Data collection

Objectives

  • Define what counts as collection (field, lab, and model outputs) across HRL hypotheses.
  • Ensure data type documentation and approved protocols appear in system-level science plans.
  • Emphasize real-time metadata capture and field QA/QC expectations.

Topics to cover

  • Inventory of common data types plus process for proposing new methods.
  • Required metadata elements (who, when, where, how, equipment, calibration routines).
  • Field QA/QC practices such as calibration logs, duplicates, controls, and environmental notes.
  • Roles for Data Producers and oversight points for the HRL Science Committee/Data Governance Group.

Inputs and outputs

  • Inputs: approved protocols, instrumentation settings, sampling designs, training materials.
  • Outputs: raw data files, field forms, preliminary QA reports ready for static publication workflows.

Decision points and governance

  • Explain when protocol updates need approval and how to document that decision.
  • Note notification pathways back to the Central Data Team following collection events.
  • Describe how sensitive/Tribal data agreements are recorded at the point of collection.