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#datavis

2 posts2 participants0 posts today
Public
Marian Dörk

“To visualize with care is to visualize with empathy: to make visible environmental harm, surface suppressed narratives, reveal shared experiences, and confront the structural biases that too often remain hidden.”

nightingaledvs.com/visualizing

by @damlacay @dario and Weidi Zhang - general chairs of this year’s VIS Arts Program @visap:

visap.net/2025/ (submissions due 20 June)

Nightingale · Visualizing as a Form of Collective Care, NightingaleCare is easy to recognize on a personal level, especially when it takes the form of small, attentive gestures woven into daily life.
#datavis#art#care
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UCLAB Potsdam

👋 Moien Luxembourg, we're happy to be at EuroVis 2025! 📊

@markjan will give a long presentation about »Fluidly revealing information: A survey of un/foldable data visualizations» - Wednesday morning. Check out the survey browser here (⚠️: lots of GIFs): uclab.fh-potsdam.de/unfoldable

Theresa Eingartner will give a short talk about »Inflecting data visualizations for web-based scrollytelling« - Thursday afternoon. See it in action here: uclab.fh-potsdam.de/inflect/

Screenshot of survey browswer with un/foldable visualizations. On the left side faceted navigation for the classification. In the main part of the interface thumbnails and project titles and references.
Four diagrams - bar chart, pie chart, line chart, and scatterplot - about visualization conferences, each chart with pink highlights.
#DataVis#DataViz#EuroVis2025
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Ele Willoughby, PhD

Happy birthday to founder of modern nursing, social reformer, statistician, data visualization innovator and writer Florence Nightingale (1820 – 1910)!⁠
Nightingale earned the nickname “The Lady with the Lamp” during the Crimean War, from a phrase used by The Times, describing her as a “ministering angel” making her solitary rounds of the hospital at night with “a little lamp in her hand”. 🧵
⁠
#linocut #printmaking #sciart #womenInSTEM #datavis #nursing #statistics #mathart #histsci #mastoArt

My linocut portrait of Florence Nightingale in blue with her oil lamp in gold with orange flame with her rose diagram of mortality for 1855 and 1856 clearly showing that lack of hygiene was the biggest killer of those injured in the Crimean War
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sport of sacred spherical cows

Updated #CDC estimates show we'd pretty much been in a JN.1.11 soup since Dec, until late March, when LP.8.1 took majority.

Data collection continues to be low priority nationally—as exactly zero regions have enough data for CDC to plot.

CDC breaks out recombinant XFC from FLiRT parent LF.7 . (Our dataviz now identifies parentage for each recombinant.) Raj's dashboard was last updated today.

❖ #ThisIsOurPolio #Covid #Covid19 #SARS2 #variants #CovidIsNotOver #CovidIsAirborne #dataviz #datavis

Chart: Estimated U.S. Variant Proportions by Common Name Sources: CDC, Cov-Lineages, NYITCOM, D. Focosi, WHN, others [ beadsland on Ko-fi ] Reskin of CDC's Variants Nowcast, and any significant variants in GISAID not broken out by CDC. Five bar-style tree-charts, for fortnights through 4/27–5/10. Legend of last fortnight, organized by subheadings of color-grouped families and convergent clusters. Percentages overlay each color key, reflecting share as of most recent tree-chart. LP.8.1 accounts for over two thirds. For 4/13–4/26, packed bubble charts fill single-variant tiles, reflecting GISAID detail not shown by CDC's Nowcast. Legend: JN.1.11 + FLiRT31 [purple]: 70% - LP.8.1.1 / NY†, LP.8.1.8† & other LP.8.1 JN.1 + FLiRT-LF [browns]: 9% - XFC 3% - LF.7.7.2 2% - XFG† & other LF.7 2% - LF.7.7.1 & LF.7.2.1 / PC JN.1 + FLiRT [reds]: 2% - LB.1.3.1 / NL 1% - XEC.4 ½% - XEQ 6% - other XEC JN.1.11 [greens]: 3% - MC.10.1 & PA.1 ⅓% - KP.3 2% - MC.1, MC.19 & other KP.3.1.1 / MC JN.1.11 + FLiRT31 [blue]: ⅓% - XEK Other [greys]: ⅐% - NB.1.8.1†, JN.1.16 & other WHO-Vaccine-Target JN.1 ⅒% - Other (not specified) _____ XEC is a child of FLiRT cluster KS.1.1 and FLuQE cluster KP.3.3. XEK is a child of FLiRT31 cluster KP.2.3 and recombinant XEC. XEQ is a child of FLiRT cluster KS.1.1.2 and FLuQE cluster KP.3. XFC is a child of FLiRT31 cluster LP.8.1.1 and FLiRT cluster LF.7. XFG is a child of FLiRT31 cluster LP.8.1.2 and FLiRT cluster LF.7. [Add'l footnotes omitted.]
Replied in thread
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sport of sacred spherical cows

Updated #CDC estimates show we'd pretty much been in a JN.1.11 soup since Dec, until late March, when LP.8.1 took majority.

Data collection continues to be a low priority nationally, as exactly zero regions have enough data for CDC to plot.

Two children of LF.7.7, dot1 and dot2, broken out by CDC, together with PA.1, a grandchild of MC.10.

Raj's dashboard was last updated a week ago today.

❖ #ThisIsOurPolio #Covid #Covid19 #SARS2 #variants #CovidIsNotOver #CovidIsAirborne #dataviz #datavis

Chart: Estimated U.S. Variant Proportions by Common Name Sources: Centers for Disease Control, CoV-Lineages, NYITCOM Research, Daniele Focosi, World Health Network, others [ beadsland on Ko-fi ] Reskin of CDC's Variants Nowcast, and any significant variants in GISAID not broken out by CDC. Five bar-style tree-charts, for fortnights through April 13–26. Legend of last fortnight, organized by subheadings of color-grouped families and convergent clusters. Percentages overlay each color key, reflecting share as of most recent tree-chart. Essentially all WHO-Vaccine-Target JN.1. LP.8.1 accounts for over two thirds; JN.1 + FLiRT for near a quarter. For fortnight of 3/30–4/12, packed bubble charts fill single-variant tiles, indicating diversification of those variants (per GISAID) that wouldn't otherwise be apparent from CDC's Nowcast. Legend: LP.8.1 [purple]: 69% - LP.8.1.1 / NY† & other LP.8.1 JN.1 + FLiRT-KS [browns]: 1% - XEC.4 ¾% - XEQ 10% - other XEC JN.1 + FLiRT [reds]: 6% - LF.7.7.2 4% - LF.7.7.1, LF.7 & LF.7.2.1 / PC 2% - LB.1.3.1 / NL JN.1.11 [greens]: 3% - MC.10.1, MC.28.1 & MC.19 2% - PA.1 ½% - KP.3 2% - MC.1 & other KP.3.1.1 / MC XEK [blue]: ⅜% - XEK Other [greys]: ⅙% - JN.1.16 ⅒% - NB.1† & other WHO-Vaccine-Target JN.1 ¼% - Other (not specified) † Dagger variants are not broken out by CDC, yet represent a significant share of recent GISAID sequences.
Continued thread
Public
Mark Dingemanse

this page 👆 is also to remind myself to think about visuals early.

sometimes I find myself talking or writing about a finding and I realise our paper actually doesn't do the best job of visualising it, so I create something new

2 examples: L how we map colours onto vowels sounds, R three common metaphors in language development. Both made after the papers (2019/2021) were published

learning from this, I now try to think ahead and make visuals clear & reusable in the first place

layered diagram showing, first, a plot of vowel space (by F2 and F1 formants on x and y axes) with 14 roughly equidistant vowel sounds plotted in a triangle. The plot is tilted in a way that makes the left side closer to the viewer. Second, a colour circle partly overlapping with the first plot, with the same kind of tilt and with grey lines showing how it projects onto the vowel space plot. In combination, it is possible to see how individual vowel sounds like /e/, /a/, and /u/ map onto regions of the colour circle like light green, red and blue.
slide showing three key metaphors from the language development literature with relevant references: bootstrapping (Pinker 1984, Gleitman 1990), scaffolding (Bruner 1978, Imai et al. 2008), bridging (Radwanska-Williams 1994, Perniss & Vigliocco 2014). Below the text are three illustrations in black and white, crayon style: a boot with a bootstrap at the back end; a scaffold; and a bridge.
#datavis
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UCLAB Potsdam

🔥 »I Want a Better Catastrophe« made it onto the short list of the Information is Beautiful Awards in the “Places, Spaces & Environment” category.

The flowchart by @jona combines audio narration with interactive elements as an invitation to join #AndrewBoyd on his narrative path and explore our predicament on your own: flowchart.bettercatastrophe.co

You can find all other short-listed entries here: informationisbeautifulawards.c

flowchart.bettercatastrophe.comI Want a Better Catastrophe · FlowchartA flowchart for navigating our climate predicament
#IIBAwards#BetterCatastrophe#Flowchart
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Marian Dörk

2-year professorship in »Information Visualization & Management« (Oct 2025–Sep 2027, W2) at #FHPotsdam with focus on data visualization, urban complexity & interdisciplinary research.

This is my parental leave replacement - feel free to reach out to me or @boris if you have any questions.

Applications already due on 9 May!

Job ad: jobs.b-ite.com/jobposting/a595
(this is in German, but teaching can be in English!)

jobs.b-ite.comVertretungsprofessur (W2) »Information Visualization & Management«
#AcademicJobs#DataVis#DataViz
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arrrrrmin

@anneroth
Der Mehrheit (alle die nicht afd wählen) wird da ein Niveau vorgesetzt das unterirdisch ist (afd wird nur schwurbeln und falsch informieren), die Ausgaben um das in Echtzeit wieder einzufangen (also den eigenen Auftrag zu erfüllen), werden ja nicht berücksichtigt.

Dazu mal eine einfache #datavis anhand echter Daten von Caren Miosga mit Alice Weidel. So lange dauert es idR bis es einen Faktencheck gibt, der wenn er überhaupt gelesen würde, zu spät ist:
chaos.social/@arrrrrmin/114116

chaos.socialarrrrrmin (@arrrrrmin@chaos.social)Attached: 1 image Unabhängig davon, ob Faktenchecks tatsächlich gelesen werden, quält mich die Frage: Wie lange hat man als Redaktion wohl Zeit, einen Faktencheck zu erarbeiten? Hier ein Versuch das Aufmerksamkeitspotential für einen Faktencheck über Bluesky bzw Mastodon Posts abzubilden: #datavis #dataviz #d3js
Replied in thread
Public
sport of sacred spherical cows

Updated #CDC estimates show we've pretty much been in a JN.1.11 soup since Dec, until late March, when LP.8.1 took majority.

Data collection continues to be a low priority nationally, as exactly zero regions have enough data for CDC to plot.

No new variants broken out, yet CDC seems to have resources to tailor their color key: hcommons.social/@beadsland/114

Raj's dashboard was updated on today.

❖ #ThisIsOurPolio #Covid #Covid19 #SARS2 #variants #CovidIsNotOver #CovidIsAirborne #dataviz #datavis

Chart: Estimated U.S. Variant Proportions by Common Name Sources: Centers for Disease Control, CoV-Lineages, NYITCOM Research, Daniele Focosi, World Health Network, others [ beadsland on Ko-fi ] Reskin of CDC's Variants Nowcast, and any significant variants in GISAID not broken out by CDC. Five bar-style tree-charts, for fortnights through Mar 30–Apr 12. Legend of last fortnight, organized by subheadings of color-grouped families and convergent clusters. Percentages overlay each color key, reflecting share as of most recent tree-chart. Essentially all WHO-Vaccine-Target JN.1. LP.8.1 accounts for over three fifths; JN.1 + FLiRT for near a quarter. For fortnight of 3/16–3/29, packed bubble charts fill single-variant tiles, indicating diversification of those variants (per GISAID) that wouldn't otherwise be apparent from CDC's Nowcast. Legend: LP.8.1 [purple]: 64% - LP.8.1.1 / NY† & other LP.8.1 XEC [browns]: 2% - XEC.4 15% - other XEC JN.1.11 + FLuQE [greens]: 5% - MC.10.1 & MC.19 1% - MC.28.1 2% - XEQ & other KP.3 3% - MC.1 & other KP.3.1.1 / MC JN.1 + FLiRT [reds]: 4% - LF.7 2% - LB.1.3.1 / NL ¾% - LF.7.2.1 / PC JN.1.11 + FLiRT [blues]: ½% - XEK 0% - KP.2.3 Other [greys]: ⅓% - JN.1.16 ⅒% - other WHO-Vaccine-Target JN.1 0% - Other (not specified) † Dagger variant is not broken out by CDC, yet represent a significant share of recent GISAID sequences.
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