism Tools
Source Guides — OpenNews

Quick reference: Data journalism workflow

| Step | Question | Key resources | |------|----------|----------------| | Learn | What skills do I need? | Data Journalism Handbook, Doing Journalism with Data, NICAR training | | Find | Where is the data? | Government portals, FOIA, Data sources | | Clean | Is the data reliable? | OpenRefine, spreadsheets, validation | | Analyze | What patterns emerge? | Python/pandas, R/tidyverse, Jupyter, statistics | | Visualize | How do I show findings? | Datawrapper, Flourish, D3.js | | Publish | How do I tell the story? | Scrollytelling, interactive dashboards, Interactive storytelling | | Verify | Can others trust this? | Documentation, data publication, methodology transparency, Fact-checking |