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Bull, L. A., Di Francesco, D., Dhada, M., Steinert, O., Lindgren, T., Parlikad, A. K., …Girolami, M. (2022). Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning. arXiv, 2204.12404. Retrieved from https://arxiv.org/abs/2204.12404v2
Forecasting
Rantanen, M., Karpechko, A. Yu., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., …Laaksonen, A. (2022). The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ., 3(168), 1–10. doi: 10.1038/s43247-022-00498-3. https://www.nature.com/articles/s43247-022-00498-3
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., …Shuckburgh, E. (2021). Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nat. Commun., 12(5124), 1–12. doi: 10.1038/s41467-021-25257-4. https://www.nature.com/articles/s41467-021-25257-4
Stowell, D., Kelly, J., Tanner, D., Taylor, J., Jones, E., Geddes, J., & Chalstrey, E. (2020). A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK. Sci. Data, 7(394), 1–15. doi: 10.1038/s41597-020-00739-0. https://www.nature.com/articles/s41597-020-00739-0
Rentschler, J., Salhab, M., & Jafino, B. A. (2022). Flood exposure and poverty in 188 countries. Nat. Commun., 13(3527), 1–11. doi: 10.1038/s41467-022-30727-4. https://www.nature.com/articles/s41467-022-30727-4
Adewoyin, R. A., Dueben, P., Watson, P., He, Y., & Dutta, R. (2021). TRU-NET: a deep learning approach to high resolution prediction of rainfall. Mach. Learn., 110(8), 2035–2062. doi: 10.1007/s10994-021-06022-6. https://link.springer.com/article/10.1007/s10994-021-06022-6
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A brief, global history of microsimulation models in health: Past applications, lessons learned and future directions | International Journal of Microsimulation. https://microsimulation.pub/articles/00175
Shukla, P. R., Skeg, J., Buendia, E. C., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., …Malley, J. (2019). Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. https://philpapers.org/rec/SHUCCA-2
Spooner, F., Abrams, J. F., Morrissey, K., Shaddick, G., Batty, M., Milton, R., …Birkin, M. (2021). A dynamic microsimulation model for epidemics. Soc. Sci. Med., 291, 114461. doi: 10.1016/j.socscimed.2021.114461. https://www.sciencedirect.com/science/article/pii/S0277953621007930
Li, K., Giles, D., Karvonen, T., Guillas, S., & Briol, F.-X. (2022). Multilevel Bayesian Quadrature. arXiv, 2210.08329. https://arxiv.org/abs/2210.08329v3
Adapting
Climate Change 2022: Impacts, Adaptation and Vulnerability, Climate Change 2022: Impacts, Adaptation and Vulnerability. https://www.ipcc.ch/report/ar6/wg2
UN Climate Change Conference (COP26) at the SEC – Glasgow 2021. Published in 2022. https://ukcop26.org
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Raza, M. M., & Bebber, D. P. (2022). Climate change, biotic yield gaps and disease pressure in cereal crops. bioRxiv, 2022.08.12.503729. (https://doi.org/10.1101/2022.08.12.503729)[https://doi.org/10.1101/2022.08.12.503729]
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Recommendations
Kusters, R., Misevic, D., Berry, H., Cully, A., Le Cunff, Y., Dandoy, L., …Wehbi, F. (2020). Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Front. Big Data, 3. doi: 10.3389/fdata.2020.577974. https://www.frontiersin.org/articles/10.3389/fdata.2020.577974/full
Forscher, P. S., Wagenmakers, E.-J., Coles, N. A., Silan, M. A., Dutra, N. B., Basnight-Brown, D., & IJzerman, H. (2022). The Benefits, Barriers, and Risks of Big Team Science. PsyArXiv. doi: 10.31234/osf.io/2mdxh. https://psyarxiv.com/2mdxh/
Hosseini, K., McDonough, K., van Strien, D., Vane, O., & Wilson, D. C. S. (2021). Maps of a Nation? The Digitized Ordnance Survey for New Historical Research. Journal Vic. Cult., 26(2), 284–299. doi: 10.1093/jvcult/vcab009.
Hosseini, K., Wilson, D. C. S., Beelen, K., & McDonough, K. (2021). MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale. arXiv, 2111.15592. https://arxiv.org/abs/2111.15592v1
Living with Machines – Harnessing digitised collections and collaborations between historians, data scientists, and curators, to model the effects of mechanisation on society. https://livingwithmachines.ac.uk
Raquel Carmo, Jamila Mifdal, and Alejandro Coca-Castro. “Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book.” ROHub. Jan 28 ,2022. https://doi.org/10.24424/g1bk-dv49. https://the-environmental-ds-book.netlify.app/welcome.html
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