DIGICHer · Task 4.4 · Decision Support Tool · Prototype v0
Decision support for heritage digitisation
Decisions based on macro-level indicators, institution-level indicators, the Heritage Monitor funding database, and cross-cutting topics — the ethical reuse of cultural heritage and citizen engagement. Say who you are, name the one to four things you are choosing between, and the tool walks you through five stages to a decision you can print and defend.
This tool helps you make good decisions about putting cultural heritage online. Say who you are, and it will walk you through — one step at a time.
⚠ Illustrative calibration — derivation thresholds and blend ratios are demo values, for workshop discussion with the WP6/D6.2 team
Start · Who is deciding?
Choose where to start
Your role opens the same five-stage flow: set your context and name what you are choosing between, then screen it, weigh engagement and ethics, stress-test it against six futures, and decide. Each stage is a DIGICHer instrument — questionnaire or database → score shown as radar, scale or index → recommendations grounded in the project's outcomes. Everything is reachable out of order, and every instrument stays available under “All tools”.
Pick the card that sounds most like you. You can change your choice at any time.
Worked examples · from the pilot contexts
See it walked through
Three illustrative walk-ins composed from the DIGICHer pilot contexts (D4.3: Sámi, Jewish and Ladin communities). Each opens a tool pre-filled with a worked example. The vignettes are fictionalised — they are not statements by, or data about, the pilot partners.
The three communities
Whose heritage this is about
Digitised heritage
What digitisation gives back
Live from Europeana
Search the digitised field
Openly-licensed digitised heritage, live from Europeana — a DIGICHer partner. Searches go through the project server only; opening an item leads to europeana.eu.
The DIGICHer suite
Monitor → Assess → Decide
The project behind this tool
Digitisation of cultural heritage of minority communities — for equity and renewed engagement
DIGICHer (Horizon Europe, GA 101132481) researches what helps and what hinders the digitisation of minority cultural heritage, working directly with Sámi, Jewish and Ladin communities. Nine partners — coordinated by Vilnius Gediminas Technical University, with Europeana, the University of Lapland, the National Archives of Finland, the Jewish Heritage Network and the Istituto Culturale Ladino among them — feed the evidence this Decision Support Tool runs on.
Looking for something else? keeps every instrument in the app, including the ones outside this flow.
Step 1 · Where you work
Your context
One answer, used by every stage that needs it: the member state whose Eurostat context data positions your assessments. You can change it at any time — nothing you have scored is lost.
Step 2 · What you are choosing between
Your projects
All tools
Step 1 · Institutional profile
Choose a worked example, or import a WP6 instrument export
Each example is a fictional but realistic profile expressed in the WP6 instrument's own 21-indicator schema (scores 0–2). The DST never re-scores the assessment — it repositions it under uncertainty.
The instrument's indicators explained (WP6 · D6.2 methodology)
What each indicator asks and what its 0 / 1 / 2 maturity levels mean — as defined by the WP6 Inclusive Digitization Monitoring instrument. The DST imports these scores and never re-grades them.
Import JSON exported from the WP6 monitoring instrument
Step 2 · Supplementary intake
Three measures the instrument does not ask
The D3.1/D3.3 evidence base identifies these as direct predictors not covered by the 21 indicators (see mapping document §4). Two minutes, pre-filled for the examples.
ENUMERATE binary — the most potent predictor in D3.1/D3.3
D3.3 models workforce as a threshold condition
D3.3: digital skills pay off only where broadband suffices
Step 1 · Jurisdiction
Select the policy context
The six contextual criteria are positioned from Eurostat data — the exact measurement proxies validated in D3.1/D3.3. Positions are min–max scaled across the EU27 (winsorized at the 10th/90th percentile), so 1.0 reads as "mid-field within the EU", not a judgment.
Contextual criteria load from the bundled Eurostat dataset
Step 2 · Institutional field conditions
Two conditions Eurostat cannot see
Field-level strategic readiness and workforce capacity are institution-side conditions measured by ENUMERATE-type surveys, which have no current EU-wide wave. Until the agreed dataset exists, position them by informed judgment of your jurisdiction's CH sector — the tool shows how conclusions shift as you move them.
Share of institutions with formal digitisation strategies; coherence of sectoral policy
Dedicated digitisation staff across the sector; D3.3 threshold condition
Step 1 · Policy context
Define the context being assessed
One context per assessment — a country, region, sector, group of institutions, programme area, or a whole digitisation ecosystem. State the purpose so the report reads as a decision record, e.g. “identify priority areas for improving cultural heritage digitisation”.
What is being assessed
Enables Eurostat-suggested scores for the six context criteria
Up to 100 characters
Optional — carried into the report
Step 2 · Structural criteria
How favourable is the context on the eight structural criteria?
Score each criterion from 0 to 10 for how favourable the current condition is for supporting cultural heritage digitisation in the selected context: 0 = extremely unfavourable · 2–3 = very unfavourable · 5 = moderately favourable · 7–8 = very favourable · 10 = extremely favourable. A higher score always means the condition supports digitisation. Question template: How favourable is [context] in terms of [criterion] for supporting cultural heritage digitisation?
Why these eight criteria — the evidence base (D3.3 §3.2)
Advanced — custom criterion weights (default: equal, 12.5% each)
Assign different weights to reflect strategic priorities. Entries are renormalised to 100%.
Step 3 · European Framework pillars
How favourable is the context for each European Framework pillar?
Score the context 0–10 for how favourably it supports each pillar of the European Framework for Action on Cultural Heritage — same scale as above. Question template: How favourable is [context] in terms of the [pillar] for cultural heritage digitisation?
Advanced — custom pillar weights (default: equal, 20% each)
If the policy goal is inclusion, weight the Inclusive pillar higher; long-term preservation → Sustainable; crisis preparedness and protection → Resilient; digital transformation → Innovative; international cooperation → Global Partnership. Entries are renormalised to 100%.
Step 4 · Results and interpretation
Where policy intervention gains the most
All three views, per the specification: radar, weighted 0–10 tables, and the index numbers. The cross-matrix multiplies the weighted criterion score by the weighted pillar score; green marks strong support (score product ≥ 80/100), red marks areas requiring attention (≤ 9/100) — thresholds reconstructed from the D3.1 worked example, pending confirmation with the methodology team. This tool compares conditions within one context; it never ranks countries or institutions against each other.
Step 1 · Project alternatives
Name and describe what you are choosing between
Compare up to four planned cultural heritage digitisation projects — one on its own is fine, and reads as a profile rather than a ranking. Name them once in the bar below and they carry through every tool in the app, not just this one; the description and expected outcome belong to the project too, and appear in the report.
Step 2 · Evaluation
Score each project across the six dimensions
Score the favourability of each project from 0 to 10, where 0 = extremely unfavourable · 2–3 = very unfavourable · 5 = moderately favourable · 7–8 = very favourable · 10 = extremely favourable. A higher score always means the criterion supports the project. For best usage, open a dimension's advanced panel and score each sub-criterion — the dimension score then becomes their weighted mean.
Step 3 · Weights
Dimension weights
Default mode: all six dimensions weigh equally (16.667% each). Advanced: assign custom weights to reflect what matters most in your selection decision — entries are renormalised to 100%. Sub-criterion weights live inside each dimension's advanced panel in Step 2.
Advanced — custom dimension weights (default: equal, 16.667% each)
Step 4 · Results and interpretation
Which alternative holds up best?
All three views, per the specification: the radar overlays every project in a different colour on the same six axes; the table shows the scores with green ≥ 7 and red ≤ 3 highlighting per the questionnaire; the index is each project's weighted overall score out of 10. With two or more projects the ranking compares your own alternatives against each other — that is this tool's purpose per its specification, unlike the scenario tools, which never rank. With one project there is nothing to rank against, so the same views read as a profile.
Step 3 · Derivation
From 21 indicator scores to eight criterion positions
Eight criterion positions from data and judgment
The computation is deliberately visible: every position traces back to instrument indicators, the intake, or Eurostat context data — nothing is a black box.
| D3.3 criterion | Source | Derivation | Position (0–2) |
|---|
Why these eight criteria — the evidence base (D3.3 §3.2)
Step 4 · Scenario position
Where this setup sits on the two critical uncertainties
The six futures — one line each, expandable to the full D3.3 narratives
Step 5 · Robustness
Stress index by scenario and pillar
Each cell answers: how much does this future fail to compensate for this setup's weaknesses, seen through this pillar? Stress = Σ criterion weight × shortfall × scenario severity, scaled 0–100. Weights are the D3.3 Table 6 BWM matrix. Hover a cell for the main contributor. Higher is more exposed — it is not a grade.
What drives the stress under
Per-criterion contribution to the stress index, averaged across the five pillars: mean weight × shortfall × scenario severity. Zero means the scenario environment is favourable on that criterion or the position is strong.
What-if sandbox — simulate improvements, watch the stress respond
Simulation only — your imported assessment stays untouched and is never re-scored. Raise any indicator one step and read the modelled effect; the sensitivity ranking below the recommendations shows the full lever order.
Step 1 · What each stage says
Everything you have scored, side by side
Step 2 · What matters most to you
Weights
The ranking below is your alternatives against your weights — that is what makes it decision support rather than a verdict. Move a weight and watch the order; if it moves easily, the ranking is not the argument, the reasons underneath it are.
Step 3 · The ranking, and how firm it is
Which one holds up best
Step 4 · The decision
Record what you decided, and why
The tool does not decide — you do. Record the choice and the reasoning in your own words; it prints with the evidence above, which is what makes the decision defensible six months later.
Per-project effect
Which of these would leave you better able to cope?
Step 6 · Early warning
Inclusion signals — the pillar that degrades first
D3.3 finds inclusion and sustainability are the first pillars to degrade under structural misalignment, and minority-heritage visibility the most sensitive diagnostic. These six instrument indicators have no criterion mapping — they operationalise that early-warning role at micro level.
Step 6 · Early warning
Inclusion and sustainability under stress
D3.3's macro diagnostic: inclusion and sustainability degrade first when institutional readiness and societal skills misalign, and minority heritage is the most sensitive signal of systemic stress. These tiles track how exposed those two pillars are across the four matrix futures.
Step 7 · Act
Matched actions from the DIGICHer toolbox
Recommendations are served scenario-aware and each carries its source: a D3.4 template, a D3.3 finding, a WP6 instrument dimension to reassess, or — from autumn 2026 — a WP6 policy recommendation.
Macro guidance derived from the D3.3 evidence base, tagged by scenario exposure. The scenario-tagged WP6 policy recommendations load into the reserved slot when finalised (autumn 2026).
Where an action can be expressed in the engine's own terms, its card shows a modelled effect: the setup is re-derived with that action completed — one maturity level on its underlying indicators or intake — and the stress index recomputed across the futures. Cards are ranked by the mean gain across S1–S4, i.e. by robustness, not by any single scenario. The displayed assessment scores are never altered; this is a projection. Modelling assumption v0, for calibration discussion.
Sensitivity — every lever ranked by modelled stress relief
Each indicator (and intake answer) raised one maturity step in isolation, ranked by the mean stress reduction across the futures. The lever order — not any single score — is this chart's message. Projection only; nothing is re-scored.
Aggregate · optional
Field view — where do assessed institutions sit?
Import several WP6 instrument exports at once and see the field as a distribution: anonymous markers on the scenario matrix, quadrant counts, the criteria where the field is weakest, and the field's mean exposure per future. Each case is derived with neutral intake assumptions (strategy in development, 2–10% staff, adequate broadband) until the instrument carries the intake questions natively (decision #4). Markers carry no names — this is a distribution, never a ranking. The anonymised WP6 cross-case dataset (D6.2) is the intended feed.
Import multiple assessments (JSON array, or one export per line)
Toolbox
The D3.4 templates, ready to use
The twelve working templates from the DIGICHer engagement toolkit (D3.4 Annex 10), condensed for use on screen. Recommendation tags and community-guide chips refer to these codes. Open one, work through it with your team or community — or
Engagement toolkit · D3.4 Annex 10
Template workbench — fill them out together
The twelve engagement templates as working forms: add rows, tick what is in place, write your agreements down — with your team or community, on screen. Everything stays in this browser (and in your saved runs, if you use an account). Each template can be printed, and the whole workbench compiles into your 📊 dashboard. The 📋 Templates menu at the top jumps straight to any template from anywhere in the DST.
Community guide
Your heritage, your say — a preparation guide
If an archive, museum, library or project wants to digitise your community's heritage, you have a say at every step — not just at the end. This guide walks the decisions of a digitisation initiative in plain language. Tick what is already settled to your satisfaction; anything left unticked is worth raising with your partner institution. Nothing you tick here leaves your browser. It builds on the DIGICHer engagement guidelines (D3.4) and the Community Guide of the Inclusive Digitization tool.
Four questions that apply the whole way through
International principles for indigenous and minority data governance.
Step 1 · Questionnaire
Score each statement from 0 to 10: 0 = not at all / extremely unfavourable · 5 = partly / moderately · 10 = fully / extremely favourable. Open each block in turn — the block score is the mean of its statements, updated as you move the sliders.
Step 2 · Results and recommendations
Your profile
All three views, per the specification: the radar, the 0–10 scales, and the index. Green ≥ 7 marks strengths; red ≤ 3 marks areas requiring attention. The recommendations are rule-based and grounded in the DIGICHer project outcomes; this profile describes your own answers — it never compares you to anyone else.
Workshop session · join code
Participants open on their phones — no account, fully anonymous. Answers aggregate here live.
Discussion · value-based dilemmas
Six themes worth a conversation
No scores here. Open a card to reveal its dilemma, sit with it, and tick the ones your team or community should actually discuss — they are carried into your dashboard and printed summary.
Knowledge check · formative, private
How well do you know the method?
Short checks on the method behind the tools — the scenarios, the eight criteria, the D3.4 lifecycle, CARE. Every answer is explained and cited, and a wrong answer teaches more than a right one. This checks your understanding, never your organisation: results stay in this browser, nothing is graded, nobody is compared.
Workshop check · join code
Participants open on their phones — no account, fully anonymous. They never see the correct answers; you reveal them here when the room is ready.
My dashboard · compiled results
Everything your tools found, in one place
Each tool's "Add to my dashboard" button drops its headline results here — the summary, overview and compilation of recommendations that closes the DST journey. It lives in this browser (nothing is transmitted); signed-in users can also keep it in their account.
Methodology
What this tool is — and is explicitly not
Per D3.3 §6.7.5, the scenario framework structures comparative interpretation under uncertainty. Accordingly, this tool:
- assigns no probabilities and selects no preferred future;
- is not a maturity model — that role belongs to the WP6 Inclusive Digitization Monitoring tool, whose scores are imported, never re-graded;
- is not a compliance checklist or ranking — the stress index compares one setup across futures, never institutions or countries against each other;
- evaluates robustness: how the same choices perform across alternative configurations of the eight validated structural criteria;
- keeps the scenario layer a stable, versioned, interrogable reference — weights and states shown in full, open to stakeholder refinement.
Administrator · content editor
Edit the published content
Everything users read — toolboxes, question banks, discussion cards, glossary, vignettes, insights, the WP6 recommendations slot — is one JSON object, published append-only through /api/content. A partial object replaces only the keys it carries; the bundled defaults fill the rest. Validate before publishing; preview applies locally without saving.
Question banks — prepare the questions per level
Each questionnaire's statements, level by level, without hand-editing JSON. Edit below (one statement per line), or let the ✦ Guide draft grounded defaults to start from — then "Insert into the editor JSON" and publish as usual. Users score every statement 0–10.
Administrators — grant or revoke access
Administrators edit and publish everything users read. The account must exist (registered and verified) before it can be granted. First administrator: set ADMIN_EMAILS in the server environment, or run --make-admin on the server.