Matters (and metaphors) of life and death: How DNA storage doubles back on its promise to the world
Bibliographic record
Abstract
For this special section on “geographies of the digital,” we explore how DNA‐based data storage is touted as an alternative to traditional storage modalities and pitched by the data storage industry as a more efficient, stable, and long‐term archival solution than that offered by current technologies. In analyzing how DNA is soaked in the language of sustainability, life, and longevity by those trumpeting the new technology, we situate emergent discourses proposing DNA as a remedy to energy‐, water‐, and land‐intensive data centres and cloud storage. While DNA is not an “online” data storage technology, we show that the prospects of biological computation have altered the imagined futurity of cloud infrastructure. We then explain how DNA data storage works, and we complete the paper with three case studies—Microvenus, The National Film and Sound Archive of Australia, and The Arch Mission Foundation's Lunar Library—offering a critique of “the archive” as it is framed through these nascent scientific and technological discourses. In sum, we argue that DNA‐based data storage is imbricated by an apocalyptic thinking, and that the temporality and timing of this technology speaks to growing, unevenly distributed, planetary anxieties.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.080 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".