2024 11 07-ish Simplification

simplification 1 (dave)

I've decided that namespaces should just be namespaces, although they are still NamedIndividuals. they can contain other NamedIndividuals, including Datasets (as long as those datasets are completely expressed in a single file), or probably even DatasetSeries (which are also NamedIndividuals), and they maybe can contain sub-namespaces. What I was thinking of before as a SFSiteRepo, basically a collection of namespaces, cam probably just be the user/org-level namespace site, and it's optional. The namespace sites are independent, can publish themselves using different SSG at different times. SFDataRepos are either for non-published data, or are themselves namespace repos (that can optionally nest under existing namespace and

simplification 1 (chatgpt)

Your updated approach shows some important conceptual clarifications. Let’s break down the implications of these decisions and how they streamline the overall organization of Semantic Flow:

Namespaces and NamedIndividuals

Namespaces as NamedIndividuals:
- Treating namespaces as NamedIndividuals simplifies the overall system by maintaining consistency—everything significant, whether an entity or a namespace, is now explicitly identifiable and traceable as a NamedIndividual.
- This also aligns well with the idea of namespaces serving as containers that can have attributes and relationships, making them integral parts of the RDF graph rather than purely abstract labels. It keeps namespaces concrete while allowing them to participate in the graph in a meaningful way.
Containing NamedIndividuals and Datasets:
- Allowing namespaces to contain other NamedIndividuals, Datasets, or DatasetSeries provides a more flexible structure. The requirement that datasets must be completely expressed in a single file ensures the simplicity of versioning and distribution, which is practical, especially when dealing with immutability and bitemporality.
- This means that datasets won’t get fragmented across multiple files within a namespace, making the management of each dataset more straightforward. This way, a dataset is treated as a holistic unit, which aligns well with the idea of “graph snapshots” at a particular point in time.
Sub-Namespaces:
- The possibility of sub-namespaces provides flexibility for hierarchical organization where needed. Sub-namespaces could help you logically partition large datasets or projects into smaller, more manageable sections without complicating relationships at the RDF level.
- Importantly, you have the choice of how granularly you want to organize namespaces. A single, broad namespace could suffice for some cases, while others might benefit from breaking down into more focused sub-namespaces.

Reconsidering SFSiteRepo and Namespace Sites

User/Org-Level Namespace Site:
- By redefining what was previously envisioned as an SFSiteRepo to simply be a user/org-level namespace site, you’re making a crucial simplification:
  - Instead of a dedicated repository type specifically for holding collections of namespaces, each namespace site can be independently managed and published.
  - The user/org-level namespace site becomes an optional umbrella for organization-level management, but is not mandatory—promoting more flexibility and reducing interdependencies.
Independent Namespace Sites:
- Allowing namespace sites to be independent and publish themselves using different SSGs at different times introduces a highly modular and scalable approach.
- Each namespace can evolve and publish at its own pace, using tools that fit its unique needs. This means you’re not tied to one static site generator for all namespaces, which can be particularly useful if namespaces differ significantly in scope, audience, or content requirements.
- It also means namespaces are loosely coupled, making your system more resilient to changes. If one namespace’s setup changes, others are unaffected, maintaining consistency and stability across your ecosystem.

SFDataRepos and Namespace Repos

SFDataRepos as Non-Published or Namespace Repos:
- SFDataRepos now serve a dual purpose:
  - For non-published data, acting as working repositories that contribute RDF but aren't meant for direct exposure or public consumption.
  - For namespace repositories, they can be used to publish complete namespaces, which adds flexibility in how data and NamedIndividuals are managed and versioned.
- This dual-role approach avoids over-defining repository types, allowing SFDataRepos to be either purely backend or fully integrated into the public-facing namespace publication process.
Optional Nesting Under Existing Namespace:
- Allowing namespace repositories to optionally nest under existing namespaces introduces another layer of granularity. This supports more complex projects that may require subdividing namespaces but keeps it optional to avoid unnecessary complications.
- This flexibility means namespaces can scale organically—starting as independent, simple entities and optionally evolving into a nested setup if complexity increases.

Streamlining Versioning and Publishing

Versioning: Since datasets within a namespace must be complete and expressed in a single file, version control becomes simpler. There’s no ambiguity about the contents of a dataset version, reducing confusion and eliminating the need for tracking fragmented pieces across multiple files.
Publishing Independence: The decision to allow different SSGs for namespace sites means that the focus can shift to what best fits the use case and audience of each namespace. Some namespaces may need a very dynamic approach (like using Docsify), while others may benefit from a more performance-oriented static site setup (like Metalsmith).

Considerations Moving Forward

Dataset Modularity:
- Ensure that the single-file rule for datasets is viable for larger or more complex datasets. If you find that complexity makes this impractical, you might need to consider splitting datasets while maintaining cohesion through clever IRI use or named graphs.
SSG Tooling and Ecosystem:
- With each namespace potentially using a different SSG, think about how you will maintain consistency and quality across all these namespaces. Setting a baseline standard for what each namespace site should include—such as a minimum set of metadata, navigation, or documentation—can be useful.
Documentation and Discoverability:
- The more distributed and independent namespace sites become, the more critical discoverability and navigation across namespaces will be. It might be worthwhile to develop an overarching catalog or cross-referencing system that provides a unified view across independently published namespaces, possibly using RDF catalogs to link everything together.

Conclusion

This realization is a significant leap towards simplifying your Semantic Flow ecosystem by removing unnecessary constraints and embracing the inherently non-hierarchical nature of semantic data. By treating namespaces as modular, independently publishable entities, and SFDataRepos as flexible contributors, you’re aligning your approach more closely with the semantic nature of RDF. This makes your system more adaptable, scalable, and less rigidly defined by arbitrary organizational constraints, ultimately facilitating both development and collaboration.

no sub-namespaces (dave)

I'm reconsidering the option of sub-namespaces. I think a published repo as a namespace is broad enough that sub-namespaces are not necessary, and having a site per namespace (or namespace per site) aligns with the github pages approach.

no sub-namespaces (chatgpt)

That makes a lot of sense, especially considering how well it fits with GitHub Pages' structure. Keeping things simple with one namespace per site avoids unnecessary complexity and allows each namespace to maintain its own independent lifecycle and publication workflow. This approach will likely make managing and scaling your namespaces far more straightforward, especially when leveraging GitHub's existing features for version control and hosting. It keeps everything modular and clearly scoped.

Let me know if you need help refining how to implement this streamlined approach!

reference page customization (dave)

Thinking about how the reference pages for each NamedIndividual are going to be built. The source won't be Markdown, it'll be at least partially RDF, specifically the _default and _catalog datasetseries. Just RDF plus a template should be able to generate most pages, but you should be able to override any template per NamedIndividual and use completely custom HTML or an HTML template (ejs?) so you almost need a CMS more than an SSG.

reference page customization (chatgpt)

This shift towards a more CMS-like solution makes sense, especially given the level of customization you want for each NamedIndividual. Here are some considerations that could help you navigate this approach:

Template Flexibility and Overrides

Default Templates vs. Custom Overrides:
- Your idea of using a default template with RDF data for most pages is efficient. This way, the bulk of the content can be generated systematically.
- However, providing custom overrides per NamedIndividual allows for tailored presentations. Using something like EJS gives the flexibility to blend templating logic with raw HTML, which might be necessary when particular NamedIndividuals require unique formatting or additional functionality.
Dynamic Template Selection:
- A mechanism for template selection per NamedIndividual could be helpful. For instance, a configuration in the _catalog DatasetSeries might specify which template to use for each NamedIndividual, or even indicate if a custom HTML file should be utilized.
- This way, the rendering system would first check for a custom template, then fall back to the default if no override is specified.

CMS-Like Features vs. Static Site Generation

SSG with CMS Features:
- The requirements you're describing—dynamic page generation based on data and customizable per-entity templates—fall somewhere between an SSG and a CMS.
- You could consider building a hybrid approach, where RDF data is processed through a templating engine, but where the tooling also has some CMS-like features for managing templates and configurations:
  - For instance, building a UI (or even a simple CLI) that allows you to easily define which template each NamedIndividual uses could provide a more CMS-like workflow while keeping the build process static.
Dynamic Content Integration:
- While keeping the generation mostly static, you could consider JavaScript-driven components for some dynamic functionality. This would allow pages to have some real-time features without converting entirely to a dynamic CMS.
- For instance, dynamically loading related NamedIndividuals or supporting client-side querying of linked data could enrich the static content.

Tech Stack and Implementation

Templating Engines:
- Using EJS or even Handlebars.js could be a good option since both are compatible with JavaScript environments like Deno or Node.js, which aligns with the rest of your stack.
- These engines allow you to inject RDF data directly, providing flexibility for custom layouts without complicating the pipeline too much.
RDF Parsing and Data Transformation:
- You’ll need to integrate RDF parsing to convert the _default and _catalog DatasetSeries into a format that the templating engine can use (e.g., JSON). You might use a library like rdflib.js or N3.js for this purpose.
- Once parsed, the transformed RDF data can be passed into templates to generate the HTML pages, ensuring consistency between the data model and the generated documentation.
Custom HTML as Overrides:
- Allowing full custom HTML overrides for some NamedIndividuals gives authors complete freedom when necessary. You might manage these overrides by checking if an override file exists for a particular NamedIndividual during the build process.
- If a custom HTML file exists, use that for rendering instead of the default templating workflow.

CMS-Like Management Layer

Editing and Metadata Handling:
- For content that needs frequent updates, consider a basic CMS-like interface that allows you to manage RDF data directly, specify which templates should be used, and preview the generated HTML.
- This interface wouldn’t need to be a full CMS but could focus on metadata management, template selection, and triggering re-generation of the static pages.
Git-Based CMS:
- You might consider a Git-based CMS like Netlify CMS or Tina that can integrate with a GitHub repository to provide a user-friendly interface for editing RDF data files and managing templates. This approach would allow non-developers to make changes through a UI while maintaining version control.

Challenges and Considerations

Complexity vs. Simplicity:
- Introducing a CMS-like layer might add some complexity to your build and deployment processes. To keep things manageable, it’s important to define which NamedIndividuals require custom treatment and which can be handled generically.
- A registry or manifest file that lists NamedIndividuals and their rendering options could help keep track of which pages are custom vs. generated.
Rendering Workflow:
- The overall rendering workflow would likely involve:
  - Parsing RDF datasets to extract relevant data.
  - Checking if a custom HTML template exists for the NamedIndividual.
  - If not, applying the default EJS/Handlebars template.
  - Rendering the page and saving it to the docs folder for publishing.
Maintenance:
- Allowing customizable HTML means you’ll need to manage version compatibility of the templates or HTML files. Keeping templates modular and limiting the complexity of per-individual customizations could help avoid scenarios where future updates break compatibility with existing custom pages.

Conclusion

You’re heading toward a hybrid model that incorporates both the strengths of an SSG and the flexibility of a CMS. By treating namespaces and NamedIndividuals as modular units with customizable templates, you’re optimizing for both consistency and flexibility. The key will be striking the right balance between automation (using RDF and default templates) and customization (allowing HTML overrides and dynamic templating where needed). This hybrid approach will likely lead to a versatile publishing system that caters well to the needs of your diverse semantic data landscape.

Let me know if you'd like me to help sketch out any specific aspect of this implementation in more detail!

implicit vs explicit IRI minting (dave)

There are two ways to mint an IRI: explicitly and implicitly. Explicitly is via creating a namespace folder for the Named Entity and an index.html file inside it so that the IRI is dereferenceable. Implicitly happens when a scan of an RDF source (could be a dataset or dataset series that's already in the namespace, or it could be an external dataset or a SPARQL-compliant service) for IRI references within the namespace.

implicit vs explicit IRI minting (dave)

This dual approach to minting IRIs offers both control and adaptability:

Explicit minting ensures that key entities are reliably and manually curated, making sure that they have clear, discoverable references.
Implicit minting allows you to grow the semantic web dynamically, as you parse and incorporate references from RDF sources, reducing the need for manual intervention.

This combination provides a flexible but structured way to manage entities, balancing between the rigor of explicit control and the efficiency of automatic discovery. If you need help refining either method—like setting policies for implicit minting or tooling for explicit creation—just let me know!

period-delimited hierarchical identifier parts (dave)

I've been toying with the idea of using dendron-style hierarchical (period-delimited). This way the data repo is flat, but NamedIndividuals can be hierarchically qualified.

period-delimited hierarchical identifier parts (chatgpt)

Adopting dendron-style hierarchical, period-delimited naming could offer an elegant solution to balancing flat data structures with the need for hierarchical qualification:

Flat Data Repo: Keeping the data repository flat avoids unnecessary complexity in the file system, making it easier to manage and scale without worrying about nested directories.
Hierarchically Qualified IRIs: Using a period-delimited structure for NamedIndividuals allows you to effectively encode relationships and hierarchies within the names themselves. This gives the semantic richness of hierarchy without the rigid structure of a nested file system.

This could simplify a lot of operational aspects—like versioning, searching, and visualization—since the hierarchy becomes implicit in the naming rather than explicit in the folder layout. Let me know if you need help exploring specific naming conventions or if you want to see potential examples of how this might look in practice!

datasetfile (dave)

I'm thinking to differentiate a DatasetFile from a Dataset, but it's problematic. A DatasetFile (needs a better name) is a dataset that's contained in a single file. It can have multiple distributions. It's not just a dataset, because a dataset could be represent in memory. A file could have multiple datasets (e.g., via named graphs), but a datasetfile dataset is like a distribution, but it isn't.

I was thinking the datasetfile isn't the physical representation. It's an abstract thing that would still have distributions, but it represents a dataset that contained in a single file, as opposed to in memory or multiple files.

datasetfile (chatgp)

Thanks for clarifying that. Let's refine the concept with that distinction in mind:

Clarifying DatasetFile as an Abstract Entity

The DatasetFile is an abstract construct representing a dataset that is logically contained within a single file, but it still has distributions like other datasets.
It describes a dataset that is cohesive within a single-file context, regardless of the specific serialization or storage. This abstraction serves to distinguish it from datasets that are distributed across multiple files or are managed in-memory.

Key Characteristics of DatasetFile

Abstract Nature:
- DatasetFile isn't a specific serialized version of a dataset; rather, it represents the concept of a dataset that is entirely contained within a single physical file. It indicates that the boundaries of the dataset are well-defined within that file, but it could still be instantiated in various formats (i.e., distributions).
Multiple Distributions:
- Even though it is logically within a single file, a DatasetFile can have multiple distributions, such as Turtle, JSON-LD, or N-Triples. Each distribution provides a different way to access or interact with the data, but they all conform to the logical structure defined by the DatasetFile.
- The DatasetFile acts as a boundary to define what data is included, but the specific way the data is serialized can vary.
Single File vs. In-Memory or Fragmented:
- A DatasetFile is different from datasets that are fragmented across multiple files, as well as from datasets that exist in-memory without any clear file boundaries. It is a more compact and manageable abstraction that makes it clear the dataset is logically contained within a singular file.

Naming Reconsiderations

BoundDataset:
- Given the updated abstraction, BoundDataset might be a good fit. It suggests that the dataset is logically bound to a single cohesive structure (in this case, a single file).
ContainedDataset:
- This name highlights that the dataset is contained within a single, unified context. The "contained" part emphasizes that there are no parts external to the file—everything about the dataset is logically within one file.
SingularDataset:
- SingularDataset continues to make sense, given that it suggests the dataset is conceptually singular in its boundary. It’s one unified entity, regardless of the different distributions it might have.
AtomicDataset (Revisited):
- Atomic could be suitable if you want to emphasize that this dataset cannot be divided across files, implying that it is the smallest cohesive unit with everything needed contained within it.

Implications for Semantic Flow

Versioning and Distribution:
- A DatasetFile would still have versioning tied to it, allowing different versions to exist that each represent the complete dataset within a single file. This could also mean that each version could have different distributions, like one version might only have a JSON-LD representation, while another might also have a Turtle distribution.
Metadata and Reference Management:
- Metadata for a DatasetFile could explicitly note that the dataset is fully represented by a single file, distinguishing it from datasets that might aggregate multiple files or pieces.
- Reference Management becomes easier, as the DatasetFile can always be referenced as a single coherent entity, ensuring that any consumers of the data know they are accessing a complete and unified set.
DatasetFile and Dataset Relationship:
- The DatasetFile represents a type of dataset, with the key property being that it’s self-contained within a single file, but it’s still distinct from the physical manifestation of that dataset. It remains abstract and could be instantiated into different physical distributions.
- In a way, a DatasetFile is a commitment to how the dataset is organized, while the actual distributions are commitments to how it is accessed.

Next Steps and Considerations

Define Metadata Clearly: Make sure there is metadata that captures the nature of a DatasetFile as an abstract, cohesive entity, and clarify how it differs from general datasets.
Ensure Consistent Terminology: As you work towards building Semantic Flow, having clear and consistent definitions for DatasetFile, Dataset, and Distribution will be crucial to avoid ambiguities.
Relationship to Distributions: Clearly define the relationship between the abstract DatasetFile and its distributions—potentially as a part-whole relationship where the DatasetFile is abstract, and its distributions are the different concrete realizations of the abstract dataset.

If you need help drafting these distinctions more formally or integrating them into your Semantic Flow workflow, feel free to ask!

data folder (dave)

I was thinking that in the "data" folder, there would be a file, say dave-richardson.ttl. That would correspond to the _default datasetseries for the {namespace}/dave-richardson IRI. Update the file and bump (or evolve, or update, or ???) the version, and we get a new version created, e.g. {namespace}/dave-richardson/dss/_default/v2, which corresponds to a new (overwritten) {namespace}/dave-richardson/dss/_default/current. We'd want the "bump" command to make sure there were no duplicate named parts, i.e., dave-richardson.jsonld.

data folder (chatgpt)

Your approach has a clear and logical flow for managing versioning of DatasetSeries. The idea of maintaining a _default DatasetSeries and evolving it with a version bump helps ensure that every update has a distinct version, while keeping a consistent "current" reference.

Version Management Considerations

"Bump" Command Process:
- The "bump" command should enforce uniqueness of each distribution to prevent any accidental duplication. Before proceeding, it could run checks against existing distributions to verify that the newly updated version doesn't have any naming conflicts.
- This helps avoid ambiguity, especially when dealing with multiple formats (Turtle, JSON-LD, etc.). The command could be designed to:
  - Validate that no duplicate named distributions exist for the current or previous versions.
  - Automatically adjust the distribution names if conflicts are detected (e.g., appending version identifiers).
"Bump" vs. "Evolve":
- The terminology is important. The term "bump" implies incrementing in a controlled way (like a version number), which works well for indicating a new, distinct state.
- "Evolve" could also be a good fit if you want to emphasize more of an iterative, natural progression of the dataset over time.
- Alternatively, "update" feels a bit generic and might imply changes to the same version rather than creating a distinct new version.
- Perhaps using "promote" might capture the idea that a "draft" or working version is moved forward as the latest "current" version.
Linking Current and Versioned Series:
- Keeping a {namespace}/dave-richardson/dss/_default/current pointer to the most recent version is an excellent practice for maintaining easy access. This pattern is consistent and helps ensure that users or systems looking for the latest data always have a simple way to get it.
- You could consider implementing a history file within each series to document changes, which can be automatically updated during a version bump. This would make it easier to track changes over time and provide context for why certain versions exist.

Ensuring Dataset Integrity

Validation Checks:
- The "bump" command should include validation checks to ensure dataset integrity—such as verifying that:
  - All named parts are unique across formats and versions.
  - No conflicting IRIs are present within the updated dataset, which could lead to confusion or broken links.
Named Parts Uniqueness:
- When it comes to duplicate named parts, like ensuring there isn't both dave-richardson.jsonld and another conflicting distribution, the bump command could also consider a naming convention that makes each version distinct:
  - Including a version identifier in each distribution file's name can prevent accidental overwrites.
  - For example: dave-richardson-v2.ttl or dave-richardson-v2.jsonld.

Automation and Workflow

Automated Workflow:
- An automated CI/CD-style workflow could be helpful to handle the version bump:
  - Commit Hook: A commit hook can be added to automatically check for modifications in the dataset file (dave-richardson.ttl), and if an update is detected, trigger the bump command.
  - New Version Generation: After validation, it could automatically generate a new dataset version and update the _default/current reference.
  - Changelog Update: It could also update a changelog for the dataset, summarizing what has changed between versions.

User-Friendly Command Structure

A user-friendly CLI command might look like:
```
sf-cli bump-version {namespace}/dave-richardson --check-uniqueness
```
This command could:
- Increment the version of the _default DatasetSeries.
- Update the current pointer.
- Validate uniqueness across distributions.
- Optionally output a summary of changes or errors found.
Alternatively, you could add an "evolve" option if you want different stages of the version lifecycle:
```
sf-cli evolve {namespace}/dave-richardson --draft
```
This could create a draft version (v2-draft) that’s distinct from a full, bumped release, providing more flexibility for previewing changes before they are fully promoted.

Summary

Your versioning system seems well-thought-out, with a balance between control and flexibility. The key aspects—ensuring no duplicate parts, keeping a consistent "current" reference, and allowing easy, automated bumping or evolving of versions—are practical and help maintain clear data integrity. If you'd like, I could help sketch out more specific command workflows or automation scripts to make this process as seamless as possible.

getting rid of _next and current (dave)

So the "_next" version of a dataset (in a dataset series) might not need representation in a namespace, because you wouldn't edit it inside the docs folder, you edit it in the "data" folder (formerly known as the src folder). changes in the data folder can get checked in, but until they're "promoted" (need a better word) there's no new version created in the namespace. To actually publish the new version of course, you have to commit it after one or more bumps and then run the SSG. (Which can be automated, of course.) If you had the docsify approach, you wouldn't need to run the SSG, a new dataset series version would be published automatically when the changed "data" folder commit happened. You also might not need the "_current" dataset because that could exist at the {ns}/dave-richardson/index.{jsonld|trig} file, likely as a named graph. The default graph would point to the catalog a _default series, and the actual data could be in a named graph that parotted the IRI for the current version.

getting rid of _next and current (dave)

Your evolving approach significantly refines the workflow and infrastructure of Semantic Flow, especially with the introduction of "_next" and rethinking the role of "_current." Here are some reflections and suggestions:

Editing in the Data Folder ("_next" Concept):
- It makes sense that "_next" versions reside in the "data" folder. This keeps the editing separate from what’s publicly visible, reducing the risk of exposing incomplete or draft work.
- "_next" as a Stage: Rather than being a published version, "_next" is effectively the working stage. It exists in the background until it's deemed ready for release. This helps prevent accidental exposure of in-progress data and keeps the "docs" folder clean and focused on stable versions.
- The term "promoted" seems useful for describing the movement from "_next" to a publicly visible state, but it could potentially be more precise. Consider terms like:
  - "Finalized" or "Stabilized": Emphasizes readiness and that the dataset has reached a stable state.
  - "Released" or "Published": Highlights the dataset moving into an accessible, publicly visible state.
Automated Workflow and Docsify Implications:
- With a Docsify-style dynamic generation, the SSG step could be skipped altogether, as the content would update automatically on commit. This is particularly appealing for a more fluid and agile workflow where changes can be rapidly iterated and pushed to production.
- Automated Publishing: As you note, this would mean that any commit to the "data" folder that triggers a "bump" would automatically update what is seen at the namespace level without having to manually regenerate static pages. This approach would favor rapid iteration cycles.
Rethinking "_current":
- "_current" dataset representation could be simplified if it lives directly at {ns}/dave-richardson/index.{jsonld|trig}. This would effectively make the "current" version inherently part of the namespace representation, avoiding the need for an explicit "_current" folder.
- Using named graphs:
  - The default graph could serve as a pointer, leading to the _default catalog or dataset series.
  - The actual data could reside within a named graph, using an IRI that mirrors the current version, providing clarity on what constitutes the current state of the dataset.
Default and Named Graph Strategy:
- This strategy elegantly keeps metadata (default graph) and dataset content (named graph) in the same location, making it easier to manage and discover data:
  - Default Graph: Contains general information about the dataset series, such as the current version and links to other versions or metadata.
  - Named Graph: Holds the actual dataset, ensuring that it’s scoped to a specific version and allowing for easy versioning and updating within the same dataset file.
- By using named graphs, you also get flexibility—if you need to revert to a prior version or link to specific historical data, it’s easier to reference by named graph IRI.

Benefits and Considerations of the Updated Workflow

Separation of Stages:
- The clear delineation between "_next" (working), "_default" (stable version catalog), and published representations enhances the clarity of data management. It lets you clearly track the lifecycle of each dataset—whether it’s being edited, stabilized, or already released.
Simplification and Discoverability:
- Simplifying "_current" by making it directly accessible at a stable IRI (index.jsonld or index.trig) helps make the latest data easy to find without unnecessary layers of abstraction.
- This approach also lends itself to easy dereferencing—any consumer of the data can simply hit a namespace IRI and immediately access both metadata and data, reducing friction for discovery and use.
Reducing Redundancy:
- Removing the explicit "_current" folder reduces redundancy and simplifies maintenance—less potential for synchronization issues between representations. Everything is unified at the namespace level, and the structure becomes easier to understand and maintain.

Potential Next Steps and Challenges

Implementation of Named Graphs:
- Ensure that your tooling, both for editing RDF data and for the SSG or Docsify-like approach, can effectively handle named graphs within a dataset. This will be critical for managing different versions effectively, especially if each version needs to be independently queryable or referenceable.
Version Bumping and Publishing:
- The "bump" process will need robust integrity checks—such as ensuring there are no duplicate named parts and verifying that the new version conforms to the expected structure.
- Consider building a lightweight pre-publication review system that could check changes in the "_next" stage before "promotion," ensuring consistency and avoiding mistakes that might end up in a public namespace.
Cross-Version Relationships:
- Managing cross-version relationships might be a bit trickier with "_next" and "_default" split across folders. You'll want to think about how these relationships are maintained, particularly if a "_next" version changes something significant that impacts other datasets.
Discoverability in a Dynamic Setup:
- With a dynamic setup (e.g., Docsify), managing discoverability becomes more about managing metadata effectively. Make sure each NamedIndividual or dataset has appropriate metadata, potentially including information about stability (draft, stable, deprecated) to help users and systems understand the dataset's lifecycle.

Conclusion

Your updated workflow is moving towards a more natural and streamlined approach to handling dataset versions and making them discoverable. By reducing redundancy, embracing "_next" as an intermediate stage, and potentially leveraging Docsify for automated publishing, you’re simplifying both the management overhead and the consumer experience. This could make Semantic Flow an even more flexible and powerful tool for RDF data publishing.

Let me know if you'd like more detailed feedback on any specific part of this workflow or further help in refining these concepts!

finalizing (dave)

I like "release" as the verb/command for evolving the dataset series. I'd forgotten, I'd introduced the vset folder to refer the datasetfile in the abstract. You would also get rid of the catalog's _current folder and integrate the _catalog/_vset/v2/index.ttl into the NamedIndividual's current default distribution. So dave-richardson/index.trig would have a default graph with pointers, a catalog graph with graph name corresponding to the latest catalog vset, and a default graph corresponding to its latest version. Maybe vset (for versioned dataset) instance are type DatasetFile, or maybe they're even the same thing I was calling a DatasetFile.

finalizing (chatgpt)

This updated structure and terminology help align the concepts more clearly and introduce a cohesive way to handle versioning and dataset evolution:

Key Changes and Their Implications

"Release" as the Command:
- Using "release" as the command to evolve a DatasetSeries is effective. It clearly conveys that a dataset version is moving from a draft or "_next" state into a published, stable state. It fits naturally into the lifecycle terminology and suggests readiness for public consumption.
Role of vset for Abstract Representation:
- Introducing a vset folder to represent the DatasetFile in the abstract provides a useful separation between the conceptual dataset and its specific versions. This gives you a clear way to:
  - Reference the dataset conceptually without committing to a particular version.
  - Maintain consistency across versions by abstracting common metadata or relationships to the vset level.
Eliminating _current Folder:
- By removing the _current folder and instead integrating _catalog/_vset/v2/index.ttl into the NamedIndividual's current distribution, you’re simplifying the representation.
- This approach makes dave-richardson/index.trig a centralized place for accessing:
  - A default graph with pointers to the various components.
  - A catalog graph, where the graph name matches the latest vset catalog.
  - A default graph pointing to the latest dataset version, giving a comprehensive overview of the NamedIndividual's current state.
Catalog and Dataset Representation:
- Having a catalog graph within the NamedIndividual's index.trig is a smart way to keep everything in one accessible location, reducing redundancy and ensuring that the latest catalog version is always easy to find.
- The default graph corresponding to the latest version of the dataset makes the data immediately accessible and up-to-date, aligning the namespace with the most current information without the need for additional layers like _current.

DatasetFile and `vset` Relationship

It seems like vset could indeed serve the role that DatasetFile was intended to play, but perhaps with a more refined scope:
- DatasetFile as previously conceptualized was a way to describe a dataset fully contained in a single file.
- vset is a logical extension of this—representing not only the dataset but also its versioning and relationship to the broader dataset series.
- You could consider making vset instances of type DatasetFile, implying that a vset instance is a specific manifestation of a DatasetFile that contributes to the versioned representation of the dataset series.
Unified Concept or Distinction?:
- If vset and DatasetFile are to be unified, it would simplify the hierarchy, reducing the need to differentiate between different kinds of abstract dataset representations.
- However, if there’s a need to distinguish between versioned (i.e., vset) and non-versioned, abstract DatasetFiles, you might keep them separate, where a DatasetFile is a more general concept and a vset is a specialized versioned instance.

Implementation Details and Workflow

Versioned Dataset Representation:
- The vset approach gives you a centralized place for storing metadata and version-specific details.
- Each release would generate a new folder within the vset, such as _vset/v2/, containing the dataset files (index.ttl) along with any distributions.
Default Graphs and Catalog Graphs:
- The default graph in dave-richardson/index.trig serves as a cohesive entry point, providing context and links to:
  - The latest dataset version.
  - The latest catalog version.
- Including catalog graphs directly within index.trig allows for efficient lookup and dereferencing by both humans and machines, keeping everything centralized.
Automation Considerations:
- Automating the "release" process could involve ensuring that:
  - The vset folder is updated.
  - The catalog graph in index.trig is appropriately updated to reflect the new release.
  - Validation checks are performed before a release to make sure all named parts are unique and consistent across versions.
Publishing Workflow:
- When a dataset in _next is released, it becomes part of the vset and the namespace representation is updated accordingly:
  - Update the catalog graph within the index.trig.
  - Set the default graph to the latest version.
  - Optionally generate a changelog that summarizes what changed between versions.

Next Steps

Clarify DatasetFile vs. vset:
- Determine if it makes sense to collapse DatasetFile and vset into a single concept or if there are meaningful distinctions that justify keeping them separate. If vset works well as an evolution of DatasetFile, it may reduce complexity.
Implement the "Release" Command:
- Finalize the "release" command to manage all aspects of evolving a dataset from _next to _vset. This command should:
  - Validate data integrity.
  - Update the appropriate parts of the index.trig.
  - Trigger necessary automations (like running the SSG if needed).
Update Documentation and Metadata Standards:
- Make sure that the documentation is updated to reflect the new role of vset, and ensure that all metadata standards accommodate the changes, including:
  - How catalogs reference vset instances.
  - How default and catalog graphs are structured to maintain consistency.

This approach seems to be converging on a highly modular and organized system that captures versioning, publishing, and discovery in a coherent way. Let me know if you’d like further assistance with specifying these commands or refining the catalog and dataset structures!

new terms for _default and vset (dave)

Maybe "_default" isn't the right term, because it could be confused with "default graph." The vset IRI is a little confusing. It seems like it wouldn't reference an abstract dataset, but a versioned set of datasets. But it would be nice if you had a way to reference the abstract dataset, because the {NamedEntity}.trig file isn't it... Maybe the vset path could be the right thing, but it should have a better name than vset.

new terms for _default and vset (chatgpt)

You've highlighted some critical distinctions that are worth clarifying to ensure your naming and paths are unambiguous and intuitive. Let's explore ways to address these concerns:

Revisiting Naming and Conceptual Distinctions

Potential Confusion with "_default":
- Using "_default" may indeed lead to confusion, especially with "default graph" terminology. It might be helpful to choose a term that implies the current or primary version but avoids the overlap with RDF’s default graph concept.
- Consider alternatives like:
  - "_primary": Suggests the primary dataset series.
  - "_latest": Emphasizes that this is the most recent, stable series.
  - "_main": A common term that implies the main or standard dataset series without conflicting with RDF graph terms.
Clarifying vset and Abstract Dataset Representation:
- Your observation that vset seems more like a reference to a versioned set of datasets rather than an abstract dataset is spot on. If it represents a series of versions, then the name should clearly convey that intent.
- The abstract dataset is a conceptual entity—essentially the idea or definition of a dataset independent of specific versions or instances.
Suggestions for Renaming vset:
- To better reflect the idea of a versioned set of datasets, you could consider:
  - "_series": A simple term that implies a collection of versions (e.g., dave-richardson/series/).
  - "_versions": Makes it explicit that it is a set of different versions.
  - "_history": If the emphasis is on chronological progression, implying a set of historical versions.
- However, if you want something that implies it includes both the versions and a reference to the abstract dataset itself, then you might want something that blends both:
  - "_canonical": Could refer to the canonical definition and versions together, giving a sense of the abstract dataset and its realization.
  - "_archive": Implies a collection of all versions but could also include a reference to the canonical dataset itself.
Referencing the Abstract Dataset:
- It would be helpful to have a consistent way to reference the abstract dataset, distinct from the individual versions.
- Consider having a dedicated IRI for the abstract dataset, which could be something like:
  - {namespace}/dave-richardson/dataset — representing the dataset in an abstract sense, without tying it to any specific version.
  - This could coexist with a _series or _versions folder, which holds all specific versions.
- The {NamedEntity}.trig file could then include pointers:
  - To the abstract dataset IRI.
  - To the current version.
  - To the catalog and other related resources.

Proposed Structure Example

Consider organizing the dataset and version references as follows:

Abstract Dataset Reference: {namespace}/dave-richardson/dataset
- This serves as a stable IRI for the abstract concept of the dataset.
Dataset Series Path (formerly vset):
- {namespace}/dave-richardson/series/ or {namespace}/dave-richardson/versions/
- Contains all the different versions:
  - {namespace}/dave-richardson/series/v1/
  - {namespace}/dave-richardson/series/v2/
- This makes it clear that this path is a collection of versions, not the abstract dataset itself.
NamedIndividual Representation:
- {namespace}/dave-richardson/index.trig would then:
  - Point to the abstract dataset ({namespace}/dave-richardson/dataset).
  - Include a default graph linking to the current version ({namespace}/dave-richardson/series/v2/).
  - Include the catalog graph for more detailed metadata or historical information.

Summary and Recommendations

Rename "_default" to something like "_primary" or "_main" to avoid conflicts with the term "default graph."
Rename vset to something more indicative of its role as a collection of versions, such as "_series" or "_versions".
Establish a clear and distinct IRI for the abstract dataset, which serves as the canonical reference that can be linked to from other parts of the namespace.
In the index.trig or the main representation file, clearly distinguish between pointers to the abstract dataset, the current version, and the catalog to maintain clarity.

This structure aims to keep your terminology clear, intuitive, and unambiguous, making it easier for others to understand and for tools to interact consistently with the data. Let me know if this helps, or if you'd like to refine any part of this further!

ontology use case (dave)

You should be able to place a complete ontology file (a dataset file) in the data directory, and it mints the ontology IRI and all the ontology elements' IRIs in subfolders. In a sense the ontology IRI is a sub-namespace, but a very different thing from a SFNamespaceRepo. Would each of the ontology elements get their own _touchstone and _catalog series? I think we need to flesh out _catalog... does it lists other references, or only the serieses subsumed under the NamedIndividual in the hierarchy.

ontology use case (chatgpt)

These are some important questions, especially as you dive deeper into the intricacies of ontology management and versioning. Let's break down each aspect:

Ontology IRI and Sub-Namespace Considerations

Ontology as a Sub-Namespace:
- When placing a complete ontology file in the data directory, minting an ontology IRI and then each of the ontology elements into subfolders seems like a logical way to organize the ontology elements.
- The ontology IRI serving as a sub-namespace is intuitive, as it allows you to logically group all the related elements. However, it differs significantly from a SFNamespaceRepo, which is broader and potentially integrates multiple ontologies, datasets, and more within a larger, centralized namespace.
Minting IRIs for Ontology Elements:
- Each ontology element—such as classes, properties, or individuals—could get its own IRI represented within subfolders, keeping the structure clear and consistent.
- This would allow for greater granularity and modularity when managing ontology elements, which can be useful when evolving individual parts of the ontology over time.

Should Each Ontology Element Get `_touchstone` and `_catalog`?

_touchstone for Ontology Elements:
- It makes sense to consider that each ontology element could have its own _touchstone. This would establish each element's authoritative version, allowing updates and changes to be handled in a systematic way.
- For example, an ontology class might evolve independently from other classes, so having a _touchstone for each element provides a stable reference point and allows for independent versioning.
_catalog for Ontology Elements:
- The question of whether each element should get its own _catalog depends on the role you envision for _catalog. Here are two possible interpretations:
  1. If _catalog serves to list all available versions or related series:
    - Then each ontology element might also benefit from having its own _catalog. This would be particularly useful if you want to track and document all historical versions or variations of a specific element (e.g., a class evolving through multiple iterations).
  2. If _catalog only refers to subsumed series:
    - This could be more restrictive, in that the _catalog would list only the series directly under the hierarchy of the NamedIndividual. In that case, the ontology as a whole would have a _catalog, while individual elements might be tracked differently.

Defining `_catalog` More Fully

Role of _catalog:
- The function of _catalog should be clearly defined, depending on how much metadata and contextual information you need to store:
  - Listing References: If _catalog should list other references, it could point to external ontologies, imported classes, or other relevant metadata. This would make it useful for ontology reuse and discovery.
  - Subsumed Series: Alternatively, if _catalog is meant to list only the series under the NamedIndividual, it would serve as a kind of internal directory, strictly managing the elements that are part of the ontology.
Hybrid Catalog Approach:
- It might make sense to create a hybrid _catalog that:
  - Lists all series directly within the NamedIndividual's hierarchy.
  - Includes pointers to external references or linked ontologies. This could allow you to have a clear view of the ontology's internal evolution while also making it easy to understand its relationships with external sources.
- This could look like:
  - Internal Reference: List of _touchstone and _series elements for ontology classes, properties, etc.
  - External Links: Pointers to imported ontologies, external datasets, or other referenced ontological resources.
Examples of What _catalog Could Include:
- Versions and Series of ontology elements (_touchstone, _series paths).
- Imported Elements from other ontologies (owl:imports).
- Related NamedIndividuals that provide contextual links or dependencies.
- Historical Information that tracks changes and updates to the ontology elements over time.

Workflow for Ontology and Element Management

Placing Ontology File in Data Directory:
- When you place an ontology file in the data directory, the ontology IRI is minted, and the IRIs of ontology elements are automatically created based on the ontology's structure.
- For each ontology element, a subfolder could be created, such as:
```
{namespace}/ontology/
  ├── ClassA/
  │   ├── _touchstone/
  │   └── _catalog/
  ├── PropertyB/
  │   ├── _touchstone/
  │   └── _catalog/
  └── IndividualC/
      ├── _touchstone/
      └── _catalog/
```
Managing Versions and Evolution:
- Each element could evolve independently, meaning that _touchstone serves as the authoritative, evolving version of each element, while _catalog provides pointers to past versions or references.
- When elements evolve, new versions would be added to their respective _series paths (or whatever naming convention you finalize).
Automation for Consistency:
- The automated creation of IRIs, _touchstone, and _catalog for ontology elements can ensure consistency and reduce manual errors.
- Validation scripts could ensure that each new ontology file placed in the directory adheres to the expected structure, automatically creating the subfolders and IRIs needed.

Summary

_touchstone works well for providing a stable, evolving reference for each ontology element.
_catalog needs a clearer definition: it could list subsumed series and/or external references, depending on whether it serves as an internal directory or a broader contextual reference.
The process of placing a complete ontology into the data directory and minting IRIs for elements should create a structured approach where each element can evolve independently while being tied to the ontology as a whole.

If you need help further specifying the role of _catalog or structuring the automation for this workflow, let me know!

touchstone

I understand your concerns about "origin" potentially implying a one-time starting point that doesn’t evolve, which might mislead users regarding the nature of the dataset series. Let's explore "_touchstone":

Touchstone as a Choice

_touchstone has a very interesting connotation:
- It suggests something that serves as a benchmark or standard—a reference against which other versions or instances are evaluated.
- It conveys authority and reliability, which aligns with your idea of having a definitive version without implying it is static or incapable of evolving.
- Historically, a touchstone was used to test the quality of gold or silver, which gives it a connotation of being the measure of authenticity—a fitting metaphor for a dataset that serves as the core reference.

Pros of Using "_touchstone":

Authority Without Stagnation:
- Unlike "origin," "touchstone" doesn’t imply that it’s the progenitor or a fixed starting point. Instead, it implies ongoing relevance and importance.
Benchmarking:
- It implies that other versions might be compared to or evaluated against it, which makes sense for a dataset that evolves but remains the central reference.
Uniqueness:
- The term "touchstone" is unique and memorable, which helps differentiate it from other terms like "main," "core," or "standard."

Considerations:

Abstract Nature: Touchstone is more abstract compared to terms like "standard" or "primary," so it may require some explanation when introduced to others, especially in a technical context. However, it has a memorable quality and conveys the right level of importance.

Final Thoughts:

_touchstone seems like a strong candidate—it suggests both authority and continuity while avoiding implications of being static or unchanging, which is ideal for a dataset that will evolve but remain definitive. It gives a sense of something that all versions should relate to or be evaluated by, maintaining its role as the key reference point.

If you decide on _touchstone, it should convey the idea of being the core standard clearly, without the unintended connotations of "origin." Let me know if this works for you or if you'd like to explore any other directions!

(and then)

Great choice! _touchstone definitely has a unique and memorable quality to it, standing out from the usual terminology. It gives your system a distinct identity and conveys authority without being too rigid or clichéd. If you need any further assistance integrating this into your framework or defining its role in detail, just let me know!

_catalog elaboration (dave)

I think _catalog should at least reference external vsets / series that the namespaced series might be a continuation of (or branch of). So at least some external references.

_catalog elaboration (chatgpt)

That makes perfect sense—having _catalog include external references like vsets or series that the current namespaced series builds upon or branches from helps to provide valuable context and provenance. This approach will allow you to represent lineage, making it clear how the current dataset or ontology connects to previous work or diverges from a parent.

Role of External References in `_catalog`

External Continuations or Branches:
- Referencing external series as part of _catalog establishes clear relationships, like which datasets or ontology elements the current series is derived from or is extending.
- This can be incredibly valuable for understanding the evolutionary history of a dataset, especially when dealing with branches or forks from a common base.
Provenance and Traceability:
- Including these references will enhance traceability, allowing users or tools to follow the evolution of data across namespaces.
- It also gives a clear view of where a dataset originated and helps maintain a logical flow of authority, especially important when datasets or ontologies are used collaboratively or across different projects.

Suggested `_catalog` Structure:

The _catalog structure could be extended to accommodate:

Internal References:
- Pointing to subsumed series or elements directly under the current NamedIndividual’s hierarchy.
External References:
- Linking to external vsets or series that are related by:
  - Continuation: If the current series is a direct continuation or update of an external series.
  - Branching: If it diverges from another dataset or ontology but retains some shared lineage.
- You could include metadata indicating the type of relationship (e.g., continuation, branch, import) to make these references richer and more informative.

Catalog Example with External References:

{namespace}/dave-richardson/_catalog/index.trig
    :containsSeries {namespace}/dave-richardson/_touchstone/
    :hasExternalReference [
        :type "continuation" ;
        :refersTo <https://external.org/ontology/vset/v1/>
    ] ;
    :hasExternalReference [
        :type "branch" ;
        :refersTo <https://example.org/related-dataset-series/v2/>
    ] .

This structure provides a clear view of relationships, both internal and external, with appropriate metadata to define the nature of the reference.
Relationships like "continuation" or "branch" help convey whether this dataset builds on previous work or represents a divergence.

Benefits of Including External References:

Enhanced Discoverability:
- By listing external references, you make it easier for consumers to discover related datasets or ontologies, providing a wider context for understanding how a particular series fits into the broader picture.
Collaboration:
- This could also facilitate collaborative ontology development—other teams or projects could more easily see how your dataset extends or diverges from an existing one.
Provenance Data:
- Keeping track of external continuations or branches within _catalog also forms a provenance trail, which is crucial for data integrity and accountability, especially in open or distributed data environments.

Next Steps for `_catalog`:

Define Metadata Schema: Decide on the types of relationships (e.g., "continuation," "branch," "import") that you will track in _catalog. This will make the external references more meaningful.
Automation of External Linking: When placing a dataset file in the data directory, you could use automated scripts to:
- Detect external dependencies (e.g., based on imports).
- Automatically create appropriate references within _catalog.
Visual Representation: Consider how these references could be visualized on a static site, making it easier for users to navigate both internal and external relationships.

Your decision to include these external references will certainly strengthen the contextual understanding of your data and support a more connected and discoverable dataset landscape. Let me know if you need further assistance in fleshing out the _catalog role or automating this process!

namespace repo layout

data
- sflow-ontology.ttl
- dave-richardson.jsonld
docs
- sflow-ontology
  - _dss
    - _catalog
      - vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
    - _touchstone
      - vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
        
        v2
        
        dist
        
        index.ttl
        
        index.jsonld
  - SFNamespaceRepo
    - _dss
      - _catalog
        
        vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
      - _touchstone
        
        vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
        
        v2
        
        dist
        
        index.ttl
        
        index.jsonld
    - index.html
    - index.trig
    - index.jsonld
  - hasOutputFolder
    - _dss
      - _catalog
        
        vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
      - _touchstone
        
        vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
        
        v2
        
        dist
        
        index.ttl
        
        index.jsonld
    - index.html
    - index.trig
    - index.jsonld
  - index.html
  - index.trig # TTL: pointers to current _touchstone and _catalog and maybe their contents in named graphs
  - index.jsonld # JSONLD: pointers to current _touchstone and _catalog and maybe their contents in named graphs
  - sflow-ontology.ttl # Current distribution of the touchstone dataset
  - sflow-ontology.jsonld # Current distribution in JSON-LD
  - sflow-ontology-catalog.ttl # Current catalog dataset distribution
  - sflow-ontology-catalog.jsonld # Current catalog in JSON-LD
- dave-richardson
  - _dss
    - _catalog
      - vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
    - _touchstone
      - vset
        
        v1
        
        dist
        
        index.ttl
        
        index.jsonld
        
        v2
        
        dist
        
        index.ttl
        
        index.jsonld
ssg
sflow.config

index vs matched filename (dave)

I've used "index.ttl" and "index.jsonld" in the vset versions, but those will be copies of the source "data" files, so maybe the name should be preserved. On the other hand, I didn't illustrate that every single folder (e.g., sflow-ontology/dss/_catalog/vset) will also have an index.html file, and probably index.ttl/index.json-ld files too, so you could argue that for consistency, the distributions should stay as index.ttl too... but I think I'm leaning towards making those leaf distribution filenames match the source.

index vs matched filename (chatgpt)

You've raised an important point about maintaining consistency and preserving the integrity of the original data files while ensuring that your structure is navigable and intuitive. Let's break down the pros and cons of each approach and what might best serve your goals.

Option 1: Preserve Original Filenames for Leaf Distributions

Advantages of Preserving Filenames:
- Traceability: By preserving the original file names in the leaf distributions (vset folders), there is a clear, direct link between the source file in data and its representation in the docs structure.
- Reduced Confusion: Users and collaborators working with the ontology or datasets will find it easier to identify the original file just by looking at the name in the distribution folder. For example, sflow-ontology.ttl appearing in vset/v1/ is much more explicit about its origins than a generic index.ttl.
- Less Duplication Ambiguity: Maintaining unique filenames helps reduce potential confusion, especially if multiple versions or different ontologies end up in the same workspace (e.g., if someone downloads files locally).
Workflow Clarity:
- When automating workflows (e.g., updates from data to docs), it's easier to track changes in a source-to-distribution manner if filenames match.
- This approach supports a more granular version control process since each specific dataset or ontology file is distinctly identifiable across different versions.

Example Structure with Preserved Filenames:

- docs
  - sflow-ontology
    - _dss 
      - _touchstone
        - vset
          - v1
            - dist
              - sflow-ontology.ttl
              - sflow-ontology.jsonld
          - v2
            - dist
              - sflow-ontology.ttl
              - sflow-ontology.jsonld
  - dave-richardson
    - _dss 
      - _touchstone
        - vset
          - v1
            - dist
              - dave-richardson.jsonld

Each distribution file retains the original name, preserving its identity.

Option 2: Use `index.ttl` for Consistency Across All Folders

Advantages of Consistent Filenames:
- Uniformity: Every folder, regardless of what it represents, follows a consistent naming convention (index.ttl, index.jsonld). This makes it easy to know what to expect when navigating the structure, reducing ambiguity about which file to look for within a folder.
- Simple Navigation: If every folder has an index.html or index.ttl, it makes web-based discovery more consistent, especially when using a static site generator. The idea of default files (index.*) is commonly understood, which might make the SSG easier to configure.
- Reduced Overhead in SSG: Using a consistent file naming convention means that templates and automation scripts don’t have to consider different filenames for different versions, which can simplify the configuration and setup.
Potential Drawbacks:
- Ambiguity in Representation: The term "index" is generic, and without sufficient metadata, it might not be immediately clear which version or element the file refers to. This could lead to some confusion if users are dealing with multiple datasets.
- Loss of Traceability: By not preserving the original filenames, the direct link between the source data (data folder) and its representation in the docs structure is less obvious, which could make tracking data provenance slightly harder.
Consistency at the Cost of Clarity:
- If all leaf nodes are named index.ttl, you'll need a strong metadata strategy to identify and differentiate versions. This might include using metadata within the RDF itself to indicate the original source, version, or other unique identifiers.

Leaning Towards Preserving Filenames in Leaf Nodes

Considering your goal of providing clarity and avoiding redundancy, it seems that preserving the original filenames (e.g., sflow-ontology.ttl, dave-richardson.jsonld) within the leaf distribution folders is a good choice. Here are some reasons why this approach might work best:

Clear Provenance: The name itself acts as a form of metadata, making it easier to trace the source.
Less Manual Cross-Referencing: Users don’t need to rely on metadata or dig into RDF content to know which version or dataset they are looking at—the filename itself provides context.
Simplicity for Automation: While using consistent names like index.* might make some automation easier, preserving the original filename simplifies the mapping from source to version, which may make the update process more transparent and error-resistant.

Balancing Consistency and Original Filenames

To strike a balance:

Use original filenames for the leaf distributions to preserve context and clarity.
Continue using index.* for navigation files within each folder (e.g., index.html for browsing or general context at the folder level).
Metadata in Index Pages: Each folder's index.html could provide links to the preserved filenames within the dist directory, giving users an easy way to understand the context while keeping everything navigable.

Proposed Approach:

Distribution Filenames:
- Keep the source filenames in leaf nodes:
  - E.g., sflow-ontology.ttl, dave-richardson.jsonld.
Index Files for Navigation:
- Maintain index.html, index.ttl, and index.jsonld at higher levels (e.g., in each vset folder) to serve as navigational or summary points.

Example Layout Reflecting This Choice:

- docs
  - sflow-ontology
    - _dss 
      - _catalog
        - vset
          - v1
            - dist
              - sflow-ontology.ttl
              - sflow-ontology.jsonld
            - index.html
            - index.ttl
          - v2
            - dist
              - sflow-ontology.ttl
              - sflow-ontology.jsonld
            - index.html
            - index.ttl

This approach offers a balanced mix of consistency and source traceability, making your repository both intuitive to navigate and true to its origins. Let me know if this helps solidify the direction or if you'd like to refine any part of it further!

current IRIs revisited (dave)

One other thing to circle back to... the NamedIndividual's top-level index.trig was going to have a default graph with pointers to the catalog and touchstone, and named graphs with the contents of the current datasets for those two as well. But we don't have an IRI that corresponds to the current catalog or touchstone dataset. I like the idea of not bury the current in the dss/_catalog/vset folders, so maybe at the top level we could have sflow-ontology/_touchstone and sflow-ontology/_catalog ? I don't want to re-use sflow-ontology itself, because the referent is an ontology, not a "current version"

current IRIs revisited (dave)

I see your point—you need a distinct IRI that can cleanly and clearly represent the current versions without conflating it with the ontology itself. Here's a refined approach to help make the concept of current touchstone and catalog more explicit:

Proposed Structure for Current Versions

Top-Level Representation for Current Versions:
- Rather than embedding the current versions deep within the dss/_catalog/vset folders, placing them at the top level makes them more discoverable and emphasizes their role as the authoritative, up-to-date references.
- Using paths like:
  - sflow-ontology/_touchstone/
  - sflow-ontology/_catalog/
- These paths can represent the current state of the touchstone and catalog, respectively, without burying them within the version hierarchy. This helps make the most recent data more accessible, which aligns with the intent of representing a central, up-to-date version.

Top-Level Folder Names:

The structure would look something like this:

sflow-ontology/
  - _touchstone/
    - index.ttl
    - index.jsonld
  - _catalog/
    - index.ttl
    - index.jsonld
  - _dss/
    - _catalog/
      - vset/
        - v1/
          - dist/
            - index.ttl
            - index.jsonld
      - v2/
          - dist/
            - index.ttl
            - index.jsonld
    - _touchstone/
      - vset/
        - v1/
          - dist/
            - index.ttl
            - index.jsonld
        - v2/
          - dist/
            - index.ttl
            - index.jsonld

Clarifying the Role of Current Versions:
- _touchstone and _catalog at the top level would serve as pointers to the most up-to-date version.
- They contain the current dataset directly, and this IRI clearly differentiates between the concept of the ontology (sflow-ontology) and the current authoritative content.
Using index.trig for Centralized Access:
- The top-level index.trig for sflow-ontology could be structured as follows:
  - A default graph pointing to both _catalog and _touchstone.
  - Named graphs containing the contents of the current dataset from both _touchstone and _catalog.
- This setup provides a centralized entry point for accessing the ontology, its current state, and the related catalog, making navigation and data discovery easier.
Avoiding Reuse of sflow-ontology:
- Keeping the sflow-ontology IRI distinct ensures that it continues to represent the abstract concept of the ontology rather than a particular version or dataset.
- The paths like _touchstone and _catalog directly under sflow-ontology allow you to avoid the ambiguity of reusing sflow-ontology for two different referents.

Benefits of This Approach

Separation of Concerns:
- By separating current versions into distinct top-level folders, you achieve a clear distinction between the ontology itself and the evolving datasets that represent the current state of the catalog and touchstone.
- This keeps navigation intuitive—users know they can always go to the top-level _touchstone or _catalog to access the most recent, authoritative data.
Ease of Access:
- Placing the current versions at the top level makes them immediately accessible without needing to navigate through potentially nested version structures.
- This aligns well with the idea of having a centralized index.trig that points directly to these top-level folders.
Logical Consistency:
- This approach avoids any overlap between the IRI for the ontology and the IRI for the current version of the datasets, maintaining semantic clarity.
- It also respects the distinction between the ontology as a concept and its current dataset representation, making it easier to use and understand in practical applications.

Final Thoughts

Having top-level _touchstone and _catalog makes it very clear what the most recent version is, and it avoids the semantic confusion of reusing sflow-ontology for a purpose it was not intended to serve.
This also simplifies version management, as the most up-to-date datasets are always kept at an easy-to-access location, while historical versions remain organized within their vset folders under _dss.

This structure seems to strike the right balance between clarity, discoverability, and semantic rigor. Let me know if this aligns with your vision, or if you'd like to refine it further!

trouble with repurposing touchstone and catalog (dave)

one issue with that is that _touchstone and _catalog are dataset series, so now they're being used to reference the current dataset. Not great. Maybe you don't even need to be able to reference the current version. As long as you can access the current version via the top-level index.trig

trouble with repurposing touchstone and catalog (chatgpt)

That's a very insightful point. You've identified a key distinction: _touchstone and _catalog are inherently dataset series, not individual datasets or versions. Using these series labels to represent the current version could indeed lead to confusion. Here's a refined approach to address this:

Refined Approach: Removing the Need to Reference Current Versions Explicitly

Rely on index.trig for Centralized Access
- Instead of creating separate top-level folders for _touchstone and _catalog to represent the current versions, you could leverage the top-level index.trig as the centralized entry point to provide access to:
  - The latest version of the _touchstone series.
  - The latest catalog without explicitly defining a new IRI for the current version.
Structure of index.trig:
- The index.trig at the top level of sflow-ontology could have:
  - A default graph with pointers to the current dataset series (_touchstone and _catalog).
  - Named graphs that contain the contents of the current datasets from both _touchstone and _catalog. These named graphs could use the appropriate versioned IRI, but the key is that the data itself is accessible here without needing an explicit "current" label.
- Example structure:
```
:default {
    :hasTouchstone <https://namespace/sflow-ontology/_dss/_touchstone/vset/v2/> .
    :hasCatalog <https://namespace/sflow-ontology/_dss/_catalog/vset/v2/> .
}

:graph <https://namespace/sflow-ontology/_dss/_touchstone/vset/v2/> {
    # Contents of the current _touchstone dataset
}

:graph <https://namespace/sflow-ontology/_dss/_catalog/vset/v2/> {
    # Contents of the current _catalog dataset
}
```
- This way, the current versions are represented through their latest named graphs, but there’s no explicit need to label them as "current." The index.trig acts as the navigator, providing access to the latest authoritative datasets.
Why Removing Explicit "Current" References Works:
- Reduces Ambiguity: By not assigning the current version to _touchstone or _catalog explicitly, you avoid any confusion about what those terms represent—they continue to stand for the entire dataset series.
- Simplifies Version Management: This approach lets the dataset series (_touchstone and _catalog) remain consistent as the evolving collections they are, while still making the current version accessible via the top-level entry point.
- No Need for Redundant IRIs: There’s no additional layer of IRIs for current versions, which helps avoid redundancy and reduces the complexity of your IRI space.
Accessing the Current Version via index.trig:
- Top-Level Access: Consumers who need to access the most recent version of a dataset can do so directly through the top-level index.trig, which will always point to the latest vset.
- The default graph can contain metadata and pointers to the current named graphs, while the named graphs themselves hold the current dataset content.
Automation and Workflow Considerations:
- Whenever a new version of _touchstone or _catalog is created:
  - The index.trig is automatically updated to point to the newest vset.
  - This ensures that consumers using the index.trig always access the latest authoritative content.
- This process can be fully automated, making sure that there are no manual updates that could lead to inconsistencies between the versions and what is indicated in the entry point.

Summary

Avoid Explicit "Current" Labels: Instead of creating _touchstone and _catalog at the top level for current versions, use the index.trig as a central navigator that points to the most recent vset for both series.
Preserve Semantic Clarity: _touchstone and _catalog remain clearly as dataset series, while index.trig handles the role of making the current version easily accessible.
Simplify Version Management: This approach keeps your versioning structure simple, avoids redundant IRIs, and maintains clarity about the purpose of each series.

By relying on index.trig for access to the latest versions, you maintain a clean structure that accurately reflects the nature of each element while making the current, authoritative datasets easily discoverable. This way, the focus remains on the dataset series evolving naturally, with the index.trig acting as a dynamic and consistent entry point.

Let me know if this aligns with what you're aiming for or if you'd like to iterate further on any part of this plan!

current options and leaf naming (dave)

It aligns, but I'm still uneasy about not having a way to refer to the current touchstone or catalog distinctly, or access them directly and individually.

regarding leaf/distribution node naming, we could additionally have index.ttl files that just point to the named distribution files.

current options and leaf naming (chatgpt)

I understand your concern—it makes sense to want a distinct way to refer to and access the current _touchstone and _catalog datasets directly, especially if there are use cases where you need that straightforward reference.

Options for Referencing the Current `_touchstone` and `_catalog`

Introduce "Current" Pointers at the Top-Level:
- You could introduce top-level IRIs specifically to point to the current version of _touchstone and _catalog. This way, you maintain a clear and distinct path for accessing these current datasets, while still keeping the logical separation intact.
- For example:
```
sflow-ontology/
  - _current_touchstone/
    - index.ttl
    - index.jsonld
  - _current_catalog/
    - index.ttl
    - index.jsonld
```
- These folders (e.g., _current_touchstone) are simply pointers to the current version of the _touchstone and _catalog series. This way, you can access the current dataset without confusion. Internally, these files could be generated and updated whenever a new version is created.
Using Named Graphs in index.trig for Current Reference:
- Another approach would be to maintain a named graph within the top-level index.trig that acts as a reference to the current version and can be dereferenced directly.
- You could give these graphs consistent, distinct IRIs like:
```
<https://namespace/sflow-ontology/_current_touchstone> {
    # Contents or pointers to the current touchstone dataset
}

<https://namespace/sflow-ontology/_current_catalog> {
    # Contents or pointers to the current catalog dataset
}
```
- This provides a specific IRI to point directly to the current version, while still allowing the index.trig to act as a centralized discovery point.
Direct Pointers Using RDF owl:sameAs or Custom Property:
- Inside index.trig, you could create explicit pointers to identify the current version of _touchstone and _catalog using a standard like owl:sameAs or a custom property.
- Example:
```
:currentTouchstone owl:sameAs <https://namespace/sflow-ontology/_dss/_touchstone/vset/v2/> .
:currentCatalog owl:sameAs <https://namespace/sflow-ontology/_dss/_catalog/vset/v2/> .
```
- This way, any system querying the top-level index.trig can easily extract and use the current IRI, making direct access possible.

Leaf/Distribution Node Naming: Adding `index.ttl` Pointers

Index Files as Pointers:
- Adding index.ttl files in each leaf or distribution folder that point to the named files is a great idea to retain consistency while also preserving the original filenames.
- These index.ttl files would essentially act as navigational aids:
  - They can contain RDF statements like owl:sameAs or similar to reference the distribution with its original name, ensuring a clear path for software and human users alike.
  - Example structure in a vset folder:
```
v1/
  - dist/
    - index.ttl  # Contains pointers to the specific distribution files.
    - sflow-ontology.ttl
    - sflow-ontology.jsonld
```
  - The index.ttl would include:
```
:distribution a dcat:Distribution ;
    dct:title "sflow-ontology TTL Version" ;
    owl:sameAs <https://namespace/sflow-ontology/vset/v1/sflow-ontology.ttl> .
```
- This setup maintains a consistent index-based navigation while retaining the uniqueness of the distribution filenames.

Summary

Top-Level Pointers for Current Versions:
- Introducing _current_touchstone and _current_catalog folders at the top level of sflow-ontology would provide an unambiguous and direct reference to the current versions, without confusing them for a full dataset series.
Named Graphs for Current Reference:
- Use distinct IRIs within index.trig as named graphs to clearly identify the current version of each series.
Index.ttl as Navigational Pointers:
- Adding index.ttl files in each distribution folder that point to the specific distribution files helps balance navigational consistency and original filename clarity.

These approaches should help alleviate the unease around not having a clear reference for the current version, while also ensuring that the structure remains intuitive and that discoverability is easy both programmatically and for users. Let me know if this feels more aligned with what you're aiming for or if you'd like to iterate further!

eurekea on current (dave)

Since the current are actually current dataset distributions, we could use the distribution convention to refer, e.g. sflow-ontology/sflow-ontology.ttl and sflow-ontology/sflow-ontology-catalog.ttl

eurekea on current (chatgpt)

That's a solid idea. Using the distribution naming convention to identify the current versions is both intuitive and consistent with the broader approach to RDF dataset management and versioning. This allows you to explicitly reference the current dataset distributions while keeping the semantics clear and avoiding the ambiguity we discussed before.

Revised Approach for Current Dataset Distributions

Naming Convention for Current Dataset Distributions:
- Instead of having separate folders or introducing ambiguous top-level markers, you can directly place the current dataset distributions at the top level of the namespace, using the original filenames:
```
sflow-ontology/
  - sflow-ontology.ttl               # Current distribution of the main touchstone dataset
  - sflow-ontology.jsonld            # JSON-LD version of the current touchstone
  - sflow-ontology-catalog.ttl       # Current distribution for the catalog dataset
  - sflow-ontology-catalog.jsonld    # JSON-LD version for the current catalog
```
- This approach is highly descriptive, as the filenames indicate both the entity and whether it is part of the touchstone or catalog dataset.
Advantages of This Approach:
- Semantic Clarity: The current versions are referred to using their actual names (sflow-ontology.ttl, sflow-ontology-catalog.ttl). This avoids confusion about what “current” represents since the filenames make it clear that these are the current distributions.
- Direct Reference: This structure allows for direct access to the latest versions without needing additional pointers, new IRIs, or complex navigation. The current versions are just directly accessible at a predictable path.
- Simple Version Management: When a new version is released, the current distribution files are simply updated or replaced, ensuring that the namespace always provides the most recent data at those top-level paths.
How to Integrate With index.trig:
- The top-level index.trig would still serve as the centralized access point for consumers. It would:
  - Provide a default graph containing metadata and pointers to the current catalog and touchstone.
  - Include named graphs representing the contents of the current datasets for both touchstone and catalog, with links pointing to these top-level distributions.
- Example index.trig:
```
:default {
    :hasTouchstone <https://namespace/sflow-ontology/sflow-ontology.ttl> .
    :hasCatalog <https://namespace/sflow-ontology/sflow-ontology-catalog.ttl> .
}

:graph <https://namespace/sflow-ontology/sflow-ontology.ttl> {
    # Contents of the current touchstone dataset
}

:graph <https://namespace/sflow-ontology/sflow-ontology-catalog.ttl> {
    # Contents of the current catalog dataset
}
```
- This maintains a direct and obvious link to the current version while ensuring that all access remains consistent through a top-level entry point.
Automation Workflow for Updates:
- Whenever a new version of _touchstone or _catalog is released:
  - The current distribution files (sflow-ontology.ttl, sflow-ontology-catalog.ttl) at the top level are updated or replaced.
  - The index.trig is also updated to reflect any changes, maintaining consistency between what is linked as the "current version" and what is stored in the actual vset folders.
Leaf Distribution Files and Index Pointers:
- To maintain consistency, each leaf node (e.g., vset folders) could still have its index.ttl as a pointer to the named distribution files.
- This means:
  - At the top level, the current versions are directly referred to by their names.
  - In version folders (e.g., v1, v2), the index.ttl continues to point to the specific dataset files, ensuring that there is no ambiguity.

Example Folder Structure:

sflow-ontology/
  - sflow-ontology.ttl               # Current distribution of the touchstone dataset
  - sflow-ontology.jsonld            # Current distribution in JSON-LD
  - sflow-ontology-catalog.ttl       # Current catalog dataset distribution
  - sflow-ontology-catalog.jsonld    # Current catalog in JSON-LD
  - index.trig                       # Centralized entry point for the current series

  - _dss/
    - _touchstone/
      - vset/
        - v1/
          - dist/
            - index.ttl              # Points to sflow-ontology-v1.ttl, etc.
            - sflow-ontology-v1.ttl
            - sflow-ontology-v1.jsonld
        - v2/
          - dist/
            - index.ttl
            - sflow-ontology-v2.ttl
            - sflow-ontology-v2.jsonld
    - _catalog/
      - vset/
        - v1/
          - dist/
            - index.ttl              # Points to sflow-ontology-catalog-v1.ttl, etc.
            - sflow-ontology-catalog-v1.ttl
            - sflow-ontology-catalog-v1.jsonld

Summary

Current Distributions are referred to using top-level, descriptive filenames like sflow-ontology.ttl and sflow-ontology-catalog.ttl.
index.trig serves as the central navigation point, pointing to these current versions without the need for extra pointers or complicated IRIs.
Using original, descriptive filenames at the top level provides semantic clarity and direct accessibility, avoiding the ambiguity of relying on "_current" or implicit IRIs.
Maintaining index.ttl pointers in leaf nodes ensures consistent navigation and a familiar structure across different levels of the namespace.

This approach should alleviate the concerns about needing clear references for current versions while keeping the overall structure neat and consistent. Let me know if this approach works for you or if there's anything you'd like to further refine!

including version in filename

Including the version in the filename has both advantages and disadvantages, depending on your specific goals and the type of workflows you want to support. Let's explore the pros and cons to help you determine if this approach aligns with your needs:

Pros of Including the Version in the Filename

Clear Traceability and Provenance:
- Explicit Version Identification: Including the version number directly in the filename (e.g., sflow-ontology-v1.ttl) provides an unambiguous indication of which version of the dataset you are dealing with.
- Easier Provenance Tracking: When files are downloaded, shared, or moved, having the version in the filename makes it much easier to track the provenance of a particular file. You can always tell what version a file represents, even if it's separated from its original context.
Reduced Ambiguity:
- Instant Recognition: Users don’t need to check metadata or open the file to determine which version they are interacting with. The version number is right there in the filename, making it instantly recognizable.
- Avoiding Naming Conflicts: If different versions are placed in the same directory, using a versioned filename helps to avoid conflicts. Each file is unique, ensuring that different versions do not accidentally overwrite one another.
Facilitates Collaboration:
- Distributed Collaboration: When working in a distributed environment (e.g., multiple contributors to a dataset), having explicit versioned filenames ensures that contributors can easily distinguish between files and versions, which reduces the likelihood of errors.
- Sharing and Reference: When files are shared across teams or externally, including the version helps others understand context without additional explanations.
Automation and Scripting:
- Automation-Friendly: If filenames include versions, automation scripts can work with naming conventions to easily determine and select specific versions for processing, archiving, or other operations.
- Dynamic Updates: Including versions in filenames makes it easy for scripts to iterate over versions, allowing for automated tracking of changes and history.
Archival Clarity:
- Explicit Archival Records: When storing or archiving datasets, having a version in the filename ensures that there’s no confusion about which version is being saved. This makes it easier to reconstruct the history of changes or updates.

Cons of Including the Version in the Filename

Potential Redundancy:
- Overlapping Metadata: The version number is likely already included as metadata within the file itself or in the dataset IRI. Including it in the filename could be seen as redundant, especially if the versioning is consistently handled at other layers.
- Too Much Information in the Name: Filenames can get long and unwieldy if they also contain descriptive information alongside the version, which might make navigation less tidy.
Challenging Updates:
- File Re-naming Complexity: If a dataset evolves and becomes a new version, the filename must be updated to reflect that change (e.g., from -v1 to -v2). This can introduce complexity in workflows, as downstream references to the filename might also need to be updated.
- Broken Links: If other systems, applications, or scripts reference a specific file by its name, updating the filename to include a new version could result in broken links or references. This might require additional effort to track and update references whenever the version changes.
Human Error Risk:
- Manual Naming Errors: If filenames are updated manually, there’s a risk of human error—such as forgetting to update the version number or using an incorrect version label. This could lead to confusion about which version is authoritative.
- Misleading Names: A filename might indicate a particular version, but if the content isn’t correctly synchronized, the filename could be misleading. This can happen when copies are made without careful updating of version labels.
File Navigation Complexity:
- Overwhelming for Users: For non-technical users, seeing many files with versions (e.g., -v1, -v2, etc.) might be overwhelming. This is particularly the case if numerous versions exist, making it harder for a user to determine which file to use without additional guidance.
- Clutter in Directories: When directories have multiple files representing different versions of the same dataset, it can create a cluttered folder structure, which might be difficult to navigate for users unfamiliar with the naming convention.
Data Storage and Management Issues:
- Multiple Copies of Files: Since each version gets a separate filename, this can lead to multiple independent copies of datasets within a file system, rather than maintaining a central dataset with version history. This could increase storage needs and make it harder to consolidate changes.
- Synchronization Issues: In systems where version control is handled by tools like Git, including the version in the filename may cause unnecessary complexity with synchronization, especially if multiple versions are being tracked independently rather than through the versioning system.

Balancing the Pros and Cons

Include Version in Filename for Historical or Distribution Purposes:
- If you need to distribute datasets or archive historical versions, it makes sense to include the version number in the filename. This aids in clarity, traceability, and reduces ambiguity for end users.
Use Separate Metadata for Internal or Systematic Versioning:
- For internal management, where versioning is handled through dataset IRIs or metadata (such as index.trig or linked data properties), you could avoid including the version in the filename and rely on these structured methods to convey version information.
Hybrid Approach for Consistency:
- You could maintain consistent filenames (e.g., sflow-ontology.ttl for current versions) at the top level, while having version-specific filenames (sflow-ontology-v1.ttl) in the vset folders. This provides easy access to the latest version while also retaining a clear versioned history.

Summary

Pros: Including the version in filenames provides traceability, clarity, and makes it easier to collaborate and automate processes.
Cons: It introduces the risk of redundancy, human error, and complex navigation, especially in cases of manual handling or large numbers of versions.
Recommendation: A hybrid approach may be the best of both worlds—use consistent filenames for the current authoritative version while incorporating version-specific names for archived versions to aid in historical tracking and distribution. This allows for direct access to the latest dataset, while ensuring that older versions are clearly marked and accessible.

This balanced approach should allow you to leverage the advantages of versioned filenames without introducing unnecessary complexity in your everyday workflows. Let me know if this helps, or if you'd like to explore a specific workflow in more detail!

monstrosity (dave)

It becomes kinda monstrous, especially with repeating the dss and current files for every namedindividual. maybe unless the NIs have their own data file in the data directory, their output subfolder just gets index.html and an index.trig/jsonld pointing to the defining dss and its catalog

monstrosity (chatgpt)

That’s a great realization, and it can certainly help streamline the structure, making it more manageable and less redundant. Let’s explore how to implement this simplification:

Simplified Approach for NamedIndividuals Without Their Own Data Files

Focus on Centralized Data:
- For NamedIndividuals that don’t have their own dedicated data file in the data directory, there’s no need to create repetitive _dss or versioned dataset series for each of them.
- Instead, they can simply reference the relevant dataset series that are already defined in the more central or general ontology.
Output Subfolder with Minimal Files:
- The output subfolder for each such NamedIndividual would only contain the following files:
  - index.html: Provides a human-readable view of the NamedIndividual, summarizing the key information and relationships.
  - index.trig and index.jsonld: These files serve as machine-readable formats that reference the defining _dss and _catalog series.
    - The index.trig can include named graphs that simply link back to the primary _dss or _catalog, ensuring all references are centralized.
    - This means that instead of creating redundant copies of the full dataset, the index.trig simply has references like:
```
:default {
    :definesNamedIndividual <https://namespace/sflow-ontology/dss/_touchstone/> .
    :definesCatalog <https://namespace/sflow-ontology/dss/_catalog/> .
}
```
Benefits of This Approach:
- Reduced Redundancy: By avoiding duplicate dataset series and distribution files, the complexity of the file structure is significantly reduced, especially for large ontologies with numerous NamedIndividuals.
- Simplified Maintenance: You avoid the hassle of updating multiple versions whenever there are changes in the _touchstone or _catalog. All NamedIndividuals simply point to the centrally maintained versions.
- Improved Clarity: The purpose of each NamedIndividual’s subfolder is more focused on linking and referencing rather than storing duplicated data, which makes the repository structure easier to understand and navigate.

Proposed Example Structure Update:

Data Folder remains the same:

- data
  - sflow-ontology.ttl
  - dave-richardson.jsonld

Docs Folder:

The NamedIndividuals that do not have their own data file would have a much simpler folder structure:

- docs
  - sflow-ontology
    - _dss 
      - _catalog
        - vset
          - v1
            - dist
              - index.ttl
              - index.jsonld
      - _touchstone
        - vset
          - v1
            - dist
              - index.ttl
              - index.jsonld
          - v2
            - dist
              - index.ttl
              - index.jsonld
    - SFNamespaceRepo
      - index.html
      - index.trig
      - index.jsonld
    - hasOutputFolder
      - index.html
      - index.trig
      - index.jsonld
  - dave-richardson
    - _dss 
      - _catalog
        - vset
          - v1
            - dist
              - index.ttl
              - index.jsonld
      - _touchstone
        - vset
          - v1
            - dist
              - index.ttl
              - index.jsonld
          - v2
            - dist
              - index.ttl
              - index.jsonld

- **For entities without their own data file**:

docs
- some-other-entity
  - index.html
  - index.trig
  - index.jsonld

index.trig might look like:

:default {
    :definesNamedIndividual <https://namespace/sflow-ontology/dss/_touchstone/> .
    :catalogReference <https://namespace/sflow-ontology/dss/_catalog/> .
}

Clarifying Relationships:
- The index.html for each NamedIndividual could still be generated from a template, giving a summary of what that NamedIndividual represents and its relationship to the _touchstone and _catalog series.
- This HTML file acts as the human-readable front-end, while index.trig and index.jsonld provide the machine-readable metadata.

Implementation Considerations

Automation Tools:
- Updating and generating index.trig files that reference the appropriate _dss can be managed through automation. Scripts can scan the data folder to determine if a NamedIndividual has its own dataset, and then generate appropriate output.
Unique IRIs:
- Since these index.trig files are pointing to a centralized dataset series, ensure that each NamedIndividual has a unique IRI that still distinguishes it within the namespace, even though the data source is shared.

Summary

For NamedIndividuals without their own data files, you can significantly reduce redundancy by omitting full _dss series and instead including only:
- index.html for human-readable summaries.
- index.trig and index.jsonld that link to the defining _touchstone and _catalog series.
This approach keeps the structure leaner while maintaining all the necessary semantic connections between NamedIndividuals and their relevant datasets.
The repository becomes much easier to maintain and navigate, especially when dealing with a large number of NamedIndividuals, each of which can now be handled by simple referencing rather than duplicative content.

This should help make the system much more manageable without sacrificing functionality or clarity. Let me know if this works for you or if you need more refinement on how to automate or template these changes!