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Various internet sources have deduced that overall we make an eye-popping 35,000 choices per day. Of this number, 227 choices daily are made on just food alone according to researchers at Cornell ...
FREE Resources: 3 articles every 2 weeks (Register and Read Program, archived journals). Also, early journals (prior to 1923 in US, 1870 elsewhere) free, no registry necessary. Free and Subscription JSTOR [88] Jurn: Multidisciplinary Jurn is a free-to-use online search tool for finding and downloading free full-text scholarly works.
d-separation; D/M/1 queue; D'Agostino's K-squared test; Dagum distribution; DAP – open source software; Data analysis; Data assimilation; Data binning; Data classification (business intelligence)
Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. . Released in beta in November 2004, the Google Scholar index includes peer-reviewed online academic journals and books, conference papers, theses and dissertations, preprints, abstracts, technical reports, and other ...
The "share" is lower than the count because for each article it is based on the number of nationals who have contributed, divided by the total number of contributors. In many cases the "share" will be much lower than the "count" because the "count" includes articles published by institutions which may have only a very few members of the ...
Faculty, Students, Staff, Equipment Owners, Research Centers, Facilities, Technologies, Units etc. as per institution's needs Yes Active and Passive Yes Community Academic Profiles - CAP Active Stanford physicians, School of Medicine faculty, students, staff and postdocs. Yes Active and Passive Unknown Curvita Profile Manager All Unknown
It uses bibliometric methods to analyze and rank the scientific paper performance. In addition to the overall ranking, it includes a list of the top universities in six fields and fourteen subjects. [4] [5] The rankings were introduced in 2007. The original ranking methodology favored toward universities with medical schools.
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2]