![]() Although getting critical feedback about their data may be initially troublesome for some data creators, developing good data design and description habits is worth the effort and ultimately benefits everyone who will use the data. Poor table organization and object naming can severely limit data understandability and ease-of-use, incomplete data definitions can render otherwise stellar data virtually useless, and failure to keep the dictionary up to date with the actual data structures suggests a lack of data stewardship. The Alaska Science Center Research Data Management Plan has excellent examples of a Data Description Form and other forms to capture metadata before, during, and at the end of a project.ĭata Dictionaries Can Reveal Poor Design Decisionsįor both data reviewers and data users, the data dictionary can reveal potential credibility problems within the data. The easiest path is to adopt and cite a data standard, thus avoiding the need to provide and manage your own documentation. Try to use naming conventions appropriate to the system or subject area. When data structures change, update the dictionary. As required and optional data elements are identified, add them to the data dictionary. Plan ahead for storing data at the start of any project by developing a schema or data model as a guide to data requirements. JPL Planetary Data System Data Dictionary.Data Dictionary for the National Database of Deep-Sea Corals (NOAA).Climate and Forecast Conventions Standard Name Table.Human Health Risk Assessment Data Dictionary (ORNL).Data Dictionary for Organic Carbon Sorption and Decomposition in Selected Global Soils (ORNL).MODIS Level 1B Products Data Dictionary (NASA).National Hydrography Dataset Data Dictionary.(Example only - updated NED Data Dictionary will be available soon) National Elevation Dataset (NED) Data Dictionary.Aerial Photo Single Frames Data Dictionary.Data Dictionary for Surficial Sediment Data from the Gulf of Maine, Georges Bank, and Vicinity GIS Compilation (USGS Open-File Report 03-001).EarthExplorer USGS Landsat Data Dictionary.Lastly, if there is a common, vetted, and documented data resource, it is not necessary to produce separate documentation for each implementation.Įxamples of Shared USGS Data Dictionaries ![]() Data dictionaries also provide information needed by those who build systems and applications that support the data. Shared dictionaries ensure that the meaning, relevance, and quality of data elements are the same for all users. Decision Making - assist in planning data collection, project development, and other collaborative effortsįor groups of people working with similar data, having a shared data dictionary facilitates standardization by documenting common data structures and providing the precise vocabulary needed for discussing specific data elements.Data Integration - clear definitions of data elements provide the contextual understanding needed when deciding how to map one data system to another, or whether to subset, merge, stack, or transform data for a specific use.Systems Analysis - enable analysts to understand overall system design and data flow, and to find where data interact with various processes or components. ![]()
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