Validating observation data in WaterML 2.0

Yu, Jonathan; Taylor, Peter; Cox, Simon J.; Walker, Gavin

doi:10.1016/j.cageo.2015.06.001

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…The authors noted that there was more value in sticking with the general implementation of data standards, as it increased portability. Relevant to this is the discussion surrounding the use of the WaterML standard for hydrologic data (Challco et al 2017;Yu et al 2015). Work in this area is iterative and serves as a check-andbalance between what the standards offer and what they necessarily sacrifice in the interest of universality.…”

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

Cracking “Open” Technology in Ecohydrology

Turner

Hill

Caton

2020

Forest-Water Interactions

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In recent years, the adjective "open" has been applied to many aspects of scientific knowledge discovery and dissemination, including open-source software and hardware, open access journal articles, massive open online courses, and open data.Applying the term open to these entities emphasizes an intention for them to be accessible-a quality increasingly emphasized as desirable in science. But there is nothing new about the idea of "open science." Science shed its character of secrecy in sixteenth-century Europe, when the wealthy players in the patronage system that was increasingly supporting scientific inquiry at that time found it in their interest for the scientists they supported to go public with their discoveries, as a form of ornamental display to enhance the patrons' own reputations and power (David 2004). Around this time, scientific discovery also became so complex and mathematically grounded that the patrons could no longer assess quality on their own; fearing charlatanism, patrons began encouraging systems of peer vetting, in which scientists would open their work to other scientists for the purpose of review and confirmation (David 2004). Clearly, the historically instituted practices of peer review and experiment replication are still followed in the current era, so what is The original version of this chapter was published non-open access.

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Section: Datamentioning

confidence: 99%

“…We are starting to see serious efforts toward optimization and standardization in how we collect and store data. Exemplifying this is the checkand-balance we are seeing with open standards such as WaterML (Yu et al 2015). This type of review of a common standard is similar to the optimization and adoption process of open-source software.…”

Section: Successesmentioning

confidence: 99%

Cracking “Open” Technology in Ecohydrology

Turner

Hill

Caton

2020

Forest-Water Interactions

View full text Add to dashboard Cite

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“…Hydrology DWG tiene como trabajo futuro abordar el área de los vocabularios controlados para el dominio de la hidrología. Sin embargo existen estudios para aumentar la capacidad de vocabularios controlados para ser reutilizados por otros sistemas y aplicaciones (Yu et al, 2015).…”

Section: Resultados Y Discusiónunclassified

“…(Hedler, Montero y Kutscherauer, 2011). Estas reglas Schematron se utilizan en el servicio de validación WaterML 2.0 (Yu et al, 2015, Yu et al 2012.…”

Section: Uml -Oandmunclassified

Estudio Del Estándar Waterml

Challco¹,

Pau²,

Pesquer³

2017

GeoFocus

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RESUMENLos estándares para la información geoespacial distribuida pueden ser de propósito general o particulares de un dominio. Algunos dominios han avanzado más que otros en la estandarización y en la interoperabilidad como por ejemplo el dominio Meteorológico-Climático o el Hidrológico. El colectivo de Hidrología detectó la necesidad del estándar WaterML, el cual ayuda a la óptima gestión de los recursos del agua. En términos generales, WaterML es un estándar que define un modelo de datos para la representación de las observaciones hidrológicas, con la intención de permitir el intercambio de este conjunto de datos a través de sistemas de información. Está basado en dos estándares: Geographic Markup Language (GML) y Observations and Measurements (O&M). WaterML en su versión 2.0 fue desarrollado por Open Geospatial Consortium (OGC), fue aprobado a mediados del 2012 y finalmente publicado a inicios del 2014. A pesar de ser un estándar emergente, diversas instituciones gubernamentales y no gubernamentales han considerado útil estandarizar la información sobre sus series temporales de datos hidrológicos y además publicarlas en la web. Este trabajo revisa y cuantifica el nivel de uso de WaterML en las aplicaciones hidrológicas actuales y sitúa a WaterML en la arquitectura del conjunto de los estándares OGC.

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“…This means not only the widespread applicability of the proposed method but also the better performance of the proposed method in input data validation in intelligent geoprocessing. It may promote research fields that require constraints formalization over geospatial input data, such as geographic models and integrated modeling environments [58][59][60].…”

Section: Discussionmentioning

confidence: 99%

Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method

Hou

Qin

Zhu

et al. 2021

IJGI

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Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods.

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Validating observation data in WaterML 2.0

Cited by 3 publications

References 6 publications

Cracking “Open” Technology in Ecohydrology

Cracking “Open” Technology in Ecohydrology

Estudio Del Estándar Waterml

Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method

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