Bibliographic record
Abstract
While digital technologies have revolutionized how we collect and visually represent data, humans continue the thousand-year tradition of producing physical representations of data. Physical representations of data can range from the mundane (hourglass egg timers in an everyday kitchen) to the spectacular (large-scale data sculptures in a museum). Physical representations of data are experiencing a dramatic renaissance, driven by new fabrication technologies, materials, and processes as well as a growing enthusiasm for all things data.The artists, designers, and scientists who create physical representations of data draw from a range of domains and traditions, and represent a fascinating, inspiring, and revealing cross-section of contemporary maker and data culture. To highlight the diversity of approaches, we are currently curating a collection of first-hand accounts from 25+ artists, designers, and researchers that document the process of designing and creating new physical representations and experiences with data. Each story describes the creators’ motivation and inspiration, their approaches for sourcing and encoding data, and their experience navigating the design and fabrication process. In our talk, we will present five themes that capture how people are “making with data” today: Data craft highlights artists and designers whose hand-crafted pieces manually (and sometimes painstakingly) integrate data into objects – from the extraordinaryexotic and bespoke to the personal and everyday. Digital production examines how digital fabrication techniques like 3D printing and digital milling can produce unique and expressive data-driven physical forms. Data automation introduces new physical platforms that use automation and robotics to dynamically and interactively encode data physically. Participatory showcases ways in which designers invite viewers into the creation process, allowing them to encode or reveal data through their interactions with a piece, material, or other people. Environmental projects, meanwhile, reveal data in the context of our surroundingsnatural environments, often exploring the use of natural processes to create new and compelling representations.Each of these approaches entails profound design choices and considerations which impact the design and production process, the tools and skills required to create the works, and ultimately the connection that is created between the creator, the viewer, and the dataIn this lighting talk, we will present highlights from our rich and exciting set of art pieces, projects, and installations. We will illustrate each theme with a case study featuring a particularly compelling work, and also provide our own first-hand reflections on creating and experiencing physical representations of data. Finally, we will turn the discussion back to the broader community, examining additional approaches not captured by our themes, and highlighting aspects of the creation process that are of particular interest to the information plus audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.197 | 0.111 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".