Research Data Curation Workflow
Systematize research dataset intake, compliance, and publication workflows
What You Can Do
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.
Features
Analyzes dataset files and generates complete, standardized metadata including data dictionaries, field descriptions, and schema specifications
Checks datasets against regulatory frameworks (HIPAA, GDPR, FAIR principles) and institutional requirements, flagging issues before publication
Evaluates data completeness, validity, structure, and documentation quality on a standardized scale with actionable improvement recommendations
Creates comprehensive README files, data dictionaries, methodology notes, and usage guides tailored to your dataset
Guides datasets through multi-step publication workflows with automated checklist generation and requirement verification
Processes multiple datasets simultaneously, generating consolidated assessment reports and managing bulk documentation tasks
Creates detailed logs of all curation decisions, compliance checks, and modifications for reproducibility and institutional records
Example Output
Metadata Report:
{
"dataset_id": "ds_2024_001",
"title": "Climate Station Observations 2020-2024",
"fields": [
{"name": "timestamp", "type": "datetime", "missing_pct": 0.0},
{"name": "temperature_c", "type": "float", "range": [-15.2, 42.8], "missing_pct": 2.1}
]
}
Compliance Check Results:
- ✓ FAIR Principles: 92% (Findable, Accessible, Interoperable, Reusable)
- ✓ GDPR Compliance: PASS (no PII detected)
- ✓ Documentation Completeness: 85% (6 of 7 required sections)
- ⚠ Access Control: Review institutional sharing policies
Quality Score Summary:
- Data Completeness: 97/100
- Format Standardization: 91/100
- Documentation Quality: 82/100
- Overall Score: 90/100 (Publication Ready)
What's Included
- Metadata Extraction Engine: Scans dataset structure and content to generate comprehensive metadata specifications
- Compliance Assessment Templates: Pre-built checks for common regulatory frameworks (FAIR, GDPR, HIPAA, institutional policies)
- Quality Rubric System: Standardized evaluation criteria for data completeness, validity, documentation, and usability
- Documentation Templates: Customizable README, data dictionary, and methodology documentation generators
- Publication Preparation Checklist: Step-by-step workflow for repository submission including format validation and metadata verification
- Assessment Report Generator: Produces professional reports summarizing metadata, compliance results, quality scores, and recommendations
Who It's For
- Research Data Managers
- Data Stewards
- Academic Librarians
- Compliance Officers
- Research Coordinators
Best For
- Standardizing dataset documentation across research programs
- Automating compliance verification for sensitive data
- Preparing datasets for open science repositories
- Batch-processing large dataset collections
- Creating audit trails for research data governance
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
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
This skill transforms raw patient intake data—whether from completed forms, verbal responses, or clinical notes—into organized, EHR-ready documentation. It systematically extracts ocular and systemic medical history, identifies urgent symptoms requiring expedited examination, flags drug interactions or contraindications relevant to vision care, and generates professional summaries that prepare optometrists for efficient, informed consultations. You'll reduce documentation rework, ensure no critical information is missed, and improve the quality of the medical record.
by kanta13jp1
You can architect well-structured knowledge graph schemas that capture complex entity relationships with semantic rigor and logical consistency. This skill helps you design optimized schemas, define entity types and relationships, validate graph coherence, and plan migrations from existing data structures to graph-native formats.
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