diff --git a/Arguments/AR-12934—AIs_Are_Capable_of_Understanding b/Arguments/AR-12934—AIs_Are_Capable_of_Understanding.md
similarity index 100%
rename from Arguments/AR-12934—AIs_Are_Capable_of_Understanding
rename to Arguments/AR-12934—AIs_Are_Capable_of_Understanding.md
diff --git a/Arguments/Arguments b/Arguments/Arguments.md
similarity index 100%
rename from Arguments/Arguments
rename to Arguments/Arguments.md
diff --git a/Claims/Claims b/Claims/Claims.md
similarity index 100%
rename from Claims/Claims
rename to Claims/Claims.md
diff --git a/Data/DE-Democracy-Metrics/DE-Democracy-Metrics.md b/Data/DE-Democracy-Metrics/DE-Democracy-Metrics.md
index b4518e9..1819aef 100644
--- a/Data/DE-Democracy-Metrics/DE-Democracy-Metrics.md
+++ b/Data/DE-Democracy-Metrics/DE-Democracy-Metrics.md
@@ -29,6 +29,7 @@
 
 Germany's democratic institutions remain functional, but key indicators signal structural stress: far-right vote shares have roughly doubled since 2017, institutional trust is declining across all major parties, and roughly a third of the population holds attitudes receptive to authoritarian framings (More in Common 2024). These are not post-election fluctuations but trend data.
 
+
 ---
 
 ## Methodology Summary
@@ -37,7 +38,7 @@ Germany's democratic institutions remain functional, but key indicators signal s
 
 **Sources:**
 - V-Dem Institute: Liberal Democracy Index (annual)
-- Reporters Without Borders: Press Freedom Index (annual)
+- Reporters Without Borders / Reporter ohne Grenzen (OR-00003): Press Freedom Index (annual)
 - More in Common: "Die andere deutsche Teilung" (2024)
 - Bertelsmann Stiftung: Democracy Report / Transformation Index
 - ARD-DeutschlandTREND: monthly tracking poll
@@ -92,6 +93,18 @@ Germany's democratic institutions remain functional, but key indicators signal s
 
 *Note: "Wütende" at ~10% here is the hard core; the ~30% figure in BEST ESTIMATE reflects all authoritarian-receptive attitudes (Wütende + significant share of Frustrierte).*
 
+### Palantir — State Surveillance Deployment (Germany)
+
+| Bundesland | Product | Status |
+|------------|---------|--------|
+| Hessen | hessenDATA | Active since 2017/18 |
+| NRW | DAR | Active since 2022 |
+| Bayern | VeRA | Active since 2024 |
+| Baden-Württemberg | Gotham | Approved July 2025 |
+| Federal level | Gotham | Blocked Feb 2026 (Hubig/SPD) |
+
+*Federal Constitutional Court (Feb 2023): Hessen and Hamburg police law provisions for Palantir use declared partially unconstitutional — legal basis required revision.*
+
 ---
 
 ## Source Analysis
@@ -140,6 +153,7 @@ These metrics directly support `Plans/de-plan1-sven.md`:
 - "Wütende" segment (~30%) → CHALLENGE 1: Epistemic fragmentation
 - Trust in Bundestag (~40%) → CHALLENGE 1 + CHALLENGE 4
 - Press Freedom Rank (#10) → STRATEGY 5: Local journalism investment
+- Palantir BRD deployment (4 Bundesländer active, federal blocked) → CHALLENGE 2: private surveillance infrastructure in state hands
 
 ---
 
@@ -148,3 +162,4 @@ These metrics directly support `Plans/de-plan1-sven.md`:
 | Date | Change | Reason |
 |------|--------|--------|
 | 2026-04-18 | Initial dataset created | Tier 2 Substrate development |
+| 2026-04-20 | Added Palantir DE state deployment table (4 Länder + federal block); DE-Plan connection | Memex: Palantir Technologies dot |
diff --git a/Data/DE-Epistemic-Competence/DE-Epistemic-Competence.md b/Data/DE-Epistemic-Competence/DE-Epistemic-Competence.md
new file mode 100644
index 0000000..f688b96
--- /dev/null
+++ b/Data/DE-Epistemic-Competence/DE-Epistemic-Competence.md
@@ -0,0 +1,160 @@
+# DE Epistemic Competence
+
+**Last Updated:** 2026-04-20
+**Update Method:** Manual — PIAAC (OECD, ~decennial), Wissenschaftsbarometer (annual), Eurobarometer (periodic)
+**Coverage:** Adult literacy, digital skills, science trust, fake news perception — Germany
+
+---
+
+## 🎯 BEST ESTIMATE
+
+| Metric | Value | Confidence | Last Updated |
+|--------|-------|------------|--------------|
+| **Adult literacy score** | **284 / ~500** | 90% | 2023 (PIAAC) |
+| **Adult numeracy score** | **299 / ~500** | 90% | 2023 (PIAAC) |
+| **Adults below literacy Level 2** | **~21%** | 85% | 2023 (PIAAC) |
+| **Trust in scientists** | **~60% "a lot/somewhat"** | 85% | 2024 (Wissenschaftsbarometer) |
+| **Adults with basic+ digital skills** | **~70%** | 80% | 2019 (Eurostat) |
+| **Feel able to identify fake news** | **~72%** | 70% | 2023 (Eurobarometer) |
+
+**One-liner:** Germany above OECD average; 21% of adults lack functional literacy.
+
+**Caveat:** Self-reported fake news identification confidence (72%) substantially overstates actual media literacy competence — experimental studies show most people cannot reliably identify disinformation.
+
+---
+
+## Quick Context
+
+Germany scores above OECD average on measured adult competencies (PIAAC 2023), but 21% of adults function below a literacy threshold sufficient for independent democratic participation. Science trust is moderate and declining. Self-assessed media literacy is high; actual competence is likely far lower.
+
+---
+
+## Methodology Summary
+
+**Approach:** PIAAC 2023 for direct competency measurement; Wissenschaftsbarometer for annual science trust tracking; Eurobarometer for EU-comparable media literacy perception data.
+
+**Sources:**
+- OECD PIAAC 2023: Programme for International Assessment of Adult Competencies (published Dec 2024)
+- Wissenschaft im Dialog: Wissenschaftsbarometer 2024
+- European Commission / Eurobarometer: Special surveys on media literacy and disinformation
+- Eurostat isoc_sk_dskl_i: Digital skills self-assessment (2019 data, older series)
+
+**Definition Used:** "Epistemic competence" = combination of literacy, numeracy, digital problem-solving, and science trust. PIAAC measures actual performance; Eurobarometer measures self-perception.
+
+---
+
+## Detailed Findings
+
+### PIAAC 2023 — Adult Competencies (Germany)
+
+OECD Survey of Adult Skills, 2023 round (results published December 2024). Scale 0–500.
+
+| Domain | Germany Score | OECD Average | Germany vs. OECD |
+|--------|--------------|--------------|-----------------|
+| Literacy | 284 | 260 | +24 points |
+| Numeracy | 299 | 263 | +36 points |
+| Adaptive Problem Solving | 255 | 251 | +4 points |
+
+**Proficiency distribution (Literacy):**
+
+| Level | Description | Germany share |
+|-------|-------------|--------------|
+| Below Level 2 | Cannot reliably process simple texts | ~21% |
+| Level 2 | Basic functional literacy | ~33% |
+| Level 3 | Moderate — adequate for most tasks | ~33% |
+| Level 4/5 | High competency | ~14% |
+
+*The ~21% below Level 2 represents ~14 million German adults who lack literacy sufficient for independent navigation of complex political or technical information.*
+
+*Source: OECD PIAAC 2023. Manual update; next round expected ~2030.*
+
+### Wissenschaftsbarometer 2024 — Science Trust (Germany)
+
+Annual tracking survey by Wissenschaft im Dialog (WiD).
+
+| Metric | Value |
+|--------|-------|
+| Trust in scientists "a lot or somewhat" | ~60% |
+| "Don't trust" scientists | ~16% |
+| "Science benefits society more than it harms" | ~75% |
+| Main information source for science: TV | ~45% |
+| Main source: Internet/Social Media | ~38% |
+
+*Source: Wissenschaftsbarometer 2024 (Wissenschaft im Dialog). Manual update required annually (published autumn).*
+
+### Eurobarometer — Media Literacy & Disinformation Perception
+
+| Metric | Value | Wave |
+|--------|-------|------|
+| Feel confident identifying fake news (DE) | ~72% | 2023 |
+| Encounter fake news daily or almost daily | ~50% | 2023 |
+| Check sources before sharing content | ~40% | 2023 |
+| Think disinformation is a problem in DE | ~85% | 2023 |
+
+*Source: Eurobarometer surveys on Media Pluralism and Democracy / Disinformation. Manual update. Note: confidence in identifying fake news is self-assessed — experimental studies consistently show actual competence is much lower.*
+
+### Social Origin & Educational Stratification
+
+Educational stratification is a structural precondition for epistemic competence gaps. Data: Destatis (2015/16), cited in Brockmeier/Gropp, IWH 2017.
+
+| Metric | Value |
+|--------|-------|
+| Gymnasium rate — children from high-education families | 61% |
+| Gymnasium rate — children from low-education families | 14% |
+| Upward mobility vs. Scandinavia | Significantly lower |
+| Upward mobility vs. USA | Comparable |
+
+*The 47-percentage-point gap in Gymnasium access by parental education is one structural mechanism producing the ~21% of adults below literacy Level 2. See DE-Social-Mobility (DS-00017) for full dataset.*
+
+### Eurostat — Digital Skills (Germany, older series)
+
+| Indicator | Year | Value |
+|-----------|------|-------|
+| Basic or above basic overall digital skills (% individuals 16–74) | 2019 | 70.2% |
+| Basic skills | 2019 | ~39% |
+| Above basic skills | 2019 | ~31% |
+
+*Source: Eurostat isoc_sk_dskl_i. Newer (post-2019) DE data not available in PC_IND series.*
+
+---
+
+## Source Analysis
+
+### Why These Sources?
+
+| Source | Strengths | Weaknesses | Weight |
+|--------|-----------|------------|--------|
+| **PIAAC 2023** | Only direct measurement of adult competencies; 40+ countries; skills tested not self-reported | Decennial; expensive; 2023 results are first update since 2012 | Very High |
+| **Wissenschaftsbarometer** | Annual; Germany-specific; consistent methodology since 2014 | Self-report only; question framing effects | High |
+| **Eurobarometer** | EU-comparable; consistent waves | Self-perception, not actual competency; question framing | Medium |
+| **Eurostat digital skills** | EU-standardized | Series discontinued/changed post-2019 for PC_IND; self-assessed | Medium |
+
+---
+
+## Research Metadata
+
+| Attribute | Value |
+|-----------|-------|
+| **Research Date** | 2026-04-20 |
+| **Researcher** | Sven / PAI |
+| **Method** | Manual aggregation from authoritative annual/decennial sources |
+| **Confidence Level** | 85–90% (PIAAC, direct measurement); 70–85% (perception surveys) |
+| **Known Gaps** | No annual direct competency measurement; digital skills Eurostat series discontinued; gap between self-assessed and actual media literacy |
+| **Update Frequency** | PIAAC ~decennial; Wissenschaftsbarometer annual (autumn); Eurobarometer periodic |
+
+---
+
+## Connection to DE-Plan
+
+- 21% of adults below literacy Level 2 (~14M people) → **CHALLENGE 5**: structural knowledge isolation; partially produced by 61% vs. 14% Gymnasium stratification by parental education (→ DE-Social-Mobility DS-00017)
+- Science trust declining trend → **CHALLENGE 1**: epistemic fragmentation as populations diverge on what counts as credible knowledge
+- High fake news confidence (72%) vs. low actual competence → **CHALLENGE 5**: citizens lack tools to evaluate claims independently despite believing they have them
+
+---
+
+## Changelog
+
+| Date | Change | Reason |
+|------|--------|--------|
+| 2026-04-20 | Initial dataset created | DE-Plan CHALLENGE 5 data gap |
+| 2026-04-20 | Added social stratification section | IWH 2017 / Destatis data; links to DE-Social-Mobility DS-00017 |
diff --git a/Data/DE-Federal-Budget/DE-Federal-Budget.md b/Data/DE-Federal-Budget/DE-Federal-Budget.md
index 84e7d15..4c07ef2 100644
--- a/Data/DE-Federal-Budget/DE-Federal-Budget.md
+++ b/Data/DE-Federal-Budget/DE-Federal-Budget.md
@@ -2,7 +2,7 @@
 
 ---
 
-## BEST ESTIMATE
+## 🎯 BEST ESTIMATE
 
 | Metric | Value | Confidence | Last Updated |
 |--------|-------|------------|--------------|
@@ -20,7 +20,7 @@
 
 ## Quick Context
 
-The German federal budget (Bundeshaushalt) is structured by Einzelpläne — ministry-level budget chapters. The 2024 Ist-Werte (actual values) reflect finalized execution. The budget is formally balanced at €474.75B but this conceals structural dependence on non-recurring measures and the Sondervermögen Bundeswehr (€100B defense special fund, off-balance). The dominance of social spending reflects the welfare state logic (Bismarckian social insurance); the defense share reflects post-2022 rearmament pressure following Russia's invasion of Ukraine and NATO 2% commitments.
+The German federal budget (Bundeshaushalt) is structured by Einzelpläne — ministry-level budget chapters. The 2024 Ist-Werte (actual spend) are formally balanced at €474.75B, but the Sondervermögen Bundeswehr (€100B defense fund) sits off-balance. Social spending (38%) dominates as largely statutory transfers; defense (10.6%) reflects post-2022 NATO rearmament pressure.
 
 ---
 
diff --git a/Data/DE-Lobby-Transparency/DE-Lobby-Transparency.md b/Data/DE-Lobby-Transparency/DE-Lobby-Transparency.md
index c538d95..4b4f360 100644
--- a/Data/DE-Lobby-Transparency/DE-Lobby-Transparency.md
+++ b/Data/DE-Lobby-Transparency/DE-Lobby-Transparency.md
@@ -103,8 +103,9 @@ Note: Registrants list multiple policy areas, so totals across sectors exceed th
 
 1. **Self-reporting**: Expenditure figures are declared ranges, not audited amounts. Large actors may report strategically.
 2. **Threshold gaps**: Organizations below certain thresholds or with limited direct Bundestag contact may not be legally required to register.
-3. **Guttenberg gap**: Known lobbying actors like Karl-Theodor zu Guttenberg (Elnet e.V., >€1M budget) are not registered — structural enforcement gaps exist.
-4. **Sub-entity ambiguity**: Large organizations may register subsidiaries separately, fragmenting their total lobbying footprint.
+3. **Guttenberg gap**: Known lobbying actors like Karl-Theodor zu Guttenberg (Elnet e.V., >€1M budget; Spitzberg Partners, Wirecard/Augustus Intelligence) are not registered — structural enforcement gaps exist.
+4. **Augustus Intelligence gap**: Augustus Intelligence GmbH — a network at the intersection of aristocratic capital (August von Finck jr.), intelligence services (August Hanning, Hans-Georg Maaßen), and AfD-adjacent politics (Phillip Amthor, Andreas Scheuer) — operates outside the Lobbyregister. Represents institutionalized elite access without democratic accountability.
+5. **Sub-entity ambiguity**: Large organizations may register subsidiaries separately, fragmenting their total lobbying footprint.
 
 ---
 
@@ -142,3 +143,4 @@ Connects to `Plans/de-plan1-sven.md`:
 | Date | Change | Reason |
 |------|--------|--------|
 | 2026-04-20 | Initial dataset created via live API fetch | bundesAPI integration in Substrate |
+| 2026-04-20 | Expanded Key Limitations: Guttenberg gap detail (Wirecard/Augustus Intelligence) + Augustus Intelligence network documented | Memex sessions: Neoaristokratismus, Guttenberg dots |
diff --git a/Data/DE-Parliament-Activity/DE-Parliament-Activity.md b/Data/DE-Parliament-Activity/DE-Parliament-Activity.md
index 5f5eaba..060480b 100644
--- a/Data/DE-Parliament-Activity/DE-Parliament-Activity.md
+++ b/Data/DE-Parliament-Activity/DE-Parliament-Activity.md
@@ -12,7 +12,7 @@
 | **Vorgänge (legislative processes)** | **12,507** | 99% | 2026-04-20 |
 | **Plenarprotokolle (plenary sessions)** | **83** | 99% | 2026-04-20 |
 
-**One-liner:** In ~6 months of WP21, the Bundestag logged 7,605 parliamentary documents and 12,507 legislative processes.
+**One-liner:** WP21 first 6 months: 7,605 Drucksachen, 1,812 Anfragen, 12,507 processes.
 
 **Caveat:** WP21 began October 2025 — all counts reflect roughly 6 months of activity, not a full legislative term.
 
@@ -20,7 +20,7 @@
 
 ## Quick Context
 
-The 21st German Bundestag convened in October 2025 following the federal election of February 2025. Parliamentary documents (Drucksachen) are the primary instrument through which MPs exercise oversight, initiate legislation, and question the government — they are the paper trail of democracy. Kleine Anfragen (1,812) dominate activity, reflecting the opposition's core instrument: forcing the government to answer on the record. The Vorgänge count (12,507) substantially exceeds Drucksachen because each legislative process may aggregate multiple documents across multiple stages.
+The 21st German Bundestag (WP21) convened in October 2025 after the February federal election — all counts reflect ~6 months of activity, not a full term. Drucksachen are the primary instrument through which MPs exercise oversight, initiate legislation, and question government — the paper trail of democracy. Kleine Anfragen (1,812) dominate, reflecting the opposition's core tool: forcing government to answer on the record.
 
 ---
 
diff --git a/Data/DE-Platform-Media/DE-Platform-Media.md b/Data/DE-Platform-Media/DE-Platform-Media.md
new file mode 100644
index 0000000..2dfa68c
--- /dev/null
+++ b/Data/DE-Platform-Media/DE-Platform-Media.md
@@ -0,0 +1,128 @@
+# DE Platform & Media
+
+**Last Updated:** 2026-04-20
+**Update Method:** Eurostat via `bun get-de-digital`; Reuters DNR + ARD/ZDF Onlinestudie manual (annual)
+**Coverage:** Digital media use, platform participation, news consumption — Germany
+
+---
+
+## 🎯 BEST ESTIMATE
+
+| Metric | Value | Confidence | Last Updated |
+|--------|-------|------------|--------------|
+| **Social network participation** | **59.2% of all individuals** | 95% | 2025 (Eurostat) |
+| **Online news reading** | **64.2% of all individuals** | 95% | 2025 (Eurostat) |
+| **Trust in news overall** | **~47%** | 75% | 2024 (Reuters DNR) |
+| **Social media as news source** | **~28%** | 75% | 2024 (Reuters DNR) |
+| **Active news avoidance** | **~36%** | 75% | 2024 (Reuters DNR) |
+| **WhatsApp monthly reach** | **~79%** | 80% | 2024 (ARD/ZDF) |
+
+**One-liner:** 59% use social networks; 47% trust news; 36% actively avoid it.
+
+**Caveat:** Eurostat measures participation rate (ever used in last 3 months), not daily active use — actual platform dependency is higher than these figures suggest.
+
+---
+
+## Quick Context
+
+Germany's digital media landscape shows a population where most citizens are online and read news digitally, but a significant third actively avoids news — and trust in news remains moderate despite relatively high press freedom. Platform infrastructure is dominated by US-owned services (Meta, Google/YouTube, ByteDance/TikTok) with no comparable European alternatives at scale.
+
+---
+
+## Methodology Summary
+
+**Approach:** Eurostat live API fetch (isoc_ci_ac_i) for standardized EU-comparable indicators; Reuters Institute Digital News Report 2024 for news trust and platform-as-news data; ARD/ZDF Onlinestudie 2024 for platform-specific monthly reach.
+
+**Sources:**
+- Eurostat: isoc_ci_ac_i — Internet use indicators, % of all individuals, Germany (annual)
+- Reuters Institute for the Study of Journalism: Digital News Report 2024, Germany country data
+- ARD/ZDF Onlinestudie 2024 (annual German media study)
+
+**Definition Used:** "Participation" = used the activity in the last 3 months before survey.
+
+---
+
+## Detailed Findings
+
+### Eurostat — Digital Activity Indicators (Germany, % all individuals)
+
+| Indicator | 2023 | 2024 | 2025 |
+|-----------|------|------|------|
+| Social networks participation | 48.6% | 58.0% | 59.2% |
+| Online news reading | 59.0% | 62.9% | 64.2% |
+| Video/voice calls | 63.8% | 76.1% | 78.7% |
+| Internet banking | 57.2% | 66.9% | 70.7% |
+| Finding information online | — | — | 80.1% |
+| Email use | — | — | 87.8% |
+| Online courses | 8.9% | 10.1% | 10.1% |
+
+*Source: Eurostat isoc_ci_ac_i. Fetch script: `bun get-de-digital`. Full data: `eurostat-digital-de.csv`*
+
+Note: Sharp jump in social networks (48.6% → 58.0% 2023→2024) may reflect methodology change or post-pandemic normalization.
+
+### Reuters Institute Digital News Report 2024 — Germany
+
+| Metric | Value |
+|--------|-------|
+| Trust in news overall | ~47% |
+| Use social media as main news source | ~28% |
+| Actively avoid news | ~36% |
+| YouTube for news (weekly) | ~15% |
+| TikTok for news (weekly) | ~7% |
+| Online news reach (weekly) | ~73% |
+
+*Source: Reuters Institute for the Study of Journalism, Digital News Report 2024. Manual update required.*
+
+### ARD/ZDF Onlinestudie 2024 — Platform Monthly Reach (Germany)
+
+| Platform | Monthly Reach |
+|----------|--------------|
+| WhatsApp | ~79% |
+| YouTube | ~55% |
+| Facebook | ~47% |
+| Instagram | ~34% |
+| TikTok | ~18% |
+| X (Twitter) | ~13% |
+
+*Source: ARD/ZDF Onlinestudie 2024. Manual update required annually.*
+
+---
+
+## Source Analysis
+
+### Why These Sources?
+
+| Source | Strengths | Weaknesses | Weight |
+|--------|-----------|------------|--------|
+| **Eurostat isoc_ci_ac_i** | EU-standardized, annual, free API, 20+ year time series | Measures any-use-in-3-months (broad definition), lags 1 year | Very High |
+| **Reuters DNR** | Dedicated news consumption study, consistent Germany sample, cross-national comparable | Annual PDF, no API, Germany N ~2,000 | High |
+| **ARD/ZDF Onlinestudie** | Germany's most comprehensive digital media study, large sample | Annual publication, Germany-only, no API | High |
+
+---
+
+## Research Metadata
+
+| Attribute | Value |
+|-----------|-------|
+| **Research Date** | 2026-04-20 |
+| **Researcher** | Sven / PAI |
+| **Method** | Eurostat live API (`bun get-de-digital`) + manual from 2024 annual reports |
+| **Confidence Level** | 95% (Eurostat) / 75–80% (Reuters, ARD/ZDF) |
+| **Known Gaps** | No real-time data; platform-specific time-spent not available; no federated/European platform baseline |
+| **Update Frequency** | Eurostat: run script annually; Reuters DNR + ARD/ZDF: manual each spring |
+
+---
+
+## Connection to DE-Plan
+
+- Social network participation 59.2%, US-platform dominance → **CHALLENGE 3**: German public discourse hosted on private US infrastructure without democratic control
+- Active news avoidance 36% → **CHALLENGE 1**: epistemic fragmentation; unreachable segment
+- Trust in news ~47% → **CHALLENGE 1 + CHALLENGE 6**: press ecosystem health
+
+---
+
+## Changelog
+
+| Date | Change | Reason |
+|------|--------|--------|
+| 2026-04-20 | Initial dataset created | DE-Plan CHALLENGE 3 data gap |
diff --git a/Data/DE-Platform-Media/eurostat-digital-de.csv b/Data/DE-Platform-Media/eurostat-digital-de.csv
new file mode 100644
index 0000000..e42265c
--- /dev/null
+++ b/Data/DE-Platform-Media/eurostat-digital-de.csv
@@ -0,0 +1,36 @@
+indicator_id,indicator_label,year,value_pct
+I_IUOLC,Online courses,2021,11.99
+I_IUOLC,Online courses,2022,9.60
+I_IUOLC,Online courses,2023,8.87
+I_IUOLC,Online courses,2024,10.10
+I_IUOLC,Online courses,2025,10.09
+I_IUEM,Email use,2021,79.57
+I_IUEM,Email use,2022,80.09
+I_IUEM,Email use,2023,83.26
+I_IUEM,Email use,2024,85.58
+I_IUEM,Email use,2025,87.76
+I_IUPH1,Video/voice calls,2021,56.10
+I_IUPH1,Video/voice calls,2022,58.54
+I_IUPH1,Video/voice calls,2023,63.83
+I_IUPH1,Video/voice calls,2024,76.10
+I_IUPH1,Video/voice calls,2025,78.69
+I_IUSNET,Social networks participation,2021,46.60
+I_IUSNET,Social networks participation,2022,47.73
+I_IUSNET,Social networks participation,2023,48.61
+I_IUSNET,Social networks participation,2024,58.02
+I_IUSNET,Social networks participation,2025,59.24
+I_IUIF,Finding information online,2021,60.93
+I_IUIF,Finding information online,2022,60.86
+I_IUIF,Finding information online,2023,41.67
+I_IUIF,Finding information online,2024,77.76
+I_IUIF,Finding information online,2025,80.06
+I_IUNW1,Online news reading,2021,56.74
+I_IUNW1,Online news reading,2022,57.75
+I_IUNW1,Online news reading,2023,59.03
+I_IUNW1,Online news reading,2024,62.92
+I_IUNW1,Online news reading,2025,64.21
+I_IUBK,Internet banking,2021,50.35
+I_IUBK,Internet banking,2022,48.58
+I_IUBK,Internet banking,2023,57.22
+I_IUBK,Internet banking,2024,66.92
+I_IUBK,Internet banking,2025,70.71
diff --git a/Data/DE-Social-Mobility/DE-Social-Mobility.md b/Data/DE-Social-Mobility/DE-Social-Mobility.md
new file mode 100644
index 0000000..2ff59f4
--- /dev/null
+++ b/Data/DE-Social-Mobility/DE-Social-Mobility.md
@@ -0,0 +1,91 @@
+# DE Social Mobility
+
+**Last Updated:** 2026-04-20
+**Update Method:** Manual — Destatis (annual), OECD EAG via SDMX API (annual), PISA (triennial)
+**Coverage:** Educational stratification by parental background, intergenerational mobility — Germany
+
+---
+
+## 🎯 BEST ESTIMATE
+
+| Metric | Value | Confidence | Last Updated |
+|--------|-------|------------|--------------|
+| **Gymnasium rate — high-education parents** | **61%** | 90% | 2015 (Destatis) |
+| **Gymnasium rate — low-education parents** | **14%** | 90% | 2015 (Destatis) |
+| **Education spending % GDP — government only** | **4.0% (2020), 3.6% (2017)** | 95% | 2020 (OECD EAG) |
+| **Education spending % GDP — all sources** | **4.6% (2020)** | 95% | 2020 (OECD EAG) |
+| **OECD average education spending — all sources** | **5.1% (2020)** | 95% | 2020 (OECD EAG) |
+
+**One-liner:** 61% vs. 14% Gymnasium rate — Germany's parental education gap.
+
+**Caveat:** Gymnasium stratification data from Destatis 2015 — needs manual refresh; education spending figures lag 4+ years (OECD EAG 2020).
+
+---
+
+## Quick Context
+
+Germany's educational success is strongly predicted by parental education: 61% of children from high-education families attend Gymnasium vs. 14% from low-education backgrounds (Destatis 2015). Germany's education spending (4.6% GDP, all sources, 2020) remains below the OECD average of 5.1%.
+
+---
+
+## Detailed Findings
+
+### Gymnasium Rate by Parental Education (Destatis 2015)
+
+| Parental education level | Share attending Gymnasium (under-15s, 2015) |
+|--------------------------|---------------------------------------------|
+| High educational attainment | 61% |
+| Low educational attainment | 14% |
+
+*Source: Statistisches Bundesamt. Manual update required — search Destatis for current Gymnasialquoten by parental education.*
+
+### Education Spending as % of GDP — Germany vs. OECD (OECD EAG, SDMX live)
+
+| Year | Government only (S13) | All sources | OECD avg (all sources) |
+|------|-----------------------|-------------|------------------------|
+| 2017 | 3.6% | 4.2% | — |
+| 2018 | 3.7% | 4.2% | — |
+| 2019 | 3.7% | 4.3% | 4.9% |
+| 2020 | 4.0% | 4.6% | 5.1% |
+
+*Source: OECD SDMX API — `OECD.EDU.IMEP,DSD_EAG_UOE_FIN@DF_UOE_INDIC_FIN_GDP`. ISCED 1T8 (primary to tertiary), direct expenditure. Fetch: see DS-00017 source catalog.*
+
+---
+
+## Source Analysis
+
+| Source | Strengths | Weaknesses | Weight |
+|--------|-----------|------------|--------|
+| **Destatis Bildungsquoten** | Official; annual; direct measurement | 2015 data — needs manual refresh | High |
+| **OECD EAG SDMX API** | Live; annual; official; cross-national comparable | ~18-month data lag | Very High |
+| **PISA/IGLU** | Direct competency + background measurement | Triennial; PISA 15-year-olds only | High (for update) |
+
+---
+
+## Research Metadata
+
+| Attribute | Value |
+|-----------|-------|
+| **Research Date** | 2026-04-20 |
+| **Researcher** | Sven / PAI |
+| **Method** | Destatis (Gymnasium rates); OECD SDMX API live fetch (education spending) |
+| **Confidence Level** | 90–95% for cited figures |
+| **Known Gaps** | Destatis Gymnasium figures from 2015; intergenerational income elasticity not yet included; PISA socioeconomic effect size not yet fetched |
+| **Update Frequency** | Destatis: annual; OECD EAG: annual (autumn) |
+
+---
+
+## Connection to DE-Plan
+
+- 61% vs. 14% Gymnasium rate → **CHALLENGE 5**: educational stratification structurally produces epistemic competence gap
+- Low upward mobility → **CHALLENGE 4**: precarity entrenches exhaustion across generations; democratic participation requires material stability
+- Bildungsmobilität gap → **CHALLENGE 1**: epistemically divided population is partially a product of structurally unequal knowledge access
+
+---
+
+## Changelog
+
+| Date | Change | Reason |
+|------|--------|--------|
+| 2026-04-20 | Initial dataset created | Structural cause of CHALLENGE 5 knowledge isolation |
+| 2026-04-20 | Education spending updated via OECD SDMX API; IWH removed | Direct authoritative source preferred over 2017 commentary |
diff --git a/Data/Knowledge-Worker-Global-Salaries/knowledge-worker-compensation-data.md b/Data/Knowledge-Worker-Global-Salaries/knowledge-worker-compensation-data.md
index 81a3d7b..c2ef572 100644
--- a/Data/Knowledge-Worker-Global-Salaries/knowledge-worker-compensation-data.md
+++ b/Data/Knowledge-Worker-Global-Salaries/knowledge-worker-compensation-data.md
@@ -17,11 +17,7 @@
 
 ## Quick Context
 
-Global GDP is ~$110 trillion (2024). Labor's share is approximately 52-53%, yielding ~$58 trillion in total global labor compensation. Knowledge workers represent roughly 30% of the global workforce but earn disproportionately more due to 38-50% wage premiums over average workers.
-
-**The math check:** For any knowledge work estimate to be valid, it cannot exceed total global labor compensation (~$58T). Our previous $70T upper bound failed this test - revised to $50T maximum.
-
-**McKinsey's $5-7T figure is a different metric:** McKinsey estimates the economic value of *automatable knowledge work tasks* (productivity gains from AI), NOT total compensation paid to knowledge workers. Comparing these numbers is apples-to-oranges.
+The $35-50T estimate derives from global GDP ($110T) × labor share (~52%) × knowledge worker premium — roughly 30% of the workforce earning 38-50% above average wages. Any valid estimate must stay under total global labor compensation (~$58T); a previous $70T upper bound failed this sanity check. McKinsey's $5-7T figure measures the productivity value of automatable knowledge tasks — not total compensation — a different metric entirely.
 
 ---
 
diff --git a/Data/UPDATES.md b/Data/UPDATES.md
index 0da06d9..65f8dff 100644
--- a/Data/UPDATES.md
+++ b/Data/UPDATES.md
@@ -4,6 +4,34 @@ This file tracks all datasets added to the Substrate Data directory.
 
 ---
 
+## 2026-04-20 - DE Social Mobility (DS-00017)
+
+**Dataset**: DE-Social-Mobility
+**Status**: Active
+**Coverage**: Gymnasium access rates by parental education (Destatis 2015), intergenerational mobility comparison (IWH 2017, PISA/IGLU)
+**Connection**: DE-Plan CHALLENGE 5 (Knowledge isolation), CHALLENGE 4 (Exhaustion/precarity)
+
+---
+
+## 2026-04-20 - DE Epistemic Competence (DS-00016)
+
+**Dataset**: DE-Epistemic-Competence
+**Status**: Active
+**Coverage**: Adult literacy (PIAAC 2023), science trust (Wissenschaftsbarometer 2024), digital skills (Eurostat), media literacy perception (Eurobarometer)
+**Connection**: DE-Plan CHALLENGE 5 (Knowledge isolation)
+
+---
+
+## 2026-04-20 - DE Platform & Media (DS-00015)
+
+**Dataset**: DE-Platform-Media
+**Status**: Active
+**Coverage**: Social network participation, online news reading, platform reach — Germany 2021–2025
+**Sources**: Eurostat live API (`bun get-de-digital`) + Reuters Institute Digital News Report 2024 + ARD/ZDF Onlinestudie 2024
+**Connection**: DE-Plan CHALLENGE 3 (Platform-mediated public sphere)
+
+---
+
 ## 2025-10-16 - U.S. Gross Domestic Product (GDP)
 
 **Dataset**: US-GDP
diff --git a/Data/sources/DS-00015—DE_Platform_Media/source.md b/Data/sources/DS-00015—DE_Platform_Media/source.md
new file mode 100644
index 0000000..027c670
--- /dev/null
+++ b/Data/sources/DS-00015—DE_Platform_Media/source.md
@@ -0,0 +1,37 @@
+# DE Platform & Media — Source Catalog
+
+**Source ID:** DS-00015
+**Record Created:** 2026-04-20
+**Cataloger:** Sven / PAI
+
+---
+
+## Sources
+
+### Eurostat — isoc_ci_ac_i
+- **URL:** https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_ci_ac_i
+- **Auth:** None
+- **Fetch script:** `bun get-de-digital` → `Data/DE-Platform-Media/eurostat-digital-de.csv`
+- **Coverage:** Germany, annual, % all individuals, 20+ indicators
+
+### Reuters Institute Digital News Report
+- **URL:** https://reutersinstitute.politics.ox.ac.uk/digital-news-report
+- **Auth:** None (free PDF + data download)
+- **Update:** Annual, published June
+- **Germany sample:** ~2,000 online panelists
+
+### ARD/ZDF Onlinestudie
+- **URL:** https://www.ard-zdf-onlinestudie.de
+- **Auth:** None
+- **Update:** Annual, published autumn
+- **Germany sample:** ~2,000 (random digit dialing + online panel)
+
+---
+
+## Dataset
+
+`Data/DE-Platform-Media/DE-Platform-Media.md`
+
+## Connection to DE-Plan
+
+CHALLENGE 3 (Platform-mediated public sphere)
diff --git a/Data/sources/DS-00016—DE_Epistemic_Competence/source.md b/Data/sources/DS-00016—DE_Epistemic_Competence/source.md
new file mode 100644
index 0000000..93a51ae
--- /dev/null
+++ b/Data/sources/DS-00016—DE_Epistemic_Competence/source.md
@@ -0,0 +1,42 @@
+# DE Epistemic Competence — Source Catalog
+
+**Source ID:** DS-00016
+**Record Created:** 2026-04-20
+**Cataloger:** Sven / PAI
+
+---
+
+## Sources
+
+### OECD PIAAC 2023
+- **URL:** https://www.oecd.org/en/topics/sub-issues/piaac.html
+- **Auth:** None (public data)
+- **Coverage:** Adult literacy, numeracy, adaptive problem solving; 40+ countries; Germany results Dec 2024
+- **Update frequency:** ~decennial
+
+### Wissenschaftsbarometer
+- **URL:** https://www.wissenschaft-im-dialog.de/projekte/wissenschaftsbarometer/
+- **Auth:** None (free PDF)
+- **Coverage:** Germany, annual, science trust tracking since 2014
+- **Update frequency:** Annual (autumn)
+
+### Eurobarometer (Media Literacy / Disinformation)
+- **URL:** https://europa.eu/eurobarometer/surveys/browse/all
+- **Auth:** None (GESIS archive for microdata)
+- **Coverage:** EU-wide, Germany subsample; periodic special surveys
+- **Update frequency:** Irregular (every 2–3 years for relevant topic)
+
+### Eurostat — isoc_sk_dskl_i
+- **URL:** https://ec.europa.eu/eurostat/databrowser/view/isoc_sk_dskl_i
+- **Auth:** None
+- **Note:** Digital skills self-assessment series, PC_IND unit ends ~2019 for DE
+
+---
+
+## Dataset
+
+`Data/DE-Epistemic-Competence/DE-Epistemic-Competence.md`
+
+## Connection to DE-Plan
+
+CHALLENGE 5 (Knowledge isolation)
diff --git a/Data/sources/DS-00017—DE_Social_Mobility/source.md b/Data/sources/DS-00017—DE_Social_Mobility/source.md
new file mode 100644
index 0000000..f1d6def
--- /dev/null
+++ b/Data/sources/DS-00017—DE_Social_Mobility/source.md
@@ -0,0 +1,40 @@
+# DE Social Mobility — Source Catalog
+
+**Source ID:** DS-00017
+**Record Created:** 2026-04-20
+**Cataloger:** Sven / PAI
+
+---
+
+## Sources
+
+### Statistisches Bundesamt (Destatis)
+- **URL:** https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Schulen/_inhalt.html
+- **Auth:** None
+- **Coverage:** Annual Gymnasialquoten by parental education; Bildungsquoten nach Migrationshintergrund
+- **Update frequency:** Annual
+
+### OECD Education at a Glance — SDMX API (live)
+- **URL:** https://data-explorer.oecd.org/
+- **API base:** `https://sdmx.oecd.org/public/rest/data/`
+- **Education spending % GDP:** `OECD.EDU.IMEP,DSD_EAG_UOE_FIN@DF_UOE_INDIC_FIN_GDP,/EXP.DEU.ISCED11_1T8.{S13|_T}.INST_EDU.DIR_EXP._Z.PT_B1GQ?format=csvfilewithlabels`
+  - `S13` = government only; `_T` = all sources
+- **Auth:** None
+- **Coverage:** Education spending % GDP, annual, all ISCED levels, Germany
+- **Update frequency:** Annual (autumn, ~18 month lag)
+
+### PISA / IGLU (IEA)
+- **URL:** https://www.oecd.org/pisa/ / https://www.iea.nl/studies/iea/pirls
+- **Auth:** None
+- **Coverage:** PISA: 15-year-olds, triennial; IGLU: Grade 4 reading, 5-year cycle
+- **Update frequency:** PISA triennial; IGLU every 5 years
+
+---
+
+## Dataset
+
+`Data/DE-Social-Mobility/DE-Social-Mobility.md`
+
+## Connection to DE-Plan
+
+CHALLENGE 5 (Knowledge isolation), CHALLENGE 4 (Exhaustion/precarity)
diff --git a/Plans/de-plan1-sven.md b/Plans/de-plan1-sven.md
index d619fb1..298c299 100644
--- a/Plans/de-plan1-sven.md
+++ b/Plans/de-plan1-sven.md
@@ -52,11 +52,19 @@ This is a Plan for the Bundesrepublik Deutschland.
 
 # DATA REFERENCES
 
-- DE-Common-Metrics: economic baseline (GDP, unemployment, housing, wages)
-- DE-Democracy-Metrics: democratic health indicators (voter turnout, trust, press freedom, V-Dem score)
+- DE-Common-Metrics: economic baseline (GDP, unemployment, housing, wages) → CHALLENGE 4
+- DE-Democracy-Metrics: democratic health indicators (voter turnout, trust, press freedom, V-Dem score, Palantir deployment) → CHALLENGE 1, 2, 6
+- DE-Lobby-Transparency: declared lobbying expenditure, top spenders, sector breakdown → CHALLENGE 1 (organized influence as epistemic asymmetry), CHALLENGE 2 (institutional trust)
+- DE-Parliament-Activity: Drucksachen, Vorgänge, Plenarprotokolle (WP21) → CHALLENGE 2 (legislative activity as democracy indicator)
+- DE-Federal-Budget: federal expenditure by ministry, income structure → CHALLENGE 4 (resource allocation for social vs. defense priorities)
+- DE-Energy-Mix: electricity generation by source, renewable share → Energiewende tracking (supporting context)
+- DE-Platform-Media: social network participation %, news trust, platform reach (Eurostat + Reuters DNR + ARD/ZDF) → CHALLENGE 3
+- DE-Epistemic-Competence: adult literacy (PIAAC 2023), science trust (Wissenschaftsbarometer), digital skills → CHALLENGE 5
+- DE-Social-Mobility: Gymnasium access by parental education (Destatis), intergenerational mobility (IWH/PISA) → CHALLENGE 5, CHALLENGE 4
 
 # RELATED SUBSTRATE COMPONENTS
 
 - Problems: PR-00001 (Meaning Crisis), PR-00003 (Exhaustion), PR-00004 (Fascization), PR-00005 (Epistemic Power), PR-00006 (Platform Feudalism), PR-00007 (Knowledge Isolation)
 - Values: VA-00001 (Epistemic Sovereignty), VA-00002 (Authority Requires Justification), VA-00005 (Digital Autonomy Is Political)
-- Models: MO-00001 (Han — Exhaustion), MO-00002 (Fisher — Capitalist Realism), MO-00003 (Foucault — Power/Knowledge), MO-00005 (Graeber — Anarchism/Domination)
+- Models: MO-00001 (Han — Exhaustion), MO-00002 (Fisher — Capitalist Realism), MO-00003 (Foucault — Power/Knowledge), MO-00004 (Vervaeke — Meaning Crisis), MO-00005 (Graeber — Anarchism/Domination)
+- Organizations: OR-00001 (IndieWeb — STRATEGY 1), OR-00002 (Wikimedia DE — STRATEGY 5), OR-00003 (Reporter ohne Grenzen — CHALLENGE 6), OR-00004 (Mehr Demokratie e.V. — MISSION 1), OR-00005 (netzpolitik.org — CHALLENGE 3)
diff --git a/Problems/Problems b/Problems/Problems.md
similarity index 100%
rename from Problems/Problems
rename to Problems/Problems.md
diff --git a/Values/Values b/Values/Values.md
similarity index 100%
rename from Values/Values
rename to Values/Values.md
diff --git a/get-de-digital b/get-de-digital
new file mode 100755
index 0000000..4793ae4
--- /dev/null
+++ b/get-de-digital
@@ -0,0 +1,112 @@
+#!/usr/bin/env bun
+// Fetch Eurostat digital society indicators for Germany
+// Endpoint: ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_ci_ac_i
+// No auth required. Dataset: PC_IND (% of all individuals), IND_TOTAL, geo=DE
+
+import { writeFileSync, mkdirSync } from "fs";
+import { join } from "path";
+
+const OUT_DIR = join(__dirname, "Data/DE-Platform-Media");
+
+const BASE = "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data";
+
+const INDICATORS: Record<string, string> = {
+  I_IUSNET: "Social networks participation",
+  I_IUNW1: "Online news reading",
+  I_IUPH1: "Video/voice calls",
+  I_IUBK: "Internet banking",
+  I_IUOLC: "Online courses",
+  I_IUUPL1: "Content upload/sharing",
+  I_IUWIKI: "Wiki consultation",
+  I_IUEM: "Email use",
+  I_IUIF: "Finding information online",
+};
+
+function csvEscape(value: string | number | undefined | null): string {
+  if (value === null || value === undefined) return "";
+  const s = String(value);
+  if (s.includes(",") || s.includes('"') || s.includes("\n"))
+    return `"${s.replace(/"/g, '""')}"`;
+  return s;
+}
+
+async function fetchEurostat(indicators: string[]): Promise<Record<string, Record<string, number>>> {
+  const indic_params = indicators.map((i) => `indic_is=${i}`).join("&");
+  const url = `${BASE}/isoc_ci_ac_i?format=JSON&lang=en&geo=DE&unit=PC_IND&ind_type=IND_TOTAL&${indic_params}`;
+
+  const res = await fetch(url);
+  if (!res.ok) throw new Error(`Eurostat HTTP ${res.status}`);
+  const data = await res.json() as any;
+
+  const dims = data.dimension ?? {};
+  const vals = data.value ?? {};
+  const idx: string[] = data.id ?? [];
+  const sizes: number[] = data.size ?? [];
+
+  const indicList = Object.keys(dims.indic_is?.category?.label ?? {});
+  const timeList = Object.keys(dims.time?.category?.label ?? {});
+
+  const nTime = sizes[idx.indexOf("time")] ?? 1;
+  const result: Record<string, Record<string, number>> = {};
+
+  for (const [k, v] of Object.entries(vals)) {
+    const ki = parseInt(k);
+    const iIndic = Math.floor(ki / nTime);
+    const iTime = ki % nTime;
+    const indic = indicList[iIndic];
+    const time = timeList[iTime];
+    if (!indic || !time) continue;
+    result[indic] ??= {};
+    result[indic][time] = v as number;
+  }
+
+  return result;
+}
+
+async function main() {
+  console.log("Fetching Eurostat digital society data for Germany...");
+  mkdirSync(OUT_DIR, { recursive: true });
+
+  const indicatorKeys = Object.keys(INDICATORS);
+  const data = await fetchEurostat(indicatorKeys);
+
+  // Determine recent years (last 5)
+  const allYears = new Set<string>();
+  for (const indic of Object.values(data)) {
+    for (const year of Object.keys(indic)) allYears.add(year);
+  }
+  const recentYears = [...allYears].sort().slice(-5);
+
+  // CSV: indicator, label, year, value_pct
+  const rows: string[] = ["indicator_id,indicator_label,year,value_pct"];
+  for (const [indicId, yearMap] of Object.entries(data)) {
+    const label = INDICATORS[indicId] ?? indicId;
+    for (const year of recentYears) {
+      const val = yearMap[year];
+      if (val !== undefined) {
+        rows.push(
+          [csvEscape(indicId), csvEscape(label), csvEscape(year), val.toFixed(2)].join(",")
+        );
+      }
+    }
+  }
+
+  const csvPath = join(OUT_DIR, "eurostat-digital-de.csv");
+  writeFileSync(csvPath, rows.join("\n") + "\n");
+  console.log(`✓ Written ${rows.length - 1} rows to ${csvPath}`);
+
+  // Print summary
+  console.log("\n=== Latest values (most recent year) ===");
+  for (const [indicId, yearMap] of Object.entries(data)) {
+    const years = Object.keys(yearMap).sort();
+    const latest = years[years.length - 1];
+    if (latest) {
+      console.log(`  ${INDICATORS[indicId] ?? indicId}: ${yearMap[latest]?.toFixed(1)}% (${latest})`);
+    }
+  }
+}
+
+main().catch((err) => {
+  console.error("Error:", err.message);
+  process.exit(1);
+});