Create a contextual Record Set projection from observational resources

Constructs a lightweight contextual Record Set projection from an observational resource table.

Record Set is a contextual aggregation concept defined by the International Council on Archives (ICA) Records in Contexts standard (RiC).

In operational terms, a Record Set may represent:

• a project workspace;

• a synchronized cloud folder;

• a repository inventory;

• a digitisation batch;

• a web archive collection;

• a reconstruction corpus;

• or another contextual grouping of related digital resources.

The function is designed as an operational bridge between:

• filesystem observations;

• web archive inventories;

• digitised heritage collections;

• repository inventories;

• and later semantically enriched Record Set representations.

The returned object is intentionally a plain tibble rather than a semantically enriched recordset_df.

This allows:

• efficient tidyverse workflows;

• exploratory analytical pipelines;

• lightweight contextual reconstruction;

• deferred semantic stabilisation;

• provenance-aware iterative enrichment.

More information:

• ICA Records in Contexts overview: https://www.ica.org/ica-network/expert-groups/egad/records-in-contexts-ric/

• RiC-O ontology repository: https://github.com/ica-egad/ric-o

In RiC-aligned operational terminology:

• rows typically represent observed or derived Record Resources, Instantiations, or other operational resource proxies;

• the resulting tibble represents a contextual Record Set projection constructed from deterministic operational rules;

• the function does not create authoritative archival arrangement, fonds hierarchy, or curatorial description;

• Record Set semantics remain analytical and operational unless later stabilised through curatorial or semantic workflows.

Typical use cases include:

• grouping filesystem observations into project-level Record Sets;

• constructing analytical corpora from repository structures;

• creating candidate archival aggregations;

• preparing Heritage Digital Twin analytical spaces;

• contextualising WARC/WACZ collections;

• constructing enrichment workspaces for knowledge-graph workflows.

The function deliberately separates:

• operational contextualisation (create_record_set())

from:

• semantic publication and metadata enrichment (as_recordset_df()).

This mirrors the package philosophy used throughout the observational pipeline:

• observe first;

• contextualise second;

• interpret later.

Usage

create_record_set(
  x,
  record_set_id,
  resource_id,
  construction_rule,
  locator_path = NULL,
  resource_title = NULL,
  resource_type = NULL
)

Arguments

x

A data.frame or tibble containing observational or derived resource rows.

record_set_id

Character scalar or existing column name defining the contextual Record Set membership of each resource.

Typical examples include:

• structural filesystem groupings;

• repository roots;

• WARC collection identifiers;

• digitisation batches;

• curatorial aggregation identifiers.

resource_id

Character scalar or existing column name defining the operational identity of each resource within the Record Set.

In many filesystem workflows, resource_id will often correspond to what users informally think of as a "file" or "file name".

However, the identifier intentionally represents an operational or contextual resource approximation rather than an authoritative or permanent file identity.

This distinction matters because digital resources frequently evolve over time:

• filenames and paths may change;

• synchronized copies may diverge;

• local and cloud versions may coexist;

• files may be copied, renamed, or reorganised;

• multiple observations may refer to evolving versions of the same underlying resource.

For example:

• the same digital resource ("file") may exist in multiple locations;

• a synchronized cloud copy may differ from a local working copy;

• a renamed file may still represent the continuation of the same evolving digital resource.

Typical examples include:

• storage_path_id (storage-scoped filesystem resource approximation);

• URI identifiers;

• WARC record identifiers;

• repository-relative identifiers;

• IIIF resource identifiers.

construction_rule

Character description documenting the deterministic operational rule used to construct the contextual Record Set projection.

Examples:

• "filtered_project_roots|structural_group"

• "warc_collection|domain_partition"

• "iiif_manifest|folder_batch"

The construction rule is stored as lightweight provenance metadata attached to the resulting tibble.

locator_path

Optional character scalar or existing column name providing a human-readable operational locator associated with the resource.

Examples include:

• filesystem paths;

• repository-relative paths;

• URIs;

• WARC locators;

• IIIF resource paths.

resource_title

Optional character scalar or existing column name containing a human-readable resource title or label.

resource_type

Optional character scalar or existing column name describing the operational resource type.

Examples:

• "file"

• "warc_record"

• "iiif_canvas"

• "rdf_resource"

• "digitised_page"

Value

A tibble representing a contextual operational Record Set projection.

The resulting tibble contains:

• record_set_id

• resource_id

• optional contextual resource variables

together with lightweight provenance attributes:

• construction_rule

• created_by

• record_set_created_at

Details

The function intentionally performs only lightweight contextual projection and validation.

It does not:

• infer authoritative documentary hierarchy;

• enforce archival arrangement;

• construct RiC-complete semantic graphs;

• perform provenance reasoning;

• stabilise resource identity across time.

Semantic enrichment and publication-oriented metadata are intended to be added later via as_recordset_df().

This staged architecture supports:

• efficient analytical workflows;

• iterative reconstruction;

• provenance-aware contextualisation;

• future alignment with RiC-O and RiC-CM.

Examples

toy_record_set <- tibble::tibble(
  structural_group = c(
    "heritage_collection",
    "heritage_collection",
    "digitisation_batch"
  ),
  storage_path_id = c(
    "laptop01::scans/photo_001.tif",
    "laptop01::ocr/photo_001.txt",
    "archive01::reports/summary.qmd"
  ),
  rel_root_path = c(
    "scans/photo_001.tif",
    "ocr/photo_001.txt",
    "reports/summary.qmd"
  )
)

toy_record_set <- create_record_set(
  toy_record_set,
  record_set_id = "structural_group",
  resource_id = "storage_path_id",
  locator_path = "rel_root_path",
  construction_rule =
    "filtered_project_roots|structural_group",
  resource_type = "file"
)