Category Archives: Funding

Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data

Understanding the complex patterns in research funding plays a fundamental role in comprehensively revealing funding preferences and informing ideas for future strategic innovation. This is especially true when the funding policies need to be constantly shifted to accommodate highly complex and ever-changing demands for technological, economic, and social development. To this end, we investigate the associations between funding agencies and the topics they fund in an attempt to understand funding patterns at both an organizational level and a topic level. In this paper, the links between heterogeneous nodes, organizations and topics, are mapped to a two-mode organization–topic network. The collaborative interactions formed by funding organizations and the semantic networks constituted by word embedding-enhanced topics are revealed and analyzed simultaneously. The methodology is demonstrated through a case study on big data research involving 9882 articles from the Web of Science over the period 2010 to 2019. The result shows a comprehensive picture of the topics that governments, academic institutions, and industrial funding organizations prefer to fund, which provide potential decision support for agencies and organizations who are exploring funding patterns, estimating funding trends, and updating their funding strategies.

Author(s): Qianqian Jin, Hongshu Chen, Ximeng Wang, Tingting Ma, Fei Xiong
Organization(s): Beijing Institute of Technology, Beijing Wuzi University, Beijing Jiaotong University
Source: Scientometrics
Year: 2022

The effect of competitive public funding on scientific output: A comparison between China and the EU

Public funding is believed to play an important role in the development of science and technology. However, whether public funding and, in particular, competitive funding from public agencies actually helps to increase scientific output (i.e. publications) remains a matter of debate. By analysing a dataset of co-publications between China and the EU and a dataset of joint project collaborations in European Framework Programs for Research and Innovation [FP7 and Horizon 2020 (H2020)], we investigate whether different public funding agencies’ competitive assets have different impact on the volume of publication output. Our results support the hypotheses that competitively funded research output varies by funding sources, so that a high level of funding does not necessarily lead to high scientific output. Our results show that FP7/H2020 funded projects do not have a positive contribution to the output of joint publications between China and the EU. Interestingly, cooperation in the form of jointly writing proposals to these EU programmes, especially when they are not granted by the European Commission, can contribute significantly to joint scientific publications in a later stage. This applies in particular to cases where funding from China is involved. Our findings highlight the key role that funding agencies play in influencing research behaviour. Our results indicate that Chinese funding triggers a high number of publications, whereas research funded by the EU does so to a much lower extent, arguably due to the EU’s strong focus on social impact and its funding schemes as tools to promote European integration.

Author(s): Lili Wang, Xianwen Wang, Fredrik Niclas Piro, Niels J Philipsen
Organization(s): Maastricht University, Dalian University of Technology, Nordic Institute for Studies in Innovation, Erasmus University Rotterdam
Source: Research Evaluation
Year: 2020

Neglected tropical diseases in Brazil: lack of correlation between disease burden, research funding and output

To assess the correlation between the burden of seven priority neglected tropical diseases (NTDs) included in the Brazilian National Agenda of Priorities in Health Research – tuberculosis, Chagas disease, leprosy, malaria, leishmaniasis, dengue and schistosomiasis – and their respective research funding and output.

This retrospective review obtained data on disease burden from the Global Burden of Disease Study and funding data from open access sources. Publications were retrieved from Scopus and SciELO, and characterised according to the type of research conducted. Correlation between funding, research output and burden was assessed by comparing the ‘expected’ and ‘observed’ values for funding and publications relative to the proportional burden for each disease.

There was an emphasis in basic biomedical research (average 30% of publications) and a shortage of health policy and systems (average 7%) and social sciences research (average 3%). Research output and funding were poorly correlated with disease burden. Tuberculosis, Chagas disease and schistosomiasis accounted for more than 75% of total NTD‐related DALYs, but accounted for only 34% of publications. Leprosy, leishmaniasis and malaria, together, received 49% of NTD‐related funding despite being responsible for only 9% of DALYs.

The analysis evidenced a lack of correlation between disease burden, research output and government funding for priority NTDs in Brazil. Our findings highlight the importance of monitoring health needs, research investments and outputs to inform policy and optimise the uptake of evidence for action, particularly in developing countries, where resources are scarce and the research capacity is limited. The results contribute to health policy by highlighting the need for improving coordination of scientific activities and public health needs for effective impact.

Author(s): Bruna de Paula Fonseca, Priscila Costa Albuquerque, Fabio Zicker
Organization(s): Oswaldo Cruz Foundation (Fiocruz)
Source: Tropical Medicine & International Health
Year: 2020

Do national funding organizations properly address the diseases with the highest burden? – Observations from China and the UK (Full-Text)

Recent years have witnessed an incipient shift in science policy from a focus mainly on academic excellence to a focus that also takes into account “societal impact”. This shift raises the question as to whether medical research has given proper attention to the diseases imposing the greatest burden on society. Therefore, with the aim of identifying correlations between research funding priorities and public demand in health, we examine grants issued by the major medical research funding bodies of China and the UK during the decade 2006-2017 and compare the focus of their funded projects with the diseases that carry the highest burden of death, risk, or loss of health. The results indicate that the funding decisions of both nations do correspond to the illnesses with the highest health impact on their citizens. For both regions, the greatest health concerns surround non-communicable diseases, and neoplasms and cardiovascular disease in particular. In China, national health priorities have remained focused on these illnesses for the benefit of its own population, whereas the UK has funded a wider variety of research, extending to projects with impacts outside its borders to some developing countries. Additionally, despite an increased incidence of mental illness and HIV/AIDs in China, there is evidence that less priority has been given to these conditions. Both of these health areas seem to require more attention from China’s national funding agencies and the society in general. Methodologically, this study can serve as an example of how to conduct analyses related to public health issues by combining informetric methods and data with data and tools from other fields, thereby inspiring other scientometrics studies.

For FULL-TEXT download at DOI: 10.31219/

Author(s): Lin Zhang, Wenjing ZHAO, Jianhua Liu, Gunnar Sivertsen, Ying HUANG
Organization(s): Wuhan University, KU Leuven, Beijing Wanfang Data Ltd., Nordic Institute for Studies in Innovation Research and Education (NIFU)
Source: SocArXiv
Year: 2020

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

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.


Author(s): Ying Huang, Yi Zhang, Jan Youtie, Alan L. Porter, Xuefeng Wang
Organization(s): Beijing Institute of Technology; Georgia Institute of Technology
Source: PLoS ONE
Year: 2016

Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations

This article contributes to the development of methods for analysing research funding systems by exploring the robustness and comparability of emerging approaches to generate funding landscapes useful for policy making. We use a novel dataset of manually extracted and coded data on the funding acknowledgements of 7,510 publications representing UK cancer research in the year 2011 and compare these ‘reference data’ with funding data provided by Web of Science (WoS) and MEDLINE/PubMed. Findings show high recall (about 93%) of WoS funding data. By contrast, MEDLINE/PubMed data retrieved less than half of the UK cancer publications acknowledging at least one funder. Conversely, both databases have high precision (+90%): i.e. few cases of publications with no acknowledgement to funders are identified as having funding data. Nonetheless, funders acknowledged in UK cancer publications were not correctly listed by MEDLINE/PubMed and WoS in about 75% and 32% of the cases, respectively. ‘Reference data’ on the UK cancer research funding system are then used as a case-study to demonstrate the utility of funding data for strategic intelligence applications (e.g. mapping of funding landscape, comparison of funders’ research portfolios).


Author(s): Nicola Grassano, Daniele Rotolo, Joshua Hutton, Frederique Lang, and Michael M. Hopkins
Organization(s): Science Policy Research Unit (SPRU), University of Sussex
Source: Journal of the Association for Information Science and Technology
Year: 2016

Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research


  • Data-driven clustering approach to group topics with high accuracy
  • Similarity measure approach to trace the interaction between topics in time series
  • Analyzing changes of TFIDF values of related topics to predict future trends
  • Technology Roadmapping to blend historical analysis and expert-based forecasting

The number and extent of current Science, Technology & Innovation topics are changing all the time, and their induced accumulative innovation, or even disruptive revolution, will heavily influence the whole of society in the near future. By addressing and predicting these changes, this paper proposes an analytic method to (1) cluster associated terms and phrases to constitute meaningful technological topics and their interactions, and (2) identify changing topical emphases. Our results are carried forward to present mechanisms that forecast prospective developments using Technology Roadmapping, combining qualitative and quantitative methodologies. An empirical case study of Awards data from the United States National Science Foundation, Division of Computer and Communication Foundation, is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and science policy in practice.

Author(s): Yi Zhang, Guangquan Zhang, Hongshu Chen, Alan L. Porter, Donghua Zhu, Jie Lu
Organization(s): University of Technology Sydney, Georgia Institute of Technology, Beijing Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2016

The Impact of Research Funding on Scientific Outputs: Evidence from Six Smaller European Countries

We investigate the relationships between the citation impacts of scientific papers and the sources of funding which are acknowledged as having supported those publications. We examine several relationships potentially associated with funding including first citation, total citations and the chances of becoming highly cited. Furthermore, we explore evidence on the links between citations and types of funding by organization and also with combined measures of funding. In particular, we examine the relationship between funding intensity and funding variety and citation. Our empirical work focuses on six small advanced European economies, applying a zero inflated negative binomial model to a set of more than 240,000 papers authored by researchers from these countries. We find that funding is not related to the first citation but is significantly related to the number of citations and top percentile citation impact. Additionally, we find that citation impact is positively related to funding variety and negatively related with funding intensity. Finally there is an inverse relationship between the relative frequency of funding and citation impact. The results presented in the paper raise insights for the design of research programs and the structure of research funding and for the behavior and strategies of researchers and sponsoring organizations.

Open Access escholar

Author(s): Abdullah Gök, John Rigby, Philip Shapira
Organization(s): MIoIR Manchester University
Source: Journal of the American Society for Information Science and Technology
Year: 2014

Upgrading the Quality of Science: Does Funding Source Matter?

Extended Abstract – MINING NOVEL DATA SOURCES session at “1st Global TechMining Conference” 2011

Author(s): Abdullah Gök and Philip Shapira (University of Manchester)

This paper examines the effect of differential and multiple funding on the quality of science in the Czech Republic. We explore several propositions. Firstly, we investigate whether European Union research sponsorship is changing the field orientation of Czech science, compared with the structural long-term trend of change since 1980. Secondly, we ask whether the European Union research sponsorship has had a positive influence on the quality of Czech scientific papers compared with other national and international funding sources. Our measures of quality include accrued citations and journal impact factors. Continue reading Upgrading the Quality of Science: Does Funding Source Matter?

Funding Acknowledgement Analysis – An Enhanced Tool to Investigate Research Sponsorship Impacts

Extended Abstract – MINING NOVEL DATA SOURCES   session at “1st Global TechMining Conference” 2011

Author(s): Jue Wang (Florida International University) and Philip Shapira (University of Manchester)

There is increasing interest in assessing how sponsored research funding influences the development and trajectory of science and technology. Traditionally, linkages between research funding and subsequent results are hard to track, often requiring access to separate funding or performance reports released by researchers or sponsors. Tracing research sponsorship and output linkages is even more challenging when researchers receive multiple funding awards and collaborate with a variety of differentially-sponsored research colleagues. Continue reading Funding Acknowledgement Analysis – An Enhanced Tool to Investigate Research Sponsorship Impacts