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Dataome

Cf.

  • The Noösphere
  • global datasphere, technosphere, infosphere (Floridi)
  • data ecology
  • data commons
  • digital colonialism, data colonialism (Couldry)
  • memory institutions, archival studies, forgetting, data sovereignty, cf. Heritage
  • materiality, e.g. material engagement theory4
  • genesity, as a as a more general and expansive characterization of habitability1
  • extended mind (Clark and Chalmers)
  • extended phenotype (Dawkins)
  • niche construction (Odling-Smee, Laland)

The Selfish Dataome - Nautilus

Scharf, Caleb, and Olaf Witkowski. “Rebuilding the Habitable Zone from the Bottom up with Computational Zones.” Astrobiology 24, no. 6 (2024): 613–27. https://doi.org/10.1089/ast.2023.0035.

Scharf, Caleb A. The Ascent of Information: Books, Bits, Genes, Machines, and Life’s Unending Algorithm. New York: Riverhead Books, 2021.

Definition

The sum total of human internal and external information (related to humans? on Earth?, what if humans travel out?).

Considerations

Conceptual

  • What counts as "data"? How is dataome different from "human stuff" or "the technosphere"?

  • The dataome seems to be collapsing memory (a recorded trace) and learning (use of that trace), cf. Learning.

  • Dataome's distinctive contribution is the information-theoretic and thermodynamic framing (a measurable cost in joules and bits). If that framing is dropped, the term adds little to the existing vocabulary?

  • Scharf's primary examples are human. Chimpanzee tools, beaver dams or pufferfish circles appear as an addendum. A more ambitious reading would treat the dataome as multi-species and ecological: tracks, scent marks, pheromone trails, mycorrhizal records, scarred trees, coral skeletons, sediment laminae. Cf. Biosemiotics.

  • Living substrates blur the boundary. Some "external" information sits in living bodies. E.g., gut microbiomes, domesticated species, cultivars, lab organisms. Is a wheat genome part of the dataome because humans selected it? If yes, the dataome includes the genome, which dissolves the original analogy. If no, we need a boundary.

Critical

  • "The selfish dataome" inherits Dawkins's gene-centric framing and its problems. It personifies information, downplays context and cooperation, and tends to naturalise whatever currently exists. Treating data as an autonomous quasi-agent pursuing its own persistence risks letting human institutions off the hook. Misinformation does not "want" to spread; particular actors and platforms reward and accelerate it.

  • Calling the aggregate an "-ome" implies a bounded, countable whole, like a genome. In practice the dataome has no edge, no version, no reference sequence and no replication mechanism. The biological analogy is suggestive but not structural, and it can mislead when pressed.

  • The accounting of computing, storage and transmission under-counts extraction of materials (rare earths, water, sand), labour conditions of data work, e-waste, land use of data centres, the attentional costs borne by users, etc. A full accounting would draw on political ecology, not just thermodynamics.2

  • The dataome is treated as a planetary commons, but in reality it is highly enclosed: paywalled journals, proprietary models, surveillance archives, classified records, deleted indigenous archives, languages without digital support. A neutral "ome" obscures who owns, controls, profits from and is harmed by which parts. Cf. Commons and data colonialism.3

  • Genomes have well-studied error rates and repair. The dataome's decay is governed by format obsolescence, link rot (and now AI-induced data rot, stochastic parroting), institutional collapse, censorship and deliberate destruction. These are political and infrastructural processes, not analogues of mutation.

  • Bartlett and Wong's image of an AI-driven dataome devouring itself treats the dynamic as a quasi-natural runaway. The same outcome is more accurately described as a failure of editorial, legal and economic institutions to regulate generative systems. Framing it ecologically can excuse human and corporate agency.

  • If the dataome were genuinely selfish in the Dawkinsian sense, we would expect it to evolve to maximise its own copying. Much of the dataome (archives, monuments, scholarship) is sustained against decay only at significant human cost, with no self-replicating mechanism.

Future Directions

  • A more-than-human dataome? Consider explicitly admitting non-human inscriptions: tree traces (channels, epicormic growths, rings),5 geological strata, animal trails, mycorrhizal maps, coral records. Position the dataome as continuous with Geology, Deep Time and biosemiotics, and align with nonhuman political representation (e.g., in Learning).

  • A taxonomy of dataomic harms such as erasure (colonial archive destruction), enclosure (paywalls, IP), poisoning (AI slop, propaganda), surveillance (extractive recording), abandonment (link rot, defunded archives). Cf. Value. A more-than-human justice is in part a justice of records: of who may leave traces, who can read them, who controls them, and who must keep them legible across generations.

    • This can supply a justice rationale for the "learnability index": a place is unjust if it strips agents of the records they need to live and to be known.
    • Cf. Learning. Loss of non-human dataomic strata is a loss of planetary learning capacity, not just of cultural heritage.
    • Design briefs that propose more sensing, dashboards or digital twins must show how they return legibility and standing to non-human agents, not only extract data about them. Cf. Respectful Design.

Examples

Shakespeare is reanimated by a dataome of documents, performances, textual derivatives, transmitted embodied knowledge, etc. that has outlived the author, himself. Note: Humans, nonhuman animals, and objects are all involved in this process, as well, just as biomediation requires the actions of bodies, platforms, and substrates in the materialization and mobilization of life as pattern.

Warren-Crow, Heather. Shakespeare and Nonhuman Intelligence. Cambridge: Cambridge University Press, 2024. https://doi.org/10.1017/9781009202633.

Cf. Roudavski, Stanislav. “Field Creativity and Post-Anthropocentrism.” Digital Creativity 27, no. 1 (2016): 7–23. https://doi.org/10.1080/14626268.2016.1151443.

The creation and utilization of the Dataome demand substantial energy, sometimes leading to low-quality data and misinformation that may diminish the Dataome's overall value.

Naudé, Wim. Economic Growth and Societal Collapse: Beyond Green Growth and Degrowth Fairy Tales. Cham: Palgrave Macmillan, 2023.

Footnotes

  1. Malafouris, Lambros. How Things Shape the Mind: A Theory of Material Engagement. Cambridge, MA: MIT Press, 2013.˄

  2. Wong, Michael L., Stuart Bartlett, Sihe Chen, and Louisa Tierney. “Searching for Life, Mindful of Lyfe’s Possibilities.” Life 12, no. 6 (2022): 783. https://doi.org/10.3390/life12060783.˄

  3. Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press, 2021. Brevini, Benedetta. Is AI Good for the Planet? Cambridge: Polity Press, 2022.˄

  4. Couldry, Nick, and Ulises Ali Mejias. The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism. Stanford: Stanford University Press, 2019.˄

  5. Yalukit Weelam Tarrang, Carolyn N’Arweet Briggs, David Tournier, Brian Martin, Julian Rutten, Alexander Holland, and Stanislav Roudavski. “Vegetal Leadership in Interspecies Cultures: Collaborative Mark-Making with Yalukit Weelam Tarrang [Tree].” Australian and New Zealand Journal of Art 25, no. 1 (2025): 7–32. https://doi.org/10.1080/14434318.2025.2515197.˄

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