This is the first episode of the Flush to Data podcast. We start with a discussion on mechanistic modelling and machine learning and venture into models for emulation, uncertainty quantification, and data quality. Bonus material includes a discussion on aspects of current scientific practice, including the lack of hypothesis testing, the evaluation of novelty, and the challenges with a generalist approach.Hosts: Jörg Rieckermann and Kris VillezGuest: Juan Pablo CarbjalLinks:* Juan Pablo's web page: https://sites.google.com/site/juanpicarbajal/* Article relating Gaussian processes and Kalman filter: www.jstor.org/stable/2984861 * BBC podcast on Gauss: https://www.bbc.co.uk/programmes/b09gbnfj* Using Lake Zurich as a heat sink: Unfortunately, we could not back-track the original source, despite considerable effort. If anyone of the listeners happens to know how to access the original source we would be grateful for a notice. The best we could find was documentation of related projects by Eawag: https://thermdis.eawag.ch/ and [1]. These show that ecological consequences have indeed been assessed in detail. * Goodhart's law: https://en.wikipedia.org/wiki/Goodhart's_law* An invitation to reproducible computational research: https://doi.org/10.1093/biostatistics/kxq028* Science in the age of selfies: https://doi.org/10.1073/pnas.1609793113 References:[1] Wüest, A. (2012). Potential zur Wärmeenergienutzung aus dem Zürichsee. Machbarkeit. Wärmeentzug (Heizen) und Einleitung von Kühlwasser. Kastanienbaum: Eawag. DORA-Link Episode guide:[0:00:00] Who is Juan Pablo Carbajal?[0:03:10] Mechanistic modelling versus artificial intelligence[0:07:08] Who is Juan Pablo Carbajal? (ctd.)[0:09:26] Cross-fertilization between robotics and wastewater engineering[0:15:05] Emulation: using models to approximate other models[0:21:22] Incorporating common sense and prior knowledge into data-driven models[0:31:31] Equivalence between Gaussian processes and Kalman filter[0:33:50] Utility of emulation[0:40:15] Utility of quantified uncertainty[0:44:50] Intermezzo[0:49:04] What can models say about data quality [1:02:15] How to communicate about data quality?[1:10:10] Preparing engineers for the future[1:15:23] Thank you and goodbye!Bonus material:[1:16:40] Interpretable machine learning models[1:22:33] Hypothesis testing[1:26:14] Critical assessment of novelty[1:30:50] Barriers to the generalist approach [1:35:48] Thank you and goodbye!

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