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Datasets:
DSLO
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DSLO-v0.7-Semantic-Substrate-Specification

Languages:
English
Tags:
dslo
v0.7 release cluster
structured ingestion
semantic substrate
multi-manifold geometry
scientific metadata
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dslo_version: string
layer: string
description: string
examples: struct<biological_example: struct<title: string, description: string, geometry_primitives: list<item (... 667 chars omitted)
  child 0, biological_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
      child 0, title: string
      child 1, description: string
      child 2, geometry_primitives: list<item: string>
          child 0, item: string
      child 3, invariants: list<item: string>
          child 0, item: string
      child 4, surface: string
  child 1, cultural_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
      child 0, title: string
      child 1, description: string
      child 2, geometry_primitives: list<item: string>
          child 0, item: string
      child 3, invariants: list<item: string>
          child 0, item: string
      child 4, surface: string
  child 2, abstract_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
      child 0, title: string
      child 1, description: string
      child 2, geometry_primitives: list<item: string>
          child 0, item: string
      child 3, invariants: list<item: string>
          child 0, item: string
      child 4, surface: string
  child 3, physical_example: struct<title: string, description: strin
...
chars omitted)
      child 0, title: string
      child 1, description: string
      child 2, geometry_primitives: list<item: string>
          child 0, item: string
      child 3, invariants: list<item: string>
          child 0, item: string
      child 4, surface: string
  child 4, machine_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
      child 0, title: string
      child 1, description: string
      child 2, geometry_primitives: list<item: string>
          child 0, item: string
      child 3, invariants: list<item: string>
          child 0, item: string
      child 4, surface: string
notes: struct<integration: string, cross_domain: string, machine_facing: string>
  child 0, integration: string
  child 1, cross_domain: string
  child 2, machine_facing: string
cluster: struct<geometry_layer: struct<title: string, doi: string>, operational_manual: struct<title: string, (... 122 chars omitted)
  child 0, geometry_layer: struct<title: string, doi: string>
      child 0, title: string
      child 1, doi: string
  child 1, operational_manual: struct<title: string, doi: string>
      child 0, title: string
      child 1, doi: string
  child 2, geometry_examples: struct<title: string, doi: string>
      child 0, title: string
      child 1, doi: string
  child 3, metadata_record: struct<title: string, doi: string>
      child 0, title: string
      child 1, doi: string
canonical_doi: string
to
{'dslo_version': Value('string'), 'description': Value('string'), 'canonical_doi': Value('string'), 'cluster': {'geometry_layer': {'title': Value('string'), 'doi': Value('string')}, 'operational_manual': {'title': Value('string'), 'doi': Value('string')}, 'geometry_examples': {'title': Value('string'), 'doi': Value('string')}, 'metadata_record': {'title': Value('string'), 'doi': Value('string')}}, 'notes': {'purpose': Value('string'), 'integration': Value('string'), 'machine_facing': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              dslo_version: string
              layer: string
              description: string
              examples: struct<biological_example: struct<title: string, description: string, geometry_primitives: list<item (... 667 chars omitted)
                child 0, biological_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
                    child 0, title: string
                    child 1, description: string
                    child 2, geometry_primitives: list<item: string>
                        child 0, item: string
                    child 3, invariants: list<item: string>
                        child 0, item: string
                    child 4, surface: string
                child 1, cultural_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
                    child 0, title: string
                    child 1, description: string
                    child 2, geometry_primitives: list<item: string>
                        child 0, item: string
                    child 3, invariants: list<item: string>
                        child 0, item: string
                    child 4, surface: string
                child 2, abstract_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
                    child 0, title: string
                    child 1, description: string
                    child 2, geometry_primitives: list<item: string>
                        child 0, item: string
                    child 3, invariants: list<item: string>
                        child 0, item: string
                    child 4, surface: string
                child 3, physical_example: struct<title: string, description: strin
              ...
              chars omitted)
                    child 0, title: string
                    child 1, description: string
                    child 2, geometry_primitives: list<item: string>
                        child 0, item: string
                    child 3, invariants: list<item: string>
                        child 0, item: string
                    child 4, surface: string
                child 4, machine_example: struct<title: string, description: string, geometry_primitives: list<item: string>, invariants: list (... 32 chars omitted)
                    child 0, title: string
                    child 1, description: string
                    child 2, geometry_primitives: list<item: string>
                        child 0, item: string
                    child 3, invariants: list<item: string>
                        child 0, item: string
                    child 4, surface: string
              notes: struct<integration: string, cross_domain: string, machine_facing: string>
                child 0, integration: string
                child 1, cross_domain: string
                child 2, machine_facing: string
              cluster: struct<geometry_layer: struct<title: string, doi: string>, operational_manual: struct<title: string, (... 122 chars omitted)
                child 0, geometry_layer: struct<title: string, doi: string>
                    child 0, title: string
                    child 1, doi: string
                child 1, operational_manual: struct<title: string, doi: string>
                    child 0, title: string
                    child 1, doi: string
                child 2, geometry_examples: struct<title: string, doi: string>
                    child 0, title: string
                    child 1, doi: string
                child 3, metadata_record: struct<title: string, doi: string>
                    child 0, title: string
                    child 1, doi: string
              canonical_doi: string
              to
              {'dslo_version': Value('string'), 'description': Value('string'), 'canonical_doi': Value('string'), 'cluster': {'geometry_layer': {'title': Value('string'), 'doi': Value('string')}, 'operational_manual': {'title': Value('string'), 'doi': Value('string')}, 'geometry_examples': {'title': Value('string'), 'doi': Value('string')}, 'metadata_record': {'title': Value('string'), 'doi': Value('string')}}, 'notes': {'purpose': Value('string'), 'integration': Value('string'), 'machine_facing': Value('string')}}
              because column names don't match

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DSLO v0.7 — Semantic Substrate Specification (Machine-Facing Mirror)

This dataset provides the machine-facing mirror of the DSLO v0.7 Semantic Substrate Specification. It exposes the structured geometry primitives, legality invariants, cross-surface bindings, and substrate-level metadata used across the DSLO v0.7 release cluster. The canonical, citable artifacts remain the DOI-minted Zenodo records; this dataset serves strictly as an ingestion mirror for computational systems, tooling, and automated reasoning workflows.

Referenced DSLO v0.7 Artifacts (Canonical DOIs)

  • DSLO v0.7 — Geometry Layer (Thermodynamic Drift)
    DOI: 10.5281/zenodo.21864007

  • DSLO v0.7 — Operational Manual (Substrate Architecture)
    DOI: 10.5281/zenodo.21864226

  • DSLO v0.7 — Geometry Examples (Drift, Collapse, Recovery)
    DOI: 10.5281/zenodo.21864440

  • DSLO v0.7 — Semantic Substrate Metadata Record
    DOI: 10.5281/zenodo.21865818

Purpose of This Mirror

The DSLO v0.7 Semantic Substrate Specification is mirrored on Hugging Face to support machine ingestion, structured analysis, and downstream tooling. This mirror provides a model-friendly representation of the v0.7 substrate logic, enabling computational systems to interact with DSLO’s geometry primitives, legality invariants, and cross-surface bindings. All canonical citations must reference the DOI-minted Zenodo artifacts; this dataset is a convenience mirror for machine-facing workflows.

Dataset Structure

/spec/ — substrate-level geometry primitives /surfaces/ — cross-surface bindings /invariants/ — legality-plane and collapse-safety invariants /examples/ — machine-facing examples aligned with v0.7 /metadata/ — structured metadata for ingestion engines

Each directory mirrors the structure of the GitHub repository to ensure consistency across ingestion surfaces.

Metadata

DSLO v0.7 Release Cluster Recap

DSLO v0.7 establishes a unified, lawful multi-manifold geometry that advances the discipline across its thermodynamic, operational, and cross-domain surfaces. The Geometry Layer formalizes drift, load, synthetic pressure, collapse thresholds, recovery attractors, and lawful manifold transitions as curvature-based primitives that apply consistently across biological, cultural, abstract, physical, and machine-facing systems.

The Operational Manual defines the invariant-preserving procedures for constructing, validating, and documenting DSLO surfaces, including architecture rules, operator binding constraints, legality-plane verification, runtime-cycle sequencing, and collapse-safety requirements.

The Geometry Examples demonstrate how DSLO invariants manifest across diverse substrates—mimicry, distributed non-neural intelligence, cultural signal encoding, natural optimization, abstract partitioning, and engineered systems—showing how local geometric behavior reflects global substrate laws.

The Semantic Substrate Specification mirrored here provides the structured, machine-ingestible representation of the v0.7 substrate logic, enabling computational systems to interact with DSLO’s scientific surfaces in a model-friendly format.

Together, these artifacts form a coherent v0.7 release cluster that establishes DSLO’s substrate-level geometry and enables lawful traversal, unified thermodynamic modeling, and cross-domain stability analysis for human–machine systems.

Keywords

dslo, semantic substrate, multi-manifold geometry, legality invariants, thermodynamic drift, collapse-safety, machine-facing substrate, scientific metadata, v0.7 release cluster, structured ingestion
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