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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2a3c99a8a008a1c87735c6a17b70d04dd/janrange",         
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         "label" : "Advancing geospatial data infrastructure in Dataverse via metadata automation, interactive tools and LLM case study",
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         "journal": "Environmental Modelling & Software","publisher":"Elsevier BV",
         "year": "2026", 
         "url": "http://dx.doi.org/10.1016/j.envsoft.2025.106792", 
         
         "author": [ 
            "Ana Trišović","Jan Range","Philip Durbin","Katherine Mika","Amber Leahey","Wei Li"
         ],
         "authors": [
         	
            	{"first" : "Ana",	"last" : "Trišović"},
            	{"first" : "Jan",	"last" : "Range"},
            	{"first" : "Philip",	"last" : "Durbin"},
            	{"first" : "Katherine",	"last" : "Mika"},
            	{"first" : "Amber",	"last" : "Leahey"},
            	{"first" : "Wei",	"last" : "Li"}
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         "volume": "199","pages": "106792","abstract": "In the era of big data and interdisciplinary research, the effective dissemination and reuse of geospatial data have become vital across various fields such as economics, biostatistics, epidemiology, environmental health, and sciences. This study investigates the challenges associated with managing geospatial data and presents the implementation of tools designed to address these challenges. We present an overview of the current state of geospatial data in a general-purpose research data repository Dataverse and outline a series of implemented advancements for improving the management and utilization of geospatial datasets. These advancements include building the capability to extract structured metadata automatically, enabling programmatic engagement with data assets, incorporating checklists, facilitating geospatial-specific searches, and providing previews of geographic dataset coverage. In this paper, we include two case studies. In the first, we evaluate the effectiveness of the automatic metadata extraction feature, part of our proposed advancements, using the large language model GPT-4 and find that the extracted metadata offers unique information, which is not typically provided by the user. In the second case study, we introduce the community of practice around climate-health data at Dataverse, coordinated through the CAFE Research Coordinating Center.",
         "language" : "English",
         
         "issn" : "1364-8152",
         
         "doi" : "10.1016/j.envsoft.2025.106792",
         
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         "label" : "Standardized data, scalable documentation, sustainable storage \u2013 EnzymeML as a basis for FAIR data management in biocatalysis",
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         "date" : "2021-12-07 19:25:35",
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         "journal": "ChemCatChem",
         "year": "2021", 
         "url": "", 
         
         "author": [ 
            "Jürgen Pleiss"
         ],
         "authors": [
         	
            	{"first" : "Jürgen",	"last" : "Pleiss"}
         ],
         "volume": "13","pages": "3909-3913",
         "bibtexKey": "pleiss2021standardized"

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