What is fscontext?
fscontext is an experimental toolkit for observing,
contextualising, and reconstructing digital information
environments.
Many digital collections contain valuable contextual information, but that information is distributed across folders, filenames, inventories, metadata records, digital surrogates, spreadsheets, repositories, and other partially documented resources. Before semantic integration, knowledge graph construction, or archival description can begin, this contextual evidence must first be identified, organised, and interpreted.
The package provides a reproducible framework for treating filesystem structures and related metadata as observations. These observations can then be transformed into contextual groupings and candidate Record Sets that support further analytical, archival, and semantic workflows.
The package is particularly relevant for:
- born-digital archives;
- research repositories;
- shared drives and network storage;
- audiovisual production environments;
- digitised cultural heritage collections;
- provenance and reconstruction workflows.
Rather than treating files as isolated technical objects,
fscontext treats them as traces of activities, processes,
and documentary contexts.
Context before semantics
Many interoperability projects begin with semantic models, ontologies, or knowledge graphs. In practice, however, organisations often face a more immediate challenge: understanding the information environments they already possess.
A cultural heritage institution may hold thousands of digital surrogates whose relationship to physical collections is only partially documented. A research organisation may maintain decades of project folders spread across multiple drives and repositories. An audiovisual archive may preserve recordings, contracts, metadata, and production artefacts that evolved through complex workflows.
In such situations the primary problem is not semantic integration but contextual reconstruction.
Before records can be linked, classified, harmonised, or federated, it is often necessary to reconstruct how digital resources relate to projects, activities, collections, and people.
fscontext provides a reproducible observational layer
for this purpose.
The fscontext workflow
The package follows a layered workflow:
Filesystem observations
↓
Snapshots
↓
Contextualisation
↓
Record Sets
↓
Semantic stabilisation
↓
Knowledge systemsFilesystem observations capture what was observed at a particular point in time.
Contextualisation groups observations into meaningful analytical or operational contexts.
Record Sets provide higher-level documentary groupings inspired by the Records in Contexts (RiC) conceptual model.
Subsequent semantic stabilisation semantic stabilisation activities can then refine these structures into more formal semantic representations.
Creating a snapshot
The starting point is a filesystem snapshot.
snapshot <- scan_storage(root = "D:/projects")Snapshots are ordinary data frames containing observed filesystem resources and associated metadata.
Working with example data
The package includes two reproducible filesystem snapshots from the
companion package fscontextdemo that is available at https://github.com/dataobservatory-eu/fscontextdemo.
fscontextdemo is a deliberately constructed
demonstration repository designed to simulate a small but realistic
digital work environment. It contains source code, datasets, generated
artefacts, documentation, tests, package metadata, and semantic
enrichment examples. The repository was created specifically to support
reproducible experimentation with filesystem reconstruction, provenance
analysis, contextualisation, and Record Set construction workflows.
The two snapshots capture the repository at different stages of development. Between the first and second observations, additional artefacts, datasets, visualisations, and semantic enrichment workflows were introduced. This creates a realistic longitudinal example that can be used to explore how digital work environments evolve over time.
data("fscontextdemo_snapshot_02")
fscontextdemo_snapshot_02 |>
dplyr::select(storage_id, rel_path, filename) |>
head()
#> storage_id rel_path
#> 1 fscontextdemo .github/.gitignore
#> 2 fscontextdemo .github/workflows/pkgdown.yaml
#> 3 fscontextdemo .gitignore
#> 4 fscontextdemo .Rbuildignore
#> 5 fscontextdemo data/fscontextdemo_snapshot_01.rda
#> 6 fscontextdemo data/fsdemo_country_data.rda
#> filename
#> 1 .gitignore
#> 2 pkgdown.yaml
#> 3 .gitignore
#> 4 .Rbuildignore
#> 5 fscontextdemo_snapshot_01.rda
#> 6 fsdemo_country_data.rdaThese observations describe files that were present when the snapshot was created.
Adding contextual information
Filesystem observations can be enriched with contextual identifiers.
snapshots <- add_snapshot_context(fscontextdemo_snapshot_02)Additional structural groupings can then be derived.
snapshots <- dplyr::bind_cols(
snapshots,
derive_structural_groups(
snapshots$rel_path
)
)
snapshots |>
dplyr::select(
rel_path,
structural_group,
component
) |>
head()
#> rel_path structural_group
#> 1 .github/.gitignore .github/.gitignore
#> 2 .github/workflows/pkgdown.yaml .github/workflows
#> 3 .gitignore .gitignore
#> 4 .Rbuildignore .Rbuildignore
#> 5 data/fscontextdemo_snapshot_01.rda data/fscontextdemo_snapshot_01.rda
#> 6 data/fsdemo_country_data.rda data/fsdemo_country_data.rda
#> component
#> 1 <NA>
#> 2 pkgdown.yaml
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>This creates lightweight contextual structures that support later reconstruction and analysis.
Creating Record Sets
One of the central goals of the package is to derive contextual Record Sets from filesystem observations.
tmp <- tempfile(fileext = ".rds")
saveRDS(fscontextdemo_snapshot_02, tmp)
record_set <- snapshot_to_recordset_df(
person = utils::person("Jane", "Doe"),
snapshot_files = tmp,
roots = "D:/_packages/fscontextdemo",
record_set_id = "fscontextdemo"
)Record Sets provide contextual documentary groupings derived from filesystem evidence.
set.seed(12)
record_set |>
dplyr::select(record_set_id, filename, quick_sig, size) |>
sample_n(10)
#> Doe (2026): The fscontextdemo filesystem record set [dataset]
#> record_set_id filename quick_sig size
#> <chr> <chr> <chr> <dbl>
#> 1 fscontextdemo hello_world.html 616bd020_0ff89400_e236c2f1 6200
#> 2 fscontextdemo fsdemo_country_data.Rd ac7b4bcb 704
#> 3 fscontextdemo fa-v4compatibility.woff2 5d71f69a_2d446c09_ba3911cf 4792
#> 4 fscontextdemo index.md 8a70c24d 339
#> 5 fscontextdemo test-label_country_data.R 0a2f05ba 926
#> 6 fscontextdemo data-deps.txt 53d99066 898
#> 7 fscontextdemo NAMESPACE d990970e 122
#> 8 fscontextdemo v4-shims.min.css 97175dde_efdbb55a_9606c83f 27593
#> 9 fscontextdemo jQuery.headroom.min.js 7a8d4ff3 589
#> 10 fscontextdemo README.Rmd ef654eb9_4d94e660 1671Repeated observation
A single snapshot provides a static view.
Multiple snapshots allow longitudinal analysis.
observe_universe(
snapshot_dir = snapshot_directory,
max_aggregation_depth = 2
)Repeated observations support analysis of:
persistence;
duplication;
growth;
disappearance;
structural change.
Semantic stabilisation
Filesystem observations often contain ambiguous or incomplete information.
fscontext supports progressive semantic enrichment
through semantic stabilisation workflows.
These workflows allow observations to be refined incrementally while preserving the underlying observational evidence.
Relationship to Records in Contexts
The package is inspired by the Records in Contexts (RiC) family of models.
It is not a formal implementation of RiC-CM or RiC-O.
Instead, it provides practical tools for moving from filesystem observations toward contextual documentary representations that may later support RiC-aligned workflows.