Deep Connection: Making Virtual Reality Artworks with Medical Scan Data
Bibliographic record
Abstract
Deep Connection is an installation and virtual reality (VR) artwork made using full body 3D and 4D magnetic resonance (MR) scan datasets. When the user enters Deep Connection, they see a scanned body lying prone in mid-air. The user can walk around the body and inspect it, lie underneath and walk through it. The user can dive inside and see its inner workings, its lungs, spine, brain. The user can take hold of the figure’s outstretched hand: holding the hand triggers the 4D dataset, making the heart beat and lungs breathe. When the user lets go the hand, the heart stops beating and the lungs stop breathing. Deep Connection creates a scenario where an embodied human becomes the companion for a virtual body. This paper maps the conceptual and theoretical framework for Deep Connection such as virtual intimacy and digitally mediated companionship. It also reflects on working with scanned bodies more generally in virtual reality by discussing transparency, the cyberbody versus the data body, as well as data privacy and data ethics. The paper also explains the technical and procedural aspects of the Deep Connection project with respect to acquiring scan data for the creation of virtual reality artworks.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".