“…when ice is present underground, it occupies space. The melting of this ice leads to ground subsidence, which creates issues such as coastal erosion, infrastructure instability, agricultural changes, landslides, and more.” Background and Research Expertise Like many researchers, Dr. Melissa Ward Jones found Arctic science by accident. As a first-generation college student and the… Read more »
Artificial intelligence (AI) and machine learning (ML) play an increasingly crucial role in Earth science teaching and research by accelerating large data analyses, data interpretation, enhancing predictive models, and making complex environmental processes more accessible to students and researchers alike. The NSF-funded Cyber2A project is a collaboration between Arizona State University (ASU), Woodwell Climate… Read more »
Background Born and raised in New England, Elias Manos was not familiar with the Arctic, but he had a deep interest in learning about this unfamiliar place. His academic background in geography more commonly dealt with geospatial data science, particularly analyzing satellite/aerial imagery with machine learning. While Manos was still an undergraduate, he began collaborating… Read more »
We are excited to announce the upcoming second “Scaling Impact: Co-Creating a Shared Framework for Teaching AI and Machine Learning with Applications in Arctic Research” workshop, to be held from October 20-24, 2025, in beautiful Santa Barbara, CA! Overview Artificial intelligence (AI) and machine learning (ML) play an increasingly crucial role in Earth science teaching and research… Read more »
Highlight: Permafrost thaw is expected to cost Alaska $37-51 billion in building and road damages by 2050 The Permafrost Discovery Gateway (PDG) is an online data visualization platform hosted by the Arctic Data Center that provides openly accessible permafrost conditions across the Arctic region for researchers, educators, and the public. The PDG leverages satellite imagery… Read more »
Computational models are growing in their capacity to consume large datasets and create complex, fine-scale outputs that sometimes reach multiple terabytes in size. Although in theory model output can be regenerated by re-running the model, doing so may require access to high-performance supercomputing, making reproduction costly and impractical. Therefore, when the model output data have… Read more »
Highlight: “Saunders emphasized the importance of having the work from her project funded and supported by the National Science Foundation and state agencies, highlighting the great value of investing in research like hers and resources like the ADC” Background and Research Expertise Prior to the Arctic, Taylor Saunders spent a few years immersed in the… Read more »
Highlight: “This course was an incredible opportunity to learn directly from the experts about best practices in scientific computing. It provided comprehensive training on data handling and computing – both training participants in fundamental coding skills and offering a broad survey of emerging trends in scalable computing. I now feel equipped to tackle bigger and… Read more »
The Arctic Data Center (ADC) Community Engagement and Outreach Coordinators, Angie Garcia & Nicole Greco, joined 1200 international scientists, researchers, Indigenous knowledge holders, and community members in attending the International Conference on Arctic Research Planning (ICARP) IV and Arctic Science Summit Week (ASSW) in Boulder, Colorado this past March. While the ICARP IV Summit occurs… Read more »
Training Overview The Arctic Data Center (ADC) held the annual virtual Reproducible Approaches to Arctic Research Using R training from February 24-28th, 2025 for 14 Arctic researchers from across the globe. Participants ranged from undergraduate students to senior-level scientists with a diverse range of research disciplines. This 4.5-day long course gave participants an overview of… Read more »
