SGPA is one of the world’s primary sources for Earth science data, making it essential for answering numerous scientific questions. It offers access to large datasets for supporting various research goals and applications ranging from single observation analyses, multi-observation process studies and assimilation into Earth system models. Furthermore, its unique infrastructure gives scientists access to high-quality data as well as high-resolution simulations.

The SGP database offers unparalleled spatial and temporal resolution for many Earth sciences data sets, making it the perfect platform for studying environmental impacts including those caused by humans, including pollution or ocean acidification. Furthermore, this comprehensive dataset also contains geochemical data as well as meteorological reports and seismic records.

As a result of this project, researchers will gain greater access to legacy and unpublished geochemical and geophysical data that otherwise wouldn’t be accessible without this effort. Its ultimate goal is to migrate these datasets into permanent geochemical data repositories.

To improve data quality, multiple methods for assessing uncertainty are being implemented. This includes statistical analyses and spatial interpolation techniques. It is anticipated that this data set will yield better uncertainty estimates for both local SGP time series as well as global ones; particularly useful when investigating changes in ocean biogeochemistry.

The SGP Observatory Network spans over 160 acres of cattle pasture and wheat fields southeast of Lamont, Oklahoma. At its core is an instrumented Central Facility which technicians and support staff use to collect data; additional smaller instruments throughout the site may also provide data. Furthermore, researchers supplement continuous observations with guest instruments during field research campaigns.

When analyzing SGP data, it’s essential to be familiar with the differences between WIDE and LONG formats. Lower level functions such as studentGrowthPercentiles and studentGrowthProjections rely on WIDE data while higher-level wrapper functions use LONG data instead. For operational analyses, LONG formatted data typically offers greater management simplicity for preparation and storage.

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