Research Data Documentation Generator
Generate FAIR-compliant dataset documentation and metadata
What You Can Do
You can create comprehensive, standards-compliant documentation for research datasets including data dictionaries, metadata records, and institutional compliance reports. The skill generates structured documentation following FAIR principles (Findable, Accessible, Interoperable, Reusable), validates against disciplinary standards like DDI, DataCite, or Dublin Core, and produces documentation your institution and publishers require for data archival and discovery.
Features
Automatically checks your documentation against all FAIR principles and flags missing elements (persistent identifiers, licenses, provenance, access rights)
Converts raw dataset metadata into structured variable descriptions with data types, value ranges, units, missing value codes, and controlled vocabulary mappings
Produces metadata in multiple formats (JSON-LD, XML, Dublin Core, MIARC) compatible with institutional repositories and data discovery services
Adapts documentation to match standards in your field—DDI-C for social science, MIAME for genomics, FGDC for geospatial, domain-specific vocabularies
Generates compliance checklists and documentation required by your institution's data management policies, IRB protocols, or funder mandates
Tracks and documents data provenance, processing steps, software versions, and transformation logic for full reproducibility
Generates automated quality checks on metadata completeness, consistency, and compliance with specified standards, with actionable recommendations
Example Output
Data Dictionary (tabular format):
| Variable | Type | Range | Unit | Missing Value | Description |
|---|---|---|---|---|---|
| temperature_c | float | -40 to 50 | Celsius | NA | Daily mean temperature measured at 14:00 UTC |
| precipitation_mm | float | 0 to 200 | mm | 999 | Total 24-hour precipitation, quality-controlled |
Metadata Record (Dublin Core):
<dc:creator>Smith, John; Lee, Maria</dc:creator>
<dc:issued>2026-01-15</dc:issued>
<dc:title>Climate Station Data 2025</dc:title>
<dc:identifier>https://doi.org/10.5281/zenodo.1234567</dc:identifier>
<dc:language>en</dc:language>
<dc:rights>CC-BY-4.0</dc:rights>
<dcat:keyword>climate, temperature, precipitation, station-data</dcat:keyword>
FAIR Compliance Checklist:
- ✓ Dataset has persistent identifier (DOI)
- ✓ Metadata includes creation date and responsible party
- ✓ License is explicitly stated (CC-BY-4.0)
- ✓ Data dictionary describes all variables and units
- ✗ Missing: Access restrictions and embargo dates
- ✗ Missing: Related publications and citations
What's Included
- Data Dictionary Template: Pre-formatted tables for variables with fields for type, range, units, missing value codes, definitions, and validation rules
- Metadata Generation System: Structured prompts to extract, organize, and standardize metadata from your dataset description and produce standards-compliant output
- FAIR Compliance Validator: Automated checklist testing documentation against FAIR principles and disciplinary standards with gap analysis and remediation guidance
- Institution-Specific Templates: Customizable templates aligned with common data management requirements from funders, IRBs, and institutional repositories
- Data Lineage Worksheet: Guided template to document data sources, processing steps, software versions, parameters, and decision logic for reproducibility
- Quality Assurance Checklist: Verification guide ensuring all required metadata fields are present, internally consistent, and machine-readable for archival systems
Who It's For
- Research Data Scientists and Data Stewards
- Data Librarians and Repository Curators
- Research Administrators and Compliance Officers
- Institutional Repository and Archive Managers
- Grant-Funded Researchers Preparing Data for Submission
Best For
- Creating documentation for datasets destined for institutional or disciplinary archives
- Ensuring FAIR compliance before publication or funder submission
- Standardizing metadata across multi-site or institutional research programs
- Preparing data for long-term preservation, discovery, and reuse
You might also like
You can design, document, and validate healthcare taxonomies and clinical classification hierarchies from scratch or refine existing ones. This skill guides you through term standardization, compliance mapping to ICD, SNOMED CT, and LOINC standards, and produces auditable taxonomies with comprehensive implementation documentation. You'll ensure consistency, regulatory compliance, and integration readiness across your healthcare systems.
by kanta13jp1
You can convert unstructured diagnostic test findings into formatted clinical documentation that meets ophthalmic standards and EHR requirements. The skill standardizes results across multiple test modalities (visual fields, OCT, fundus photography, refraction, tonometry), identifies clinically significant abnormalities requiring immediate physician review, and validates test protocols for completeness before submission—reducing documentation errors and improving patient safety.
by kanta13jp1
Create hierarchical taxonomies and classification schemes that organize legal documents, compliance frameworks, and regulatory requirements into coherent information architectures. You'll generate consistent, navigable category structures with cross-references and validation rules that ensure compliance teams can quickly locate and categorize documents. The skill produces documented taxonomy frameworks with implementation guides, classification rules, and audit-ready documentation.
by kanta13jp1
Create comprehensive technical specifications for ophthalmic instruments with built-in regulatory compliance markers, systematically analyze competitor instruments to identify market gaps and clinical differentiators, and develop value-based positioning statements that support pricing strategy and sales enablement. This skill transforms product knowledge into actionable competitive intelligence and positioning documents tailored for product managers, sales teams, and executive stakeholders in vision care.
by kanta13jp1
You can upload geospatial datasets (shapefiles, GeoJSON, rasters) and receive comprehensive validation reports, metadata standardization recommendations, and ISO 19115 compliance checks. The skill identifies data quality issues like projection mismatches, missing attributes, invalid geometries, and inconsistent coordinate systems, then generates standardized metadata templates ready for data catalogs.
by kanta13jp1
You'll design scalable, hierarchical content taxonomies tailored to your organization's structure and compliance needs. This skill generates comprehensive governance frameworks that define roles, responsibilities, approval workflows, and decision-making authority. You'll receive implementation roadmaps, metadata standards, stewardship models, and compliance matrices ready to deploy across departments.
by kanta13jp1
Build sophisticated taxonomy frameworks that organize legal concepts, documents, and case law into coherent hierarchies. The skill helps you create standardized classification systems for law firms, legal departments, and compliance teams, enabling faster document retrieval, better knowledge management, and clearer legal reasoning. You can design custom taxonomies for specific practice areas, regulatory frameworks, or organizational needs.
by kanta13jp1
Systematically evaluate geospatial datasets for quality issues, completeness, and compliance with spatial standards. You'll receive detailed audit reports identifying data gaps, CRS inconsistencies, geometric errors, and attribute validation failures. The skill also auto-generates structured metadata documentation following ISO 19115 and OGC standards, complete with data lineage, processing history, and recommended corrections.
by kanta13jp1