Ontology engineering in provenance enablement for the National Climate Assessment

Ma, Xiaogang; Zheng, Jin; Goldstein, J.; Zednik, S.; Fu, Linyun; Duggan, Brian; Aulenbach, S.; West, Patrick; Tilmes, Curt; Fox, Peter

doi:10.1016/j.envsoft.2014.08.002

Cited by 32 publications

(10 citation statements)

References 32 publications

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“…Our work of provenance records in DCvO leveraged the W3C standard prov (https://www.w3.org/TR/prov-o/), which represents a high level framework. Components in a few domain ontologies, such as dco, foaf, and vivo can be mapped as subclasses or subproperties of corresponding parts in prov (Ma et al, 2014c).…”

Section: Add Structures and Meanings To Contentmentioning

confidence: 99%

Weaving a Knowledge Network for Deep Carbon Science

West

Zednik

et al. 2017

Front. Earth Sci.

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Geoscience researchers are increasingly dependent on informatics and the Web to conduct their research. Geoscience is one of the first domains that take lead in initiatives such as open data, open code, open access, and open collections, which comprise key topics of Open Science in academia. The meaning of being open can be understood at two levels. The lower level is to make data, code, sample collections, and publications, etc., freely accessible online and allow reuse, modification, and sharing. The higher level is the annotation and connection between those resources to establish a network for collaborative scientific research. In the data science component of the Deep Carbon Observatory (DCO), we have leveraged state-of-the-art information technologies and existing online resources to deploy a web portal for the over 1,000 researchers in the DCO community. An initial aim of the portal is to keep track of all research and outputs related to the DCO community. Further, we intend for the portal to establish a knowledge network, which supports various stages of an open scientific process within and beyond the DCO community. Annotation and linking are the key characteristics of the knowledge network. Not only are key assets, including DCO data and methods, published in an open and inter-linked fashion, but the people, organizations, groups, grants, projects, samples, field sites, instruments, software programs, activities, meetings, etc., are recorded and connected to each other through relationships based on well-defined, formal conceptual models. The network promotes collaboration among DCO participants, improves the openness and reproducibility of carbon-related research, facilitates accreditation to resource contributors, and eventually stimulates new ideas and findings in deep carbon-related studies.

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Section: Add Structures and Meanings To Contentmentioning

confidence: 99%

Weaving a Knowledge Network for Deep Carbon Science

West

Zednik

et al. 2017

Front. Earth Sci.

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“…Some research activities already report the use of PROV-O in the scope of environmental data management. Thus, for example, in [42] PROV-O is used to design an information model that represents the provenance information related to a climate assessment report, in the scope of climate change analysis. In [43], the gaps between ISO 19115 lineage model and PROV-O are analyzed to undertake a PROV-O extension with ISO 19115 based semantics, enabling this way PROV-O to be effectively used in geospatial application domains.…”

Section: Geospatial Smart Representationsmentioning

confidence: 99%

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

et al. 2020

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The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains.

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“…Figure 2 shows an example of how to collect provenance information from peer-review journal articles and distributed data sources for the global sea-level rise scenarios. From the published paper, with the GCIS (Global Change Information System) information model and corresponding ontology model [51,52], the climate data behind the Figure "Global sea-level rise" was traced back to the raw dataset and platforms. Tracing CNES' and NASA's contribution to the global sea-level rise scenario results.…”

Section: Defining Product Traceabilitymentioning

confidence: 99%

Towards a Traceable Climate Service: Assessment of Quality and Usability of Essential Climate Variables

Zeng

Barmpadimos³

et al. 2019

Remote Sensing

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Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, including their value to users, to establish the relation of trust between providers of climate data and information and various downstream users. The climate data and information products (i.e., from satellite, in-situ and reanalysis) shall be fully traceable, adequately documented and uncertainty quantified and can provide sufficient guidance for users to address their specific needs and feedbacks. This paper discusses details on how to apply the quality assurance framework to deliver timely assessments of the quality and usability of Essential Climate Variable (ECV) products. It identifies an overarching structure for the quality assessment of single product ECVs (i.e., consists of only one single variable), multi-product ECVs (i.e., more than one single parameter), thematic products (i.e., water, energy and carbon cycles), as well as the usability assessment. To support a traceable climate service, other than rigorously evaluating the technical and scientific quality of ECV products, which represent the upstream of climate services, how the uncertainty propagates into the resulting benefit (utility) for the users of the climate service needs to be detailed.

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Ontology engineering in provenance enablement for the National Climate Assessment

Cited by 32 publications

References 32 publications

Weaving a Knowledge Network for Deep Carbon Science

Weaving a Knowledge Network for Deep Carbon Science

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

Towards a Traceable Climate Service: Assessment of Quality and Usability of Essential Climate Variables

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