BEGIN:VCALENDAR PRODID:-//Grails iCalendar plugin//NONSGML Grails iCalendar plugin//EN VERSION:2.0 CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20240328T095152Z DTSTART;TZID=Europe/Zurich:20200820T090000 DTEND;TZID=Europe/Zurich:20200821T130000 SUMMARY:Data Science in Climate and Climate Impact Research. Conceptual Issues\, Challenges\, and Opportunities. UID:20240328T112354Z-iCalPlugin-Grails@philevents-web-6f97df9687-7c6q9 TZID:Europe/Zurich LOCATION:Universitätstrasse 16\, Zürich\, Switzerland\, 8092 DESCRIPTION:Workshop "Data Science in Climate and Climate Impact Research"\nMain content\n\n\n\n\n\nConceptual Issues\, Challenges\, and Opportunities\nConference Venue and Date:\n
ETH Zü\;rich\, Zentrum.
\nAugust 20 (afternoon) and 21 (full day)\, 2020.
\nOrganizers\nBenedikt Knü\;sel\, Marius Zumwald\, Christoph Baumberger\, David Bresch\, Reto Knutti\, ETH Zü\;rich
\nDisciplines\nIn recent decades\, the production and storage of data about practically every aspect of human life has increased. This concerns scientific research in general\, including environmental sciences. Increasing volumes of data help environmental scientists to observe more phenomena at a finer spatial and temporal scale and to model phenomena with machine learning. These new possibilities raise a host of interesting methodological questions. In a policy-relevant field such as climate science and climate impact research\, transparency of and confidence in results is key. However\, it is unclear how these features can be achieved when employing data science. Furthermore\, data science projects often require extensive interdisciplinary collaboration in order to combine expertise in handling and analyzing data and domain expertise. This helps to obtain meaningful insights from the data. \;However\, this interdisciplinarity can be associated with new challenges.
\nThere are numerous examples of interesting data-science projects in climate and climate impact research\, which have addressed a variety of \;purposes. For example\, innovative methods that combine physical modeling and machine learning might be employed to analyze environmental data while ensuring interpretability and physical plausibility of the models. New forms of data might help to measure environmental conditions and monitor policy implementation or to monitor disaster response and climate change adaptation. But how should these attempts be evaluated? What role does interdisciplinary collaboration play in these efforts? What purposes are data sciences tools most fruitful for and under what conditions?
\nAt this interdisciplinary conference\, we aim to bring together researchers from all climate-related subdisciplines (including environmental social science and climate impact research)\, philosophers\, and environmental data scientists to discuss questions like these. While case studies are welcome\, the emphasis on the talks should be on conceptual issues.
\nTopics\nQuestions that can be discussed include (but are not limited to) the following:
\nFrom Philosophy:
\n\nFrom scientific practice:
\nAbstracts (max. 300 words excluding references) for slots of 30 minutes (20 minutes of presentation plus discussion of 10 minutes) should be submitted until March 15\, 2020. Please prepare your abstract for anonymous review (no information identifying the authors). We will notify all applicants by April 1\, 2020.
\nThe workshop fee is CHF 100 and will cover the full participation\, food\, and refreshments during the conference. A conference dinner will take place after the first day of the workshop\, which is not covered by the fee.
\nFor submissions: Please submit your abstract to \;sarah.spitzauer@usys.ethz.ch.
\nFor questions regarding the conference\, please contact Benedikt Knü\;sel (benedikt.knuesel@usys.ethz.ch).
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