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  "creator_user_id": "a21a024c-343c-4b53-a214-49d6fe62a720",
            
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  "groups": [],
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  "groups": [
            
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    {
            
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      "description": "Water Infrastructure in the Alaskan 
            
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Yukon\r\n\r\nOne of the Sites explored by this project is the Yukon 
            
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Delta in Alaska through the Navigating the New Arctic (NNA) 
            
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initiative. This project addresses key challenges for remote 
            
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communities in Alaska which impact the nation's economy, security, and 
            
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resilience. This project focuses on improving water infrastructure in 
            
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rural Arctic areas by bridging the gap between engineering knowledge 
            
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and local expertise. It employs an interdisciplinary approach in three 
            
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phases to develop practical OMM resources grounded in local knowledge. 
            
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The phases combine civil and environmental engineering, sociology, 
            
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anthropology, and human-computer interaction to yield a 
            
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multidisciplinary and multifaceted appraisal of the 
            
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socio-environmental challenges in these communities.\r\n\r\nPrinciple 
            
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Funder: National Science Foundation Navigating the New Arctic Program 
            
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(Award No. #1952196)",
            
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      "display_name": "Alaska: Y-K Delta",
            
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      "id": "09ec5aad-6cbf-4d89-bd31-8f1091498eeb",
            
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      "image_display_url": 
            
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exas.edu/uploads/group/2025-02-21-163516.738771NunapMapElevation.png",
            
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      "name": "alaskan-yukon",
            
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      "title": "Alaska: Y-K Delta"
            
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    },
            
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    {
            
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      "description": "The Smart & Connected Communities project 
            
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focuses on improving resilience in communities facing extreme weather 
            
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events and chronic stressors. It aims to create a secure online portal 
            
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where residents can share their local knowledge about these 
            
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challenges. Researchers will work with the community to integrate this 
            
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local knowledge with existing data sets used for planning and disaster 
            
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preparation. The project not only enhances community resilience but 
            
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also contributes to the national interest by providing a model that 
            
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other communities can adapt. It involves creating innovative tools, 
            
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data privacy processes, and knowledge integration frameworks, 
            
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promoting local expertise in decision-making, and enhancing 
            
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computer-based modeling for better resilience and 
            
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preparedness.\r\n\r\nPrinciple Funder: National Science Foundation 
            
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Smart & Connected Communities (Award No. 2127353)",
            
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      "display_name": "Texas: Rio Grande Valley Region",
            
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      "id": "06257bd5-3ba2-47dd-ba84-ad3a83092297",
            
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      "image_display_url": 
            
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exas.edu/uploads/group/2025-06-27-031854.161623BeulahModelScreen.png",
            
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      "name": "rgv",
            
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      "title": "Texas: Rio Grande Valley Region"
            
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    }
            
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  "id": "9d3bf417-3d04-4e0a-9d9c-75b6f1080023",
            
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  "isopen": true,
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  "isopen": true,
            
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  "license_id": "cc-by",
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  "license_id": "cc-by",
            
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  "license_title": "Creative Commons Attribution",
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  "license_title": "Creative Commons Attribution",
            
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  "license_url": "http://www.opendefinition.org/licenses/cc-by",
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  "license_url": "http://www.opendefinition.org/licenses/cc-by",
            
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  "maintainer": "",
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  "maintainer": "",
            
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  "maintainer_email": "",
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  "maintainer_email": "",
            
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  "metadata_created": "2025-08-27T04:53:48.929877",
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  "metadata_created": "2025-08-27T04:53:48.929877",
            
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  "metadata_modified": "2025-10-07T17:49:56.986758",
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  "metadata_modified": "2025-11-18T16:40:04.337716",
            
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  "name": "interactive-sites-stories",
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  "name": "interactive-sites-stories",
            
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  "notes": "Based on the annual report, here are the significant 
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  "notes": "Based on the annual report, here are the significant 
            
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results for the Sites & Stories Application and AI approaches that 
63
results for the Sites & Stories Application and AI approaches that 
            
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Suzanne Pierce and Texas Advanced Computing Center contributed 
64
Suzanne Pierce and Texas Advanced Computing Center contributed 
            
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to:\r\n\r\n## 1. Deployed Scalable AI-Enabled Analytical 
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to:\r\n\r\n## 1. Deployed Scalable AI-Enabled Analytical 
            
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Infrastructure\r\n\r\nThe Sites & Stories (S&S) application advanced 
66
Infrastructure\r\n\r\nThe Sites & Stories (S&S) application advanced 
            
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from testing to full deployment as a reusable tool on Texas Advanced 
67
from testing to full deployment as a reusable tool on Texas Advanced 
            
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Computing Center Resources. The system now provides cross-scale 
68
Computing Center Resources. The system now provides cross-scale 
            
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applications of Natural Language Processing, topic modeling, and Large 
69
applications of Natural Language Processing, topic modeling, and Large 
            
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Language Model capabilities through a science gateway data portal that 
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Language Model capabilities through a science gateway data portal that 
            
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enables analyses at scale with HPC and GPU compute. LLM tools were 
71
enables analyses at scale with HPC and GPU compute. LLM tools were 
            
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successfully incorporated into the S&S application suite with open and 
72
successfully incorporated into the S&S application suite with open and 
            
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reproducible access as of June 2024.\r\n\r\n## 2. Validated 
73
reproducible access as of June 2024.\r\n\r\n## 2. Validated 
            
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Field-Ready Data Collection and Analysis Capabilities  \r\n\r\nThe 
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Field-Ready Data Collection and Analysis Capabilities  \r\n\r\nThe 
            
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team completed 25 performance field tests in Nunapitchuk to evaluate 
75
team completed 25 performance field tests in Nunapitchuk to evaluate 
            
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the Sites & Stories data collection and ingest services using 
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the Sites & Stories data collection and ingest services using 
            
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variations in hardware, software, and data catalog capabilities. These 
77
variations in hardware, software, and data catalog capabilities. These 
            
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tests demonstrated the system's ability to function in remote Arctic 
78
tests demonstrated the system's ability to function in remote Arctic 
            
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environments while supporting both interview data collection and drone 
79
environments while supporting both interview data collection and drone 
            
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terrain data processing. The application successfully automated 
80
terrain data processing. The application successfully automated 
            
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analytical approaches with AI to connect qualitative and quantitative 
81
analytical approaches with AI to connect qualitative and quantitative 
            
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knowledge in well-defined workflows.\r\n\r\n## 3. Advanced Semantic 
82
knowledge in well-defined workflows.\r\n\r\n## 3. Advanced Semantic 
            
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Linking and Automated Knowledge Integration\r\n\r\nThe S&S application 
83
Linking and Automated Knowledge Integration\r\n\r\nThe S&S application 
            
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was enhanced with semantic linking mechanisms to automate 
84
was enhanced with semantic linking mechanisms to automate 
            
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identification of relevant data collections and models for locally 
85
identification of relevant data collections and models for locally 
            
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identified stakeholder problems. The system now enables semantic 
86
identified stakeholder problems. The system now enables semantic 
            
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linking of stakeholder interview information with digital terrain 
87
linking of stakeholder interview information with digital terrain 
            
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models, creating culturally-informed technical resources. This 
88
models, creating culturally-informed technical resources. This 
            
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advancement allows for automated connection of community knowledge 
89
advancement allows for automated connection of community knowledge 
            
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with scientific models and observational data.\r\n\r\n## 4. 
90
with scientific models and observational data.\r\n\r\n## 4. 
            
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Demonstrated Transferability and Scalability Across 
91
Demonstrated Transferability and Scalability Across 
            
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Contexts\r\n\r\nThe capabilities were successfully replicated and 
92
Contexts\r\n\r\nThe capabilities were successfully replicated and 
            
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tested for transferability in the Rio Grande Valley of Texas, proving 
93
tested for transferability in the Rio Grande Valley of Texas, proving 
            
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the system's applicability beyond Arctic contexts. The S&S application 
94
the system's applicability beyond Arctic contexts. The S&S application 
            
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was deployed for broad use by researchers with training provided to 
95
was deployed for broad use by researchers with training provided to 
            
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large research teams and community partners. The system is now fully 
96
large research teams and community partners. The system is now fully 
            
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scalable, reusable, and easily modified across different settings and 
97
scalable, reusable, and easily modified across different settings and 
            
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use cases.\r\n\r\n## 5. Established Reusable Computational Cookbook 
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use cases.\r\n\r\n## 5. Established Reusable Computational Cookbook 
            
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Framework  \r\n\r\nAI-enabled analytical workflows are now available 
99
Framework  \r\n\r\nAI-enabled analytical workflows are now available 
            
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across multiple scales, from individual narrative analysis through 
100
across multiple scales, from individual narrative analysis through 
            
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Natural Language Processing computational cookbooks to topical 
101
Natural Language Processing computational cookbooks to topical 
            
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analysis using BERTtopic cookbook, plus open-ended analysis tools 
102
analysis using BERTtopic cookbook, plus open-ended analysis tools 
            
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using four Large Language Foundation Model options on TACC systems. 
103
using four Large Language Foundation Model options on TACC systems. 
            
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These workflows provide mechanisms for linking lived experience 
104
These workflows provide mechanisms for linking lived experience 
            
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information with observational data and physics-based models in 
105
information with observational data and physics-based models in 
            
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convergent applications, with the full code base and reusable products 
106
convergent applications, with the full code base and reusable products 
            
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available via Science Gateway applications and GitHub.",
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available via Science Gateway applications and GitHub.",
            
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    "title": "Arctic Infrastructure",
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    "title": "Arctic Infrastructure",
            
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summarizes the state of features and capabilities developed as 
178
summarizes the state of features and capabilities developed as 
            
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decision support systems. The slides walkthrough a demo and 
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decision support systems. The slides walkthrough a demo and 
            
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description of analyzing qualitative data using natural language 
180
description of analyzing qualitative data using natural language 
            
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processing and large language modeling tools on the science gateways 
181
processing and large language modeling tools on the science gateways 
            
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portals. Additionally, features for an interactive annotation 
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portals. Additionally, features for an interactive annotation 
            
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interface that allows users to label and describe text tracebacks to 
183
interface that allows users to label and describe text tracebacks to 
            
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interviews or corpora on an interactive 3D terrain data representation 
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interviews or corpora on an interactive 3D terrain data representation 
            
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built on lidar point datasets.",
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