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Making Data Playable? (workshop slides)


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The slides present the context of a workshop on using card games to interpret datasets as well as defamiliarize and reassess the role of tools in data practices and the computational humanities. An abstract with more information and literature can be found here:

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Making Data Playable? (workshop slides)

  1. 1. Slide No. 1“Playing with Datasets?”, December 10th, 2019 Making Datasets Playable…? Dr. Karin van Es Dr. Stefan Werning (Utrecht University)
  2. 2. Slide No. 2“Playing with Datasets?”, December 10th, 2019 11.15 Intro ‘Playful Datasets’ 11.30 Play sample card game & plenary discussion 12.15 Lunch 13.00 Explain card creation with Nandeck 13.20 Redesign sample game (incl. creating new cards) 14.30 Team pitches 15.00 Redesign card game made by other team 16.00 Team pitches and prototype comparison 16.30 Closing remarks/theoretical embedding (incl. Galey/Ruecker) Program today
  3. 3. Slide No. 3“Playing with Datasets?”, December 10th, 2019 Premise • As games become a ‚reference medium‘, more people interpret their lives through the ‚lens‘ of games and game co-creation • NRC article on changing Hasbro‘s Game of Life to reflect real-world lived experience – “Spelkaart 1: Je besluit dat tijd meer waard is dan geld. Verkoop je bezittingen en trek je terug in het groen” – “Spelkaart 2: Je wil géén standaardgezin. Geef je buggy weg en start een ander soort familie” – “Spelkaart 3: Je hebt geen huis nodig. Leen een tent of camper en zwerf een tijdje rond”
  4. 4. Slide No. 4“Playing with Datasets?”, December 10th, 2019 Playfulness in Engaging with Data • Playfulness (Lieberman) – Cognitive Spontaneity – Physical Spontaneity – Social Spontaneity – Humor – Manifest Joy • Dear Data • “Playing With Real(ish) Data: Visualizing the Books I Read Last Year” • Play can but need not be critical! Lieberman, J. Nina. 2014. Playfulness: Its Relationship to Imagination and Creativity. Brooklyn, NY: Academic Press.
  5. 5. Slide No. 5“Playing with Datasets?”, December 10th, 2019 • How can games and play make data ‘experienceable’ while simultaneously contributing to a more nuanced understanding of tools in data analysis? • Primacy of visual evidence vs. multimodality – Making Data Matter ⇔ Music by Oceans • Defamiliarization – Making both familiar data and techniques ‘strange’ • Foregrounding play, performativity and participation – Often excluded from “scientific method” and rhetoric – Cf. e.g. (Roberts-Smith 2017) Question Roberts-Smith, J. (2017) ‘Théâtre et humanités numériques. Du développement des outils aux design experimental’, Revue d’Historiographie du Théâtre, 4(Special Issue: Études théâtrales et humanités numériques).
  6. 6. Slide No. 6“Playing with Datasets?”, December 10th, 2019 Game Co-Creation ⇔ Critical Making
  7. 7. Slide No. 7“Playing with Datasets?”, December 10th, 2019 Epistemologies of ‘Making’ • Stephen Ramsay, “On Building” – “All the technai of digital humanities – data mining, xml encoding, text analysis, gIs, Web design, visualization, programming, tool design, database design, etc. – involve building” • Galey/Ruecker – Prototypes can constitute scholarly arguments – Exhibit a rhetoric of their own • Ratto, “Critical Making” • Dunne & Raby, “Critical Design”, Feminist Data Set • Tharp & Tharp, “Discursive Design” – Focus on “alternative things” – Commonalities and differences
  8. 8. Slide No. 8“Playing with Datasets?”, December 10th, 2019 Analytical Game Design • Some existing perspectives on critical (board/card) game-making – Critical board game design (Zavala/Odendaal 2018) – Indigenous board game design (LaPensée 2016) – Critical Modification (Loring-Albright 2015) – “Executable thought experiments” (Schulzke 2014) – All focus on one games as ‘product’ • Analytical Game Design • a) Game co-creation as an ongoing discourse • b) Game prototypes as epistemic objects (cf. Humanities Today, objects) • c) Unfinishedness affords ‘productive Irritations’ • d) Playgiarism (Raymond Federman) LaPensée, E. (2016) ‘Indigenous Board Game Design in The Gift of Food’, Analogue Game Studies, 3(2). Available at: design-in-the-gift-of-food/. Loring-Albright, G. (2015) ‘The First Nations of Catan: Practices in Critical Modification’, Analogue Game Studies, 2(7). Available at: practices-in-critical-modification/. Schulzke, M. (2014) ‘Simulating philosophy: Interpreting video games as executable thought experiments’, Philosophy and Technology. Springer Netherlands, 27(2), pp. 251–265. Zavala, K. and Odendaal, A. (2018) ‘Black Boxes out of Cardboard: Algorithmic Literacy through Critical Board Game Design’, Analogue Game Studies, 5(4). Available at: cardboard-algorithmic-literacy-through-critical-board-game- design/.
  9. 9. Slide No. 9“Playing with Datasets?”, December 10th, 2019 Defamiliarization in Critical Practice and Games • “Scholars in various fields have argued that one of the great merits of digital tools is their capacity for ostranenie: for ‘making strange’, or defamiliarizing us from, our objects of study – and by the same token, for calling into question our most profound assumptions about them” (Masson 2017, 31) • Defamiliarization in/through games – E.g. games devised to reinvigorate artistic practice and unsettle routines (Dadaism, Surrealism, OuLiPo) Knight’s tour diagram used in creating Georges Perec’s Life: A User's Manual Masson, E. (2017) ‘Humanistic Data Research. An Encounter between Epistemic Traditions’, in Schäfer, M. and Van Es, K. (eds) The Datafied Society. Studying Culture through Data. Amsterdam: Amsterdam University Press, pp. 25–38.
  10. 10. Slide No. 10“Playing with Datasets?”, December 10th, 2019 Card Games as ‘Tools’ for Thought
  11. 11. Slide No. 11“Playing with Datasets?”, December 10th, 2019 Card Games as Data Storage Devices • The playing card as “platform” (Altice 2014) • Material affordances – “Planar, uniform, ordinal, spatial, and textural” – Tapping, turning, stacking, shuffling (cf. e.g. Magic: The Gathering) • Specific ‘data type’ qua layout – Selection and position of values, ‘flavor text’ • “Programmable” (Bogost/Montfort 2009) – “A ‘platform’ is a system that can be programmed and therefore customized by outside developers -- users -- and in that way, adapted to countless needs and niches” Altice, Nathan. 2014. “The Playing Card Platform.” Analogue Game Studies 4 (2). Bogost, I., & Montfort, N. 2009. “Platform Studies: Frequently Questioned Answers.” UC Irvine: Digital Arts and Culture 2009. Retrieved from
  12. 12. Slide No. 12“Playing with Datasets?”, December 10th, 2019 Playing with Data: The ‘Language’ of Card Games • Top-down vs. Bottom-up – Modify mechanics and basic parameters (players, goals, rules, external tokens/board etc.) • Sequence/set-making (incl. hand management) – Open (e.g. Domino and derivatives) – Hidden (e.g. Vroeger of Later) • Collectible cards (asymmetrical) • Deck building • Worker/engine placement • Asymmetrical goals • Trick taking • Distributed information (e.g. Hanabi) • Trading/auction (e.g. You’re Bluffing!/Koehandel) • Memorization • Self-modifying rules (e.g. Fluxx) • Narrative/scripts
  13. 13. Slide No. 13“Playing with Datasets?”, December 10th, 2019 Playing with Data: Mechanics • New gameplay mechanics (like layout algorithms) enable new ways of looking at data • Randomly extended sequences – All players choose a result that fits their role – Random results are added to the sequence, which is shuffled – Players need to guess who selected which result (=> identify bias of examination work, e.g. reliance on SD etc.)
  14. 14. Slide No. 14“Playing with Datasets?”, December 10th, 2019 A Sample Game
  15. 15. Slide No. 15“Playing with Datasets?”, December 10th, 2019 Our Sample Dataset • Scraped from the Google Play Store – Imported from Google Sheet – Can be extended (collaboratively) • Adaptable to other datasets – Kickstarter projects – Trending videos on YouTube • Yet: Playing the same game with different datasets/metrics produces different ‘meanings’
  16. 16. Slide No. 16“Playing with Datasets?”, December 10th, 2019 Sample Game • Players ‘launch’ apps to compete with each other in four categories – Install size, installs, review score, number of reviews (buzz) • ‘Gaps’ in the rules as productive irritations – E.g. can you compete with your own apps? • ⇔ Leaves room for co-creation – E.g. what role do rating and category play? • Maybe extend using set- making or event cards (limiting points earned for apps rated T and above?)
  17. 17. Slide No. 17“Playing with Datasets?”, December 10th, 2019 Playtesting
  18. 18. Slide No. 18“Playing with Datasets?”, December 10th, 2019 Redesign • Co-creating games requires a bit of practice and getting used to a. Start by modifying individual parameters b. Use Caillois’ taxonomy as a heuristic c. Try out different rule variations and determine whether they fit one prototype or should be split into multiple • Template – Setup phase • Setup action 1 • Setup action 2 – Play phase(s) (per player) • (Potential) Action 1 • (Potential) Action 2 • Check win/lose conditions • Game response before player switching – Wrap-up phase • Determine win/lose state • Consequences for next play session?
  19. 19. Slide No. 19“Playing with Datasets?”, December 10th, 2019 Connections to the three concepts established earlier in class? • How could Caillois’ taxonomy of games be of use here? • How can Sutton-Smith’s emphasis on the ambiguity of play be made productive in working with data (even if it contradicts the traditional ‘scientific method’) • How can data games facilitate performative engagement with data? • What are the “terms of participation” (Jenkins) in this case and what role do tools and “DIY cultures” play? • (How) Can this approach contribute to the notion of cultural citizenship? “Play, Perform, Participate” Questions
  20. 20. Slide No. 20“Playing with Datasets?”, December 10th, 2019 • What classifications and standards are employed? What is made visible (and invisible) in this system? To what end and at what consequences? • What are the material affordances of the tools (i.e. the cards) employed? How do they impact the game(play)? • What interpretive acts have been made in the construction of the interface (i.e. the cards)? How is this partial? • What are underlying values and norms? (the basic logics or principles of the game) Tool Criticism questions in Playable Datasets
  21. 21. Slide No. 21“Playing with Datasets?”, December 10th, 2019 • Duration presentation: 20-30 minutes • Make use of visuals that support the rationale • Address the following elements in your presentation: – Main results of your project – Reflection on cycle and position of your question – Role of digital tools and the concept of tool criticism – Connect to wider debates and theories in Data-driven research as addressed in literature and seminars – Position in relation to Tools Criticism symposium in Leiden Presentation: Playful Datasets workshop from a tool criticism perspective
  22. 22. Slide No. 22“Playing with Datasets?”, December 10th, 2019