OCTAVIA OME-Zarr

*The term Data Object is often used loosely to describe a table, file, API payload, business entity, or data product. Such ambiguity undermines the very stability the concept is intended to provide. This essay offers a rigorous definition: a Data Object is a versioned logical contract representing a coherent entity, event, state, or document-like assertion, with explicit identity, temporal semantics, attributes, relations, quality constraints, provenance, and governance. It is independent of any single source or physical storage format, though it must be implementable in concrete technologies. The essay develops a formal tuple for Data Objects, distinguishes them from source records and consumer views, and analyses granularity, lifecycle, compatibility, and ownership. Philosophically, the Data Object is treated as an institutional object: neither a natural thing nor an arbitrary schema, but a stabilised representation whose legitimacy depends on transparent rules and continued use.*

Data platforms frequently promise that the same input will produce the same output. The promise is attractive, but often false because “the same input” is defined too narrowly. A computation depends not only on source records but on code, configuration, reference data, library versions, execution engines, clocks, seeds, policies, and hidden environmental state. This essay distinguishes repetition, determinism, reproducibility, replicability, and auditability. It formalises a data computation as a function over an extended state space, examines sources of non-determinism in distributed and temporal systems, and proposes an evidence model for reproducible outputs. The philosophical question is one of identity through time: when are two executions instances of the same computation, and when is a numerically equal result merely accidental? The essay argues that reproducibility is not achieved by rerunning code but by preserving the computational world in which a claim was produced.