The National Science Foundation Ocean Sciences Division (NSF-OCE) provides the majority of the support for ocean research in the United States. Knowledge of the trends in research and funding for NSF-OCE awards is important to investigators, academic institutions, policy analysts, and advocacy organizations. Here, we apply topic modeling to NSF-OCE award abstracts to uncover underlying research topics, examine the interrelationships between awards, and identify research and funding trends. The 20 topics identified by the model capture NSF-OCE’s 10 largest programs (~90% of awards) remarkably well and provide better resolution into research subjects. The distribution of awards in topic space shows how the different topics relate to each other based on their similarity and how awards transition from one topic to another. Awards have become more interdisciplinary over time, with increasing trends in 13 of the 20 topics (65%). Seven topics show a growing fraction of the number of awards while six topics have a declining share. Both the annual inflation-adjusted amount of money awarded and the fraction of the annual funding have been increasing over time in four of the 20 topics. Three other topics show a decline in both the annual amount awarded and the fraction of total annual funding. The identified topics can be grouped into three major themes: infrastructure, education, and science. After 2011, increases in the mean annual cost per project result in a relatively constant fraction of annual funding for infrastructure, despite a significant decline in the infrastructure fraction of awards. The information presented on research and funding trends is useful to scientists and academic institutions in planning and decision-making, while the metrics we employed can be used by NSF to quantify the effects of policy decisions.

Lima, I.D., and J.E. Rheuban. 2018. Topics and trends in NSF ocean sciences awards. Oceanography 31(4):164–170, https://doi.org/10.5670/oceanog.2018.404.

» Supplementary Materials 1–4

Balsiger, P.W. 2004. Supradisciplinary research practices: History, objectives and rationale. Futures 36(4):407–421, https://doi.org/10.1016/​j.futures.2003.10.002.
1. Blei, D.M. 2012. Probabilistic topic models. Communications of the ACM 55(4):77–84, https://doi.org/​10.1145/2133806.2133826.
2. Blei, D.M., and J. Lafferty. 2009. Topic models. Pp. 71–94 in Text Mining: Classification, Clustering, and Applications, 1^st ed. A.N. Srivastava and M. Sahami, eds, Chapman and Hall/CRC, Boca Raton, FL.
3. Blei, D.M., A.Y. Ng, and M.I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3(Jan):993–1,022.
4. DiMaggio, P., M. Nag, and D. Blei. 2013. Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding. Poetics 41(6):570–606, https://doi.org/10.1016/​j.poetic.2013.08.004.
5. Doney, S.C. 2006. The dangers of ocean acidification. Scientific American 294(3):58–65, https://doi.org/​10.1038/scientificamerican0306-58.
6. Griffiths, T.L., and M. Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101(suppl 1):5,228–5,235, https://doi.org/​10.1073/pnas.0307752101.
7. Hicks, C.C., C. Fitzsimmons, and N.V.C. Polunin. 2010. Interdisciplinarity in the environmental sciences: Barriers and frontiers. Environmental Conservation 37(4):464–477, https://doi.org/10.1017/S0376892910000822.
8. Klein, J.T. 1990. Interdisciplinarity: History, Theory, and Practice. Wayne State University Press, 340 pp.
9. Ludwig, J.A., and J.F. Reynolds. 1988. Statistical Ecology: A Primer in Methods and Computing. John Wiley & Sons, 362 pp.
10. McFarland, D.A., D. Ramage, J. Chuang, J. Heer, C.D. Manning, and D. Jurafsky. 2013. Differentiating language usage through topic models. Poetics 41(6):607–625, https://doi.org/10.1016/​j.poetic.2013.06.004.
11. Mix, A.C. 2017. Follow the money. Oceanography 30(2):7–9, https://doi.org/10.5670/oceanog.2017.202.
12. Mohr, J.W., and P. Bogdanov. 2013. Introduction—topic models: What they are and why they matter. Poetics 41(6):545–569, https://doi.org/10.1016/​j.poetic.2013.10.001.
13. NRC (National Research Council). 2000. 50 Years of Ocean Discovery: National Science Foundation 1950–2000. The National Academies Press, 276 pp., https://doi.org/10.17226/9702.
14. NRC. 2015. Sea Change: 2015–2025 Decadal Survey of Ocean Sciences. The National Academies Press, 98 pp., https://doi.org/10.17226/21655.
15. Orr, J.C., V.J. Fabry, O. Aumont, L. Bopp, S.C. Doney, R.A. Feely, A. Gnanadesikan, N. Gruber, A. Ishida, F. Joos, and others. 2005. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437(7059):681–686, https://doi.org/10.1038/nature04095.
16. Pachauri, R.K., M.R. Allen, V.R. Barros, J. Broome, W. Cramer, R. Christ, J.A. Church, L. Clarke, Q. Dahe, P. Dasgupta, and others. 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. R.K. Pachauri, and L. Meyer, eds, IPCC, Geneva, Switzerland, 151 pp.
17. van der Maaten, L., and G. Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9(Nov):2,579–2,605.
18. Witze, A. 2015. US ocean sciences told to steer a new course. Nature News 517(7536):538, https://doi.org/​10.1038/nature.2015.16780.

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