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Full-Text Articles in Physical Sciences and Mathematics
Harmonization Of Vocabularies For Water Data, Simon J.D. Cox, Jonathan Yu, Bruce A. Simons
Harmonization Of Vocabularies For Water Data, Simon J.D. Cox, Jonathan Yu, Bruce A. Simons
International Conference on Hydroinformatics
Observational data encodes values of properties associated with a feature of interest, estimated by a specified procedure. For water the properties are physical parameters like level, volume, flow and pressure, and concentrations and counts of chemicals, substances and organisms. Water property vocabularies have been assembled at project, agency and jurisdictional level. Organizations such as EPA, USGS, CEH, GA and BoM maintain vocabularies for internal use, and may make them available externally as text files. BODC and MMI have harvested many water vocabularies alongside others of interest in their domain, formalized the content using SKOS, and published them through web interfaces. …
Environmental Data Store: Design And Implementation, Peng Ji, Michael Piasecki
Environmental Data Store: Design And Implementation, Peng Ji, Michael Piasecki
International Conference on Hydroinformatics
In this paper we present the design and implementation of Environmental data store (EDS). We also highlight the Environmental Thesaurus Server (EnvThs), a standalone web application developed by us, providing semantic support on submission and search within EDS. With the rapid growth in data volumes, data diversity and data demands from multi-disciplinary research effort, data management and exploitation are increasingly facing significant challenges for environmental scientific community. We describe Environmental data store (EDS), a system we are developing that is a web-based system following an open source implementation to manage and exploit multi-data-type environmental data. EDS provides repository services for …
Trustworthy, Useful Languages For Probabilistic Modeling And Inference, Neil B. Toronto
Trustworthy, Useful Languages For Probabilistic Modeling And Inference, Neil B. Toronto
Theses and Dissertations
The ideals of exact modeling, and of putting off approximations as long as possible, make Bayesian practice both successful and difficult. Languages for modeling probabilistic processes, whose implementations answer questions about them under asserted conditions, promise to ease much of the difficulty. Unfortunately, very few of these languages have mathematical specifications. This makes them difficult to trust: there is no way to distinguish between an implementation error and a feature, and there is no standard by which to prove optimizations correct. Further, because the languages are based on the incomplete theories of probability typically used in Bayesian practice, they place …