Design reading notes

Giorgia Lupi — legal full-text readings

Giorgia Lupi — legal full-text readings

Loop stopped when further WebSearches returned the same author-site / Medium / Print URLs already fetched. Dear Data the book was not downloaded or pirated; only author-posted essays and project pages.

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1. Data Humanism, the Revolution will be Visualized (key manifesto)

Notes (what THIS text argues)

  1. 1. We are past “peak infographics”: a first wave popularized dataviz but often as cosmetic simplification of Big Data.
  2. 2. Marketing-style “cool” infographics falsely claim pictograms and big numbers can “simplify complexity”; complexity is inherent and should not be dumbed down for crucial decisions.
  3. 3. Numbers are placeholders for knowledge, behaviors, and people—not freestanding truths; blind charting is like reviewing a film via cellulose chemistry.
  4. 4. Second-wave dataviz should be personalized, contextual, and intimate as data becomes ubiquitous.
  5. 5. Embrace complexity: revelation often needs depth; Accurat’s La Lettura work layered dense multi-attribute narratives rather than single-glance charts.
  6. 6. “Nonlinear storytelling”: clarity need not arrive all at once; readers may get lost in sub-tales while still entering via a main construct.
  7. 7. Dense unconventional visuals promote slowness—valuable against ever-shortening attention.
  8. 8. Move beyond standards: BI/out-of-the-box chart pickers throw technology at ill-framed questions and can be useless or wrong.
  9. 9. Sketch first off-screen—“draw with data in mind, no data in the pen”—to discover structure before digital polish.
  10. 10. Borrow encoding vocabularies from music notation and avant-garde geometric art (shared perception principles).
  11. 11. Sneak context in (always): collection choices and omissions shape a dataset’s life; subjectivity matters especially when data are about people.
  12. 12. Dear Data (with Posavec) is the manifesto’s living experiment: manual, anecdotal logging (e.e., why she checked the time) as “personal documentary,” not quantified-self efficiency.
  13. 13. Remember data is imperfect: “data-driven” ≠ unmistakably true; viz should show uncertainty, error, and nuance while remaining scientifically careful.
  14. 14. Goal: convey knowledge and feeling simultaneously—bring data to human life.

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2. Dear Data — project page (author site)

Notes

  1. 1. Year-long analog exchange: 52 weekly hand-drawn postcards between Lupi (NY) and Posavec (London).
  2. 2. Each week: collect/measure a shared life topic → draw on card → mail (“slow data” with postal wear).
  3. 3. Front = unique weekly viz; back = detailed key so the other can decode and imagine that week.
  4. 4. Collecting became ritual: noticing before drawing.
  5. 5. Explicit framing: personal documentary, not quantified-self.
  6. 6. Thesis: data can make us more human and deepen connection, not only more efficient.
  7. 7. Original cards/sketchbooks entered MoMA’s permanent collection.
  8. 8. Page points to Eyeo 2016 keynote and Somerset House animation (video, not transcribed here).

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3. Dear Data has been acquired by MoMA, but this isn’t what we are most excited about

Notes

  1. 1. Origin story: nearly strangers, collaboration sparked over a beer; two-year side project evenings/weekends.
  2. 2. Challenge: get to know each other through data as material.
  3. 3. Process was vulnerable: flaws, bad habits, shameful pasts shared as drawn data—a shared diary.
  4. 4. Manual + contextual details (not device metrics) made logs “about us and us alone.”
  5. 5. MoMA acquired 104 original postcards + sketchbooks (Paola Antonelli quoted on beauty/poetry of snail-mail data friendship).
  6. 6. True pride: public uptake—people seeking data penpals, children drawing data, teachers adopting the format.
  7. 7. Starting small is how they hope to increase data literacy (people can’t grasp “big/open data” without knowing what data is).
  8. 8. Success = impact beyond designer circles, not only institutional validation.
  9. 9. Dear Data doesn’t fit neat “art vs design vs dataviz” boxes; interdisciplinary fuzziness is an asset.
  10. 10. Following strict disciplinary rules risks treading water; working “in-between” extended what viz/communication can do.
  11. 11. “There is more power in being that which doesn’t ‘fit’ than that which does.”
  12. 12. Personal goal met: they became deeply close friends through data.

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4. Sketching with Data Opens the Mind’s Eye

Notes

  1. 1. Many dataviz designers still use paper sketching as primary design tool despite digital output.
  2. 2. Heller: accurate infographics are as artful as other design, but must tell a factual/linear story.
  3. 3. Drawing becomes design when lines/symbols organize choices toward a solution.
  4. 4. Three sketch phases: (1) macro architecture without real numbers; (2) encode singular data-point forms; (3) prototype after digital tests for team/client communication.
  5. 5. Early sketches raise new analysis questions the raw numbers alone wouldn’t suggest.
  6. 6. Starting from what tools easily produce yields easy-but-wrong solutions.
  7. 7. Self-imposed limit of sketching without pen-access to actual numbers focuses on meaning/organization, not overwhelm by millions of values.
  8. 8. Drawing opens mental spaces and discovers ideas you don’t yet have.

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5. Beautiful Reasons (Engaging Aesthetics for Data Narratives)

Notes

  1. 1. Asks whether aesthetic features can matter as much as data for curiosity, understanding, and exploration.
  2. 2. Beauty cannot replace functionality, but together they achieve more; aesthetics shape perceived functionality/credibility and emotion.
  3. 3. Aim for “Oh, that is beautiful. And strange” → then “I want to know what this is about.”
  4. 4. Cases: “steal” painters’ styles for painter-life timelines; musical-notation metaphors for science citation landscapes; accidental 3D cones for indie-film budget/sales/gross.
  5. 5. Unexpected aesthetics trigger attention and memorability (grandmother’s “candleholders”; meetup’s “jellyfishes”).
  6. 6. Emotional investment can produce attention rather than distraction—if values in data are not manipulated.
  7. 7. Success = balance convention (familiar forms) and novelty.
  8. 8. No unique truth in dataviz; appropriateness depends on goals, data, readers, context.
  9. 9. Dense nonconventional work promotes slowness and deeper engagement.

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6. Learning to See: Visual Inspirations and Data Visualization

Notes

  1. 1. Abstract art and dataviz share Gestalt/perception roots (Kandinsky, Mondrian, etc.).
  2. 2. “Learning how to see is essential to learn how to design.”
  3. 3. Brain Drain La Lettura piece borrowed Malevich/Mondrian compositional rules after MoMA Inventing Abstraction.
  4. 4. Combined World Bank, mobility survey, and university rankings; normalized by population for comparison.
  5. 5. Unexpected findings surfaced only once the viz was built (e.g., researchers migrate more than general population; Latin exceptions; female employment / English-language correlations noted interpretively).
  6. 6. Seek inspiration in familiar visual languages: abstract art, music notation, architectural layers, natural forms.
  7. 7. Practice: ask “What do I like in what I see—and why?” then abstract those qualities into design principles.
  8. 8. Open invitation to the art of observation.

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7. The Architecture of a Data Visualization

Notes

  1. 1. Accurat/La Lettura method: compound stories that keep complexity accessible without reducing to simple diagrams.
  2. 2. Move from quantity toward qualitative transformation—unexpected parallels and secondary tales.
  3. 3. Non-linear storytelling for print full-spreads: catch with aesthetics, then sequential depth.
  4. 4. Eight-step build: main architecture → place elements → shaped quantitative/qualitative forms → relationships → labels → tangential enrichments → legend-as-miniature-layers → fine-tune hierarchy/negative space.
  5. 5. Process is iterative, not strictly linear.
  6. 6. When purpose is revelation/engagement (not rapid decision dashboards), visual complexity is often necessary.
  7. 7. Experiment with new metaphors; elegance = understandable and appealing enough to trigger curiosity.
  8. 8. Layering hierarchies in both analysis and composition lets readers “get lost” productively.

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8. Bruises — The Data We Don’t See

Notes

  1. 1. Collaboration with musician Kaki King during daughter Cooper’s ITP (autoimmune low platelets).
  2. 2. Clinical numbers alone miss family impact; Lupi asked whether viz can evoke empathy, not only cognition.
  3. 3. Soft data: daily bruises/petechiae, steroids, incidents, travel stress, hope/fear (1–10), handwritten notes—juxtaposed with platelet counts.
  4. 4. Visual grammar: day blocks between labs; red platelet dots; purple/green bruise splotches; pink petechiae density; grey steroid strokes; yellow positive moments; floating hope/fear lines.
  5. 5. Companion song: 120 measures mapping 120 days; guitar as musical map of bruises.
  6. 6. Not a scientific clinical chart—but an accurate sensorial picture of the journey.
  7. 7. Data always include overlooked fluid/nuanced layers needing new representational forms.
  8. 8. Data collection itself was cathartic for King (parallels between bruises and stress; reclaiming control).
  9. 9. Ties explicitly to Data Humanism: include empathy, imperfection, human qualities in how we collect/process/display.
  10. 10. Future: data not only for efficiency but to become more human; healthcare innovation interest noted.

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9. Data ITEMS: Exploring the Power and Depth of Soft Data for MoMA

Notes

  1. 1. Site-specific hand-drawn wall viz for MoMA Items: Is Fashion Modern? (111 iconic garments/accessories).
  2. 2. Built own dataset from curatorial research via qualitative questions (medium vs message; conform vs escape; cause vs effect, etc.).
  3. 3. “Just because you don’t immediately see numbers doesn’t mean there is no data.”
  4. 4. Landscape overview + eight Capstones exploding commercial lifecycle / sustainability (Quantum Redesign / SDGs).
  5. 5. Soft data’s shapelessness invites departure from hard statistical viz conventions toward hand drawing and deliberate imprecision.
  6. 6. Hand-drawn form matches subjective concepts (culture, identity, rebellion, belonging)—not cold perfect observation.
  7. 7. Restates Data Humanism: empathy, imperfection, human qualities in data practice.
  8. 8. Sometimes meaning is macro structure (squint), not micro detail.

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10. My 2020 in Data (So Far)

Notes

  1. 1. Hand-drawn NYT Sunday At Home timeline of pandemic “lasts” and socially distanced “firsts.”
  2. 2. Echoes Dear Data / Data Humanism: personal data to humanize lockdown statistics.
  3. 3. Sources: calendar, journal, texts—categories for friends, family, team, NYC life, self-care.
  4. 4. Encoding: white dots = events; red = lasts; blue = firsts; wavy fields = blurred virtual middle; marks for true lasts / restarted / brand-new.
  5. 5. Density drops after March; relationship “firsts” increase—connection can flourish in quarantine.
  6. 6. Simplified palette for newsprint; invites readers to track their own months.

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11. Philosophy (author site statement)

Notes

  1. 1. Data surrounds us as an invisible ecosystem; hard to ignore.
  2. 2. Data ≠ facts; it is an imperfect abstraction of reality, quantitative and qualitative, human-made and bias-prone.
  3. 3. Origin story: childhood sorting in grandmother’s tailor shop; architecture/urban mapping.
  4. 4. At Pentagram, data is a design material (charts, but also sculpture, mural, clothing).
  5. 5. Data Humanism analogy to Renaissance humanism: center people, not numbers/God, as source of meaning.
  6. 6. “Despite how it may sometimes appear, data is always the product of human hands.”
  7. 7. Multiple definitions of data must coexist; critical scrutiny of production/consumption is required.

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Not read in full / excluded

ItemStatus
Dear Data book (Penguin / PAP)Excluded — copyrighted book; not pirated
Observe, Collect, Draw! journalPromotional page only; book not taken
Medium “Bruises” URLPartial fetch; full text taken from author site instead
TED talks (video)Linked from philosophy page; not transcripted as full text here
MoMA collection object pagesCatalog entries, not Lupi essays

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Search-loop note

Repeated searches for Lupi manifesto / Dear Data / Medium Accurat essays returned the same URLs above. Stopped per “next searches are duplicates” rule.