Education research occupies a distinctive position in public science because it is expected to advance scholarly knowledge while also informing learning, teaching, participation, and workforce development. This study examines how the intellectual characteristics of NSF-funded education proposals are associated with the subsequent academic performance of funded scholars. Linking 8,715 NSF education awards from 1990 to 2020 with 84,519 publications by principal investigators, the analysis focuses on four major NSF education divisions that collectively span undergraduate and graduate levels, formal and informal learning environments, and inclusive educational initiatives. Proposal novelty is measured as semantic distance from prior funded projects within the same division, topical diversity as breadth across latent research themes, and intellectual orientation as theoretical, practical, or balanced. The results show that NSF education funding is consistently associated with higher publication output across divisions. However, this increase is not accompanied by stronger citation performance or higher journal-level visibility; citation and CiteScore estimates are often negative, partic
This study explores funding, authorship patterns, and citation impact of articles funded by the Ministry of Education and Science of Ukraine (MESU), the National Academy of Sciences of Ukraine (NASU), and the National Research Foundation of Ukraine (NRFU). The analysis focuses on articles published in Scopus-indexed journals between 2020 and 2023. The findings show that the share of articles funded by these agencies increased from 8.6% in 2020-2021 to 11.9% in 2022-2023. Foreign co-funding as well as international co-authorship and co-affiliations are consistently associated with higher citation impact. In particular, foreign co-affiliations are associated with higher field-normalised citation impact (FNCI) for MESU-funded articles in 2022-2023, exceeding that of articles jointly funded by MESU and foreign agencies. NASU funding is associated with only modest differences in citation impact relative to unfunded articles. These effects are small and not consistently significant across authorship patterns and become less pronounced in 2022-2023, as the citation impact of unfunded articles partially converges with that of funded articles. While the results should be interpreted as aver
Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with
Over the past four decades, artificial intelligence (AI) research has flourished at the nexus of academia and industry. However, Big Tech companies have increasingly acquired the edge in computational resources, big data, and talent. So far, it has been largely unclear how many papers the industry funds, how their citation impact compares to non-funded papers, and what drives industry interest. This study fills that gap by quantifying the number of industry-funded papers at 10 top AI conferences (e.g., ICLR, CVPR, AAAI, ACL) and their citation influence. We analyze about 49.8K papers, about 1.8M citations from AI papers to other papers, and about 2.3M citations from other papers to AI papers from 1998-2022 in Scopus. Through seven research questions, we examine the volume and evolution of industry funding in AI research, the citation impact of funded papers, the diversity and temporal range of their citations, and the subfields in which industry predominantly acts. Our findings reveal that industry presence has grown markedly since 2015, from less than 2 percent to more than 11 percent in 2020. Between 2018 and 2022, 12 percent of industry-funded papers achieved high citation rates
Every year many scholars are funded by the China Scholarship Council (CSC). The CSC is a funding agency established by the Chinese government with the main initiative of training Chinese scholars to conduct research abroad and to promote international collaboration. In this study, we identified these CSC-funded scholars sponsored by the China Scholarship Council based on the acknowledgments text indexed by the Web of Science. Bibliometric data of their publications were collected to track their scientific mobility in different fields, and to evaluate the performance of the CSC scholarship in promoting international collaboration by sponsoring the mobility of scholars. Papers funded by the China Scholarship Council are mainly from the fields of natural sciences and engineering sciences. There are few CSC-funded papers in the field of social sciences and humanities. CSC-funded scholars from mainland China have the United States, Australia, Canada, and some European countries, such as Germany, the UK, and the Netherlands, as their preferential mobility destinations across all fields of science. CSC-funded scholars published most of their papers with international collaboration during
In countries with a growing number of elderly and a shrinking workforce, one of which is Russia, it becomes impossible to maintain a solidary pension system and a need to switch to a more stable funded system appears. This paper analyzes various scenarios of Russia's transition to such a system. This is the first study on the Russian economy in which an Overlapping Generations Model is used to simulate the pension transition. It is demonstrated that in the long term, the transition to a funded system slightly reduces the welfare of pensioners, and during the transition, the situation of pensioners deteriorates strongly. However, it is also important to emphasize that the transition imposes a heavy burden on all generations living during the reform, they are forced to consume less and greatly change their savings, while also often starting to work more. Such conclusions are made concerning average population cohorts, and the results may not be the same for different groups of individuals within these cohorts. In different scenarios, the pension system transition can cause both economic growth and economic recession, as well as a corresponding increase or decrease in wages and consum
India is now among the major knowledge producers of the world, ranking among the top 5 countries in total research output, as per some recent reports. The institutional setup for Research & Development (R&D) in India comprises a diverse set of Institutions, including Universities, government departments, research laboratories, and private sector institutions etc. It may be noted that more than 45% share of India's Gross Expenditure on Research and Development (GERD) comes from the central government. In this context, this article attempts to explore the quantum of research contribution of centrally funded institutions and institution systems of India. The volume, proportionate share and growth patterns of research publications from the major centrally funded institutions, organised in 16 groups, is analysed. These institutions taken together account for 67.54% of Indian research output during 2001 to 2020. The research output of the centrally funded institutions in India has increased steadily since 2001 with a good value for CAGR. The paper presents noteworthy insights about scientific research production of India that may be useful to policymakers, researchers and science
On August 25, 2022, the White House Office of Science and Technology Policy (OSTP) released a memo regarding public access to scientific research. Signed by Director Alondra Nelson, this updated guidance eliminated the 12-month embargo period on publications arising from U.S. federal funding that had been allowed from a previous 2013 OSTP memo. While reactions to this updated federal guidance have been plentiful, to date there has not been a detailed analysis of the publications which would fall under this new framework. The OSTP released a companion report along with the memo, but it only provided a broad estimate of total numbers affected per year. Therefore, this study seeks to more deeply investigate the characteristics of U.S. federally funded research over a 5-year period from 2017-2021 to better understand the updated guidance's impact. It uses a manually created custom filter in the Dimensions database to return only publications that arise from U.S. federal funding. Results show that an average of 265,000 articles were published each year that acknowledge U.S. federal funding agencies, and these research outputs are further examined by publisher, journal title, institution
We show how the cost of funding the collateral in a particular set up can be equal to the Bilateral Valuation Adjustment with the "funded" probability of default, leading to the definition of a Funded Bilateral Valuation Adjustment (FBVA). That set up can also be viewed by an investor as an effective way to restructure the counterparty risk arising from an uncollateralized transaction with a counterparty, mitigating or even avoiding entirely the additional capital charge introduced by the new Basel III framework.
Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diffusing into scientific practice, holding substantial promise while raising widespread concerns. Despite growing attention to AI use in scientific writing and evaluation, little is known about how the rise of LLMs is reshaping the public funding landscape. Here, we examine LLM involvement at key stages of the federal funding pipeline by combining two complementary data sources: confidential National Science Foundation (NSF) and National Institutes of Health (NIH) proposal submissions from two large US R1 universities, including funded, unfunded, and pending proposals, and the full population of publicly released NSF and NIH awards. We find that LLM use rises sharply beginning in 2023 and exhibits a bimodal distribution, indicating a clear split between minimal and substantive use. Across both private submissions and public awards, higher LLM involvement is consistently associated with lower semantic distinctiveness, positioning projects closer to recently funded work within the same agency. The consequences of this shift are agency-depende
We model an electorate voting on the funding of a public good in a two-party system in an evolutionary game theory framework. Voters adopt one of four strategies: Consensus-makers, Gridlockers, Party 1 Zealots, and Party 2 Zealots, which they may change via imitation. The public good benefits both individuals locally and those in neighbouring regions due to spillover effects. A system of differential equations governs the spatial movement of individuals and shifts in their voting strategies. Local social interactions drive strategy evolution, while migration occurs toward areas of higher utility, which is a function of both social and economic factors. Our results reveal bistability and significant spatial variations. Locally, populations converge to a politically gridlocked state or a mix of consensus-makers and zealots, determining public good provisioning. We find that public good spillovers generate a free-rider effect and poorly funded regions become spatially tied to, and dependent upon, well-funded ones.
In response to the 2008 global financial crisis, Science Foundation Ireland (SFI), now Research Ireland, pivoted to research with potential socioeconomic impact. Given that the latter can encompass higher technology readiness levels, which typically correlates with lower academic impact, it is interesting to understand how academic impact holds up in SFI funded research. Here we decompose SFI \textit{Investigator Awards} - arguably the most academic funding call - into $3,243$ constituent publications and field weighted citation impact (FWCI) values searchable in the SCOPUS database. Given that citation counts are skewed, we highlight the limitation of FWCI as a paper metric, which naively restricts one to comparisons of average FWCI ($\overline{\mathrm{FWCI}}$) in large samples. Neglecting publications with $\textrm{FWCI} < 0.1$ ($8.8\%$), SFI funded publications are well approximated by a lognormal distribution with $μ= -0.0761^{+0.017}_{-0.0039}$ and $ σ= 0.933^{+0.011}_{-0.012}$ at $95 \%$ confidence level. This equates to an $\overline{\mathrm{FWCI}} = 1.433^{+0.029}_{-0.015}$ well above $\overline{\mathrm{FWCI}}=1$ internationally. Broken down by award, we correct $\overli
We present a reconstruction of UKRI's Gateway to Research (GtR) database that links funding opportunities to their resulting project proposals through panel meeting outcomes. Unlike existing work that focuses primarily on funded projects and their outcomes, we close the complete funding lifecycle by integrating three previously disconnected data sources: the GtR project database, UKRI funding opportunities, and competitive funding decision records across UKRI's research councils. We describe the technical challenges of data collection, including navigating inconsistent publication formats and restricted access to panel decisions. The resulting dataset enables a holistic interrogation of the entire funding process, from opportunity announcement to research outcomes. We release the database and associated code.
Public funding plays a central role in driving scientific discovery. To better understand the link between research inputs and outputs, we introduce FIND (Funding-Impact NSF Database), an open-access dataset that systematically links NSF grant proposals to their downstream research outputs, including publication metadata and abstracts. The primary contribution of this project is the creation of a large-scale, structured dataset that enables transparency, impact evaluation, and metascience research on the returns to public funding. To illustrate the potential of FIND, we present two proof-of-concept NLP applications. First, we analyze whether the language of grant proposals can predict the subsequent citation impact of funded research. Second, we leverage large language models to extract scientific claims from both proposals and resulting publications, allowing us to measure the extent to which funded projects deliver on their stated goals. Together, these applications highlight the utility of FIND for advancing metascience, informing funding policy, and enabling novel AI-driven analyses of the scientific process.
Artificial Intelligence for Social Good (AI4SG) is a growing area that explores AI's potential to address social issues, such as public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face real-world deployment and sustainability challenges. While existing HCI literature on AI4SG initiatives primarily focuses on the mechanisms of funded projects and their outcomes, much less attention has been given to the upstream funding agendas that influence project approaches. In this work, we conducted a reflexive thematic analysis of 35 funding documents, representing about $410 million USD in total investments. We uncovered a spectrum of conceptual framings of AI4SG and the approaches that funding rhetoric promoted: from biasing towards technology capacities (more techno-centric) to emphasizing contextual understanding of the social problems at hand alongside technology capacities (more balanced). Drawing on our findings on how funding documents construct AI4SG, we offer recommendations for funders to embed more balanced approaches in future funding call designs. We further discuss implications for how the HCI comm
Academic grant programs are widely used to motivate international research collaboration and boost scientific impact across borders. Among these, bi-national funding schemes -- pairing researchers from just two designated countries - are common yet understudied compared with national and multinational funding. In this study, we explore whether bi-national programs genuinely foster new collaborations, high-quality research, and lasting partnerships. To this end, we conducted a bibliometric case study of the German-Israeli Foundation (GIF), covering 642 grants, 2,386 researchers, and 52,847 publications. Our results show that GIF funding catalyzes collaboration during, and even slightly before, the grant period, but rarely produces long-lasting partnerships that persist once the funding concludes. By tracing co-authorship before, during, and after the funding period, clustering collaboration trajectories with temporally-aware K-means, and predicting cluster membership with ML models (best: XGBoost, 74% accuracy), we find that 45% of teams with no prior joint work become active while funded, yet activity declines symmetrically post-award; roughly one-third sustain collaboration longer
Many open source software (OSS) projects need more human resources for maintenance, improvements, and sometimes even their survival. These needs allegedly apply even to vital OSS projects that can be seen as being a part of the world's critical infrastructures. To address this resourcing problem, new funding instruments for OSS projects have been established in recent years. The paper examines two such funding bodies for OSS and the projects they have funded. The focus of both funding bodies is on software security and cyber security in general. Based on qualitative thematic analysis, the results indicate that particularly OSS supply chains, network and cryptography libraries, programming languages, and operating systems and their low-level components have been funded and thus seen as critical in terms of cyber security. In addition to the qualitative results presented, the paper makes a contribution by connecting the research branches of critical infrastructure and sustainability of OSS projects. A further contribution is made by connecting the topic examined to recent cyber security regulations. Finally, an important argument is raised that neither cyber security nor project sust
The hedge fund industry presents significant challenges for investors due to its opacity and limited disclosure requirements. This pioneering study introduces two major innovations in financial text analysis. First, we apply topic modeling to hedge fund documents-an unexplored domain for automated text analysis-using a unique dataset of over 35,000 documents from 1,125 hedge fund managers. We compared three state-of-the-art methods: Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic. Our findings reveal that LDA with 20 topics produces the most interpretable results for human users and demonstrates higher robustness in topic assignments when the number of topics varies, while Top2Vec shows superior classification performance. Second, we establish a novel quantitative framework linking document sentiment to fund performance, transforming qualitative information traditionally requiring expert interpretation into systematic investment signals. In sentiment analysis, contrary to expectations, the general-purpose DistilBERT outperforms the finance-specific FinBERT in generating sentiment scores, demonstrating superior adaptability to diverse linguistic patterns found in hedge fund
Science and scientific research activities, in addition to the involvement of the researchers, require resources like research infrastructure, materials and reagents, databases and computational tools, journal subscriptions and publication charges etc. In order to meet these requirements, researchers try to attract research funding from different funding sources, both intramural and extramural. Though some recent reports provide details of the amount of funding provided by different funding agencies in India, it is not known what quantum of research output resulted from such funding. This paper, therefore, attempts to quantify the research output produced with the funding provided by different funding agencies to Indian researchers. The major funding agencies that supported Indian research publications are identified and are further characterized in terms of being national or international, and public or private. The analytical results not only provide a quantitative estimate of funded research from India and the major funding agencies supporting the research, but also discusses the overall context of research funding in India, particularly in the context of upcoming operationaliza
Germany is tasked with ensuring the safe and final storage of high-level radioactive waste in a deep geological repository. Since 2022, the ambitious target year of 2031 to identify a suitable location for such a site has been deferred by most public actors. The target year was pushed back by several decades to 2046 or even 2068, consequently delaying the completion of all waste management activities well into the 22nd century. Most radioactive waste management activities in Germany are funded via the external fund KENFO that was initiated with an initial endowment of EUR24.1 bn. in 2017. KENFO hopes to achieve average returns on invest (ROI) of 3.7% over the coming decades to ensure that sufficient funds remain. However, the delays in the current process will likely result in overall cost increases. Thus, in this analysis, we conduct a stochastic analysis of the potential delays in the site selection procedure and their corresponding cost effects to assess whether KENFO's target ROI will suffice for the long-term funding requirements. We find that even under optimistic assumptions, KENFO's ROI would have to be increased to at least 5.91%, up to 6.63%. Alternatively, lump sum injec