AI for Biometrics

Study Knowledge Graph

A visual, runnable guide to the study knowledge base

Run / share / read / build - pick your mode. This page shows the knowledge base and embeds the live graph, the run-and-build page shows how to run and build the Shiny app that lets you filter and click through it, and the interactive graph is a shareable no-server widget. Synthetic study, no PHI.

What it is

Every study artifact - protocol, SAP, ADaM specs, DMP, monitoring plan, milestones - is a note, and every dependency between them is a link. Instead of a folder tree, you navigate the study the way it actually connects.

%%{init: {'theme':'base','themeVariables':{'primaryColor':'#28344f','primaryTextColor':'#eef2fb','primaryBorderColor':'#7d9bd6','lineColor':'#aab8d6','clusterBkg':'#161d33','clusterBorder':'#3a476a','tertiaryColor':'#20406a','tertiaryTextColor':'#eef2fb','tertiaryBorderColor':'#7d9bd6','secondaryColor':'#20406a','secondaryTextColor':'#eef2fb'}}}%%
flowchart LR
  H["Study hub"] --> D["Design &<br/>data collection"]
  H --> S["Statistics &<br/>programming"]
  H --> DM["Data management<br/>& flow"]
  H --> SF["Safety &<br/>oversight"]
  H --> SU["Randomization,<br/>blinding & drug"]
  H --> P["Deliverables<br/>& timeline"]
  H --> R["People, standards<br/>& vendors"]

The structure - by the numbers

28

Notes

59

Links

8

Clusters

9

Note types

The most-connected note is the CP-101 Knowledge Hub (it links to 15 others); the top broker - the note that most sits between clusters - is CP-101 Knowledge Hub. Those are computed with igraph, not asserted.

The live graph

Drag a note, hover for its detail, or use the type selector. The whole vault is three CSV files you can open right now: nodes.csv (one row per note), edges.csv (one row per link), and types.csv (the note-type colours). Edit those and the graph moves.

Run it - and change it

shiny::runApp("knowledge_vault/shiny")
# needs: install.packages(c("shiny","bslib","visNetwork","igraph"))

The three CSVs are the vault. Add a note as a row in nodes.csv, wire it with rows in edges.csv, and it appears - sized by how many notes link to it. The run-and-build page shows how the graph is built from the logic in R/logic.R.

The other formats

Mode	Artifact
Run / explore	Run & build the Shiny app
Share (no R)	Interactive graph
Build	Run-and-build walkthrough

Synthetic study knowledge base. No PHI, no participant-level data.

--- title: "Study Knowledge Graph" subtitle: "A visual, runnable guide to the study knowledge base" format: html: theme: - darkly - ../../_quarto-mc.scss include-before-body: ../../_mc-topbar.html toc: true toc-location: left toc-title: On this page embed-resources: true code-tools: true code-fold: true execute: echo: false warning: false message: false --- ```{r setup} library(visNetwork); library(knitr) source("../shiny/R/logic.R") kg <- kg_load("../shiny/data"); st <- kg_stats(kg); V <- kg_vis(kg) ``` ```{=html} <style> svg[id^="mermaid"]{ width:100% !important; max-width:940px; height:auto; display:block; margin:8px auto; } svg[id^="mermaid"] .nodeLabel, svg[id^="mermaid"] .edgeLabel { font-size:16px !important; } </style> ``` ::: {.callout-note appearance="simple"} **Run / share / read / build - pick your mode.** This page *shows* the knowledge base and embeds the live graph, the [**run-and-build page**](kg-build.html) shows how to run and build the Shiny app that lets you filter and click through it, and the [interactive graph](kg-graph.html) is a shareable no-server widget. Synthetic study, no PHI. ::: ## What it is Every study artifact - protocol, SAP, ADaM specs, DMP, monitoring plan, milestones - is a **note**, and every dependency between them is a **link**. Instead of a folder tree, you navigate the study the way it actually connects. ```{mermaid} %%{init: {'theme':'base','themeVariables':{'primaryColor':'#28344f','primaryTextColor':'#eef2fb','primaryBorderColor':'#7d9bd6','lineColor':'#aab8d6','clusterBkg':'#161d33','clusterBorder':'#3a476a','tertiaryColor':'#20406a','tertiaryTextColor':'#eef2fb','tertiaryBorderColor':'#7d9bd6','secondaryColor':'#20406a','secondaryTextColor':'#eef2fb'}}}%% flowchart LR H["Study hub"] --> D["Design &<br/>data collection"] H --> S["Statistics &<br/>programming"] H --> DM["Data management<br/>& flow"] H --> SF["Safety &<br/>oversight"] H --> SU["Randomization,<br/>blinding & drug"] H --> P["Deliverables<br/>& timeline"] H --> R["People, standards<br/>& vendors"] ``` ## The structure - by the numbers {#live} ```{r kpis, results='asis'} box <- function(label, value, col="#e9edf7") sprintf( '<div style="flex:1 1 120px;background:#222630;border:1px solid #3a4157;border-radius:10px;padding:14px 16px"><div style="font-size:28px;font-weight:700;color:%s;font-family:monospace;line-height:1">%s</div><div style="font-size:11px;color:#94a1c0;text-transform:uppercase;letter-spacing:.05em;margin-top:6px">%s</div></div>', col, value, label) cat('<div style="display:flex;flex-wrap:wrap;gap:10px;margin:6px 0 18px">') cat(box("Notes", st$n_nodes)); cat(box("Links", st$n_edges)) cat(box("Clusters", st$n_clusters, "#3fd0c9")); cat(box("Note types", st$n_types, "#e0a64c")) cat('</div>') ``` The most-connected note is the **`r st$hub_title`** (it links to `r st$hub_degree` others); the top **broker** - the note that most sits *between* clusters - is **`r st$broker_title`**. Those are computed with `igraph`, not asserted. ## The live graph Drag a note, hover for its detail, or use the type selector. The whole vault is three CSV files you can open right now: [`nodes.csv`](../shiny/data/nodes.csv) (one row per note), [`edges.csv`](../shiny/data/edges.csv) (one row per link), and [`types.csv`](../shiny/data/types.csv) (the note-type colours). Edit those and the graph moves. ```{r graph} legend <- data.frame(label = kg$types$label, color = kg$types$color, shape = "dot") visNetwork(V$nodes, V$edges, background = "#141925", height = "520px", width = "100%") |> visNodes(font = list(color = "#e9edf7", size = 16)) |> visEdges(color = list(color = "#2A3242", highlight = "#5b8cff"), smooth = FALSE) |> visOptions(highlightNearest = list(enabled = TRUE, degree = 1, hover = TRUE), selectedBy = "group") |> visPhysics(solver = "forceAtlas2Based", forceAtlas2Based = list(gravitationalConstant = -55), stabilization = TRUE) |> visLegend(useGroups = FALSE, addNodes = legend, width = 0.2, position = "left", ncol = 1) ``` ## Run it - and change it {#run} ```r shiny::runApp("knowledge_vault/shiny") # needs: install.packages(c("shiny","bslib","visNetwork","igraph")) ``` ::: {.callout-tip appearance="simple"} **The three CSVs are the vault.** Add a note as a row in [`nodes.csv`](../shiny/data/nodes.csv), wire it with rows in [`edges.csv`](../shiny/data/edges.csv), and it appears - sized by how many notes link to it. The [run-and-build page](kg-build.html) shows how the graph is built from the logic in `R/logic.R`. ::: ## The other formats | Mode | Artifact | |---|---| | Run / explore | [Run & build the Shiny app](kg-build.html) | | Share (no R) | [Interactive graph](kg-graph.html) | | Build | [Run-and-build walkthrough](kg-build.html) | *Synthetic study knowledge base. No PHI, no participant-level data.*