id: 05958391
dt: a
an: 05958391
au: He, Dan; Parker, D.S.
ti: Learning the funding momentum of research projects.
so: Huang, Joshua Zhexue (ed.) et al., Advances in knowledge discovery and data
    mining. 15th Pacific-Asia conference, PAKDD 2011, Shenzhen, China, May
    24‒27, 2011. Proceedings, Part II. Berlin: Springer (ISBN
    978-3-642-20846-1/pbk). Lecture Notes in Computer Science 6635. Lecture
    Notes in Artificial Intelligence, 532-543 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: Grants; Momentum; Bursts; Technical stock analysis; Classification
ci: 
li: doi:10.1007/978-3-642-20847-8_44
ab: Summary: In developing grant proposals for funding agencies like NIH or
    NSF, it is often important to determine whether a research topic is
    gaining momentum ‒ where by ‘momentum’ we mean the rate of change
    of a certain measure such as popularity, impact or significance ‒ to
    evaluate whether the topic is more likely to receive grants. Analysis
    of data about past grant awards reveals interesting patterns about
    successful grant topics, suggesting it is sometimes possible to measure
    the degree to which a given research topic has ‘increasing
    momentum’. In this paper, we develop a framework for quantitative
    modeling of the funding momentum of a project, based on the momentum of
    the individual topics in the project. This momentum follows certain
    patterns that rise and fall in a predictable fashion. To our knowledge,
    this is the first attempt to quantify the momentum of research topics
    or projects.
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