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How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China.

ABSTRACT: 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.

SUBMITTER: Huang Y

PROVIDER: S-EPMC4878788 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China.

Huang Ying Y Zhang Yi Y Youtie Jan J Porter Alan L AL Wang Xuefeng X

PloS one 20160524 5

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 ...[more]

PMID: 27219466

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Project description:Background Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study. Methods To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery. Results Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. Conclusion We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s13023-021-02120-9.

Project description:Inadequate research capacity impedes the development of evidence-based health programming in sub-Saharan Africa. However, funding for research capacity building (RCB) is often insufficient and restricted, limiting institutions' ability to address current RCB needs. The Doris Duke Charitable Foundation's African Health Initiative (AHI) funded Population Health Implementation and Training (PHIT) partnership projects in five African countries (Ghana, Mozambique, Rwanda, Tanzania and Zambia) to implement health systems strengthening initiatives inclusive of RCB.Using Cooke's framework for RCB, RCB activity leaders from each country reported on RCB priorities, activities, program metrics, ongoing challenges and solutions. These were synthesized by the authorship team, identifying common challenges and lessons learned.For most countries, each of the RCB domains from Cooke's framework was a high priority. In about half of the countries, domain specific activities happened prior to PHIT. During PHIT, specific RCB activities varied across countries. However, all five countries used AHI funding to improve research administrative support and infrastructure, implement research trainings and support mentorship activities and research dissemination. While outcomes data were not systematically collected, countries reported holding 54 research trainings, forming 56 mentor-mentee relationships, training 201 individuals and awarding 22 PhD and Masters-level scholarships. Over the 5 years, 116 manuscripts were developed. Of the 59 manuscripts published in peer-reviewed journals, 29 had national first authors and 18 had national senior authors. Trainees participated in 99 conferences and projects held 37 forums with policy makers to facilitate research translation into policy.All five PHIT projects strongly reported an increase in RCB activities and commended the Doris Duke Charitable Foundation for prioritizing RCB, funding RCB at adequate levels and time frames and for allowing flexibility in funding so that each project could implement activities according to their trainees' needs. As a result, many common challenges for RCB, such as adequate resources and local and international institutional support, were not identified as major challenges for these projects. Overall recommendations are for funders to provide adequate and flexible funding for RCB activities and for institutions to offer a spectrum of RCB activities to enable continued growth, provide adequate mentorship for trainees and systematically monitor RCB activities.

Project description:BackgroundAllocation of health research funds among diseases has never been evaluated in China. This study aimed to examine the relationship between disease-specific funding levels of National Nature Science Foundation of China (NSFC), the main governmental resource for health research in China, and burden of disease.MethodsFunding magnitudes for 53 diseases or conditions were obtained from the website of NSFC. Measures of disease burden, mortality, years of life lost (YLLs) and disability-adjusted life years (DALYs), were derived from the Global Burden of Disease Study 2010. The relationship between NSFC funding and disease burden was analyzed with univariate linear regression. For each measure associated with funding, regression-derived estimates were used to calculate the expected funds for each disease. The actual and expected funds were then compared. We also evaluated the impacts of changes of disease burden metrics since 1990, and differences from the world averages on NSFC funding.ResultsNSFC health research funding was associated with disease burden measured in mortality (R = 0.33, P = 0.02), YLLs (R = 0.39, P = 0.004), and DALYs (R? = 0.40, P = 0.003). But none of the changes of mortality (R = 0.22, P = 0.12), YLLs (R =? -0.04, P = 0.79) and DALYs (R =? -0.003, P = 0.98) since 1990 was associated with the funding magnitudes. None of the differences of mortality (R =? -0.11, P = 0.45), YLLs (R =? -0.11, P = 0.43) and DALYs (R = ?-0.12, P = 0.38) from that of the concurrent world averages were associated with the funding magnitudes. Measured by DALY, stroke and COPD received the least funding compared to expected; while leukemia and diabetes received the most funding compared to expected.ConclusionAlthough NSFC funding were roughly associated with disease burden as measured in mortality, YLLs and DALYs. Some major diseases such as stroke were underfunded; while others such as leukaemia were overfunded. Change of disease burden during the last 20 years and country-specialized disease burden were not reflected in current allocation of NSFC funds.

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How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China.

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How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China.

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