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Data corruption refers to errors in computer data that occur during writing, reading, storage, transmission, or processing, which introduce unintended changes to the original data. Computer, transmission, and storage systems use a number of measures to provide end-to-end data integrity , or lack of errors.
Data degradation is the gradual corruption of computer data due to an accumulation of non-critical failures in a data storage device. It is also referred to as data decay, data rot or bit rot. [1] This results in a decline in data quality over time, even when the data is not being utilized.
This includes data corruption and the term is often used as a synonym for corrupt data. [1] It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use unstructured text. Noisy data can adversely affect the results of any data analysis and skew conclusions if not handled properly.
Data transparency (2) Analytic methods transparency (3) Research materials transparency (4) with all the relevant data, code and research materials stored on a "trusted repository" and all analysis being already reproduced independently prior to publication. [56] Design and analysis transparency (5) with dedicated standards for "review and ...
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...
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 ...
In scientific inquiry and academic research, data fabrication is the intentional misrepresentation of research results. As with other forms of scientific misconduct , it is the intent to deceive that marks fabrication as unethical, and thus different from scientists deceiving themselves .
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method.For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated.