Curate Data Management

Overview#

Curate is NeuroNexus' data management and curation application within the Radiens Clarity platform. Designed to implement FAIR data principles (Findable, Accessible, Interoperable, Reusable), Curate provides comprehensive metadata management, version-controlled analysis workflows, and collaborative tools that ensure reproducible neuroscience research.

Core Capabilities#

FAIR Data Compliance#

Implement globally recognized FAIR principles for scientific data management and sharing.

FAIR Components:

• Findable: Rich metadata with unique identifiers and searchable repositories
• Accessible: Standardized retrieval protocols and authentication mechanisms
• Interoperable: Format standards (NWB, BIDS) and controlled vocabularies
• Reusable: Clear licensing, provenance tracking, and community standards

Standards Supported:

• Neurodata Without Borders (NWB) format
• Brain Imaging Data Structure (BIDS) for behavioral data
• Dublin Core metadata standards
• DataCite DOI integration
• ORCID researcher identification
• Research Organization Registry (ROR)

Reproducible Pipelines#

Version-controlled analysis workflows with complete provenance tracking from raw data to published results.

Pipeline Features:

• Workflow definition with visual editor or YAML configuration
• Parameter versioning with Git-style change tracking
• Automatic dependency detection and resolution
• Container-based reproducibility (Docker, Singularity)
• Computational environment capture (Python, R, MATLAB versions)
• Result validation and checksums
• Re-execution from any pipeline stage

Metadata Rich#

Comprehensive metadata capture with standardized schemas and flexible custom fields.

Metadata Categories:

• Experimental: Protocols, hypotheses, conditions, variables
• Subject Information: Species, strain, age, sex, weight, IDs
• Recording Details: Probes, channels, sampling rates, filters
• Analysis Parameters: Algorithms, thresholds, software versions
• Provenance: Data lineage, processing history, contributors
• Administrative: Funding sources, ethics approvals, collaborators

Schema Support:

• Template-based metadata entry
• Custom field definitions
• Validation rules and constraints
• Ontology integration (Gene Ontology, Brain Architecture Ontology)
• Auto-completion from controlled vocabularies

Collaboration Ready#

Share datasets and workflows with built-in permissions, commenting, and team coordination.

Collaboration Features:

• Role-based access control (owner, editor, viewer, analyst)
• Granular permissions at dataset and file level
• Team workspaces with shared resources
• Real-time activity feed and notifications
• Inline commenting on datasets and results
• Task assignment and workflow management
• External collaborator invitations
• Audit logs for compliance and tracking

Key Advantages#

1. Zero File Migration#

Organize data without moving, copying, or reorganizing files. Curate creates virtual collections pointing to files in their original locations, preserving established folder structures and naming conventions. No storage duplication, no broken paths, no workflow disruption—metadata layer sits above existing data organization.

2. FAIR Compliance Built-In#

Implement Findable, Accessible, Interoperable, Reusable data principles through native NWB and BIDS format support, standardized metadata schemas, controlled vocabularies, and DOI integration. Meet funding agency requirements (NIH Data Management and Sharing Policy, NSF Data Management Plans) without custom development or external consultants.

3. Version-Controlled Workflows#

Complete provenance tracking from raw data through all processing steps to published results. Git-style workflow versioning with change diffs, rollback capabilities, and branch/merge for experimental pipelines. Reproduce any analysis from any point in project history—essential for publications, grant reporting, and regulatory compliance.

4. Team Collaboration Without Barriers#

Role-based access control balances security and productivity. Granular permissions at dataset and file level enable appropriate access for owners, editors, analysts, and viewers. Activity feeds, inline commenting, task assignment, and team workspaces coordinate distributed research teams. Integration with Slack and Microsoft Teams keeps communication centralized.

5. Regulatory Compliance Ready#

Built-in support for GDPR, HIPAA, FERPA, and institutional data governance policies. Audit logs track all data access and modifications. Consent tracking, data anonymization tools, secure deletion with verification, and access controls meet stringent regulatory requirements. Ethics approval documentation and data usage agreements integrated into metadata.

6. One-Click Repository Publication#

Prepare and publish datasets to DANDI, NIH BRAIN Initiative repositories, CRCNS, Zenodo, and Figshare with automatic metadata export to repository standards. DOI assignment, citation generation, embargo management, and contributor credit (CRediT taxonomy) streamline data sharing. Turn internal datasets into public resources with minimal additional effort.

Integration with Radiens Platform#

Curate operates as the data governance layer binding the entire Radiens ecosystem:

• Allego Integration: Capture acquisition metadata automatically during recording
• Videre Integration: Document analysis workflows and parameter choices
• Summa Integration: Track cloud processing jobs and resource usage
• RadiensPy Integration: Programmatic metadata access and batch curation
• Zero Migration: Organize existing data folders without reorganization

Technical Specifications#

Performance:

• Handle datasets with 10,000+ files
• Sub-second search across metadata catalogs
• Efficient storage with deduplication
• Incremental backup and synchronization
• Optimized for large time-series data

Platforms:

• Windows 10/11
• macOS 12+
• Ubuntu 20.04+
• Web interface for remote access

System Requirements:

• CPU: Intel Core i5 or AMD Ryzen 5 (6th gen or newer)
• RAM: 8 GB minimum, 16 GB recommended
• Storage: SSD for metadata database, HDD acceptable for archived data
• Network: Gigabit Ethernet for team collaboration features

Data Organization#

Virtual Collections#

Organize data without moving or copying files through virtual dataset groupings.

Collection Types:

• Project-Based: All data for a specific research project
• Subject-Based: Longitudinal data from individual subjects
• Condition-Based: Data grouped by experimental conditions
• Temporal: Data organized by recording date or session
• Quality-Based: Filtered collections by QC metrics
• Publication-Based: Datasets associated with manuscripts

Collection Management:

• Nested collection hierarchies
• Cross-collection references
• Dynamic smart collections with auto-update rules
• Collection sharing and duplication
• Bulk operations across collection members

Metadata Templates#

Standardized metadata entry with reusable templates for consistency.

Template Features:

• Lab-specific custom templates
• Protocol-based templates (acute, chronic, behavioral)
• Equipment configuration templates
• Subject information templates
• Analysis workflow templates
• Import/export for cross-lab standardization

Search and Discovery#

Powerful search capabilities for rapid dataset discovery and filtering.

Search Features:

• Full-text search across all metadata fields
• Structured queries with field-specific filters
• Date range and numerical range filtering
• Tag-based search and filtering
• Saved searches and alerts
• Boolean operators (AND, OR, NOT)
• Fuzzy matching for typo tolerance

Workflow Management#

Pipeline Definition#

Define multi-step analysis workflows with dependencies and parameters.

Pipeline Components:

• Visual workflow editor with drag-and-drop nodes
• YAML-based pipeline configuration for version control
• Pre-built pipeline templates (spike sorting, LFP, population analysis)
• Custom script integration (Python, MATLAB, R, shell)
• Conditional branching based on data characteristics
• Parallel execution for independent steps
• Error handling and retry logic

Version Control#

Track all changes to pipelines, parameters, and datasets with Git-style versioning.

Versioning Features:

• Automatic versioning on pipeline modification
• Semantic versioning (major.minor.patch)
• Change diff visualization
• Rollback to previous versions
• Branch and merge for experimental pipelines
• Release tagging for stable versions
• Change log with annotation

Provenance Tracking#

Complete audit trail from raw data through all processing steps to final results.

Provenance Information:

• Input dataset identifiers and versions
• Processing software and versions
• Parameter values for all steps
• Execution timestamps and durations
• Computational environment details
• User and system information
• Output checksums and validation

Execution Management#

Monitor and control pipeline execution with detailed status tracking.

Execution Features:

• Local or cloud execution options
• Progress monitoring with estimated completion
• Resource usage tracking (CPU, memory, disk)
• Automatic result validation
• Failed step restart without full re-execution
• Batch execution across multiple datasets
• Scheduling for off-peak processing

Collaboration Tools#

Access Control#

Flexible permissions system balancing collaboration and data security.

Permission Levels:

• Owner: Full control including deletion and permission management
• Editor: Modify data, metadata, and workflows
• Analyst: Run analyses but cannot modify source data
• Viewer: Read-only access to data and results
• Guest: Limited temporary access with expiration

Permission Scope:

• Dataset-level permissions
• File-level permissions for sensitive data
• Collection-level permissions
• Pipeline-level permissions
• Time-limited access grants

Team Workspaces#

Dedicated collaborative environments for research groups.

Workspace Features:

• Shared data repositories
• Team-wide metadata standards
• Centralized pipeline library
• Common result galleries
• Team calendar and scheduling
• Resource quotas and usage monitoring
• Workspace-level settings and preferences

Communication#

Integrated communication tools for dataset and analysis discussions.

Communication Features:

• Inline comments on datasets
• Annotation threads on specific results
• At-mentions for team notifications
• Activity feed showing team actions
• Email notifications for key events
• Integration with Slack and Microsoft Teams

Compliance and Standards#

Regulatory Compliance#

Meet institutional, national, and international data governance requirements.

Supported Regulations:

• GDPR (General Data Protection Regulation)
• HIPAA (Health Insurance Portability and Accountability Act)
• FERPA (Family Educational Rights and Privacy Act)
• NIH Data Management and Sharing Policy
• NSF Data Management Plan requirements
• Institutional data retention policies

Compliance Features:

• Data anonymization tools
• Access logging and audit trails
• Secure data deletion with verification
• Consent tracking and management
• Data usage agreements
• Ethics approval documentation

Data Sharing#

Prepare and publish datasets to public repositories with proper documentation.

Repository Integration:

• DANDI (Distributed Archives for Neurophysiology Data Integration)
• NIH Brain Initiative repositories
• CRCNS (Collaborative Research in Computational Neuroscience)
• Zenodo and Figshare
• Institutional repositories
• Custom repository connections

Publication Tools:

• Automatic metadata export to repository standards
• DOI assignment and management
• Citation generation
• Embargo period management
• License selection and assignment
• Contributor credit assignment (CRediT taxonomy)

Quality Assurance#

Data Validation#

Automated checks ensuring data integrity and completeness.

Validation Checks:

• File format validation
• Metadata completeness checks
• Cross-reference consistency
• Numerical range validation
• Required field enforcement
• Custom validation rules
• Pre-submission validation for sharing

Quality Metrics#

Track dataset quality for informed analysis decisions.

Quality Indicators:

• Recording quality scores from Allego
• Metadata completeness percentage
• Standards compliance level
• Processing success rates
• Result reproducibility verification
• Peer review ratings

Advanced Features#

Smart Curation#

AI-assisted metadata entry and quality assessment.

AI Features:

• Automatic metadata extraction from filenames and protocols
• Anomaly detection in metadata patterns
• Suggested tags based on content analysis
• Missing metadata identification
• Duplicate dataset detection
• Recommended related datasets

Bulk Operations#

Efficient handling of large dataset collections.

Batch Capabilities:

• Bulk metadata editing
• Batch file operations (move, copy, delete)
• Mass permission updates
• Collection-wide tag application
• Bulk export and sharing
• Batch pipeline execution

Integration APIs#

Connect Curate with external systems and custom workflows.

API Capabilities:

• RESTful API for metadata operations
• GraphQL API for flexible queries
• Webhook notifications for events
• LIMS integration (LabGuru, Benchling, others)
• Electronic lab notebook integration
• Institutional authentication (LDAP, Shibboleth, OAuth)

Use Cases#

Lab Data Management#

Centralized data organization for research groups.

Benefits:

• Consistent metadata across all lab members
• Easy discovery of previous experiments
• Knowledge transfer to new lab members
• Standard operating procedure compliance

Multi-Site Collaborations#

Coordinated data collection and analysis across institutions.

Benefits:

• Standardized protocols and metadata
• Secure data sharing with partner sites
• Unified analysis pipelines
• Coordinated publication preparation

Reproducible Publications#

Ensure published results are fully reproducible.

Benefits:

• Complete provenance documentation
• Version-locked analysis pipelines
• Archived computational environments
• Public dataset preparation
• Supplementary material generation

Grant Reporting#

Meet funding agency data management requirements.

Benefits:

• Automated data management plan compliance
• Usage metrics and statistics
• Progress tracking and reporting
• Public data sharing documentation

Licensing and Availability#

License Tiers:

• Radiens BASE: Core curation with local metadata storage
• Radiens LIVE: Enhanced collaboration and team features
• Radiens SUITE: Full Curate with cloud backup and advanced features

Trial Access: 30-day full-featured trial available

Updates: Regular updates with new repository integrations and standards

Support and Documentation#

• User Guide: Comprehensive FAIR data principles and implementation
• Video Tutorials: Metadata management and workflow creation
• Best Practices: Lab data management strategies
• Template Library: Pre-built metadata and workflow templates
• API Documentation: Complete reference for integration
• Community Forum: Share templates and discuss data management
• Direct Support: Assistance with compliance and setup

Scientific Validation#

Curate implements standards developed by the neuroscience community, including NWB (supported by NIH BRAIN Initiative), BIDS (endorsed by major journals), and FAIR principles (recognized by major funding agencies). NeuroNexus actively participates in standards development and contributes to open neuroscience initiatives.

Future Roadmap#

• Enhanced AI Curation: Automatic protocol extraction from notes and papers
• Blockchain Provenance: Immutable audit trails for regulatory compliance
• Federated Search: Discover datasets across institutional boundaries
• Real-Time Collaboration: Google Docs-style simultaneous editing
• Mobile Application: Field data collection and annotation
• Advanced Visualization: Interactive metadata exploration and insights