Open Data Curation Assistant
Validate, document, and publish datasets with FAIR compliance verification
What You Can Do
Prepare open datasets for publication by validating metadata completeness, assessing data quality against FAIR principles (Findable, Accessible, Interoperable, Reusable), and generating comprehensive documentation. You'll receive structured reports identifying compliance gaps, quality scores by dimension, and actionable recommendations for improving dataset discoverability and usability.
Features
Evaluate your dataset against FAIR principles with dimension-by-dimension scoring (Findability, Accessibility, Interoperability, Reusability) and specific recommendations to increase scores
Verify metadata completeness against Dublin Core, DCAT, and domain-specific standards; flag missing or malformed elements with guidance for correction
Analyze data for completeness, consistency, accuracy, and validity; generate quality profiles by field with actionable improvement suggestions
Create data dictionaries, README files, and usage guides from your dataset structure and existing metadata with domain-specific context
Suggest optimal file formats, encoding standards, and schema structures for maximum interoperability and platform compatibility
Review data licensing declarations for compatibility with open standards; flag potential legal or attribution issues
Receive step-by-step workflows tailored to your dataset type (tabular, geospatial, timeseries, text) with role-based checklists
Get a comprehensive go/no-go assessment with prioritized action items to meet repository and platform requirements
Example Output
FAIR Compliance Report for Air Quality Dataset:
- Findability Score: 3.5/5 (missing DOI, needs structured keywords)
- Accessibility: 4/5 (well-documented API, recommend adding data availability statement)
- Interoperability: 3/5 (CSV format good, add RDF conversion for linked data)
- Reusability: 4/5 (clear license, missing data quality documentation)
Quality Assessment:
- Completeness: 94% (3 fields have >5% missing values)
- Consistency: 98% (minor format inconsistencies in date fields detected)
- Recommendation: Add 50 missing values for geographic region field, standardize timestamps to ISO 8601
Auto-Generated Data Dictionary (excerpt):
Field: temperature_celsius
Type: Numeric (float)
Range: -10 to 45
Missing: 2% (12 records)
Description: Daily maximum temperature in Celsius, measured at ground level
Unit: °C
Source: Weather Station ID WS-042
What's Included
- FAIR Assessment Engine: Multi-dimensional compliance checker evaluating Findability, Accessibility, Interoperability, and Reusability with scoring rubric and remediation guidance
- Metadata Schema Validator: Validates against Dublin Core, DCAT, DataCite, and custom schemas; generates structured metadata in multiple serialization formats (JSON-LD, XML, TTL)
- Quality Profiler: Statistical and structural analysis of datasets generating completeness, consistency, and accuracy scores by field with distribution visualizations
- Documentation Generator: Produces README, data dictionary, methodology documentation, and usage examples in Markdown, automatically formatted for platforms like GitHub and Zenodo
- Curation Checklists: Role-based and dataset-type-specific workflows (e.g., for repository curators, data stewards, researchers) with progress tracking and task prioritization
- Publication Readiness Report: Executive summary with go/no-go decision, compliance gaps ranked by impact, specific actions to meet target repository requirements (Zenodo, DataHub, CKAN, etc.)
Who It's For
- Research Data Managers
- Open Data Librarians & Curators
- Academic Researchers & Data Stewards
- Government & Public Sector Data Officers
- NGO & Foundation Data Publication Leads
Best For
- Preparing datasets for institutional or open repositories
- Validating compliance before data publication
- Creating standardized metadata and documentation
- Improving dataset quality and discoverability scores
- Training data teams on FAIR principles and best practices
You might also like
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 design and validate information architectures that align with how users actually search and think about content. This skill analyzes user mental models, search patterns, and existing structures to identify discoverability gaps and recommend strategic improvements that make finding information intuitive and fast.
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
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 audit healthcare taxonomies and data models against clinical standards like SNOMED CT, ICD, and HL7 to identify gaps, overlaps, and semantic inconsistencies. Claude analyzes your taxonomy structure, suggests remediation strategies, and generates compliance reports. This ensures your clinical data is interoperable, standards-compliant, and semantically consistent across systems.
by kanta13jp1
Automate the curation of research datasets by generating standardized metadata, performing compliance verification, and producing quality assessments. This skill standardizes your data documentation process, ensures datasets meet regulatory requirements, and prepares them for repository publication—reducing manual work and improving consistency across your research organization.
by kanta13jp1
Create structured, standards-compliant metadata and finding aids that make cultural collections discoverable and accessible. Generate DACS-aligned descriptions, EAD XML exports, and scope-and-content narratives that support both public access and archival management. Produce comprehensive cataloging information that follows international archival best practices.
by kanta13jp1
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