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
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