II.
Workflow overview
Reference · liveworkflow:nano-characterization-pipeline
Nano-Characterization Pipeline overview
Processes nanoscale characterization data through automated analysis -- ingesting AFM topography scans, TEM/STEM micrographs, XPS spectra, and nanoindentation curves, applying image segmentation for particle size distribution, fitting spectral peaks for composition analysis, computing hardness/modulus from Oliver-Pharr method, and populating the nanomaterials property database with uncertainty quantification. Excludes instrument operation.
Attributes
displayName
Nano-Characterization Pipeline
workflowKind
operational
triggerType
event-driven
typicalCadence
per-sample-batch
complexity
single-team
description
Processes nanoscale characterization data through automated analysis
-- ingesting AFM topography scans, TEM/STEM micrographs, XPS
spectra, and nanoindentation curves, applying image segmentation for
particle size distribution, fitting spectral peaks for composition
analysis, computing hardness/modulus from Oliver-Pharr method, and
populating the nanomaterials property database with uncertainty
quantification. Excludes instrument operation.
Outgoing edges
applies_to_domain2
- domain:nanotechnology·DomainNanotechnology
- domain:scientific-computing·DomainScientific Computing
involves_role3
- role:data-scientist·RoleData Scientist
- role:ml-engineer·RoleMachine Learning Engineer
- role:staff-engineer·RoleStaff Engineer
performed_by_org_unit2
- org-unit:research-engineering·OrgUnitResearch Engineering
- org-unit:data-team·OrgUnitData Team
requires_skill_area2
- skill-area:python-data-pipelines·SkillAreaPython Data Pipelines
- skill-area:ml-fine-tuning·SkillAreaML Fine-Tuning
triggers_responsibility1
- responsibility:data-quality-monitoring·ResponsibilityData quality monitoring
Incoming edges
None.