Portfolio Mining (original) (raw)
Related papers
Is the Distribution of Research Grants Sustainable? An Empirical Study of Grant Assessment
Sustainability
Financing of basic research is an important task in supporting research activities and development of dynamically advancing interdisciplinary fields of science. A significant challenge in this aspect is the correct distribution of limited finances sustainably. In this paper, we present an empirical study related to National Science Centre (NSC), which is the main government agency in Poland. NSC funds projects in Arts, Humanities and Social Sciences, Life Sciences and Physical Sciences and Engineering. In this work, we analyse three primary funding schemes of NSC, which are called PRELUDIUM, SONATA and OPUS. Each of theses programms is asigned to another group of scientists from beginners to experts. Projects’ data concerning PRELUDIUM, SONATA and OPUS schemes are collected from NSC projects database (only completed projects) and proccessed for further investigation. Effectiveness and sustainability of projects implemented in scientific fields are analysed concerning criteria such a...
Large-Scale Public R&D Portfolio Selection by Maximizing a Biobjective Impact Measure
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2000
This paper addresses R&D portfolio selection in social institutions, state-owned enterprises, and other nonprofit organizations which periodically launch a call for proposals and distribute funds among accepted projects. A nonlinear discontinuous bicriterion optimization model is developed in order to find a compromise between a portfolio quality measure and the number of projects selected for funding. This model is then transformed into a linear mixed-integer formulation to present the Pareto front. Numerical experiments with up to 25 000 projects competing for funding demonstrate a high computational efficiency of the proposed approach. The acceptance/rejection rules are obtained for a portfolio using the rough set methodology.
Using Visualization to Derive Insights from Research Funding Portfolios
IEEE Computer Graphics and Applications, 2015
Characterizing the existing funding portfolio of any federal agency becomes difficult due to the number, complexity, and diversity of funded projects and associated metadata. Deep Insights Anywhere, Anytime (DIA2) is a new platform that makes it easy to access and understand funding portfolios. Providing insights to determine the impact of any funded project can be challenging, especially in terms of qualifying the return on investment of the research activity. This paper presents results of assessing DIA2's usability and explains how DIA2 can provide meaningful representations that contribute to determining the impact of a research portfolio. The results show that DIA2 has good usability. Further, participants identified several indicators of impact as a result of the visualizations that can be realized through DIA2.
The relative success of private funders and government funders in funding important science
European Journal of Law and Economics, 2006
Regression analysis is used to test the effects of funding source (and of various control variables) on the importance of the article, as measured by the number of citations that the article receives. Funding source is measured by the number of private and the number of government grants mentioned in the acknowledgements section. The importance of an article is measured by an "early" count (of citations through October 1992), and a "late" count (of citations through July 2002). Using either measure of article importance, the evidence suggests that private funders are more successful than the government at identifying important research.
IEEE Transactions on Engineering Management, 2000
This paper presents a portfolio building process for large public and/or nonprofit research organizations. The proposed approach allocates a research and development (R&D) program budget, taking into consideration both applications and technological areas. It starts with the definition of program objectives and covers the allocation of an R&D program budget, including a final activity selection, according to specific criteria. Budget allocation decisions rely on estimations of risk and return for areas and projects, based on the Markowitz portfolio selection model. A scenario implementation of the proposed portfolio building process in the context of a space agency is included. The application to other areas is also discussed. Index Terms-Decision making, portfolio management, project selection, research and development (R&D).
Estimating the NIH Efficient Frontier
PLoS ONE, 2012
Background: The National Institutes of Health (NIH) is among the world's largest investors in biomedical research, with a mandate to: ''…lengthen life, and reduce the burdens of illness and disability.'' Its funding decisions have been criticized as insufficiently focused on disease burden. We hypothesize that modern portfolio theory can create a closer link between basic research and outcome, and offer insight into basic-science related improvements in public health. We propose portfolio theory as a systematic framework for making biomedical funding allocation decisions-one that is directly tied to the risk/reward trade-off of burden-of-disease outcomes. Methods and Findings: Using data from 1965 to 2007, we provide estimates of the NIH ''efficient frontier'', the set of funding allocations across 7 groups of disease-oriented NIH institutes that yield the greatest expected return on investment for a given level of risk, where return on investment is measured by subsequent impact on U.S. years of life lost (YLL). The results suggest that NIH may be actively managing its research risk, given that the volatility of its current allocation is 17% less than that of an equal-allocation portfolio with similar expected returns. The estimated efficient frontier suggests that further improvements in expected return (89% to 119% vs. current) or reduction in risk (22% to 35% vs. current) are available holding risk or expected return, respectively, constant, and that 28% to 89% greater decrease in average years-oflife-lost per unit risk may be achievable. However, these results also reflect the imprecision of YLL as a measure of disease burden, the noisy statistical link between basic research and YLL, and other known limitations of portfolio theory itself. Conclusions: Our analysis is intended to serve as a proof-of-concept and starting point for applying quantitative methods to allocating biomedical research funding that are objective, systematic, transparent, repeatable, and expressly designed to reduce the burden of disease. By approaching funding decisions in a more analytical fashion, it may be possible to improve their ultimate outcomes while reducing unintended consequences.
Http Dx Doi Org 10 1080 02691720903364092, 2009
The National Science Foundation (NSF) has, since 1997, attempted to diversify and enrich science research and education in the USA through the Broader Impacts Criterion (BIC), also known as "Criterion Two" or the "Second Criterion". In doing so, NSF has so successfully integrated BIC into its discovery grant funding programmes that it has become difficult to assess the efficiency (in an economic sense) of BIC activities, as opposed to cataloguing its products (number of trainees, publications, etc.). Moreover, current practice at NSF requires that each and every Principal Investigator receiving a discovery grant address both Science, Technology, Engineering and Math activities and broader impacts, despite the fact that their formal training is most likely to be in only one of these areas. Against this backdrop, I consider NSF spending on broader impacts, conduct a microeconomic analysis of effectiveness of BIC expenditures, and discuss alternative funding models and Principal Investigator profiles and expertise sets that might not only accelerate the goals of expanding NSF's broader impact, but additionally enhance the quality of science funded by this agency.