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Researcher Learning Path

A structured path for scientists and academic researchers who want to use the FCC framework for knowledge management, open science workflows, FAIR data compliance, and cross-project federation. This path emphasizes knowledge graphs, federation, RAG pipelines, and the open science module.

Estimated time: 14--18 hours

Prerequisites: Python 3.10+, familiarity with research data management, basic understanding of ontologies (RDF/OWL helpful but not required), comfort with Jupyter notebooks.

Quick-Start Checklist

# Activity Resource Time
1 Install FCC and run the Quickstart Quickstart 15 min
2 Study the knowledge graph module Notebook 16: Knowledge Graphs 45 min
3 Build a RAG pipeline for literature retrieval Notebook 17: RAG Pipeline 45 min
4 Explore federation and cross-project resolution Notebook 18: Federation 45 min
5 Study semantic search for persona discovery Notebook 15: Semantic Search 45 min
6 Learn the Open Science module Open Science persona definitions 30 min
7 Complete the open science guided demo fcc demo open-science 30 min
8 Read the Knowledge Graph walkthroughs kg_walkthroughs.yaml 30 min
9 Study the For Scientists documentation For Scientists 45 min
10 Complete the federation tutorial Federation prompts 45 min

Key Personas for Researchers

Open Science (4 personas)

ID Name Phase Why It Matters
FDS FAIR Data Steward Find Ensures datasets meet FAIR principles (Findable, Accessible, Interoperable, Reusable)
RSN Research Software Notary Critique Validates reproducibility of computational workflows
CSL Citation Style Librarian Create Manages citation formats, DOI resolution, bibliographic metadata
OAA Open Access Advocate Create Guides open access publishing strategy and licensing

Knowledge Graph (3 personas)

ID Name Phase Why It Matters
OA Ontology Architect Create Designs ontology schemas, class hierarchies, property definitions
KB Knowledge Base Engineer Build Populates and maintains knowledge graphs at scale
SDE Semantic Data Engineer Build Implements RDF/SPARQL pipelines, linked data workflows

Supporting Personas

ID Name Why It Matters
STE Semantic Taxonomy Engineer Builds taxonomy schemas and ontology graphs
RC Research Crafter Gathers and synthesizes research into structured inventories
IRE Interpretability Researcher Explains model decisions for reproducibility

Skill Progression

Stage 1: Knowledge Graphs (3--4 hours)

Goal: Build, query, and export knowledge graphs using the FCC knowledge module.

  • Complete Notebook 16 (Knowledge Graphs)
  • Construct a KG with 9 node types (persona, action, artifact, concept, workflow, event, metric, gate, constitution) and 9 edge types
  • Export to OWL, RDF, SKOS, or JSON-LD using the serializer module
  • Use the build_full_fcc_graph() builder to generate a complete framework knowledge graph
from fcc.knowledge.graph import KnowledgeGraph
from fcc.knowledge.builders import build_full_fcc_graph
from fcc.knowledge.serializers import RDFSerializer

graph = build_full_fcc_graph()
serializer = RDFSerializer()
rdf_output = serializer.serialize(graph)

Milestone: You have a knowledge graph with persona, action, and artifact nodes, and can query it for relationship paths.

Stage 2: RAG Pipeline (3--4 hours)

Goal: Build a retrieval-augmented generation pipeline for research literature.

  • Complete Notebook 17 (RAG Pipeline)
  • Experiment with 6 chunking strategies: sentence, paragraph, sliding window, semantic, section, and page
  • Configure the SemanticRetriever for persona-aware queries
  • Build a pipeline that retrieves relevant passages given a research question

Milestone: You can chunk a research paper, index it, and retrieve relevant passages using persona-aware queries.

Stage 3: Federation and Cross-Project (3--4 hours)

Goal: Resolve entities across projects and manage federated knowledge.

  • Complete Notebook 18 (Federation)
  • Register namespaces for your research projects using the NamespaceRegistry (11 pre-defined ecosystems)
  • Use the EntityResolver to find equivalent concepts across projects
  • Track changes with the ChangeTracker for provenance
  • Build a FederatedKnowledgeGraph that spans multiple namespaces

Milestone: You can resolve a persona or concept across two federated knowledge graphs and trace its provenance.

Stage 4: FAIR Compliance and Open Science (3--4 hours)

Goal: Use FCC to enforce FAIR data principles in your research workflows.

  • Study the FDS (FAIR Data Steward) persona's R.I.S.C.E.A.R. specification
  • Design a workflow that validates datasets against FAIR criteria
  • Use the RSN (Research Software Notary) to validate computational reproducibility
  • Run the fcc demo open-science guided demo

Milestone: You have a FAIR compliance checklist automated through FCC personas and can generate compliance reports.

Stage 5: Advanced Research Applications (2--4 hours)

Goal: Design custom research workflows using FCC primitives.

  • Create custom personas for your research domain using the custom persona design guide
  • Build a knowledge graph specific to your field
  • Configure a RAG pipeline using your own corpus
  • Integrate with existing tools via the protocol bridge (A2A/MCP)

Milestone: You have a working FCC deployment tailored to your research domain.

FAIR Compliance Workflow

The FCC framework supports FAIR data principles through a structured workflow:

FDS (Find) --> OA (Create) --> RSN (Critique) --> CSL (Create) --> OAA (Create)
  |               |                |                 |               |
  v               v                v                 v               v
 Discover       Design          Validate          Format          Publish
 datasets       ontology        reproducibility   citations       open access

Each transition is governed by quality gates defined in quality_gates.yaml.

Application Purpose When to Use
persona_explorer.py Search and browse all 102 personas When selecting research team composition
ecosystem_dashboard.py View cross-project dependencies When federating knowledge across projects
collaboration_dashboard.py Track multi-persona research sessions During collaborative analysis

Connections to Other Paths

Further Reading