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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 Curation Lifecycle Model, [9] developed by the DCC, was first published in 2007 and finalised in 2008. It is a graphic which describes the overarching digital curation process with archival and preservation processes being only portions of this overall digital curation process. The model outlines various curation actions.
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]
MLOps is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering. MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software ...
The cornerstone of digital preservation, "data integrity" refers to the assurance that the data is "complete and unaltered in all essential respects"; a program designed to maintain integrity aims to "ensure data is recorded exactly as intended, and upon later retrieval, ensure the data is the same as it was when it was originally recorded".
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]
Knowledge management (KM) is the set of procedures for producing, disseminating, utilizing, and overseeing an organization's knowledge and data.It alludes to a multidisciplinary strategy that maximizes knowledge utilization to accomplish organizational goals.