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The EPA metadata registry furnishes an example of data stewardship. Note that each data element therein has a "POC" (point of contact). In 2023, ETH Zurich launched the Data Stewardship Network (DSN) to facilitate collaboration among employees engaged in data management, analysis, and code development across research groups. The DSN serves as a ...
Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. [ 1 ] [ 2 ] Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure.
The merger, which came in response to increasing demand for environmental information, was intended to make NOAA's data more useful through the application of consistent data stewardship practices across all science disciplines. NCEI works with the ISC World Data System to make data free and accessible. [4]
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. (Meta)data use vocabularies that follow FAIR principles I3.
Enterprise data management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. EDM focuses on the creation of accurate, consistent, and transparent content.
Stewardship is a practice committed to ethical value that embodies the responsible planning and management of resources. The concepts of stewardship can be applied to the environment and nature, [ 1 ] [ 2 ] [ 3 ] economics, [ 4 ] [ 5 ] health, [ 6 ] places, [ 7 ] property, [ 8 ] information, [ 9 ] theology, [ 10 ] and cultural resources.
One example of United States government's attempt to implement one of these design patterns (one to one) is the National Information Exchange Model (NIEM). [7] [8] One-to-one exchange models fall short of supporting all of the required design patterns needed to fully implement data exploitation technology.
In contrast, data holdings are collections of gathered data that are informally kept, and not necessarily prepared for long-term preservation. For example, a collection or back-up of personal files. Data holdings are generally the storage methods used in the past when data has been lost due to environmental and other historical disasters. [4]