Integration & data-flow checklist

This practical checklist focuses on the common technical and data-related gaps that derail automation pilots.

Before you run

• Define the canonical sample identifier and ensure it appears in every data record.
• Decide storage location and ownership (LIMS, network share, object store) and confirm access rights.
• Agree on data format(s) (CSV, JSON, API schema) and a mapping document for each instrument output.
• Capture provenance: instrument model/version, protocol version, operator ID, timestamps, and any calibration notes.
• Confirm network and security requirements for instrument connectivity (firewall rules, credentials management).

During the pilot

• Log every run: operator, start/end times, run identifier, and any exceptions.
• Ensure time synchronization across devices (NTP) to make logs coherent.
• Automatically persist raw instrument logs in addition to processed outputs.
• Apply straightforward validation checks on data upon capture (expected ranges, missing fields).
• Record manual interventions with a short reason code and operator initials.

Error handling & resilience

• Define how the system behaves on failure: retry automatically, pause and notify, or stop and require manual restart.
• Ensure there is a clear rollback path to manual operation with minimal downtime.
• Store error logs separately and include enough detail for root-cause analysis (stack traces, instrument state).

Post-run and reproducibility

• Archive raw and processed outputs with clear versioning of protocols and software.
• Keep a change log for protocol adjustments and note their effective date/time and reason.
• Plan periodic reviews of data quality during the pilot (e.g., weekly data sanity checks).

Governance and people

• Assign a data owner responsible for storage, retention, and access controls.
• Train operators on minimal required metadata entry and the daily log process.
• Decide how pilot data will be used in audits or publications—capture consent or approvals if needed.