Sample Detail View
The Sample Detail View is the authoritative execution record for an individual biological sample within Flask Track.
It provides a complete operational history of the sample, including:
- Workflow progression
- Protocol execution
- Step completion history
- State transitions
- Timeline events
- Compliance activity
- Attached files and evidence
- Audit-relevant operational data
Every sample has a single canonical detail view that acts as the source of truth for its biological and operational lifecycle.
What Is a Sample?
A sample represents an individual biological execution unit within laboratory workflows.
Examples include:
- Tissue culture explants
- Plantlets
- Fungal cultures
- Transformation candidates
- Experimental isolates
- Fermentation samples
- Derived biological material
Samples move through workflows over time and accumulate operational history as laboratory work is performed.
Samples are central to:
- Traceability
- Reproducibility
- Compliance
- Reporting
- Experimental accountability
Sample Overview
The Sample Detail View consolidates all operational information associated with a sample into a unified execution record.
This includes:
- Current biological state
- Workflow progression
- Protocol execution history
- Operational events
- Compliance context
- Evidence and attachments
- Audit activity
The page is designed to provide both:
- Real-time operational visibility
- Long-term historical traceability
Sample Header
The top section of the page displays high-level sample metadata and operational status.
This section provides quick visibility into the sample’s identity, biological context, and current lifecycle state.
Sample Metadata
Typical metadata includes:
- Sample name or identifier
- Biological domain
- Associated species
- Batch association
- Workflow association
- Current lifecycle state
- Creation timestamp
- Compliance status
This information provides immediate operational context for users reviewing the sample.
Domains
Samples belong to a biological or operational domain.
Examples include:
- Tissue Culture
- Fungus
- Agrobacterium
- Fermentation
The domain influences:
- Available workflows
- Valid lifecycle states
- Execution behavior
- Compliance applicability
- Operational actions
QR Codes & Printable Identifiers
Samples may include printable identifiers and QR codes.
These support:
- Physical sample labeling
- Bench tracking
- Rapid lookup
- Operational scanning workflows
- Multi-user coordination
QR-enabled workflows improve traceability during laboratory execution.
Sample Actions
The Sample Actions section displays operational actions currently available for the sample.
Available actions are dynamically determined based on:
- Current sample state
- Workflow progression
- Protocol execution state
- User permissions
- Compliance rules
- Organizational policies
This ensures only valid operational actions are available at any given time.
Example Actions
Examples may include:
- Subculture
- Transfer
- Expand
- Harvest
- Root induction
- Mark contaminated
- Archive sample
- Record observation
- Complete protocol step
Unavailable or restricted actions are automatically hidden or blocked.
Workflow & Protocol Progress
The Protocol Progress section visualizes how the sample has progressed through its workflow over time.
This provides a structured operational view of execution history.
Protocols are displayed in execution order and typically include:
- Protocol name
- Workflow stage
- Completion state
- Execution timestamps
- Scheduling information
- Links to protocol definitions
- Associated execution records
This creates a traceable operational map of the sample lifecycle.
Protocol Steps
Within each protocol, individual protocol steps are displayed as discrete execution units.
Step-level information may include:
- Step title
- Execution status
- Completion timestamps
- Assigned users
- Scheduling information
- Compliance indicators
- Linked evidence or files
Step-level tracking enables detailed operational traceability and audit visibility.
Execution Scheduling
Protocol execution may include structured scheduling logic.
Examples include:
- Delayed progression
- Incubation periods
- Rest windows
- Timing offsets
- Conditional execution windows
Scheduling information helps users understand:
- What happened
- When it occurred
- Whether timing expectations were met
This is particularly important for regulated or time-sensitive workflows.
Timeline
The Timeline provides a chronological history of all operational activity associated with the sample.
The timeline acts as a unified operational event stream.
Events may include:
- Workflow initiation
- Protocol progression
- Step completion
- State transitions
- Compliance actions
- File uploads
- Manual observations
- Automated system events
- Alert-related activity
Timeline events are ordered chronologically and preserved as part of the permanent execution history.
Timeline Event Details
Each timeline event may contain:
- Event type
- Timestamp
- Associated protocol or workflow
- User attribution
- Structured metadata
- Attached files
- Compliance references
- Operational notes
This allows organizations to reconstruct the full lifecycle of the sample over time.
Files & Attachments
Samples may contain files attached directly or indirectly through execution events and operational records.
Examples include:
- Images
- PDFs
- Experimental documentation
- Environmental readings
- Compliance evidence
- Data exports
- SOP references
Files may originate from:
- Sample-level uploads
- Protocol step execution
- Compliance workflows
- Timeline events
All file activity remains attributable and auditable.
Structured Data Capture
Sample execution may include structured operational data collected during protocol steps.
Examples include:
- Environmental measurements
- Instrument readings
- Reagent tracking
- Growth observations
- Yield measurements
- Compliance evidence
- Custom operational forms
Structured data improves:
- Reproducibility
- Searchability
- Reporting
- Long-term analytics
Captured data becomes part of the permanent sample history.
Compliance Context
Samples may carry operational or regulatory compliance context.
Examples include:
- GMO workflows
- Restricted biological material
- Biosafety procedures
- Incident records
- Deviations and exceptions
- Environmental monitoring requirements
Compliance information may appear throughout the sample detail view and associated execution records.
Sample States
Samples move through domain-specific lifecycle states as execution progresses.
States provide operational visibility into biological progression and workflow status.
Tissue Culture Example States
Examples may include:
- initiated
- tc_subcultured
- tc_rooting
- tc_hardened
- tc_contaminated
Fungus Example States
Examples may include:
- mush_agar
- mush_grain_spawn
- mush_fruiting
- mush_harvested
- mush_contaminated
Agrobacterium Example States
Examples may include:
- agro_transformation
- agro_selection
- agro_confirmed
- agro_failed
State transitions are typically driven by protocol execution and operational events.
All state changes remain historically traceable.
Alerts & Operational Visibility
Samples integrate directly with the Flask Track alerting system.
Alerts may indicate:
- Upcoming protocol steps
- Overdue execution
- Compliance review requirements
- Operational delays
- Missing evidence
Alert visibility helps laboratories coordinate multi-user and multi-day workflows more effectively.
Exporting Sample Records
Sample records may be exported for operational review, reporting, or external analysis.
Supported formats may include:
- Markdown
- HTML
- Structured data exports
Exports may contain:
- Sample metadata
- Workflow history
- Timeline events
- Protocol execution
- Compliance context
- Linked operational files
Export permissions depend on organizational policies and user roles.
Archiving Samples
When laboratory work is complete, samples may be archived.
Archived samples:
- Remain searchable
- Preserve full execution history
- Retain audit attribution
- Remain reportable
- Become protected from normal operational modification
Archival improves long-term traceability while preventing accidental operational changes.
Auditability & Traceability
The Sample Detail View is heavily integrated with Flask Track’s audit systems.
Audit visibility may include:
- Execution history
- User attribution
- State changes
- Compliance actions
- File activity
- Operational modifications
This ensures organizations can reconstruct exactly what happened to a sample throughout its lifecycle.
Multi-User Collaboration
Samples are designed for collaborative operational environments.
Multiple users may contribute to:
- Protocol execution
- Observations
- Compliance review
- File uploads
- Corrective actions
- Workflow progression
Centralized execution visibility reduces reliance on manual communication and disconnected records.
Relationship to Batches & Workflows
Samples are typically created as part of a batch executing a workflow.
The relationship is generally:
- Workflow defines operational progression
- Batch instantiates execution
- Samples represent biological execution units
- Protocols define operational procedures
- Timeline events record what actually occurred
This layered model provides both operational flexibility and strong traceability.
Who Uses This Page?
Technicians
Technicians use the page to:
- Execute protocol steps
- Record operational activity
- Upload evidence
- Review scheduling requirements
Scientists
Scientists use the page to:
- Review experimental progression
- Analyze outcomes
- Verify execution quality
- Investigate biological performance
Administrators
Administrators use the page to:
- Monitor operational workflows
- Review compliance context
- Verify execution traceability
- Coordinate laboratory activity
Auditors & Reviewers
Auditors use the page to verify:
- Execution integrity
- Workflow adherence
- Timing compliance
- Evidence collection
- Operational accountability
The Sample Detail View acts as the definitive operational history for the sample.
Summary
The Sample Detail View is the operational core of biological execution within Flask Track.
By combining workflow progression, protocol execution, timeline events, structured data capture, compliance integration, auditability, and operational traceability into a single unified record, Flask Track enables laboratories to:
- Track biological lifecycle progression
- Maintain reproducible execution records
- Coordinate multi-user workflows
- Preserve audit-ready operational history
- Support regulated laboratory operations
- Improve experimental visibility
- Centralize sample execution data
Samples are not simply records — they are the living operational history of laboratory execution.