Blar i Brage IMR på forfatter "Martin, Angela Helen"

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    • Coastal Research Seen Through an Early Career Lens—A Perspective on Barriers to Interdisciplinarity in Norway 

      Deininger, Anne; Martin, Angela Helen; Pardo, Juan; Berg, Paul Ragnar; Bhardwaj, Jyotirmoy; Catarino, Diana; Fernández-Chacón, Albert; Martinez-Swatson, Karen Agatha; Ono, Kotaro; Oomen, Rebekah Alice; Sodeland, Marte; Sørdalen, Tonje Knutsen; Synnes, Ann-Elin; Thorbjørnsen, Susanna Huneide; Thormar, Jonas (Peer reviewed; Journal article, 2021)
      The value of interdisciplinarity for solving complex coastal problems is widely recognized. Many early career researchers (ECRs) therefore actively seek this type of collaboration through choice or necessity, for professional ...

    • Integral functions of marine vertebrates in the ocean carbon cycle and climate change mitigation 

      Martin, Angela Helen; Pearson, Heidi; Saba, Grace; Olsen, Esben Moland (Peer reviewed; Journal article, 2021)
      In the last decade, the ocean has absorbed a quarter of the Earth’s greenhouse gas emissions through the carbon (C) cycle, a naturally occurring process. Aspects of the ocean C cycle are now being incorporated into climate ...

    • Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook 

      Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen; Moyano, Marta; Oomen, Rebekah Alice; Rasmussen, Jeppe Have; Sørdalen, Tonje Knutsen; Thorbjørnsen, Susanna Huneide (Peer reviewed; Journal article, 2022)
      The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from ...

    • Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook 

      Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen; Moyano, Marta; Oomen, Rebekah Alice; Rasmussen, Jeppe Have; Sørdalen, Tonje Knutsen; Thorbjørnsen, Susanna Huneide (Peer reviewed; Journal article, 2022)
      The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from ...