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Published bySabrina Stewart Modified over 9 years ago
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Strategies for Adding EML Support to the GCE Data Toolbox for Matlab Wade Sheldon Georgia Coastal Ecosystems LTER (WWW: gce-lter.marsci.uga.edu/lter)
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Background Needed universal solution for processing tabular data sets (majority of IM work) Goals: Import from various data sources Standardize units, date formats, attribute names Assign metadata descriptors Validate/QAQC Generate statistical summaries, plots, maps Export to various data/metadata formats Support sub-setting & queries, super-setting (unions/joins) Support automation of all steps Automatically capture metadata throughout interactive processing
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Background Developed Matlab data structure specification for storing data table tightly coupled with metadata Developed ‘Toolbox’ (function library) for working with data structures Many roles in GCE IS: Primary tool for acquisition, QAQC of data from monitoring network, PI submissions Data/metadata packaging (linked to RDMS) Data distribution (flexible formats) New Role: Automated harvesting/processing/QC/web posting of remote data stores (USGS, NOAA) and post-processing of CSI arrays downloaded via modem Began public distribution of toolbox in 2002 (primarily for end-user analysis of GCE data)
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Toolbox Metadata Standard Full implementation of FLED (+ user- extensible content) Attribute-level metadata managed with data General documentation descriptors stored in simple array format (Category, Field, Value) – designed for pre-formatted metadata, but parseable/updateable Simple user-editable style definition tables used to produce formatted ASCII metadata
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EML Differences Higher granularity Hierarchical structure (vs flatter 3-tier) Different delineation of semantic/numerical attribute descriptors (much overlap, but different philosophy) New unit dictionary requirements for validation contrary to units/unit conversion conventions (at odds with non-IM end-user focus of toolbox) XML-based (requires extra steps for presentation)
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Strategy Short term: develop XSLT to convert EML (primarily dataset, entity, attribute) to ASCII headers for importing metadata along with data Medium term: switch to EML-oriented metadata schema (e.g. use similar arrays, but support direct eml schema mapping by using xpath syntax for category/field info) Long term: add support for direct caching of EML docs, include native xml routines for syncing metadata during processing (requires more users adopt latest Matlab version - R13)
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Significance Allow IM community take full advantage of these tools/capabilities for their own site’s data with minimal re- mastering (EML + ASCII/Matlab table) Allow LTER IM community to showcase research- oriented, metadata-driven tools to bolster support for EML efforts immediately If full EML support achieved, could become a useful mechanism for automatically producing EML- documented/validated data sets (datalogging -> harvest -> process -> QC -> dataset+EML -> validation)
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