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+# Knowledge Worker Global Compensation - Research Compilation
+
+**Source ID:** DS-00005
+**Record Created:** 2025-10-25
+**Last Updated:** 2025-10-25
+**Cataloger:** Substrate Data Curation
+**Review Status:** Reviewed
+
+---
+
+## Bibliographic Information
+
+### Title Statement
+- **Main Title:** Knowledge Worker Global Compensation: Summary Table (2024-2025)
+- **Subtitle:** Multi-Source Research Compilation on Global Knowledge Worker Salaries
+- **Abbreviated Title:** Knowledge Worker Compensation
+- **Variant Titles:** Global Tech Salaries, Knowledge Economy Compensation Data
+
+### Responsibility Statement
+- **Publisher/Issuing Body:** Substrate Data Curation (Kai Personal AI Infrastructure)
+- **Department/Division:** Multi-Agent Research System
+- **Contributors:** 10 parallel AI research agents (Perplexity, Claude, Gemini researchers)
+- **Contact Information:** Research compiled via automated research system
+
+### Publication Information
+- **Place of Publication:** Digital research compilation
+- **Date of First Publication:** 2025-10-19
+- **Publication Frequency:** On-demand research updates
+- **Current Status:** Active
+
+### Edition/Version Information
+- **Current Version:** 2025-10-19 research snapshot
+- **Version History:** Initial research compilation
+- **Versioning Scheme:** Date-based research snapshots
+
+---
+
+## Authority Statement
+
+### Organizational Authority
+
+**Issuing Organization Analysis:**
+- **Official Name:** Substrate Data Curation (Kai Personal AI Infrastructure)
+- **Type:** Research compilation via multi-agent AI system
+- **Established:** 2025
+- **Mandate:** Aggregate authoritative compensation data from multiple sources
+- **Parent Organization:** Independent research project
+- **Governance Structure:** Automated research with human validation
+
+**Domain Authority:**
+- **Subject Expertise:** Aggregation of authoritative salary data from government agencies, consulting firms, industry reports
+- **Recognition:** Synthesizes data from BLS (U.S. Bureau of Labor Statistics), OECD, ILO, major consulting firms
+- **Publication History:** Initial research compilation (2025)
+- **Peer Recognition:** Sources include recognized authorities (BLS, OECD, Dice, Glassdoor, Robert Half)
+
+**Quality Oversight:**
+- **Peer Review:** Multi-agent cross-validation (10 parallel agents)
+- **Editorial Board:** Human review of aggregated findings
+- **Scientific Committee:** Source validation against authoritative data providers
+- **External Audit:** Not applicable (research compilation)
+- **Certification:** None (data aggregation from certified sources)
+
+**Independence Assessment:**
+- **Funding Model:** Independent research project
+- **Political Independence:** No political affiliations
+- **Commercial Interests:** None (non-commercial research)
+- **Transparency:** Full source attribution; research methodology documented
+
+### Data Authority
+
+**Provenance Classification:**
+- **Source Type:** Tertiary (aggregates secondary and primary sources)
+- **Data Origin:** Multi-source aggregation from government agencies, consulting firms, industry reports
+- **Chain of Custody:** Primary sources (BLS, OECD, ILO, consulting firms) → AI research agents → Data synthesis → Public documentation
+
+**Tertiary Source Characteristics:**
+- Synthesizes data from 20+ primary and secondary sources
+- Adds value through cross-validation and regional aggregation
+- Provides confidence levels based on source quality
+- Identifies data gaps and methodological limitations
+
+---
+
+## Scope Note
+
+### Content Description
+
+**Subject Coverage:**
+- **Primary Subjects:** Labor Economics, Knowledge Worker Compensation, Technology Sector Salaries, Global Workforce Statistics
+- **Secondary Subjects:** AI/ML Premium Roles, Regional Salary Comparisons, Freelance Knowledge Work
+- **Subject Classification:**
+  - LC: HD (Labor Economics), HB (Economic Theory)
+  - Dewey: 331.2 (Labor Economics - Wages)
+- **Keywords:** knowledge workers, technology salaries, global compensation, tech workers, AI/ML roles, software engineer salaries, consulting compensation, STEM workers, professional services salaries
+
+**Geographic Coverage:**
+- **Spatial Scope:** Global (U.S., Europe, Asia-Pacific, Latin America)
+- **Countries/Regions Included:** United States, Switzerland, Denmark, Germany, Singapore, Eastern Europe, China, India, Latin America
+- **Geographic Granularity:** Country and regional aggregates
+- **Coverage Completeness:** U.S. high (85% confidence); Global medium (65% confidence)
+- **Notable Exclusions:** Africa, Middle East (limited data availability)
+
+**Temporal Coverage:**
+- **Start Date:** 2024 (primary data year)
+- **End Date:** 2025 (latest projections)
+- **Historical Depth:** 1-2 years (snapshot with growth trends)
+- **Frequency of Observations:** Annual salary data
+- **Temporal Granularity:** Year-level
+- **Time Series Continuity:** Not applicable (single research snapshot)
+
+**Population/Cases Covered:**
+- **Target Population:** Global knowledge workers (~1+ billion estimated)
+- **Inclusion Criteria:** Knowledge-intensive roles (software engineers, data scientists, consultants, healthcare professionals, financial analysts)
+- **Exclusion Criteria:** Manual labor, routine clerical work, retail service workers
+- **Coverage Rate:** U.S. ~100 million workers (38-42% of workforce); Global ~1+ billion
+- **Sample vs. Census:** Aggregation of sample surveys and employment statistics
+
+**Variables/Indicators:**
+- **Number of Variables:** 15+ compensation and workforce metrics
+- **Core Indicators:**
+  - Total annual compensation by geography and sector
+  - Average/median salaries by role
+  - Year-over-year growth rates
+  - Workforce size estimates
+  - AI/ML premium percentages
+  - Freelance knowledge worker statistics
+- **Derived Variables:** Total market value calculations, regional averages, growth trend projections
+- **Data Dictionary Available:** Yes - see knowledge-worker-compensation-data.md
+
+### Content Boundaries
+
+**What This Source IS:**
+- Comprehensive multi-source aggregation of knowledge worker compensation data
+- Best available synthesis of U.S. knowledge worker salaries (high confidence)
+- Useful global overview with regional estimates (medium confidence)
+- Identifies data gaps and methodological limitations transparently
+
+**What This Source IS NOT:**
+- NOT primary salary survey data (use BLS OEWS, Dice, Glassdoor directly)
+- NOT real-time (2024-2025 snapshot; annual updates required)
+- NOT granular below country/regional level (no city-specific data)
+- NOT comprehensive for all countries (Africa, Middle East gaps)
+- NOT peer-reviewed academic research (research compilation)
+
+**Comparison with Similar Sources:**
+
+| Source | Advantages Over This Source | Disadvantages vs. This Source |
+|--------|----------------------------|-------------------------------|
+| BLS OEWS (U.S. only) | Primary government data; high accuracy | U.S. only; no global coverage; annual lag |
+| OECD Average Wages | Official international data; high credibility | Country-level only; no occupation-specific knowledge worker data |
+| Dice Tech Salary Report | Tech sector depth; annual trends | U.S. tech only; narrow scope |
+| Glassdoor Salaries | User-generated; granular roles | Self-reported bias; quality varies |
+| Payscale/Robert Half | Consulting firm depth; market insights | Subscription models; narrower scope |
+
+---
+
+## Access Conditions
+
+### Technical Access
+
+**API Information:**
+- **Endpoint URL:** N/A (static research compilation)
+- **API Type:** N/A
+- **API Version:** N/A
+- **OpenAPI/Swagger Spec:** N/A
+- **SDKs/Libraries:** N/A
+
+**Authentication:**
+- **Authentication Required:** No
+- **Authentication Type:** None (public research documentation)
+- **Registration Process:** Not applicable
+- **Approval Required:** No
+- **Approval Timeframe:** N/A
+
+**Rate Limits:**
+- Not applicable (static document)
+
+**Query Capabilities:**
+- Not applicable (static document)
+
+**Data Formats:**
+- **Available Formats:** Markdown (knowledge-worker-compensation-data.md)
+- **Format Quality:** Structured markdown tables
+- **Compression:** Not compressed
+- **Encoding:** UTF-8
+
+**Download Options:**
+- **Bulk Download:** Yes - markdown file
+- **Streaming API:** No
+- **FTP/SFTP:** No
+- **Torrent:** No
+- **Data Dumps:** Single markdown document
+
+**Reliability Metrics:**
+- Not applicable (static research document)
+
+### Legal/Policy Access
+
+**License:**
+- **License Type:** Research compilation (individual sources retain original licenses)
+- **License Version:** N/A
+- **License URL:** See individual source licenses (BLS: public domain, OECD: CC-BY, etc.)
+- **SPDX Identifier:** Mixed (varies by source)
+
+**Usage Rights:**
+- **Redistribution Allowed:** Yes (with source attribution)
+- **Commercial Use Allowed:** Check individual source licenses
+- **Modification Allowed:** Yes (with source attribution)
+- **Attribution Required:** Yes - cite original sources
+- **Share-Alike Required:** No
+
+**Cost Structure:**
+- **Access Cost:** Free
+
+**Terms of Service:**
+- **TOS URL:** N/A (research compilation)
+- **Key Restrictions:** Cite original sources; verify currency of data
+- **Liability Disclaimers:** Research compilation "as is"; users responsible for validating currency
+- **Privacy Policy:** No personal data collected
+
+---
+
+## Collection Development Policy Fit
+
+### Relevance Assessment
+
+**Substrate Mission Alignment:**
+- **Human Progress Focus:** Knowledge worker compensation central to understanding economic progress and human capital value
+- **Problem-Solution Connection:**
+  - Links to Problems: Wage stagnation, skills gaps, labor market inefficiencies
+  - Links to Solutions: Education investment, skills development, labor mobility
+- **Evidence Quality:** Medium-High for U.S. (85% confidence); Medium for global (65% confidence)
+
+**Collection Priorities Match:**
+- **Priority Level:** IMPORTANT - valuable for labor economics and human capital domain
+- **Uniqueness:** Best available multi-source synthesis of global knowledge worker compensation
+- **Comprehensiveness:** Fills critical gap for global knowledge economy salary data
+
+### Comparison with Holdings
+
+**Overlapping Sources:**
+- None currently in Substrate
+
+**Unique Contribution:**
+- Only global knowledge worker compensation dataset in Substrate
+- Multi-source validation (20+ authoritative sources)
+- Identifies data gaps and confidence levels transparently
+- U.S. + global coverage in single compilation
+
+**Preferred Use Cases:**
+- When global knowledge worker compensation overview needed
+- Cross-country salary comparisons
+- Understanding knowledge economy labor market
+- AI/ML skills premium analysis
+
+---
+
+## Technical Specifications
+
+### Data Model
+
+**Schema Documentation:**
+- **Schema Type:** Markdown tables (structured text)
+- **Schema URL:** knowledge-worker-compensation-data.md
+- **Schema Version:** 2025-10-19
+
+**Entity Types:**
+- Geographic regions (U.S., Global, countries)
+- Sectors (Technology, Finance, Healthcare, Professional Services)
+- Roles (Software Engineer, Data Scientist, Consultant, etc.)
+- Compensation metrics (average, median, growth rates)
+
+**Key Relationships:**
+- Geography → Sector → Role → Compensation
+- Time → Geography → Growth Rate
+
+**Primary Keys:**
+- Composite: (Geography, Sector, Role, Year)
+
+**Foreign Keys:**
+- Not applicable (summary tables)
+
+### Metadata Standards Compliance
+
+**Standards Followed:**
+- [x] Dublin Core
+- [ ] DCAT - not applicable
+- [ ] Schema.org - not applicable
+- [ ] SDMX - not applicable
+- [ ] DDI - not applicable
+- [ ] ISO 19115 - not applicable
+- [ ] MARC - not applicable
+
+**Metadata Quality:**
+- **Completeness:** 80% (source attribution comprehensive; some estimates)
+- **Accuracy:** High for U.S. (85% confidence); Medium for global (65% confidence)
+- **Consistency:** Good - standardized table format
+
+### API Documentation Quality
+
+**Documentation Assessment:**
+- Not applicable (static research document)
+
+---
+
+## Source Evaluation Narrative
+
+### Methodological Assessment
+
+**Data Collection Methodology:**
+
+**Sampling Design:**
+- **Method:** Multi-source aggregation (not original sampling)
+- **Sample Size:** Varies by source (BLS: millions of establishments; Dice: survey data)
+- **Sampling Frame:** Aggregates from multiple sampling frames
+- **Stratification:** By source methodology (varies)
+- **Weighting:** Not applicable (summary compilation)
+
+**Data Collection Instruments:**
+- **Instrument Type:** AI research agents querying authoritative sources
+- **Validation:** Multi-agent cross-validation (10 parallel agents)
+- **Question Wording:** Not applicable (secondary data aggregation)
+- **Mode:** Web-based research + API queries
+
+**Quality Control Procedures:**
+- **Field Supervision:** Automated multi-agent validation
+- **Validation Rules:** Cross-source consistency checks
+- **Consistency Checks:** Regional and sector-level validation
+- **Verification:** Human review of aggregated findings
+- **Outlier Treatment:** Flagged inconsistencies noted in research
+
+**Error Characteristics:**
+- **Sampling Error:** Varies by source (BLS low; industry surveys higher)
+- **Non-sampling Error:** Aggregation errors; definitional differences across sources
+- **Known Biases:** U.S.-centric (better data coverage); self-reported bias in some sources
+- **Accuracy Bounds:** U.S. ±5-10% (high confidence); Global ±15-30% (medium confidence)
+
+**Methodology Documentation:**
+- **Transparency Level:** 4/5 (Comprehensive)
+- **Documentation URL:** knowledge-worker-compensation-data.md (full source attribution)
+- **Peer Review Status:** Multi-agent validation; not academic peer review
+- **Reproducibility:** SPARQL/API queries documented; research snapshot date-stamped
+
+### Currency Assessment
+
+**Update Characteristics:**
+- **Update Frequency:** On-demand research updates (not automated)
+- **Update Reliability:** Requires manual re-research
+- **Update Notification:** None (static snapshot)
+- **Last Updated:** 2025-10-19
+
+**Timeliness:**
+- **Collection to Publication Lag:** Varies by source (BLS ~6 months; Dice ~1 month; OECD ~1 year)
+- **Factors Affecting Timeliness:** Annual salary survey cycles; government reporting schedules
+- **Historical Timeliness:** Single snapshot (not time series)
+
+**Currency for Different Uses:**
+- **Real-time Analysis:** Unsuitable (static snapshot)
+- **Recent Trends:** Suitable for 2024-2025 estimates
+- **Historical Research:** Not applicable (single snapshot)
+
+### Objectivity Assessment
+
+**Potential Biases:**
+
+**Political Bias:**
+- **Government Influence:** Government sources (BLS, OECD) professional independence
+- **Editorial Stance:** Research compilation neutral
+- **Political Pressure:** None
+
+**Commercial Bias:**
+- **Funding Sources:** Consulting firms (Robert Half, Payscale) have commercial interests in salary data
+- **Advertising Influence:** Not applicable
+- **Proprietary Interests:** Some sources proprietary (Glassdoor, Payscale)
+
+**Cultural/Social Bias:**
+- **Geographic Bias:** U.S.-centric (better data coverage); Western Europe overrepresented
+- **Social Perspective:** Knowledge worker focus excludes non-knowledge sectors
+- **Language Bias:** English-language sources predominate
+- **Selection Bias:** "Knowledge worker" definition varies by source
+
+**Transparency:**
+- **Bias Disclosure:** Data gaps acknowledged; confidence levels provided
+- **Limitations Stated:** Comprehensive limitation documentation
+- **Raw Data Available:** Source links provided; original data at individual sources
+
+### Reliability Assessment
+
+**Consistency:**
+- **Internal Consistency:** Cross-source validation performed
+- **Temporal Consistency:** Not applicable (single snapshot)
+- **Cross-source Consistency:** Reasonable agreement for U.S. (±10%); wider variation globally
+
+**Stability:**
+- **Definition Changes:** "Knowledge worker" definition varies by source
+- **Methodology Changes:** Not applicable (single snapshot)
+- **Series Breaks:** Not applicable
+
+**Verification:**
+- **Independent Verification:** 10 parallel AI agents cross-validated findings
+- **Replication Studies:** Not applicable (research compilation)
+- **Audit Results:** Multi-agent validation; human review
+
+### Accuracy Assessment
+
+**Validation Evidence:**
+- **Benchmark Comparisons:** U.S. BLS data cross-checked with Dice, Glassdoor (±10% agreement)
+- **Coverage Assessments:** U.S. 85% confidence; Global 65% confidence
+- **Error Studies:** Not applicable
+
+**Accuracy for Different Uses:**
+- **Point Estimates:** Reliable for U.S. averages; moderate for global
+- **Trend Analysis:** Limited (single snapshot with YoY growth rates)
+- **Cross-sectional Comparison:** Reliable for U.S. sectors; moderate for cross-country
+- **Sub-population Analysis:** Limited (sector-level; no demographics)
+
+---
+
+## Known Limitations and Caveats
+
+### Coverage Limitations
+
+**Geographic Gaps:**
+- Africa (minimal data available)
+- Middle East (limited coverage)
+- Rural areas (knowledge workers concentrated in urban centers)
+
+**Temporal Gaps:**
+- Single snapshot (2024-2025)
+- No historical time series
+
+**Population Exclusions:**
+- Non-knowledge workers (by design)
+- Informal economy knowledge workers
+- Gig economy workers (partial coverage)
+
+**Variable Gaps:**
+- Equity compensation not captured in most sources (stock options, RSUs)
+- Benefits variation across countries
+- Demographic breakdowns limited
+
+### Methodological Limitations
+
+**Sampling Limitations:**
+- Varies by source (BLS high quality; self-reported surveys lower quality)
+- Self-selection bias in Glassdoor, Payscale user-generated data
+
+**Measurement Limitations:**
+- "Knowledge worker" definition inconsistent across sources
+- Purchasing power parity adjustments not uniformly applied
+- Exchange rate fluctuations affect cross-country comparisons
+
+**Processing Limitations:**
+- Aggregation across sources with different methodologies
+- Confidence levels estimated (not statistically rigorous)
+
+### Comparability Limitations
+
+**Cross-national Comparability:**
+- Definitional differences (what constitutes "knowledge worker")
+- PPP adjustments needed for true cost-of-living comparisons
+- Tax and benefit systems vary (gross vs. net compensation)
+
+**Temporal Comparability:**
+- Single snapshot (cannot assess trends)
+
+**Sub-group Comparability:**
+- Limited demographic data (gender, race, education level)
+
+### Usage Caveats
+
+**Inappropriate Uses:**
+1. **DO NOT use for precise individual salary negotiations** - broad averages only
+2. **DO NOT assume global estimates are highly accurate** - medium confidence (65%)
+3. **DO NOT use for historical trend analysis** - single snapshot
+4. **DO NOT assume equity compensation included** - most sources cash compensation only
+5. **DO NOT use without PPP adjustment** - cross-country comparisons need cost-of-living adjustment
+
+**Ecological Fallacy Risks:**
+- National/sector averages do not reflect individual company or role compensation
+- Regional averages mask within-country variation
+
+**Correlation vs. Causation:**
+- Compensation levels do not imply causation
+- Appropriate for descriptive analysis only
+
+---
+
+## Recommended Use Cases
+
+### Ideal Applications
+
+**Research Questions Well-Suited:**
+1. "What is the global knowledge worker compensation market size?"
+2. "How do U.S. tech salaries compare to European tech salaries?"
+3. "What is the AI/ML skills premium in 2025?"
+4. "What percentage of the U.S. workforce are knowledge workers?"
+
+**Analysis Types Supported:**
+- Descriptive statistics (compensation averages by geography/sector)
+- Cross-country comparison (regional salary differences)
+- Sector analysis (technology vs. finance vs. healthcare)
+- Skills premium analysis (AI/ML vs. general software engineering)
+
+### Appropriate Contexts
+
+**Geographic Contexts:**
+- U.S. national analysis (high confidence)
+- Western Europe comparison (medium confidence)
+- Global overview (medium confidence)
+
+**Temporal Contexts:**
+- Current snapshot (2024-2025)
+- Short-term growth trends (YoY)
+
+**Subject Contexts:**
+- Knowledge economy labor markets
+- Technology sector compensation
+- STEM workforce analysis
+- Consulting and professional services
+
+### Use Warnings
+
+**Avoid Using This Source For:**
+1. **Individual salary negotiation** → Use role-specific Glassdoor, Payscale
+2. **Historical trend analysis** → Single snapshot; need time series data
+3. **Precise global estimates** → Medium confidence (65%); use OECD, ILO for official stats
+4. **Equity compensation analysis** → Most sources exclude stock options/RSUs
+5. **Demographic analysis** → Limited demographic breakdowns
+
+**Recommended Alternatives For:**
+- U.S. official statistics → BLS OEWS (Occupational Employment and Wage Statistics)
+- Global official statistics → OECD Average Wages, ILO Global Wage Report
+- Tech sector depth → Dice Tech Salary Report, Stack Overflow Developer Survey
+- Equity compensation → Carta Equity Report, Glassdoor total compensation
+- Real-time data → Glassdoor, Payscale (updated continuously)
+
+---
+
+## Citation
+
+### Preferred Citation Format
+
+**APA 7th:**
+Substrate Data Curation. (2025, October 19). *Knowledge worker global compensation: Summary table (2024-2025)* [Research compilation]. https://github.com/danielmiessler/substrate
+
+**Chicago 17th:**
+Substrate Data Curation. "Knowledge Worker Global Compensation: Summary Table (2024-2025)." Research compilation. October 19, 2025. https://github.com/danielmiessler/substrate.
+
+**MLA 9th:**
+Substrate Data Curation. *Knowledge Worker Global Compensation: Summary Table (2024-2025)*. Research compilation, 19 Oct. 2025, github.com/danielmiessler/substrate.
+
+**Vancouver:**
+Substrate Data Curation. Knowledge worker global compensation: summary table (2024-2025) [Internet]. Research compilation; 2025 Oct 19 [cited 2025 Oct 25]. Available from: https://github.com/danielmiessler/substrate
+
+**BibTeX:**
+```bibtex
+@misc{substrate_knowledge_worker_2025,
+  author = {{Substrate Data Curation}},
+  title = {Knowledge Worker Global Compensation: Summary Table (2024-2025)},
+  year = {2025},
+  month = {October},
+  howpublished = {Research compilation},
+  url = {https://github.com/danielmiessler/substrate},
+  note = {Accessed: 2025-10-25; Multi-source aggregation via 10 parallel AI research agents}
+}
+```
+
+### Data Citation Principles
+
+Following FORCE11 Data Citation Principles:
+- **Importance:** Research compilation is citable output; cite original sources when possible
+- **Credit and Attribution:** Citations credit both compilation and original sources (BLS, OECD, etc.)
+- **Evidence:** Citations enable readers to verify compensation claims
+- **Unique Identification:** Date + version for exact reproducibility
+- **Access:** Citation provides access to research compilation
+- **Persistence:** Static snapshot preserved; future updates versioned
+- **Specificity and Verifiability:** Research date ensures snapshot reproducibility
+- **Interoperability:** Standard citation formats for reference managers
+- **Flexibility:** Adaptable to various research contexts
+
+**IMPORTANT:** Always cite original sources (BLS, OECD, Dice, etc.) for primary data claims. This compilation provides aggregated overview with source attribution.
+
+---
+
+## Version History
+
+### Current Version
+- **Version:** 2025-10-19 Research Snapshot
+- **Date:** 2025-10-19
+- **Changes:** Initial multi-source research compilation (10 parallel AI agents)
+
+### Previous Versions
+- None (initial research)
+
+---
+
+## Review Log
+
+### Internal Reviews
+- **Date:** 2025-10-25 | **Reviewer:** Substrate Data Curation | **Status:** Approved | **Notes:** Initial catalog entry; research compilation with transparent methodology
+
+### Quality Checks
+- **Last Metadata Validation:** 2025-10-25
+- **Last Authority Verification:** 2025-10-25 (source attribution verified)
+- **Last Link Check:** 2025-10-25
+- **Last Access Test:** 2025-10-25 (markdown file accessible)
+
+---
+
+## Related Resources
+
+### Cross-References
+
+**Related Substrate Entities:**
+- **Problems:**
+  - Wage stagnation
+  - Skills gaps
+  - Labor market inefficiencies
+- **Solutions:**
+  - Education investment
+  - Skills development programs
+  - Labor mobility initiatives
+- **Organizations:**
+  - U.S. Bureau of Labor Statistics
+  - OECD
+  - International Labour Organization
+- **Other Data Sources:**
+  - DS-00002: U.S. GDP (economic output context)
+  - DS-00003: U.S. Inflation (real wage purchasing power)
+
+**External Resources:**
+- **Alternative Sources:**
+  - BLS OEWS: https://www.bls.gov/oes/
+  - OECD Average Wages: https://data.oecd.org/earnwage/average-wages.htm
+  - ILO Global Wage Report: https://www.ilo.org/global/research/global-reports/global-wage-report/
+- **Complementary Sources:**
+  - Dice Tech Salary Report: https://www.dice.com/recruiting/ebooks/tech-salary-report/
+  - Glassdoor Salaries: https://www.glassdoor.com/Salaries/
+  - Stack Overflow Developer Survey: https://survey.stackoverflow.co/
+- **Source Comparison Studies:**
+  - Academic research on knowledge worker compensation trends
+
+### Additional Documentation
+
+**User Guides:**
+- knowledge-worker-compensation-data.md (detailed summary tables)
+- Full research report: https://gist.github.com/danielmiessler/2dc039762a202b083753b1400452614d
+
+**Research Using This Source:**
+- Initial research compilation (2025)
+
+**Methodology Papers:**
+- Research methodology: Multi-agent AI research system (10 parallel agents)
+- Sources: BLS, OECD, ILO, Dice, Glassdoor, Robert Half, Payscale, industry reports
+
+---
+
+## Cataloger Notes
+
+**Internal Notes:**
+- Unique tertiary source; multi-agent research compilation
+- U.S. data high confidence (85%); Global medium confidence (65%)
+- Future updates require re-research (not automated)
+- Consider annual update schedule (October/November)
+- Equity compensation gap noted (most sources exclude stock options/RSUs)
+
+**To Do:**
+- [ ] Annual update (October 2026) with new research cycle
+- [ ] Consider adding demographic breakdowns if data becomes available
+- [ ] Explore equity compensation data sources (Carta, Glassdoor total comp)
+- [ ] Add Africa and Middle East data if sources improve
+
+**Questions for Review:**
+- Should this be updated quarterly or annually?
+- How to handle exchange rate fluctuations in cross-country comparisons?
+- Should we add PPP-adjusted values?
+
+---
+
+**END OF SOURCE RECORD**
--- a/Data/Knowledge-Worker-Global-Salaries/update.log
+++ b/Data/Knowledge-Worker-Global-Salaries/update.log
@@ -0,0 +1,29 @@
+[2025-10-25T00:47:04.763Z] === Update Check Started ===
+[2025-10-25T00:47:04.763Z] Source: Knowledge Worker Global Compensation
+[2025-10-25T00:47:04.763Z] Source ID: DS-00005
+[2025-10-25T00:47:04.763Z] 
+[2025-10-25T00:47:04.763Z] NOTE: This data source requires manual research via multi-agent AI system.
+[2025-10-25T00:47:04.763Z] This script validates data freshness and documents the update process.
+[2025-10-25T00:47:04.763Z] 
+[2025-10-25T00:47:04.763Z] Current research date: October 19, 2025
+[2025-10-25T00:47:04.763Z] Research age: 6 days (0 months)
+[2025-10-25T00:47:04.763Z] 
+[2025-10-25T00:47:04.763Z] ✅ Research is current (< 6 months old)
+[2025-10-25T00:47:04.763Z] 
+[2025-10-25T00:47:04.763Z] Validating data file structure...
+[2025-10-25T00:47:04.763Z] ✅ All required sections present
+[2025-10-25T00:47:04.763Z] Data source citations: 58 references found
+[2025-10-25T00:47:04.763Z] ✅ Adequate source citations
+[2025-10-25T00:47:04.763Z] Updating source record...
+[2025-10-25T00:47:04.763Z] Source record updated (validation timestamp)
+[2025-10-25T00:47:04.763Z] 
+Update Check Summary:
+- Timestamp: 2025-10-25T00:47:04.763Z
+- Research Date: October 19, 2025
+- Data File: Present
+- Structure Validation: PASSED
+- Source Citations: 58 found
+- Manual Update Required: No (current)
+
+[2025-10-25T00:47:04.763Z] Checking for git repository...
+[2025-10-25T00:47:04.763Z] Git repository detected - committing validation update...