2011
DOI: 10.1016/j.envsoft.2011.07.005
|View full text |Cite
|
Sign up to set email alerts
|

Visualisation of hydrological observations in the water data transfer format

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2013
2013
2020
2020

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 1 publication
0
2
0
Order By: Relevance
“…From a scientific perspective, having data published in a comparable form considerably facilitate data acquisition, interpretation and comprehension [43]. Several studies have already demonstrated the benefits of interoperability and web services in the water domain for data visualization [44], data publication [45], data distribution [46], data discovery and retrieval [47] and modeling [48]. All these authors stress the fact that interoperability offers new and promising opportunities to the water research community for systematic data management, publication and analysis [49].…”
Section: A Benefitsmentioning
confidence: 99%
“…From a scientific perspective, having data published in a comparable form considerably facilitate data acquisition, interpretation and comprehension [43]. Several studies have already demonstrated the benefits of interoperability and web services in the water domain for data visualization [44], data publication [45], data distribution [46], data discovery and retrieval [47] and modeling [48]. All these authors stress the fact that interoperability offers new and promising opportunities to the water research community for systematic data management, publication and analysis [49].…”
Section: A Benefitsmentioning
confidence: 99%
“…One of the pillars of effective data governance is data management (Brown, 1997;Otto, 2011;Wende, 2007). Data management is successful when data are harmonised, collected, structured, stored, available, understandable, reusable, and checked for errors, ensuring that the data model can be maintained (Carleton et al, 2005;Fitch et al, 2016;Horsburgh et al, 2009;Kao et al, 2011;Tartar, 2008). These aspects ensure data integrity, avoid loss or duplication of raw data (Altheide, 2008;Wortman, 1992) and reduce uncertainty in the spatial and non-spatial data quality (Guptill and Morrison, 1995).…”
Section: Introductionmentioning
confidence: 99%