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The user, rather than the database itself, typically initiates data curation and maintains metadata. [8] According to the University of Illinois' Graduate School of Library and Information Science, "Data curation is the active and on-going management of data through its lifecycle of interest and usefulness to scholarship, science, and education; curation activities enable data discovery and ...
As an increasing portion of the world’s information output shifts from analog to digital form, preservation metadata is an essential component of most digital preservation strategies, including digital curation, data management, digital collections management and the preservation of digital information over the long-term.
The DCC Curation Lifecycle Model is especially relevant to three key participants in the digital curation process: data creators, data archivists, and data reusers. The model highlights the importance of data creation, such as metadata, in successful, sustainable curation practices. This is relevant to data creators.
Data can be described as the elements or units in which knowledge and information is created, [2] and metadata are the summarizing subsets of the elements of data; or the data about the data. [3] The main goal of data preservation is to protect data from being lost or destroyed and to contribute to the reuse and progression of the data.
The term "digital curation" was first used in the e-science and biological science fields as a means of differentiating the additional suite of activities ordinarily employed by library and museum curators to add value to their collections and enable its reuse [12] [13] [14] from the smaller subtask of simply preserving the data, a significantly more concise archival task. [12]
Algorithmic curation, curation using computer algorithms; Content curation, the collection and sorting of information; Data curation, management activities required to maintain research data; Digital curation, the preservation and maintenance of digital assets; Evidence management, the indexing and cataloguing of evidence related to an event
Code generation is the process of generating executable code (e.g. SQL, Python, R, or other executable instructions) that will transform the data based on the desired and defined data mapping rules. [4] Typically, the data transformation technologies generate this code [5] based on the definitions or metadata defined by the developers.
A properly designed ETL system extracts data from source systems and enforces data type and data validity standards and ensures it conforms structurally to the requirements of the output. Some ETL systems can also deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions.