Rubric-Q: Adding Quality-Related Elements to the GEOSS Clearinghouse Datasets

Zabala, Alaitz; Riverola, A.; Serral, Ivette; Díaz, Paula; Lush, Victoria; Pau, Joan Masó; Pons, Xavier; Habermann, Ted

doi:10.1109/jstars.2013.2259580

Cited by 8 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As data quality elements are an integral part of the metadata model we should expect that most producers provide a comprehensive description of the different components of the data quality in their metadata records, but this is not the common case. An analysis of the metadata harvested by an old version of the GEOSS 6 portal reveals that most datasets include no data quality indicators and if they do they rarely go beyond positional accuracy (Zabala et al 2013).…”

Section: Critical Analysis Of International Standard Iso 19157 and Its Applicationmentioning

confidence: 99%

Geospatial data quality (ISO 19157-1): evolve or perish

López¹,

González²,

Pau³

et al. 2020

rcarto

View full text Add to dashboard Cite

Given the circumstance that the process for the revision of the international standard ISO 19157 is currently open, this article presents a critical reflection on its content, application and some challenges posed by the new types of data (e.g. big data, BIM data, etc.), that also have a geospatial component and to which, therefore, this international standard can be applied as well. Proposals are put forward going along three lines of improvement, on the one hand the consideration of new data quality elements and on the other, the reinforcement of the interoperability of this international standard with other standards related to data quality, and finally various improvements (e.g. standardization of evaluation methods, clearly introducing the life cycle, improvement of the definition of metaquality, etc.) of the standard, which come from experience.

show abstract

Section: Critical Analysis Of International Standard Iso 19157 and Its Applicationmentioning

confidence: 99%

Geospatial data quality (ISO 19157-1): evolve or perish

López¹,

González²,

Pau³

et al. 2020

rcarto

View full text Add to dashboard Cite

show abstract

“…Generally, assessments have focused on metadata content. NOAA (2014) has developed rubrics and metrics for metadata, which has been extended with summaries for specific use cases, e.g., for spatial data in GEOSS (Zabala et al 2013). Habermann (2014) extends that approach further by evaluating XML metadata records using abstract metadata concepts (mapped to individual metadata specifications) against community-defined compliance levels and rubrics.…”

Section: Introductionmentioning

confidence: 99%

Ensuring the quality of data packages in the LTER network data management system

O'Brien

Costa

Servilla

2016

Ecological Informatics

View full text Add to dashboard Cite

Considerable data analyses use automated workflows to ingest data from public repositories, and rely on data packages of high structural quality. The Long Term Ecological Research (LTER) Network now screens all packages entering its long-term archive to ensure completeness and quality, and to ascertain that metadata and data are structurally congruent, i.e., that the data typing and formats expressed in metadata agree with that found in data entities. The EML Congruence Checker (ECC) system is a component of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA), and operates on data tables in packages described with Ecological Metadata Language using the EML Data Manager Library, written in Java. Checking is extensible for other data types and customizable via a template. Reports are retained as part of the submitted data package, and summaries here reflect the general usability of LTER data for a variety of purposes. On average in 2015, site-contributed data in the LTER catalog were 95 % compliant (valid) with the current suite of checks.

show abstract

“…Much attention has been recently paid to providing geospatial data quality indicators and reliability in such EO large-scale service-oriented systems as GEOSS [26], [27]. Therefore, under changing conditions, the system should be able to provided trusted and reliable services to the users.…”

Section: Introductionmentioning

confidence: 99%

Resilience Aspects in the Sensor Web Infrastructure for Natural Disaster Monitoring and Risk Assessment Based on Earth Observation Data

Kussul

Skakun

Shelestov

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

This paper focuses on enabling resilience of the Sensor Web system for disaster monitoring and risk assessment that is based on the use of Earth Observation (EO) data. Resilience is the ability of the system to maintain trustworthy service delivery in spite of changes. Resilience of the EO infrastructure becomes an extremely important issue, especially in the disaster management domain. We propose to enable resilience through: 1) the use of grid resources and services to meet high performance computing requirements; and 2) assessment of resource reputation to select reliable resources. Grid services enable redundancy through integration of heterogeneous resources administrated by geographically distributed organizations. The same services could be provided by different organizations within the infrastructure, and could be of different levels of quality. Therefore, we propose to incorporate a utilitybased reputation model to assess reliability of the resources. The proposed approach is implemented within the Namibia Sensor Web Pilot Project that was created as a testbed for evaluating and prototyping key technologies for rapid acquisition and distribution of data products for decision support systems to monitor floods.

show abstract

Rubric-Q: Adding Quality-Related Elements to the GEOSS Clearinghouse Datasets

Cited by 8 publications

References 14 publications

Geospatial data quality (ISO 19157-1): evolve or perish

Geospatial data quality (ISO 19157-1): evolve or perish

Ensuring the quality of data packages in the LTER network data management system

Resilience Aspects in the Sensor Web Infrastructure for Natural Disaster Monitoring and Risk Assessment Based on Earth Observation Data

Contact Info

Product

Resources

About