fscontext provides a provenance-aware contextual reconstruction
framework for file systems and related digital resource collections.
The package creates reproducible observational snapshots of files, repository structures, and related operational resources, and supports their contextual abstraction, semantic stabilization, and reconstruction-oriented analysis.
# CRAN release
install.packages("fscontext")
# Latest development version
pak::pak("dataobservatory-eu/fscontext")
The package includes four introductory vignettes that follow the typical
fscontext workflow.
-
Introduction to fscontext introduces filesystem observations, reproducible snapshots, and contextual reconstruction.
-
Working with Record Sets demonstrates how observational data can be transformed into provenance-aware
recordset_dfobjects inspired by the Records in Contexts (RiC) conceptual model. -
Prelabelled values and semantic stabilisation introduces lightweight semantic enrichment, rulebooks, and human-in-the-loop refinement workflows.
-
Organising Evidence with Structural Aggregations demonstrates how structural aggregation metadata can identify potentially informative objects and candidate Record Sets across folders, ZIP archives, and WACZ packages.
Together these vignettes introduce the observational, contextual, documentary, and semantic layers of the package.
Many digital collections contain valuable contextual information but little documentation explaining how files, datasets, reports, source code, inventories, or digital surrogates relate to one another.
Examples include research projects spread across multiple repositories, digitised archival collections with evolving inventories, audiovisual production environments, long-running analytical projects, and shared drives that have accumulated over many years.
Before semantic integration, archival description, provenance modelling, or knowledge graph construction can begin, it is often necessary to reconstruct the context in which digital resources were created and used.
fscontext approaches filesystems as observational environments. Files,
folders, timestamps, repository structures, and other digital traces are
treated as evidence from which contextual structures can be
reconstructed.
Filesystem observations
↓
Snapshots
↓
Contextual reconstruction
↓
Record Sets
↓
Semantic stabilisation
↓
Knowledge systems
Rather than replacing archival description or provenance models,
fscontext focuses on the earlier task of contextual reconstruction.
The package is inspired by the archival conceptual model Records in
Contexts
(RiC), developed by the International Council on Archives. Rather than
implementing RiC-CM or RiC-O directly, fscontext focuses on the
earlier task of contextual reconstruction: deriving contextual
relationships and candidate Record Sets from filesystem observations,
repository structures, inventories, and other digital traces.
For more information, see:
- RiC-CM 1.0 (Records in Contexts Conceptual Model)
- RiC-O 1.1 (Records in Contexts Ontology)
The package includes two example filesystem snapshots derived from the
companion repository fscontextdemo.
The demonstration repository is available at: https://github.com/dataobservatory-eu/fscontextdemo
It contains a small but realistic digital work environment including source code, datasets, generated artefacts, documentation, tests, package metadata, semantic enrichment examples.
The snapshots, fscontextdemo_snapshot_01 and
fscontextdemo_snapshot_02, capture the repository at different points
in time, allowing reconstruction and longitudinal analysis workflows to
be demonstrated reproducibly.
library(fscontext)
data("fscontextdemo_snapshot_02")
fscontextdemo_snapshot_02 |>
subset(
select = c(storage_id, rel_path, filename, quick_sig)
) |>
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 quick_sig
#> 1 .gitignore db6ad734
#> 2 pkgdown.yaml 5eb4aaba_6cbfbdf4
#> 3 .gitignore e73cf12f
#> 4 .Rbuildignore 09ab8617
#> 5 fscontextdemo_snapshot_01.rda 03dd3533_36abd309_cb1736f4
#> 6 fsdemo_country_data.rda f7e65210The snapshot records observed filesystem resources together with contextual information such as relative paths, timestamps, extensions, and storage identifiers.
Contextual identifiers can then be added:
data("fscontextdemo_snapshot_02")
snapshot <- add_snapshot_context(fscontextdemo_snapshot_02)
snapshot |>
subset(
select = c(storage_path_id, observation_id, rel_path)
) |>
head()
#> storage_path_id
#> 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
#> observation_id
#> 1 fscontextdemo::.github/.gitignore::20260525-174640
#> 2 fscontextdemo::.github/workflows/pkgdown.yaml::20260525-174640
#> 3 fscontextdemo::.gitignore::20260525-174640
#> 4 fscontextdemo::.Rbuildignore::20260525-174640
#> 5 fscontextdemo::data/fscontextdemo_snapshot_01.rda::20260525-174640
#> 6 fscontextdemo::data/fsdemo_country_data.rda::20260525-174640
#> rel_path
#> 1 .github/.gitignore
#> 2 .github/workflows/pkgdown.yaml
#> 3 .gitignore
#> 4 .Rbuildignore
#> 5 data/fscontextdemo_snapshot_01.rda
#> 6 data/fsdemo_country_data.rdaThe examples above demonstrate only the observational layer. Subsequent workflows can derive contextual Record Sets, compare repeated observations over time, identify duplicate resources, analyse activity patterns, and support semantic stabilisation.
See the package vignettes for complete end-to-end examples.
The package separates three complementary analytical layers:
| Layer | Purpose |
|---|---|
| Observation | Observe filesystems and related digital environments as reproducible snapshots. |
| Context | Derive contextual identifiers, structural aggregations, and candidate Record Sets from observations. |
| Record Sets | Create lightweight documentary objects using recordset_df, inspired by RiC. |
| Semantic stabilisation | Support progressive semantic enrichment through prelabelled values, rulebooks, and human review. |
| Analysis | Compare snapshots, detect duplicates, reconstruct activity, and analyse evolving digital work environments. |
In RiC-inspired terms, filesystem observations represent observed
digital resources and their associated instantiations at a particular
point in time. These observations may later be aggregated into
contextual Record Sets while preserving the distinction between the
observed resources themselves and the contextual structures derived from
them. The framework intentionally separates observation, contextual
organisation, semantic stabilisation, and domain-specific
interpretation. This allows the same observational evidence to support
different analytical perspectives—including archival description,
business process reconstruction, software development, digital
forensics, historical research, and other forms of contextual
analysis—without conflating the evidence with its interpretation.
fscontext does not attempt to replace archival description, provenance
ontologies, or knowledge graph platforms. Instead, it provides a
reproducible observational and contextual layer that can support those
systems by making digital working environments easier to understand,
review, and reconstruct.
- Large scans may require substantial time on slower or networked storage systems.
- Some files may be inaccessible due to permissions or synchronization state.
- Observational snapshots are intended for reproducible local or institutional analysis workflows.
- Contextual and analytical layers may evolve independently from the original observational corpus.