8.2. Data-Driven Research: Streamlining Collaboration Between Repositories and Researchers

27 March 2025 | 16:00 - 18:00 (MDT)

Open Session - HYBRID

Room:  UMC Fourth Floor - 425

Organisers:  Nicole Greco (Arctic Data Center); Shannon McAllister (Arctic Institute of North America); Chantelle Verhey (International Science Council)

Session Description:

Open access data repositories serve as a connection between researchers, data scientists, policy makers, and Arctic communities. These repositories play a role in ensuring that published data is findable, accessible, and re-usable by people and machines. This connection motivates the need to streamline collaboration between repositories and researchers for their mutual benefit and determine researcher needs for repository resources. This session will highlight advances in data services that meet researcher and community needs, and will include topics such as making data ready for use with AI and machine learning, automation and standardization of data quality assurance across systems, data integration and harmonization to create value-added data products, improving researcher experiences with data deposit and access systems, and identifying current and future user needs for data systems. These efforts will aid in ensuring researchers around the globe have access to Arctic data and reimagine and address requirements for the next decade of Arctic research to ensure data of all sizes are accessible, organized, and re-usable.

Instructions for Speakers:  Oral presentations in this session should be at most 10-minutes in length, with an additional 2 minutes for questions (unless more detailed instructions are provided by session conveners). See more detailed presenter instructions here.

Oral Presentations: 

• unfold_moreAmplifying Impact Through Collaboration: Knowledge Mobilization and Data Sharing in Arctic Research — Chantelle Verhey 

  Chantelle Verhey ^{1 
  1} World Data System - International Technology Office

  Format: Oral in-person

  Abstract:

  Knowledge mobilization and data sharing are top priorities for researchers and communities of practice worldwide. While numerous initiatives are gaining momentum, Arctic research presents a unique challenge due to the region's diversity and the responsibilities that come with it. This presentation aims to showcase these initiatives at the international, national, and regional levels, specifically examining how they interconnect and build upon one another to amplify their impact. This impact stems from the complex and interdisciplinary research currently being conducted in polar regions and the associated challenges related to knowledge mobilization and knowledge-to-action frameworks. The presentation will explore bottom-up approaches to the flow of data from spatiotemporal configurations, with examples from initiatives such as SmartICE, the Canadian Consortium for Arctic Data Interoperability (CCADI), and the Polar Data Search (PDS). Furthermore, it will emphasize the contributions of these initiatives and how they have benefited from coordination efforts by organizations such as the Arctic Data Committee (ADC) and the World Data System - International Technology Office (WDS-ITO). The polar region embodies a multitude of perspectives, and collaboration between interdisciplinary fields is essential to addressing the challenges of the open data movement, exemplified by the principle of 'as open as possible, as closed as necessary.'

• unfold_moreLeveraging Large Language Models (LLMs) for enhanced access to polar datasets through Natural Language Queries — Alice Cavaliere 

  Alice Cavaliere ¹; Claudia Frangipani ¹; Angelo Lupi ¹; Mauro Mazzola ¹; Chiara Ripa ¹; Giulio Verazzo ¹; Simone Pulimeno ¹; Vito Vitale ^{1
  1} Italian Institute of Polar Sciences, National Research Council of Italy (ISP-CNR)

  Format: Oral in-person

  Abstract:

  This presentation aims to build a local generative search engine that demonstrates how generative AI can be effectively integrated with semantic search to enhance information retrieval and user interaction. The proposed interface, powered by large language models (LLMs), will simplify access to polar datasets stored in catalogs applications such as GeoNetwork and ERDDAP. By leveraging LLMs, the system will enable users to query polar data repositories using everyday language, significantly enhancing accessibility for researchers, policymakers, and the general public. The project will focus on three key aspects: (1) LLM-driven query translation, allowing users to input natural language requests; (2) interactive query refinement, where the system engages in dynamic dialogue with users to clarify and adjust search parameters for more accurate results; and (3) enhanced result summaries, enabling LLMs to condense complex metadata into concise, relevant descriptions for quick interpretation. This system aims to automatically generate structured queries and provide summarized metadata results detailing essential dataset attributes, such as spatial, temporal, and thematic coverage. By making polar datasets more accessible and easier to query, this project will support a wide range of applications - from climate change research to environmental policy planning - ultimately improving our ability to monitor, understand, and respond to critical developments in polar regions.

• unfold_moreThe Italian Arctic Data Center (IADC): current state and future perspectives — Giulio Verazzo 

  Giulio Verazzo ¹; Chiara Ripa ¹; Alice Cavaliere ¹; Livio Ruggiero ¹; Alberto Salvati ¹; Antonino Principato ¹; Vito Vitale ^{1
  1} Italian Institute of Polar Sciences, National Research Council of Italy (ISP-CNR)

  Format: Oral in-person

  Abstract:

  The Italian Arctic Data Center (IADC) is a scientific and technological infrastructure designed to gather, manage, publish, and provide access to data and metadata on the Arctic region. Research in the Arctic Area is promoted by the Italian Arctic Research Program (PRA) and ITINERIS. The IADC infrastructure consists of several interconnected services, including a metadata catalog, a data server, a virtual research environment, and a visualization dashboard, all based on open-source software. Metadata is managed using GeoNetwork, a geospatial cataloging software compatible with common metadata standards and controlled vocabularies, featuring machine-to-machine metadata sharing and harvesting. Data is hosted via ERDDAP, a server that ingests and returns various data formats, automatically handling conversions on-the-fly. JupyterHub serves as the web-based Virtual Research Environment, allowing users to leverage programming languages like Python, R, and Julia for data analysis, retrieving data directly from ERDDAP via pre-built libraries. Lastly, Streamlit is used to build elegant dashboards for quick data visualization and comparison. The IADC infrastructure follows the Open Science and FAIR data principles. The data and metadata collection process follows a specific flow from measurement to repository. Raw data undergoes QA/QC analysis before being uploaded to ERDDAP. Data is described using the ISO 19115 metadata standard and controlled vocabularies such as GCMD, Natural Earth, SeaVox, GEMET - INSPIRE etc. Metadata is accessible through GeoNetwork and linked to the data on ERDDAP, forming the foundation for applications like the Streamlit dashboard and the Jupyter Notebook-based virtual research environment.

• unfold_moreBest practices for designing, developing and implementing data repositories to ensure effective collobrations with researchers — Munish Madan 

  Munish Madan ^{1 
  1} University of Calgary, Arctic Institute of North America, Canadian Consortium for Arctic Data Interoperability

  Format: Oral in-person

  Abstract:

  To maintain an interoperable Arctic data infrastructure, promoting effective collaboration between data repositories and researchers, individual repository components should follow best industry practices for documentation, code standards, system deployment and code management. By following best industry practices repository owners can ensure the best possible outcomes for researchers.

  An arctic data infrastructure will only be robust and useful if individual system components are robust. Since many system components are open source, and maintained by large groups of people with varying skill levels with high turnover rates, following best practices is especially important. Equally important, there are often budgetary constraints that limit the selection of tools that can be used to assist with development in an arctic research environment. Despite these challenges, in order for data to be ready for research use, underlying repository infrastructure needs to be well maintained and designed. Systems incorporating arctic data sensors also presents specific challenges as sensors often do not have reliable power or internet access. Coding for these sensor-based applications requires redundancy and reliability.

  This presentation provides implementation guidelines for creating an automated, high quality data repository useful for researchers. With this objective in mind, the following topics are discussed:

  • Properly documenting code functionality;
  • Effective use of automated testing frameworks;
  • Implementing continuous deployment and continuous integration methodologies;
  • Managing code in repositories

   A brief overview is also provided of tools that can be used to assist with these objectives (for example semantic mediators and system registries) in the context of arctic and scientific data.

• unfold_moreThe Evolution of the Polar Data Ecosystem – Lessons learned from and since the last IPY for the next IPY — Mark Parsons 

  Mark Parsons ¹; Øystein Godøy ²; Matthew Jones ³; Peter Pulsifer ^{4
  1} CODATA; ² MetNo; ³ University of California Santa Barbara; ⁴ Carleton University

  Format: Oral in-person

  Abstract:

  The IPY 2007-8 Data Policy was a forward looking and influential document promoting ethically open data and more specific requirements ranging from data citation to respectful use of Indigenous knowledge. Moreover, IPY 2007-8 led to more polar and Arctic data repositories, the formation of the Arctic Data Committee, greatly increased collaboration around data, and a general realization that data flow through a complex ecosystem involving players well beyond researchers and repositories. These developments have echoes beyond the polar regions with initiatives such as the FAIR and CARE principles, establishment of international data collaboratives such as the Research Data Alliance, and the broad promotion of Open Science at all levels.

  IPY 2007-8 marked a step change in polar data management and sharing, and it had a global influence, but it was not an unqualified success. Indeed, much of the data collected during IPY 2007-8 are still not broadly available. This presentation will review the successes and failures of IPY data management and, more importantly, how data systems and cultures have evolved over the last 20 years. Polar data are now much more open, discoverable, and accessible. Rapid advances in technology, standardization, and cooperation have made data more useful and usable despite increasing complexity. Nonetheless fundamental challenges remain in data preservation and stewardship and especially in making data discoverable, interoperable, and genuinely useful across different disciplines and ways of knowing. IPY 2032-3 should mark another major advance in Arctic understanding through greatly improved data stewardship, sharing, and use, provided preparations start now.

• unfold_moreHitchhiker’s Guide to Arctic Data Portals: Where to find and store your data — Ilkka Matero 

  Ilkka Matero ¹; Øystein Godøy ^{2 
  1} Svalbard Integrated Arctic Earth Observing System; ² Norwegian Meteorological Institute, Svalbard Integrated Arctic Earth Observing System

  Format: Oral in-person

  Abstract:

  Monitoring of Arctic environmental and socio-economic changes requires access to a wide variety of data from multiple sources and disciplines, including climate sciences, biology, oceanography, and indigenous knowledge. In response to this need, an increasing number of datasets are being published using formats that are openly available and easy to both understand and use. These developments are critical for facilitating cross-disciplinary research and enhancing global efforts to understand the evolving Arctic. However, the growing number of available data portals poses challenges for users trying to find the most useful resources. While several portals provide access to a wealth of data, the degree of readiness for data sharing and adherence to FAIR principles varies significantly. This inconsistency can hinder efficient data discovery and integration.

  To address these challenges, the Arctic PASSION project is working to map the Polar Data Ecosystem into a comprehensive atlas, providing a structured overview of available data portals and helping users navigate the complex Arctic data landscape.

  This presentation will discuss the current state of Arctic data portals, the ongoing efforts to map the Polar Data Ecosystem, and the importance of making research data universally accessible and understandable. We will highlight a few examples from the data portals that the authors are part of, such as the Sustaining Arctic Observing Networks (SAON) and Svalbard Integrated Arctic Earth System (SIOS) data portals.

• unfold_morePolar Data Management: A Rough Guide for Researchers — Shannon McAllister 

  Shannon McAllister ^{1 
  1} Arctic Institute of North America, University of Calgary, School of Interactive Arts & Technology, Simon Fraser University

  Format: Oral in-person

  Abstract:

  The Canadian Polar Data Consortium (CPDC), formerly the Canadian Consortium for Arctic Data Interoperability (CCADI), hosts and plans workshops, training sessions, and online resources to assist researchers in managing their data throughout the data life cycle. CPDC resources for researchers include: data management planning, understanding data policies, applying standards in data collection, ethical considerations, data visualization, data publishing, and data storage. This session will provide an overview of CPDC’s data management training efforts, its collaborations with researchers, and potential models for use by other data repositories.

• unfold_moreHelping Create A Resilient Arctic Future With NRCS Soil Survey Data Products — Blaine Spellman 

  Blaine Spellman ¹; Michael Sousa ¹; Gabriel Benitez ¹; Stephanie Schmit ¹; Marji Patz
  ¹ USDA Natural Resources Conservation Service

  Format: Oral in-person

  Abstract:

  The United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) provides open access soil information with plot data and mapping products useful for addressing research and infrastructure topics in the Arctic. The NRCS is developing an initial soil survey in the National Petroleum Reserve, focusing on lands within and adjacent to the Willow Project area. Several future projects are planned across Arctic Alaska, including portions of the Seward Peninsula and villages in the Yukon-Kuskokwim Delta.

  Soil surveys provide critical soil characteristics, physical and chemical properties, and soil limitations and potentials to help landowners and managers make informed, science-based land management decisions. In the soil survey process, hundreds of paired soil and vegetation plots are sampled to generate ecological site and soil unit concepts. Soil map unit concepts address important soil properties like where near-surface permafrost occurs within a survey area and provide interpretations such as state-and-transition models, permafrost sensitivity, and ground subsidence.

  NRCS collaborations with researchers, landowners, and managers can provide tacit knowledge and understanding of important infrastructure and research topics. An example of such collaboration is the use of NRCS plot data to predict how subsistence berry habitat distributions might change under various future climate scenarios in western Alaska. NRCS is seeking partnerships nationwide to determine areas of interest for future surveys, better leverage other existing and archived data to address priority research and infrastructure topics, and facilitate private, local, State, and Federal land managers to make informed, science-based decisions involving soils and vegetation across Arctic Alaska.

• unfold_morePOLARIN: Unlocking Polar Research with integrated infrastructures, FAIR data management and Open Science — Vito Vitale 

  Antonio Novellino ¹; Vito Vitale ²; MariaClaudia Paolini ¹; Alice Cavaliere ²; Giulio Verrazzo ²; Ilkka Matero ³; Daan Kivits ³; Oystein Godoy ⁴; Jonas Koefoed Rømer ⁵; Veronica Willmott ⁶; Nicole Biebow ^{6 
  1} ETT; ² Italian Institute of Polar Sciences, National Research Council of Italy (ISP-CNR); ³ Svalbard Integrated Arctic Earth Observing System; ⁴ This email address is being protected from spambots. You need JavaScript enabled to view it.; ⁵ Aarhus University Institute of Ecoscience; ⁶ Alfred-Wegener-Institut

  Format: Oral in-person

  Abstract:

  POLARIN is an international network of polar research infrastructures and services designed to tackle the scientific challenges of the Arctic and Antarctic regions. It comprises a wide array of high-quality research infrastructures, including research stations, vessels, icebreakers, observatories, and data repositories for ice and sediment cores. By offering integrated and challenge-driven access to these resources, POLARIN supports interdisciplinary research on complex polar processes.

  To enhance data accessibility, POLARIN is developing a comprehensive Data Management Policy. This policy introduces POLARIN's data and outlines strategies for reusing and accessing existing data, while ensuring that newly generated data is made widely available. Central to the policy is a commitment to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), promoting openness in research and publishing results as openly as possible (CC-BY license) as soon as possible. POLARIN actively participates in the ZENODO open research pilot flagship to foster open science. Additionally, the network employs FAIR-enabling tools such as ERDDAP, GEOSERVER, and GEONETWORK, integrating AI-based natural language search tools to enhance data access.

 

Poster Presentations (during Poster Exhibit and Session on Wednesday 26 March): 

• unfold_moreEnhancing arctic dataset quality and accessibility: integration with ERDDAP and advanced QA methods using statistical techniques and machine learning — Alice Cavaliere 

  Alice Cavaliere ¹; Claudia Frangipani ¹; Angelo Lupi ¹; Mauro Mazzola ¹; Simone Pulimeno ¹; Chiara Ripa ¹; Alberto Salvati ¹; Giulio Verazzo ¹; Vito Vitale ^{1 
  1} Italian Institute of Polar Sciences, National Research Council of Italy (ISP-CNR)

  Format: Poster in-person

  Poster number: #174

  Abstract:

  This presentation will focus on datasets collected in the Svalbard Archipelago and their integration into the ERDDAP system of the Italian Arctic Data Center. By aligning with FAIR principles (Findable, Accessible, Interoperable, and Reusable), this integration enhances accessibility, optimizes data processing, and ensures the delivery of high-quality, standardized datasets for Arctic research. We will specifically focus on the Climate Change Tower and Ozone datasets. Our discussion will highlight advanced quality assurance (QA) methods, showcasing statistical techniques designed to detect anomalies, outliers, and missing data. Additionally, we will present applications of how machine learning can improve automatic methodology, boosting the consistency and efficiency of QA across large datasets. The QA-assured datasets will be utilized to derive higher-level products, which will be presented using visualization tools developed in conjunction with a virtual research environment. This automation enables researchers to focus on in-depth analysis while ensuring the reliability and robustness of the datasets. These advancements are crucial for global climate change studies and contribute significantly to enhancing our understanding of Arctic environmental dynamics.

• unfold_moreTaiPI Data Repository: An Asia-based polar data infrastructure built for open science — Ilham Adi Panuntun 

  Ilham Adi Panuntun ¹; Whyjay Zheng ¹; Chuen-Fa Ni ^{2
  1} Center for Space and Remote Sensing Research, National Central University, Taiwan, Taiwan Polar Institute, National Central University, Taiwan; ² Taiwan Polar Institute, National Central University, Taiwan, Graduate Institute of Applied Geology, National Central University, Taiwan

  Format: Poster in-person

  Poster number: #99

  Abstract:

  The Arctic warming has attracted global attention since we do not have much time to understand, predict, and mitigate the subsequent challenges to our societal and ecological systems. Taiwan Polar Institute (TaiPI) was founded in 2023 with a mission to provide resources and infrastructure for the Earth science community in Taiwan towards contributions to timely polar research. To reach this goal, TaiPI plans to host a data repository at the end of 2024 with initial data sets focusing on the Arctic glaciers. The TaiPI repository is designed with the FAIR practices to speed up scientific data sharing and collaboration. It assigns a DOI to each data set and offers multiple data access options, including an interactive map interface and bulk downloading. We employ different data formats and structures best compatible with the reported spatial information and cloud environment. We perform the quality check for each submission to ensure its metadata provides sufficient context for data reuse. The TaiPI repository aims to reduce the access threshold and expedite the exchange and exploration of scientific data. In the coming years, we will continue to host research output performed by the TaiPI and international partners, with technical advances such as an API-based data query and access for cross-disciplinary purposes.

• unfold_moreIs a world with Indigenous Data Sovereignty and Data Openness Possible? Rethinking Public data ecosystems under an indigenous community perspective — Dimitrios Symeonidis 

  Dimitrios Symeonidis ¹ 
  Lead Author Affiliation

  Format: Poster in-person

  Poster number: #297

  Abstract:

  The Arctic lies amidst several Grand Challenges. These include climate justice and the socio-cultural dimension of the digital transformation. A promising solution that resolve some of the aforementioned issues includes harnessing Public Data Ecosystems (PDEs) and utilizing them within the context of a novel paradigm, that includes collective, data and artificial intelligence. PDEs collect data from all stakeholders of the stakeholder map and, in this way, help make well-informed decisions at the local or national level. Connecting PDEs to collective and data intelligence concepts and using artificial intelligence for analysis of the findings can generate novel insights in finding the most socially inclusive and financially and environmentally sustainable solutions to deal with the foregoing challenges. Openness of data always, however, raises concerns with regards to data sovereingty. Indigenous Data Sovereignty (IDS), in particular, has long been considered a challenging topic, as European colonialism has resulted in control of indigenous data collection and management. Achieving IDS includes using indigenous data in a way that results in collective well-being and self-determination of indigenous communities. Taking this challenge into account, this research paper performs a systematic literature review to understand how data sovereignty is dealt with in countries where PDE initiatives are taking place, on one hand, and on the other hand what are the key challenges with indigenous data sovereignty reported. This will be complemented by a thematic analysis of the results, to find existing and novel solutions, using the new intelligence paradigm, to ensure both data sovereignty and openness of data.

• unfold_moreTransforming Snow and Sea Ice Data for Machine Learning: Developing an AI-Ready Process — Joseph Rotondo, Sky Gale, & Geraint Webb 

  Joseph Rotondo ¹; Sky Gale ¹; Geraint Webb ^{1
  1} University of Washington

  Format: Poster in-person

  Poster number: #275

  Abstract:

  This project focuses on creating "AI-ready" snow and sea ice datasets, which involves transforming raw observational or model-derived data into structured, machine-readable formats suitable for use in machine learning (ML) models ranging from random forests to neural networks. The process starts with data collection from various sources, including satellite imagery, in-situ measurements, and climate models, often in diverse formats and spatial/temporal resolutions. The next critical step is data preprocessing, which includes cleaning, normalization, and interpolation to handle missing values and unify resolutions.

  Spatial data often requires resampling or regridding to align with a desired scale, while temporal data may need aggregation or smoothing to balance detail with computational efficiency. Feature manipulation follows, where relevant variables like snow depth, sea ice concentration, temperature, and albedo are extracted or synthesized, creating representative inputs for the ML workflow. Transformation of categorical data into numerical features is achieved through encoding methods, such as one-hot encoding or label encoding.

  Data quality assurance is a fundamental component to ensure outliers and noise are appropriately addressed without losing essential variability. Normalization or standardization is applied to keep input values consistent, facilitating better training stability and model convergence. Additionally, dimensionality reduction techniques like PCA can be used to reduce feature space complexity without significant information loss. Finally, the dataset is split into training, validation, and testing sets to ensure robust model development, evaluation, and deployment. These steps, performed meticulously, transform snow and sea ice data into a standardized, feature-rich format, maximizing their utility for future ML applications.

• unfold_moreHuman-connected wild lands: How social science and mobility research can assist in predicting human-mediated dispersal of vegetation and other species — Tobias Schwoerer 

  Tobias Schwoerer ¹; Jennifer Schmidt ²; Aaron Martin ³; Tammy Davis ^{4
  1} University of Alaska Fairbanks International Arctic Research Center; ² University of Alaska Anchorage Institute of Social and Economic Research; ³ U.S. Fish and Wildlife Service; ⁴ Alaska Department of Fish and Wildlife

  Format: Poster in-person

  Poster number: #347

  Abstract:

  Wild landscapes are critical strongholds for biodiversity, yet even in the remotest parts of the globe, increasing human use and development are leading to an influx of biodiversity threats including invasive species. Natural resource management agencies, and those that rely on public lands, need a better understanding of the long-distance dispersal pathways in which invasive species are introduced to remote locations. Pathway information is essential for targeting prevention and early detection across vast landscapes, but it is often challenged by information gaps and high surveillance costs. Data-driven approaches centered around a participating public can help resource managers and biosecurity professionals to better prioritize prevention and early detection activities to minimize incipient and secondary invasions. We employed surveys with resource users to integrate and analyze multiple human-mediated dispersal networks for aquatic invasive species (AIS) across Alaska's part of the North American Boreal Forest. Through network analysis we combined floatplane and watercraft movements to provide a waterbody-specific tool for prioritizing monitoring and informing pathway-specific interventions. We will discuss available geo-spatial models for the habitat suitability and potential human-mediated distribution of Elodea spp., Alaska’s first AIS, the need for more research to better understand aquatic systems in the circumpolar North, and what participatory data can provide to better understand human-mediated stressors.

• unfold_moreFRIDGE: Empowering Polar Research with Dynamic Access to High-Resolution Satellite Imagery and Digital Elevation Models — Cole Kelleher 

  Cole Kelleher ¹; Rory Johnson ¹; Jesse Bakker ¹; Claire Porter ^{1
  1} Polar Geospatial Center - University of Minnesota

  Format: Poster in-person

  Poster number: #431

  Abstract:

  FRIDGE (Federal Research Imagery and DEM Geospatial Explorer) is an innovative tool developed by the Polar Geospatial Center (PGC) at the University of Minnesota, designed to facilitate efficient access to PGC’s extensive catalog of high-resolution satellite imagery and Digital Elevation Models (DEMs). FRIDGE allows researchers to dynamically query PGC’s data by various parameters such as geographic location, time, atmospheric conditions, sensors, band combinations, and illumination angles, providing tailored data sets that meet specific research needs. With access to a repository of approximately 8 petabytes (PB) of sub-meter resolution commercial satellite imagery of the polar regions, FRIDGE supports a wide array of scientific investigations, enabling decade-scale studies of surface changes and environmental dynamics. This tool is particularly valuable for NSF Office of Polar Program (OPP) funded researchers, who can order sub-meter resolution imagery with specific processing options for bulk deliveries, while DEMs are freely accessible to all users and the general public. FRIDGE democratizes data access, empowering researchers to conduct high-resolution spatial analyses essential for studies in glaciology, climatology, ecology, geology, and many other scientific disciplines. By offering easy access to high-resolution polar imagery and DEMs, FRIDGE enhances the ability of scientists to monitor and understand changes in the Earth's polar regions, supporting both academic research and operational applications in some of the most challenging environments on the planet. Through FRIDGE, PGC continues to advance geospatial science, fostering collaboration and innovation in understanding Earth’s dynamic systems.

• unfold_moreNOAA PolarWatch: Advancing Polar Research through FAIR Principles and Collaborative Open Science Practices — Sunny Hospital 

  Sunny Hospital ^{1 
  1} NOAA/UCSC

  Format: Poster in-person

  Poster number: #539

  Abstract:

  NOAA’s PolarWatch, the polar node of the CoastWatch program, offers remote sensing and geospatial data services for polar regions, supporting a diverse community of users. Guided by FAIR (Findable, Accessible, Interoperable, Reusable) principles, PolarWatch facilitates access to essential snow and sea ice data, empowering both novice and expert users to explore high-resolution spatial and temporal data and uncover patterns and anomalies in polar environments.

  Committed to advancing open science, PolarWatch has implemented a workflow that embodies FAIR principles, creating well-organized, publicly accessible resources and code repositories. Our initiative extends beyond data delivery, as we provide training and tailored services to enhance research and resource management efforts in polar science, fostering a collaborative community dedicated to sustainable data practices and impactful insights.

  This presentation will highlight the technical innovations and collaborative strategies employed to make data accessible, emphasizing effective data management and community engagement. By reflecting on both successes and challenges, we aim to share insights that can guide future development and collaboration efforts in advancing open science and maximizing the impact of polar data resources.

• unfold_moreDynamic STAC API for Searching and Retrieving ArcticDEM Data — Jesse Bakker 

  Jesse Bakker ¹; Cole Kelleher ^{1 
  1} Polar Geospatial Center, University of Minnesota

  Format: Poster virtual

  Poster number: #550

  Abstract:

  High-quality elevation data is critical for understanding a rapidly changing Arctic environment. To that end, ArcticDEM, a stereo-photogrammetric satellite-derived high-resolution (2m) pan-Arctic time series digital elevation model, has become a foundational dataset for a wide range of applications in polar science. Published as both a temporal stack of repeat time-stamped DEM strips and as a seamless mosaic built from strip components, the DEMs can serve a variety of analytical and mapping needs. While it has been publicly available to users since it was published, the implementation of a dynamic STAC (Spatio Temporal Asset Catalog) API for ArcticDEM now means the data is more accessible than ever. STAC is a common standard for exposing spatiotemporal data online to be utilized in downstream workflows. By hosting the DEMs as Cloud Optimized Geotiffs (COGs) in an Amazon S3 bucket and leveraging STAC functionality, the ArcticDEM catalog is machine-readable and can be queried dynamically, allowing users to access just the necessary pixels without having to download entire files. This change enables more flexible integration into cloud and distributed computing workflows, reduces barriers to accessibility, and enables better collaboration and reproducibility. This session will provide an overview of the ArcticDEM STAC catalog and demonstrate how to use the API to query and retrieve data.

• unfold_moreReading maps, reading extraction businesses — Gudrun Havsteen-Mikkelsen 

  Gudrun Havsteen-Mikkelsen ^{1 
  1} Polar Research Design

  Format: Poster virtual

  Poster number: #265

  Abstract:

  Licenses and geological maps are active agents used to territorialize zones in the Arctic. The extractive industries use this agency within maps to develop and expand the mining sector.

  In Emanuela Casti’s critiques on cartography and her semiotic analysis, she argues that that maps are agents with performative aesthetics and realities. According to Casti, maps can generate and amplify political tensions through acts of symbolic control, material control, and finally domination of meaning in contrast to simply storing information.

  I would like to stress Casti’s publication Reality as representation - The semiotics of cartography and the generation of meaning (Bergamo: Bergamo University Press, 2000) and the article “Towards a Theory of Interpretation: Cartographic Semiosis” (in cartographica, vol. 40, issue 3, October 2005: 1-16) as sources of inspiration and reference.

  From maps being intended as a mediation of territory, to maps being active agents that invite processes of resource extraction it demonstrates that maps indicate the potential for mining businesses. This enhances the double role of maps. On the one hand, maps are intended to transfer knowledge of territory, while on the other hand, are also active agents that invite processes of resource extraction, claiming territories and at the right moment open for investments. However, the paradox of mapping resources, is that it often, if not certainly, leaves the social and environmental issues in the periphery.