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Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning".
Also known as "research on research", it aims to reduce waste and increase the quality of research in all fields. Meta-research concerns itself with the detection of bias, methodological flaws, and other errors and inefficiencies. Among the finding of meta-research is a low rates of reproducibility across a large number of fields. [47]
Negative consequences of rankings are generally well-documented and relate to the performativity of using journal rankings for performance measurement purposes. [20] [21] Studies of methodological quality and reliability have found that "reliability of published research works in several fields may be decreasing with increasing journal rank", [22] contrary to widespread expectations.
Research has indicated that bibliometrics figures, particularly the impact factor, decrease the quality of peer review an article receives, [80] cause a reluctance to share data, [21] decrease the quality of articles, [81] and a reduce the scope in of publishable research. "For many researchers the only research questions and projects that ...
"Information quality" is a measure of the value which the information provides to the user of that information. [1] "Quality" is often perceived as subjective and the quality of information can then vary among users and among uses of the information. Nevertheless, a high degree of quality increases its objectivity or at least the ...
However, the use of non-article-level metrics to determine the impact of a single article is statistically invalid. Moreover, studies of methodological quality and reliability have found that "reliability of published research works in several fields may be decreasing with increasing journal rank", [22] contrary to widespread expectations. [23]
A large number of hierarchies of evidence have been proposed. Similar protocols for evaluation of research quality are still in development. So far, the available protocols pay relatively little attention to whether outcome research is relevant to efficacy (the outcome of a treatment performed under ideal conditions) or to effectiveness (the outcome of the treatment performed under ordinary ...
For example, Jensen et al. (2021) emphasized that high-quality research impact evaluations should integrate evidence-based methods to ensure societal benefits. [4] Critics argue, inter alia, that there is too much focus on the impact of research outside of the university system, and that impact has no real relevance to the quality of research.