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Dra. Arancha Lana holds a B.S. in Physics from the Complutense University of Madrid. After her studies she was awarded a postgraduate fellowship at the AEMET, where she began her research career by studying atmosphere patterns with the use of a European data model. She was then awarded with a competitive national Fellowship to complete her Ph.D. (FPI) at the Institute of Marine Science (ICM-CSIC) in Barcelona. Her Ph.D. focused on the role of the marine biosphere in the oceans and its influence on cloud formation. The aforementioned studies require a profound exploration of large amounts of data, and the use of observational systems, mainly satellites, and in situ data. Moreover, she created a global ocean climatology widely used by climate modelers (445 citations) which derived exclusively from a database with the use of statistical and data analysis tools. Upon completion of her Ph.D., she was awarded with a three-year post-doctorate (JAE-doc) at the Mediterranean Institute for Advanced Studies (IMEDEA, UIB-CSIC) in Mallorca. The research focused on ocean surface data with the use of a new High-Frequency Radar located in the Balearic Islands. Her research in recent years concentrates in managing big data to study the atmosphere, oceans, and currently, animal behaviour. After her post-doctorate, her experience with big data allowed her to consolidate her knowledge of new techniques, like Computer Vision and Deep Learning. Furthermore, she joined the Ecology Fish Group at the Department of Marine Ecology at IMEDEA as a technician where she develops computer algorithms and networks. She uses these new techniques to identify fish, which allow to study their behaviour (in the sea and in the laboratory) and associate it with recreational fisheries activities. Computer Vision and Deep Learning techniques have entered in all fields of science, and they are changing the way we work in science, in addition to the influence they already have on our daily lives. Over the last few years, Dra. Lana has specialized in these new techniques, which gives her a privileged position to incorporate them into any scientific discipline. She has become passionate of these new techniques due to the wide range of utilities, especially in science. Deep Learning is used at the IMEDEA to predict satellite data where there are no data available, to the study of fishes’ activity with the combination of robotics (Raspberry Pi and Pi Cameras) and to obtain real-time tracking data. Beside her work as a technician at the Fish Ecology Group, she has collaborated with IMEDEA scientists for the study of marine fauna, and with other groups, such as the IEO, for species recognition. She has also advised other land fauna groups in order to apply automatic monitoring by the use of single-plate computers (Raspberry Pi) in the study of pollinators. Her experience with these technologies allows her to adapt her knowledge to the improvements and changes continuously developed in this new field.
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FISHERIES RESEARCH (2024): 106924
Scientific Reportsno. 1 (2023): 1-15
Scientific Reportsno. 1 (2023): 20281-14
LIMNOLOGY AND OCEANOGRAPHYno. 8 (2022): 1647-1669
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