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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 ...
The Data Asset Framework or DAF is a data audit methodology developed by HATII at the University of Glasgow in conjunction with the Digital Curation Centre. Originally the Data Audit Framework, the Data Asset Framework is an interview protocol utilised by educational institutions to better understand their growing research data collections ...
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]
The development of the toolkit follows a concentrated period of repository pilot audits undertaken by the DCC, conducted at a diverse range of organisations including national libraries, scientific data centres and cultural and heritage data archives. The construction of a toolkit of this kind is a dynamic process and this is the second stage ...
The Contributor Roles Taxonomy, commonly known as CRediT, is a controlled vocabulary of types of contributions to a research project. [1] CRediT is commonly used by scientific journals to provide an indication of what each contributor to a project did. The CRediT standard includes machine-readable metadata. [2]
A web-based tool Canto; [41] was developed to facilitate community submissions. Since Canto is freely available, generic and highly configurable, it has been adopted by other projects. [42] Curation is subjected to review by professional curators resulting in high quality in-depth curation of all molecular data-types. [43]
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data.
Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as: