DDI-CDI Tools Development and Integration

This topic will focus on software  development to support the use of DDI-CDI. Areas to be addressed include development of a specification for integrating DDI-CDI into the Harvard Dataverse platform, continued work on DDI-CDI support within the “Nectar” framework, productisation of SDMX-to-DDI-CDI tools; development of tools for leveraging other DDI specifications (DDI Codebook, DDI Lifecycle). Other topics will be considered.

Google Folder

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Link to nectar publisher:

GitHub - ddi-developers/nectar-publisher: Early work on a simple variable documentation tool

Link to the DDI-CDI sample generator:

GitHub - ddialliance/ddi-cdi-sample-generator: JavaScript example application to generate ddi-cdi (json-ld)

DDI Hackathon info and registration:

DDI Developers

Links from Adrian’s DEMO

WebGRUD, the Swiss Fertilization middleware code:

• https://github.com/zazuko/webgrud-app

• Live demo: Fertilization (P, K, Mg)

DSL that creates Notation-3 rules for it:

• GitHub - zazuko/webgrud-lang: Langium DSL for WebGRUD fertilization rule definitions

It is using Langium framework, see links in Github Repo. Note that this
is a minimal PoC, not fully functional yet.

The more "fancy" UI using ReactFlow:

• GitHub - zazuko/webgrud-xyflow-demo: Calculation visualisation using xyflow.

Also very PoC state right now, goal is to integrate it in the middleware
directly.

Trifid, a linked data server & SPARQL proxy:

• GitHub - zazuko/trifid: Lightweight Linked Data Server and Proxy

• Example resource of it:
  
  https://geo.ld.admin.ch/boundaries/municipality/5072

This is a modern replacement for "Pubby", for those who remember it.

Blueprint, the decoupling of UI & specific visualizations from RDF
vocabularies:

• GitHub - zazuko/blueprint: Zazuko Blueprint is an enterprise knowledge graph frontend and browser, designed to make RDF Knowledge Graphs accessible and customizable for domain users.

QLever, a highly scalable RDF triplestore

• GitHub - ad-freiburg/qlever: Very fast SPARQL Engine, which can handle very large knowledge graphs like the complete Wikidata, offers context-sensitive autocompletion for SPARQL queries, and allows combination with text search. It's faster than engines like Blazegraph or Virtuoso, especially for queries involving large result sets.