Science AMA Series: I’m an artist who translate scientific data into sculptures and musical scores. AMA!
Bibliographic record
Abstract
Hi Reddit, My name is Nathalie Miebach and I am Boston-based artist who translates scientific data related to ecology, climate change, and meteorology into woven sculptures and musical scores. I find data very poetic. By using somewhat unorthodox ways of representing data, I’m trying to tap into more nuanced stories embedded in data that traditional ways of scientific representations have a harder time tapping into. My method of translation is principally that of weaving—in particular, basket weaving—as it provides me with a simple yet highly effective grid through which to interpret data in three-dimensional space. Central to this work is my desire to explore the role visual aesthetics play in the translation and understanding of scientific information. I also translate weather data into musical scores that are build entirely of weather data, but integrate human experiences and interpretations of weather events. The juxtaposition of objective data and more nuanced, subjective readings of weather, lead to a musical/sculptural translation that explores how human emotions and experiences influence the perception of weather. These musical scores are translated into woven sculptures and are used in collaborative performances with musicians / composers all over the country. We’ve had over 11 concerts, called Weather Scores, and I’m getting ready to organize the next one this Summer in Montreal, Canada! Check out my work here and don’t miss my TED Talk as well as this BrainPickings write-up of my work. My friends over at NOVA PBS (where some of my work is featured on Instagram today: @novapbs) have a whole vertical dedicated to climate change, they’re been reporting on it in their email newsletter—sign up here, and their film, “Decoding the Weather Machine,” premieres April 18 at 9/8c on PBS. One of the questions I wrestle with in the studio everyday is whether or not data can ever be approached and treated as an artistic medium or if the very act of translating data into art destroys its objectivity that is part of the integrity of information. Ask me any questions you have about data, art / science collaborations, data translation into 3D and music, or anything else you’d like.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.216 | 0.106 |
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".