US NOAA Fisheries and UK SAHFOS CPR surveys: comparison of methods and data

The US National Oceanic and Atmospheric Administration (NOAA) Fisheries Continuous Plankton Recorder (CPR) Survey has sampled four routes: Boston–Nova Scotia (1961–present), New York toward Bermuda (1976–present), Narragansett Bay–Mount Hope Bay–Rhode Island Sound (1998–present) and eastward of Chesapeake Bay (1974–1980). NOAA involvement began in 1974 when it assumed responsibility for the existing Boston–Nova Scotia route from what is now the UK's Sir Alister Hardy Foundation for Ocean Science (SAHFOS). Training, equipment and computer software were provided by SAHFOS to ensure continuity for this and standard protocols for any new routes. Data for the first 14 years of this route were provided to NOAA by SAHFOS. Comparison of collection methods; sample processing; and sample identification, staging and counting techniques revealed near-consistency between NOAA and SAHFOS. One departure involved phytoplankton counting standards. This has since been addressed and the data corrected. Within- and between-survey taxonomic and life-stage names and their consistency through time were, and continue to be, an issue. For this, a cross-reference table has been generated that contains the SAHFOS taxonomic code, NOAA taxonomic code, NOAA life-stage code, National Oceanographic Data Center (NODC) taxonomic code, Integrated Taxonomic Information System (ITIS) serial number and authority and consistent use/route. This table is available for review/use by other CPR surveys. Details of the NOAA and SAHFOS comparison and analytical techniques unique to NOAA are presented.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.