iiinova — where your digital self keeps evolving

Wajdi @wajdi.bsky.social · 29/09/2026

I'll be @neuripsconf.bsky.social in #Paris this year 🎉 my paper "Discovering Translation-Worthy Languages with E-Values" will be presented at the NeurIPS workshop "E-Values: From Statistics to ML" Greatful for my co-author Safa MADIOUNI and for her contribution and amazing work!

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4-Bit Daddy (After Dark) @xfourxbitxdaddy.bsky.social · 22/09/2026

Alright, so now my computer scans my bluesky, and maintains my "VIP Mutuals" list to keep me focused, auto-follows people that just popped up on my radar (unless banned), and auto-unfollows people that have gone radio silent (unless protected). Yeah, I'm a geek like that, old school and cyberpunk.

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GoldenViz @goldenviz.bsky.social · 05/08/2026

✨GoldenVizR v0.1.0 is now available on CRAN. review your ggplot2 visualizations against 25 rules covering completeness, readability, and integrity, and generate structured feedback and HTML reports. 🔽 install.packages("GoldenVizR") cran.r-project.org/package=Gold... #rstats #Dataviz #Tidytuesday

cran.r-project.org

GoldenVizR: Rule-Based Quality Checks for 'ggplot2' Visualizations Based on the 25 Golden Rules of Data Visualization

Quality-checking tools for reviewing R data visualizations built with the 'ggplot2' library against the 25 GoldenViz rules described at <<a href="https://goldenviz.org/" target="_top">https://golde...

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GoldenViz @goldenviz.bsky.social · 24/07/2026

🔹A chart should represent data honestly without misleading the reader. 🔹The Integrity principle covers : - truthful scales, - baselines, - uncertainty, - outliers, - proportional areas, - data selection, - granularity, - and neutral framing. 🟣 Nine rules from GoldenViz. goldenviz.org

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GoldenViz @goldenviz.bsky.social · 24/07/2026

🔹 A chart should be readable without unnecessary effort. 🔹 The Readability principle addresses: - typography, - accessible colors, clutter, scales, formatting, chart choice, direct labels, and visual hierarchy. 🟣 Ten rules from the GoldenViz framework. goldenviz.org

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GoldenViz @goldenviz.bsky.social · 24/07/2026

🔹 A chart should give readers everything they need to interpret it 🔹 The Completeness principle covers: -clear titles, - labelled axes, - missing data, - legends, - sources, - context, - and useful annotations. 🟣 Six rules from the 25 Golden Rules of Data Visualization goldenviz.org

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Wajdi @wajdi.bsky.social · 22/07/2026

It took a (long) time but it's finaly out! an #rstats adaptation of the 25 golden rules of data visualization is out ! Feedbacks and comment are welcome!

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Wajdi @wajdi.bsky.social · 17/07/2026

I've just seen this posted on reddit.. someone put it in their mailbox, encouraging people to switch to #Linux.. and I find it awesome! instead of getting marketing crappy flyers and catalogues, I'd rather open my mailbox to find this ! #OSS #OpenSource #LibreOffice #Flathub

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Georgios Karamanis @karaman.is · 15/07/2026

This week's #TidyTuesday is about the penguins in the AVONET database of bird traits (Tobias et al. 2022). Ten body measurements for 93 penguins, across 18 species and six genera Code: github.com/gkaramanis/t... #RStats #dataviz

A grid of small ridgeline plots titled "Measuring penguins". It compares ten body measurements across six penguin genera. The genera run down the rows: Aptenodytes, Eudyptes, Eudyptula, Megadyptes, Pygoscelis and Spheniscus. The ten measurements run across the columns: beak depth, beak length at the culmen and at the nares, beak width, first secondary length, hand-wing index, Kipp's distance, tail length, tarsus length and wing length. In each panel, three smoothed curves show how the measurements are distributed by sex, with females in red, males in orange and unknowns in blue, and a small triangle marks each penguin. Eudyptes and Pygoscelis have the fullest data across all three groups. Eudyptula, Megadyptes and Spheniscus have much less, often just a few birds of unknown sex. Aptenodytes, the largest penguins, sit furthest to the right on wing and tail length.

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Wajdi @wajdi.bsky.social · 14/07/2026

here is this week’s #TidyTuesday visualization! 🐧 I explored how ten anatomical measurements vary across six penguin genera, combining individual specimens with medians and interquartile ranges. I gave it a light Antarctic field-atlas look as much as i could 😅 #dataViz #rstats

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Wajdi @wajdi.bsky.social · 14/07/2026

🎮 also made an interactive version of the chart Hovering over each pixel reveals the film, release date, scores, director and game publisher, while the level cards filter the films by decade.. Here’s a quick preview of how it works. 🎮 #TidyTuesday #RStats #DataViz

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Wajdi @wajdi.bsky.social · 14/07/2026

Still trying to catch-up! I finally finished my #TidyTuesday #Rstats #DataViz on "Films based on video games" 🎮📊 So for this one, I wanted to see whether these adaptations have actually improved over time. the median Rotten Tomatoes score rose from 17 in the 1990s to 51 in the 2020s

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Wajdi @wajdi.bsky.social · 10/07/2026

and here’s how the visualization came together, from a regular heatmap to the final UFC-inspired design... I gradually added the octagons, red palette, dark background, cage texture, typography, logo, and finishing details... #TidyTuesday #RStats #DataViz

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Wajdi @wajdi.bsky.social · 10/07/2026

I finally managed to finish this week’s #TidyTuesday visualization! the topic is something I’m familiar with, but it still took me a few hours and many iterations to make the chart look decent. Here it is: "INSIDE THE FINAL ROUND" : how UFC fights end across five rounds #RStats #DataViz

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LibreOffice @libreoffice.bsky.social · 08/07/2026

Our next major update is coming! #LibreOffice 26.8 will be released in August, with many new features, improvements and fixes. Help our community to test the first beta version: qa.blog.documentfoundation.org/2026/07/08/l... #foss #openSource #freesoftware

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Compute! Paris @computeparis.bsky.social · 07/07/2026

At Compute! we celebrate people who produce code that is accessible to all and maintain the software hundreds of us rely on every day. They do work most users never see, and the result is infrastructure anyone can build on, inspect, and improve.

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Wajdi @wajdi.bsky.social · 08/07/2026

and here’s how it came to life. This one took me longer than most plots, but I wanted the final map to feel thematic and to honor the legacy, languages, and heritage of the African continent... #TidyTuesday #DataViz #RStats #ggplot2 #Maps

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Wajdi @wajdi.bsky.social · 08/07/2026

Catching up on a January #TidyTuesday dataset: The Languages of Africa. I turned the continent into a hex-tessellated map, with each country split by language-family composition, sand texture & scattered letters background & borders to echo the theme visually #DataViz #RStats #ggplot2 #Maps

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Manish Datt @manishdatt.com · 07/07/2026

AI-assisted categorization of Nicknames. Dashboard: dataviz.manishdatt.com/posts/2671 #TidyTuesday #dataviz #Javascript

AI-assisted categorization of Nicknames from the UFC athletes and fight data.

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Denis Mongin @denismongin.bsky.social · 07/07/2026

ça y est. 2026 est passée 2ieme en sévérité cumulée de vague de chaleur depuis 1950. @sergezaka.bsky.social Quelques #dataviz depuis les données @infoclimat.fr pour illustrer cette folie ⬇️🧵 #1: sévérité cumulée des vagues de chaleur sur l'année. 2023 est encore devant, mais pas pour longtemps

Diagramme en barres horizontales : vagues de chaleur en France de 1959 à 2026, années classées par sévérité cumulée (somme des °C-jours au-dessus de 23,4°C). 2003 domine très largement, à environ 73 °C-jours. 2026 arrive 2e, à environ 57, devant 2022 et 2025 (~54). Suivent 2019 et 2006 (~36–38), puis une longue traîne d'années plus modérées jusqu'à 1959 (~4) tout en bas. Chaque barre est segmentée et étiquetée par mois (juin, juillet, août), indiquant quand la sévérité s'est accumulée. Les années records 2003, 2026 et 1976 sont en rouge.

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Wajdi @wajdi.bsky.social · 06/07/2026

at the Academy of Athens.. few days ago a blend of ancient inspiration and neoclassical splendor... #Greece 🇬🇷

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Wajdi @wajdi.bsky.social · 06/07/2026

@goldenviz.bsky.social started from a simple idea: before making charts more beautiful, we should first make them more useful, easier to read, and more honest. That’s why the whole book and library are built around 3 principles: more here: goldenviz.org #DataViz #TidyTuesday

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Deepali Kank @deepalikank.in · 06/07/2026

#TidyTuesday week 26 - Wreck Inventory of Ireland Built with #Rstats Quarto, #D3js and a help with AI. #DataViz

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Calle Börstell @cborstell.bsky.social · 22/09/2025

Which countries have must rated chess players per age group? ♟️ India rising to the top in the youngest age groups. #TidyTuesday github.com/borstell/tid... #R4DS #DataViz #ggplot2

A plot titled "FIDE chess players by country & birth year: Ranking of the top International Chess Federation (FIDE) countries by the number of rated players (Elo rating ⩾1400) per age group (year of birth). Number of players shown under each flag (percentage of age group in brackets). Numbers under the flags show the number of players (with percentage of totals in brackets)". The plot resembles a chessboard, with a grayish purple background and the rankings being displayed as country flags on top of the chessboard's squares. In the oldest age brackets (left side), European countries are dominating with Germany, Spain and France having the most players. On the right side with the younger age groups, India is quickly rising to the top, in the youngest age group (2010—2021), Sri Lanka is also up-and-coming. Data: FIDE (September 2025) via TidyTuesday; Packages: {ggtext, tidyverse}; Visualization: C. Börstell

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Jessica Moore @jessimoore.bsky.social · 24/09/2025

#tidytuesday and #chess data is my favourite combo

Scatterplot showing rating change (from August to September 2025) of FIDE-rated chess players. Rating gain and loss both peak before the age of 20.

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Compute! Paris @computeparis.bsky.social · 26/06/2026

⏳ Early Bird Tickets End This Sunday! Don’t miss out! The Early Bird sale for Compute! Paris 2026 ends on Sunday, June 28th. Grab your discounted tickets now and save your spot at the first edition of this exciting event. compute.events/paris2026/ti...

compute.events

Tickets — Compute! Paris 2026

Get your ticket for Compute! Paris 2026. Two days of open source computation and data science. November 25–26 at Sorbonne Université.

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Wajdi @wajdi.bsky.social · 05/07/2026

Catching up on previous #TidyTuesday data challenge: "One Million Digits of Pi" Here, I turned the first 3,141 digits of π into a spiral... Each digit is printed in place, with prime digits shown larger to reveal the hidden radial ridges in the sequence... #DataViz #RStats #ggplot2 #Pi

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Wajdi @wajdi.bsky.social · 03/07/2026

I couldn’t settle on one chart, so I’m posting the 4 attempts... This week’s #TidyTuesday explores Ireland’s Wreck Inventory: mapped wreck density, seasonal patterns, and words used to describe loss at sea. #DataViz #RStats #ggplot2

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Mickaël CANOUIL, Ph.D. @mickael.canouil.fr · 03/07/2026

Let's dive into ships wrecks for this week TidyTuesday with Gribouille for Typst. #Typst #Gribouille #TidyTuesday

A diverging bar chart styled as an underwater cross-section of Irish shipwrecks by decade from 1750 to 2010. A pale surface at the top deepens through blue into the dark deep at the bottom. A waterline at zero splits each decade into wrecks that have been located, rising above it in bright teal, and wrecks with no recorded position, sinking below it in steel blue. Before 1840 almost every column plunges into the deep, peaking in the 1850s with 1,382 lost, 96 percent never located. From the 1910s the found share climbs sharply: the vermillion-outlined World War One column is the turning point, with about half of that decade's wrecks found (630). After 1950 the small columns sit almost entirely above the waterline, nearly every wreck charted. A small sinking-ship motif rests in the deep bottom-right corner.

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Nicola Rennie @nrennie.bsky.social · 02/07/2026

An experimental chart for #TidyTuesday this week on the topic of shipwrecks around Ireland! Putting time on the y axis to create a very long static scrolly chart with lots of annotations 📊 ft. bubble effect for points #RStats #ggplot2 #DataViz

Vertical dot plot showing shipwreck records from the Wreck Inventory of Ireland Database by year (1300–2020) and vessel type (Boat, Brigantine, Smack, Sloop, Ship, Barque, Brig, Steamship, Schooner, Other). Each dot represents a known or potential shipwreck with a known date and classification. A few early wrecks from the 1300s–1600s are annotated with historical events, followed by sparse records until numbers increase from the 18th century onward. Shipwrecks become much more frequent during the 19th century across most vessel types, with especially dense clusters for ships and other vessels. Steamship and submarine wrecks increase markedly around the periods of World War I and World War II. The chart's main message is that while weather and warfare caused shipwrecks throughout history, recorded wrecks rise substantially from the 18th century, peaking during the age of sail, steam, and the two World Wars.

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Georgios Karamanis @karaman.is · 01/07/2026

This week's #TidyTuesday maps shipwrecks off Ireland, from the Wreck Inventory of Ireland Database Code: github.com/gkaramanis/t... #RStats #dataviz

A map titled "Shipwrecks off Ireland" showing the island of Ireland on a blue sea shaded by depth. Thousands of dark red dots mark recorded shipwrecks. They cluster most densely along the east and south coasts and in the north, thin out along the west coast, and continue as a sparse, widely spaced scatter far out into the Atlantic. A pale hatched band marks Ireland's maritime boundary, curving out to the west and south. The seas are labelled in italics: Atlantic Ocean to the west, Irish Sea to the east, Celtic Sea to the south. Ports are marked with star symbols and labelled: Dublin, Cork, Waterford, Shannon Foynes and Rosslare, with Derry (Foyle Port) and Greencastle in the north. A callout box in the lower right points to a wreck east of Dublin and reads: "RMS Leinster, 1918. Torpedoed by the German submarine UB-123, she sank with the loss of more than 500 of those aboard, the Irish Sea's worst single loss of life. The wreck now lies buried in sand about 25 metres down."

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Georgios Karamanis @karaman.is · 23/05/2026

This week's #TidyTuesday is about Crossref metadata quality. I made a table showing how well the top 20 countries by journal article volume document their research Made with {gt} Code: github.com/gkaramanis/t... #RStats #dataviz

A table titled "Research Nexus Readiness" showing Crossref metadata coverage for the top 20 countries by journal article DOI volume as of April 2026. Countries are grouped by world region and each row displays a country flag, country name, total DOI count in millions, and percentage of articles with references, abstract, an author ORCID ID, funding acknowledgment, funder ID, and license metadata. The percentage columns are shaded with a blue color scale, darker means higher coverage. The United States leads with 64.71 million DOIs. Stark differences are visible: reference coverage is generally high, while ORCID and license metadata rates are much lower across the board.

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Georgios Karamanis @karaman.is · 06/06/2026

This week's #TidyTuesday looks at maternity leave across Europe: How many weeks before and after birth, and how much is mandatory vs voluntary. Ireland leads at around 40 weeks total, the UK at just 2.

A horizontal diverging bar chart showing maternity leave before and after childbirth for 21 European countries in 2024. Each country has two bars extending from a central vertical line: blue-grey bars to the left show weeks before birth, orange bars to the right show weeks after. Within each bar, a crosshatch pattern marks the mandatory portion; the remaining outlined space is voluntary. Countries are sorted by total leave. Ireland is by far the longest at around 42 weeks total, with a large voluntary after-birth portion. Slovakia and Czechia follow at around 28 weeks each. The UK is the shortest at 2 weeks. Several countries, including Germany and Poland, have no before-birth leave at all. The chart title reads "Maternity leave in the EU, 2024".

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Ansgar Wolsing @ansgarw.bsky.social · 14/05/2026

Heute ist Vatertag - und mit großer Wahrscheinlichkeit der Tag mit den meisten Verkehrsunfällen unter Alkoholeinfluss in diesem Jahr. Daten: Statistisches Bundesamt Tool: R und #ggplot2 Also: Don’t drink and drive - außer im Bollerwagen.

Ein Diagramm, das die tägliche Anzahl der Verkehrsunfälle unter Alkoholeinfluss mit Personenschäden in Deutschland 2020-2024 in Kacheln, die nach Wochentag (senkrecht) und Kalenderwoche (waagerecht) angeordnet sind, anzeigt. Die Färbung der Kacheln gibt die Anzahl an. Hellorange = geringe Anzahl, dunkelrot = hohe Anzahl

Für jedes Jahr gibt es eine separate Darstellung.

In allen Jahren (außer 2021) erscheint ein Donnerstag im Mai mit der dunkelsten Farbe: Christi Himmelfahrt. Außerdem fällt Neujahr in jedem Jahr sowie der 1. Mai in den Jahren 2023 und 2024 auf.

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Byte Charts @byte-charts.dev · 30/06/2026

The spike in that #accidentalart meant something! Taking a closer look at the deadliest day in Ireland's shipwreck history for this week's #TidyTuesday. Made in #ggplot2, greatly inspired by @ansgarw.bsky.social past work. Edited in Figma for the final highlights. #RStats #DataViz

Calendar heatmaps show the distribution of Irish shipwrecks by day of the year, with each panel representing a century compressed into a single
calendar year. The standout date is 9 February in the 19th century, with 161 recorded wrecks. An accompanying bar chart shows that this spike is driven by the Great Gale of 9 February 1861, which caused the largest number of shipwrecks recorded on a single day in the dataset.

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GoldenViz @goldenviz.bsky.social · 01/07/2026

Before publishing a chart, ask 3 questions: 1. Is it complete? 2. Is it easy to read? 3. Can the reader trust it? That’s the idea behind this GoldenViz cheat sheet: 25 practical rules to make charts clearer, cleaner, and more honest. #DataViz #Python #TidyTuesday #DataScience

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Rajo @rajodm.bsky.social · 29/06/2026

A simple map for #TidyTuesday 2026 week 26 on Ireland shipwrecks. Code: github.com/rajodm/TidyT... #dataviz #rstats #ggplot2

Map of Ireland and surrounding waters on a dark blue bathymetric background, with 3,564 yellow-green points marking shipwreck locations from 1550 to 2017 clustered along the coastline and scattered across the continental shelf.

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Steven Ponce @sponce1.bsky.social · 29/06/2026

📊 #TidyTuesday – 2026 W26 | Wreck Inventory of Ireland . 🔗: stevenponce.netlify.app/data_visuali... . #rstats | #r4ds | #dataviz | #ggplot2

Line chart titled "Ireland's Shipwrecks Chronicle the End of the Age of Sail." The x-axis spans 1800 to 1940 by decade; the y-axis shows the share of classified wrecks from 0% to 100%. A blue line representing sail-powered vessels starts near 97% in 1800 and declines gradually, then steeply, crossing below 50% during the 1910s before reaching 4% by the 1940s. A burgundy line representing steam and motor vessels begins near 3% and rises slowly through the 1880s, accelerating sharply after 1900 to reach 96% by the 1940s. The two lines cross between 1910 and 1920, marking the decade when steam became the dominant vessel type in Irish waters. Among records with known vessel type, approximately 6,500 of 17,981 total; unknown classification excluded. Source: Wreck Inventory of Ireland, National Monuments Service.

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Mitsuo Shiota @mitsuoxv.bsky.social · 30/06/2026

My submission for #TidyTuesday, Week 26 on Wreck Inventory of Ireland. I explore number of wrecks by month. Code: github.com/mitsuoxv/tid...

Bar charts faceted by in or out of the World Wars (I and II); x-axis is months from 1 to 12, and y-axis is number of wrecks. Bars are filled with ship types.

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Joachim Schork @joachimschork.bsky.social · 25/06/2026

Recently found this excellent R best practices cheat sheet by Jacob Scott. You can find the original cheat sheet here: github.com/wurli/r-best... #rstats #datascience #programming #statistics #rprogramming

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Wajdi @wajdi.bsky.social · 23/06/2026

From Leo XIII to Leo XIV: shared words, different weight. For this week’s #TidyTuesday, I compared vocabulary in two papal encyclicals: Rerum Novarum (1891) and Magnifica Humanitas (2026). #DataViz #RStats #ggplot2

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Wajdi @wajdi.bsky.social · 23/06/2026

We need to bring back Open source knowledge as a central pillar of humanity's progress... Sharing knowledge, software, educational material..etc should never be behind a paywall!

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Compute! Paris @computeparis.bsky.social · 23/06/2026

Networking at Compute! Paris 2026 (Nov 25-26) isn’t about exchanging business cards, it’s about exchanging ideas. Connect with people who are solving the same problems as you, in ways you haven’t thought of. compute.events/paris2026/ #ComputeParis

compute.events

Compute! Paris 2026

Understanding your tools and algorithms is what gives you agency in a digital world. Paris, November 25–26, 2026.

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Wajdi @wajdi.bsky.social · 23/06/2026

it took me a while to polish this course/book, but I'm happy it finally came to light the 25 Golden Rules of Data Visulization 👇 #30DayChartChallenge #DataViz

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Wajdi @wajdi.bsky.social · 22/06/2026

Starting the educational communication with @goldenviz.bsky.social library... they are 25 rules, and now we start with just 5 ! Learn more about the library and the book 👇 goldenviz.org/en/ #30DayChartChallenge #DataViz #DataScience #DataAnalytics

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Wajdi @wajdi.bsky.social · 21/06/2026

📣 I’m happy to share the first stable release of GoldenViz: v0.1.1. This project started from my teaching: I wanted a more systematic way to help students review charts, explain what is wrong, and connect feedback to clear data visualization principles. goldenviz.readthedocs.io

goldenviz.readthedocs.io

GoldenViz documentation — GoldenViz 0.1.1 documentation

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Wajdi @wajdi.bsky.social · 18/06/2026

Walking around Rennes, capital of Bretagne 𝍎𝌆 🇫🇷 I stepped into the "Musée des Beaux-Arts" (mba.rennes.fr) and stumbled upon 3 Picasso masterpieces in the most unexpected setting... and stumbled upon 3 Picasso masterpieces in the most unexpected setting....

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Lisa Hornung @lisahornung.bsky.social · 27/05/2026

I’m finally wrapping up the #30DayChartChallenge. I managed to create 11 visuals (not 30 but more than I ever did). I mostly wanted to uplevel my #Svelte + #D3 skills for interactive charts. Of course I used AI, but mostly as a coding buddy not for vibe coding. Link to project below 👇

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Lisa Hornung @lisahornung.bsky.social · 26/05/2026

Accessing cash is getting harder. The number of ATMs has declined substantially in almost all Local Authorities in England in the past five years. Across the UK ATM numbers have fallen continuously since 2015. 💷 📉 One last #Dataviz for #30DayChartChallenge Day 11 | Physical. Made in Svelte + D3

Horizontal beeswarm plot showing percentage change in ATM counts by local authority from June 2021 to Jan 2026, with regions listed vertically (London to Yorkshire & H). Each dot marks a local authority’s percent change on a −50% to 0% axis; dot colour encodes ATMs per 1,000 people from yellow (0.1) to blue‑green (>1.2). Most dots cluster around −30% to −20%. Main takeaway: ATM access has declined across all regions, with most local authorities losing about 20–30% of machines, reducing cash availability.

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