Ontology dynamics in a data life cycle: Challenges and recommendations from a Geoscience Perspective

Ma, Xiaogang; Fox, Peter; Rozell, Eric; West, Patrick; Zednik, S.

doi:10.1007/s12583-014-0408-8

Cited by 26 publications

(8 citation statements)

References 23 publications

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“…With the advent of Semantic Web technologies (Berners-Lee et al, 2001), Web 3.0 became possible, a combination of principles and technology that enables us to link various objects and resources together in ways that both humans and computers can understand. In the Semantic Web, structure and interoperability of datasets are facilitated by the use and reuse of ontologies (Ma and Fox, 2013;Ma et al, 2014a). The reuse of ontologies on the one hand lower the technical burden for ontology maintenance, and on the other hand also promote the sustainability of the knowledge network in the DCvO.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Weaving a Knowledge Network for Deep Carbon Science

West

Zednik

et al. 2017

Front. Earth Sci.

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“…In [26], a data lifecycle is defined as a representation of the set of data management procedures. Data Documentation Initiative (DDI) presents a combined lifecycle model, which is linear but becomes circular in 8 steps.…”

Section: ) Ddi Lifecyclementioning

confidence: 99%

“…• Distribution: in [26], the operation of this phase has not been explained, but we still understand that this is the phase where the data are ready to be diffused in various systems.…”

Section: ) Ddi Lifecyclementioning

confidence: 99%

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Data lifecycles analysis: Towards intelligent cycle

Arass

Tikito

Souissi

2017

2017 Intelligent Systems and Computer Vision (ISCV)

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Abstract-As companies generate and handle increasingly large amounts of data along with the Big Data era, several model of data lifecycle have been proposed to deal with this situation. The analysis, the management and the use of data becomes more complicated or almost impossible in some cases for the companies. To transform these data to a knowledge, the choice of the adequate lifecycle that matches with the company expectations becomes essential.For this goal, this paper aims to be a guide to assist companies to choose a lifecycle that fits their data management vision. For this, we identify the relevant criteria of selection cycles and defined a rating system to each of these criteria. In this paper, we study the available lifecycles of data in the literature that we consider relevant. As a result of this study, we classify these cycles following two types: first analysis oriented phases and the second based on relevant criteria.

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“…From repurposing, the life cycle may go back to the processing step restarting the cycle. Recent studies (Ma et al 2014) show that before data collection there is another step called concept, which includes works on the conceptual model, ontology and terminology of datasets. Knowledge of the ontology and terminology has many implications for the ensuing steps in the data life cycle.…”

Section: Rethinking the Data Life Cyclementioning

confidence: 99%