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2. Pre Cruise Planning and Preparation

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Thomas Morrow edited this page Dec 2, 2025 · 10 revisions

Before departure, create detailed plans for data collection, processing, storage, and dissemination. Confirm roles and responsibilities with vessel and vehicle operators. Each vessel and vehicle has different expectations for what operators provide to science teams.

Data Management and Processing Plans

Answer these critical questions with your science and operator team:

What data types are being collected? Confirm expectations with vessel and vehicle operators, including data formats and hardware needed for data transfer.

What processing is occurring, when, where, and by whom? Plan for any processing beyond raw (level 0) data products. Many data types require at-sea processing to inform the next dive or deployment. Document when and where processing happens, the resulting products, and who is responsible. What processing software or hardware is required, what is provided by the science mission team and what is provided by the operator?

Where will your data go? Confirm requirements for data release timelines, accessibility, and archival with your funding sources and vessel/vehicle operators. Check ingest requirements with your intended repositories, including metadata requirements.

Create a Data Management Plan and Data Processing Plan in collaboration with vessel and vehicle operators.

Data Management Plan

A document that outlines how data will be handled during and after a research project.

A Data Management Plan is a document that describes how data will be collected, documented, stored, shared, and preserved during and after a research project. Many funding agencies, especially government sources, require a DMP as part of their application processes. Even without funding requirements, having a DMP is considered a best practice for responsible research.

What a DMP Typically Covers

  • Data types and formats: What data will be generated and in what formats
  • Metadata and documentation: How data will be described and documented
  • Storage and security: Where and how data will be stored during the project
  • Access and sharing: How others can discover and use the data
  • Archiving and preservation: Long-term plans for data accessibility
  • Roles and responsibilities: Who manages data throughout the project

Key Principles

Federal funders increasingly expect adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) and, where relevant, CARE principles for Indigenous data governance. Data must typically be made publicly accessible within two years of collection, free of charge or at minimal cost.

Format

A DMP is a living document—as research evolves, the plan may need revision. Most agencies require plans of two pages or less.

Resources

Data Processing Plan

A Data Processing Plan describes how data will be handled immediately following acquisition—the critical period between collection and archive- or publication-ready products. While Data Management Plans address the full data lifecycle, a DPP focuses specifically on the processing workflow: who does what, with which tools, and on what timeline.

This is an emerging concept that our community is formalizing to address a common gap in research data documentation and the unique environment of rapidly evolving at-sea operations.

What a DPP Typically Covers

  • Processing responsibility: Who is responsible for processing each data type, and what happens if key personnel are unavailable
  • Software and tools: What software, scripts, or workflows will be used (with version information), and which entity will supply the tools (usually science mission team or operator)
  • Timeline: When processing will occur relative to acquisition (at sea, post-cruise, etc.) and expected completion dates
  • Quality control: What QC steps will be applied and how issues will be flagged
  • Intermediate products: What intermediate data products will be retained vs. discarded
  • Handoffs: How processed data moves to the next stage (archive, collaborators, etc.)

Why This Matters

Processing is often where data quality is won or lost, yet it's frequently undocumented or assumed. A DPP ensures that processing decisions are made deliberately, institutional knowledge is captured, and data provenance is traceable.

Relationship to Other Plans

A DPP complements but does not replace a Data Management Plan. Think of it as the detailed "how" for the processing phase that a DMP references but doesn't fully specify.

Vessel and Vehicle Information

Deep Submergence Asset Documentation for Scientists

These resources provide example guidance on vehicle capacity, configurations, and sampling capabilities, useful both for proposal writing and operational planning. Contact operators directly for the most up to date documentation on their assets. Each operator may provide different levels of documentation.

United States

National Deep Submergence Facility (NDSF) – WHOI

The most comprehensive publicly available documentation for US academic deep submergence assets.

HOV Alvin

ROV Jason

AUV Sentry

General NDSF Resources

MBARI – Monterey Bay Aquarium Research Institute

ROV Doc Ricketts

ROV Ventana

NOAA Ocean Exploration – NOAA Ship Okeanos Explorer

ROV Deep Discoverer (D2)

Europe

IFREMER – France

ROV Victor 6000

GEOMAR – Germany

ROV KIEL 6000

Japan

JAMSTEC – Japan Agency for Marine-Earth Science and Technology

HOV Shinkai 6500

ROV KAIKO (Mk-IV)

Private / Non-Profit

Schmidt Ocean Institute – R/V Falkor (too)

ROV SuBastian

OceanX – R/V OceanXplorer

ROV Chimaera

Ocean Exploration Trust – E/V Nautilus

ROV Hercules

ROV Argus

What to Request From Your Operator

If documentation isn't publicly available, request the following from your vehicle operator:

  1. Vehicle specifications – depth rating, dimensions, weight, payload capacity
  2. Basket/sample storage configurations – dimensions, weight limits (wet/dry)
  3. Manipulator capabilities – reach, grip strength, degrees of freedom
  4. Instrument ports – available power (voltage/amperage), data interfaces (serial, ethernet), mounting locations
  5. Imaging systems – camera specifications, lighting, recording formats
  6. Navigation and positioning – USBL, DVL, INS specifications and accuracy
  7. Sampling equipment inventory – push corers, Niskin bottles, bio boxes, suction samplers, space for other equipment
  8. Data deliverables – file formats, metadata standards, delivery timeline
  9. Pre-cruise planning timeline – deadlines for equipment integration, pressure testing requirements
  10. User-supplied equipment requirements – electrical isolation, pressure certifications, weight/buoyancy measurements

Operational Information and Expectations

Data Planning Resources

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