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Most of ResearchGate's users are involved in medicine or biology, [10] [12] though it also has participants from engineering, law, computer science, agricultural sciences, and psychology, among others. [10] ResearchGate published an author-level metric in the form of an "RG Score" since 2012. [15] RG score is not a citation impact measure.
The information retrieval community has emphasized the use of test collections and benchmark tasks to measure topical relevance, starting with the Cranfield Experiments of the early 1960s and culminating in the TREC evaluations that continue to this day as the main evaluation framework for information retrieval research.
Leek summarized the key points of agreement as: when talking about the science-wise false discovery rate one has to bring data; there are different frameworks for estimating the science-wise false discovery rate; and "it is pretty unlikely that most published research is false", but that probably varies by one's definition of "most" and "false".
Author-level metrics are citation metrics that measure the bibliometric impact of individual authors, researchers, academics, and scholars. Many metrics have been developed that take into account varying numbers of factors (from only considering the total number of citations, to looking at their distribution across papers or journals using statistical or graph-theoretic principles).
Source criticism (or information evaluation) is the process of evaluating an information source, i.e.: a document, a person, a speech, a fingerprint, a photo, an observation, or anything used in order to obtain knowledge.
The majority of social media influencers share information with their followers without verifying its accuracy, according to a new U.N. report that was released on Tuesday. The new study, done by ...
Here is the information about the bowl games that make up the newly formatted College Football Playoff and when the action will start: What bowl games are in College Football Playoff?
All data sourced from a third party to organization's internal teams may undergo accuracy (DQ) check against the third party data. These DQ check results are valuable when administered on data that made multiple hops after the point of entry of that data but before that data becomes authorized or stored for enterprise intelligence.