Curating a 23-year weekly time-series of oceanographic data in Monterey Bay, California, or: how I learned to stop worrying and love the data
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- September 08, 2016
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- Whitmire, Amanda L.
- In 1951, the Hopkins Marine Station of Stanford University became a partner in the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program in order to collect oceanographic data in and near Monterey Bay. The aim of the program was to conduct joint fisheries-oceanographic cruises that would help researchers understand what contributed to observed fluctuations in the California sardine fishery. Hopkins conducted weekly sampling (more or less) continuously from March 1951 through June 1974. The raw and aggregated data for most of these cruises currently reside in analog form (handwritten data logs, annual reports, etc.) in the library at the Hopkins Marine Station. The dataset includes variables such as temperature, salinity, oxygen, phosphate, silicate, phytoplankton and zooplankton community structure and abundance, meteorological conditions, fish and marine mammal counts, and more. The collection includes forty-four 3-ring or loose-bound notebooks, twenty-two small, bound notebooks, minutes from annual meetings, annual data reports, and other ephemera. The Hopkins CalCOFI collection is large, completely analog, and very heterogeneous. We are in the early phases of planning a curation strategy, but our general objectives for the dataset are to digitize it, add metadata, convert sampling data to actionable formats, and make it all public. This poster presents a description of the collection, our proposed general approach toward curating it, and discusses some potential challenges. [presented at The Rescue of Data At Risk workshop in Boulder, CO, on 8–9 September 2016. Co-hosted by the Research Data Alliance (RDA) and the Committee on Data for Science and Technology (CODATA)].
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- Whitmire, Amanda L.. (2016). Curating a 23-year weekly time-series of oceanographic data in Monterey Bay, California, or: how I learned to stop worrying and love the data. Stanford Digital Repository. Poster. Available at: http://purl.stanford.edu/hp802gp8515
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