1. School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

2. R. Lukas, D. Karl and many others ALOHA Cabled Observatory (ACO) Node installed June 2011 – deepest at 4728 m Station ALOHA ACO F. Duennebier et al. started 2002

3. ACO bottom configuration ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✗ ✗ ✗ ✗ ✗ ✗✗ ✗ ✗✗

4. Data and results Aloha.manoa.hawaii.edu

5. Camera OBS light failed 3 July CAM lights failed 18 July  Jason on left – last moments on bottom “shrimp” swimming by

6. Deep-sea lizard fish attempting to eat an aristeid shrimp Jeff Drazen and Aharon Fleury, UHawaii

7. One day, one minute of sound

8. First blues 31 October 2012 x10

9. Humpbacks returning 25 Nov 2012

10. ACO Network Seafloor 100 Mb/s Makaha 1 Gb/s Makaha – Manoa HT 3 Mb/s MPLS SOEST network 1 Gb/s Internet 2 10 Gb/s Real-time data display and access 50 in-water IPs 55 dry IPs http://aloha.manoa.hawaii.edu Future 1 Gb/s

11. ACO Data Management NB ! MBARI SSDS and SIAM

12. Data numbers Two hydrophones, each sampled (16 bit) 96,000 Hz, total 3 Mb/s, 12 TB/y Other – temperature, velocity, etc – small When video starts working again this September, H.264 < 1 Mb/s Using NAS drives now Starting to use Blu-ray Buffering needed at shore station 3 Mb/s link to UH Manoa now ($) In process: 1 Gb/s link over state/UH network (< $) Network complexity – IP addresses –Subsea 55 –Shore 50

13. Data Management SIAM (Software infrastructure and application for MOOS) – instrument servers, from MBARI (Monterey Bay Aquarium Research Institute) SSDS – Shore-side Data System – MBARI Custom – hydrophone, pressure, … Practical problems – what are best practices for acoustic and video data?

14. Lessons Don’t underestimate shore-side needs –Network management – intensive! –Data management – intensive! –Documentation – intensive! –Real-time – intensive! Include good time stamps – cross-correlating data (and problems) Understanding problems with data – intensive! Observatories – sustained long-term observations – prove value with un-aliased data