GIJN’s Data Journalism Top 10: Dear Abby Data, South Africa’s Pit Toilets, The Economist’s Inequality, Politico Goes Open Source

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from November 19 to 25 finds society’s most urgent concerns in the textual data of a long-standing @dearabby column, a kerfuffle over @The Economist’s regional inequality graph, dangerous pit toilets in Africa highlighted by @SECTION27news and a gift of sorts from @politico, who open sourced their elections data management system.

GIJN’s Data Journalism Top 10: Stories with Maps, Tools with Charts, Casting for Shakespeare

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from October 15 to 21 finds @Esri’s @AllenCarroll talking the power of maps in storytelling, @visualizingdata’s catalogue of charts and corresponding tools to help information designers, and @ericwilliamlin’s deep dive into the data of casting decisions in Shakespearean plays since the 1900s.

Document of the Day: DataViz Cheatsheet

Economist Jonathan Schwabish created a handy data visualization cheatsheet with straightforward key principles to adhere to when creating data visualizations. Remember: avoid 3D, make labels easy to read and try small multiples.

GIJN’s Data Journalism Top 10: Muslims to Mecca, Women (Not) in Netflix, Inside Airbnb Europe, London’s Foul Air

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from Aug 20 to 26 finds @Numerama analyzing the representation of women in @netflix series and finds it lacking, @AJEnglish creating an interactive explainer on how 2 million Muslims make the pilgrimage to Mecca every year, @FT readers brainstorming air pollution solutions for London, and @Bastamag digging into data of @Airbnb — a cash cow for home renters.

GIJN’s Data Journalism Top 10: Egyptian Bots, Pocket Inequality, Loner Jobs and Knife Emergencies

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from Aug 13 to 19 finds @puddingviz proving that women’s pockets are inferior to men’s once and for all, @vizzuality pondering the impact cartographers have on our understanding of the world by presenting nocturnal activity in daytime maps, @InfoTimes_ discovering the bots behind the political debate in Egypt and @hnrklndbrg’s visualizations on everything from loner jobs to knife emergency room visits.

GIJN’s Data Journalism Top 10: Dam Disaster in Laos, Global Star Gazing, Why We Love Pie Charts

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from July 23 to 29 finds @NadiehBremer visualizing beautiful constellations imagined by different cultures, @mslima diving deep into why we love pie charts, @leigh_tami18 explaining the various methods of joining datasets and the Reuters Graphic’s team visualization of the dam disaster in Laos.

GIJN’s Data Journalism Top 10: Nike’s Vaporflys, Trump’s Trade War, FastCharts, Datapasta

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from July 16 to 22 finds @UpshotNYT confirming Nike’s claim that their Vaporflys really do give runners an advantage, @FinancialTimes visualizes Trump’s escalating trade war, Britain’s digital divide and introduces their new charting tool and @MilesMcBain makes some data journalists very happy by creating Datapasta.

This Week’s Top 10 in Data Journalism

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from March 19 to 25 finds a sobering study on income inequality between black and white males visualized by @nytimes, a cool time-lapse graphic of snow fall in the United States by @PostGraphics and peak baby-making seasons by country by @VismeApp and @ddjournalism.

This Week’s Top 10 in Data Journalism

What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from February 12 to 18 finds @MattLWilliams discussing the ethics of publishing Twitter content, @MaryJoWebster explaining several common “dirty data” problems and @MediaShiftOrg showing examples of the powerful impact of small data teams in newsrooms.