![]() Later versions of the templates might include the contiguous ragged array representation. We wanted to start with the simplest representations first that we believe will handle most of the data that we receive at NCEI, and then move to more complex representations. In this version, we have chosen to focus on the multidimensional array approaches. Where the storage efficiency is important, contiguous ragged array representation could be used instead. Multidimensional representations can instead be used in all the cases where these representations could be used, but are space inefficient. While there are benefits to using these representations over multidimensional array representations, they are generally more complex. ![]() For example, if the depth levels for the first XBT are and for the second XBT are, then the new depth variable containing data from both the XBTs would be two dimensional array Z = ] where '_' is a missing value, and the data values for the first XBT would be padded with missing values for indices i = 5 & i = 6.Īpart from Orthogonal multidimensional array and incomplete multidimensional array, CF allows two additional methods of packing data in an array – contiguous ragged array and indexed ragged array. A variable would need to be generated that would act as an indexed array to the depth levels of all the profiles combined. This is a useful approach, for example, if multiple XBT profiles with different vertical resolution or levels are to be stored in the same netCDF file. ![]() This representation is used when the variables of a dataset contain different coordinate values along an axis. The second representation is referred to as the Incomplete Multidimensional Array representation.Therefore, if the depth levels for the first buoy are and the second buoy are, then the depth coordinate variable Z in the Orthogonal Multidimensional Array representation would be a single dimensional array Z =, and data value for the first buoy at z = 25 would be marked as a missing value. ![]() The depth coordinate variable would store the array of the identical depth levels from the buoys. An example could be the vertical coordinates of variables measured on moored buoys with identical depth levels. The first representation is referred to as an Orthogonal Multidimensional Array representation, in which variables of a dataset contain identical coordinate values along an axis.If your situation doesn't match one of the templates, that is okay! Just use as much of the templates as you can and consider using some of the more complicated representations detailed in the full CF documentation.įor most of the discrete sampling geometries, the relationship between the data can be categorized in two ways: Dropzone 3 3 6 4. We focused on the templates that we felt would cover most situations involving environmental observations, primarily of the ocean. Our templates don't try to cover every possibility.Again, for richer information your best bet is to look to ISO 19115-2 as your metadata transfer standard. The attributes don't try to capture every possible detail of the provenance or processing of the data, for example. Basically, this kind of information focuses on helping you find the file, then using it in your application. Also remember that ACDD and CF tend to focus on what are sometimes known as 'discovery' and 'use' metadata.For broader information about the collection, consider providing an FGDC or an ISO 19115-2 (preferred by NCEI) metadata record. Keep your attributes focused on the content of the file, not the overall collection to which it might belong.Avoid 'NA' or 'N/A' as they can have multiple meanings. Be explicit! Do your best to provide accurate values for the attributes, and if you don't know them, state that clearly.A few key 'principles' appear to be emerging based on initial use of these templates:
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