Authors : Kevin Gross, Carl T. Bergstrom
Scientific research funding is allocated largely through a system of soliciting and ranking competitive grant proposals. In these competitions, the proposals themselves are not the deliverables that the funder seeks, but instead are used by the funder to screen for the most promising research ideas.
Consequently, some of the funding program’s impact on science is squandered because applying researchers must spend time writing proposals instead of doing science. To what extent does the community’s aggregate investment in proposal preparation negate the scientific impact of the funding program?
Are there alternative mechanisms for awarding funds that advance science more efficiently? We use the economic theory of contests to analyze how efficiently grant proposal competitions advance science, and compare them with recently proposed, partially randomized alternatives such as lotteries.
We find that the effort researchers waste in writing proposals may be comparable to the total scientific value of the research that the funding supports, especially when only a few proposals can be funded.
Moreover, when professional pressures motivate investigators to seek funding for reasons that extend beyond the value of the proposed science (e.g., promotion, prestige), the entire program can actually hamper scientific progress when the number of awards is small.
We suggest that lost efficiency may be restored either by partial lotteries for funding or by funding researchers based on past scientific success instead of proposals for future work.
URL : Contest models highlight inherent inefficiencies of scientific funding competitions
DOI : https://doi.org/10.1371/journal.pbio.3000065
Author : Ryan P. Womack
While providing the resources and tools that make advanced research possible is a primary mission of academic libraries at large research universities, many other elements also contribute to the success of the research enterprise, such as institutional funding, staffing, labs, and equipment.
This study focuses on U.S. members of the ARL, the Association for Research Libraries. Research success is measured by the total grant funding received by the University, creating an ordered set of categories.
Combining data from the NSF National Center for Science and Engineering Statistics, ARL Statistics, and IPEDS, the primary explanatory factors for research success are examined.
Using linear regression, logistic regression, and the cumulative logit model, the best-fitting models generated by ARL data, NSF data, and the combined data set for both nominal and per capita funding are compared. These models produce the most relevant explanatory variables for research funding, which do not include library-related variables in most cases.
URL : http://arxiv.org/abs/1601.05104
Author : Eerke A. Boiten
“Big data” has become a major area of research and associated funding, as well as a focus of utopian thinking. In the still growing research community, one of the favourite optimistic analogies for data processing is that of the oil refinery, extracting the essence out of the raw data. Pessimists look for their imagery to the other end of the petrol cycle, and talk about the “data exhausts” of our society.
Obviously, the refinement community knows how to do “refining”. This paper explores the extent to which notions of refinement and data in the formal methods community relate to the core concepts in “big data”. In particular, can the data refinement paradigm can be used to explain aspects of big data processing?
URL : http://arxiv.org/abs/1606.02017
Authors : Changhui Peng, Xinzhang Song, Hong Jiang, Qiuan Zhu, Huai Chen, Jing M. Chen, Peng Gong, Chang Jie, Wenhua Xiang, Guirui Yu, Xiaolu Zhou
Despite great progress in data sharing that has been made in China in recent decades, cultural, policy, and technological challenges have prevented Chinese researchers from maximizing the availability of their data to the global change science community.
To achieve full and open exchange and sharing of scientific data, Chinese research funding agencies need to recognize that preservation of, and access to, digital data are central to their mission, and must support these tasks accordingly.
The Chinese government also needs to develop better mechanisms, incentives, and rewards, while scientists need to change their behavior and culture to recognize the need to maximize the usefulness of their data to society as well as to other researchers.
The Chinese research community and individual researchers should think globally and act personally to promote a paradigm of open, free, and timely data sharing, and to increase the effectiveness of knowledge development.
URL : Towards a paradigm for open and free sharing of scientific data on global change science in China
DOI : http://dx.doi.org/10.1002/ehs2.1225
Authors : Ying Huang, Yi Zhang, Jan Youtie, Alan L. Porter, Xuefeng Wang
How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area?
This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data.
Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period.
We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields.
We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.
URL : How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China
DOI : 10.1371/journal.pone.0154509
Open Access to scholarly literature seems to dominate current discussions in the academic publishing, research funding and science policy arenas. Several international initiatives have been recently started calling for a large-scale transformation of the majority of scholarly journals from subscription model to Open Access.
Such a massive transition would indeed affect not only business models and related cash flows but might be also expected to generate new inequalities in distributing resources among different regions or research fields.
Thus, the paper at hand aims to serve as an input statement for the upcoming discussion and to provide some background information on Open Access debates.
URL : http://eprints.rclis.org/29269/
“There is increasing interest among funding agencies to understand how they can best contribute to enhancing the socio-economic impact of research. Interdisciplinarity is often presented as a research mode that can facilitate impact but there exist a limited number of analytical studies that have attempted to examine whether or how interdisciplinarity can affect the societal relevance of research. We investigate fifteen Social Sciences research investments in the UK to examine how they have achieved impact. We analyse research drivers, cognitive distances, degree of integration, collaborative practices, stakeholder engagement and the type of impact generated. The analysis suggests that interdisciplinarity cannot be associated with a single type of impact mechanism. Also, interdisciplinarity is neither a sufficient nor a necessary condition for achieving societal relevance and impact. However, we identify a specific modality — “long-range” interdisciplinarity, which appears more likely to be associated with societal impact because of its focused problem-orientation and its strong interaction with stakeholders.”
URL : http://arxiv.org/abs/1412.6684