A Collaborative Framework for Data Management Services: The Experience of the University of California

Starr, Joan; Willett, P.; Federer, Lisa; Horning, Claudia; Bergstrom, Mary Linn

doi:10.7191/jeslib.2012.1014

Cited by 22 publications

(18 citation statements)

References 2 publications

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“…To turn PeriodO identifiers into URLs we rely on the ARK identifier scheme (Starr et al, 2012) provided by the California Digital Library (CDL). First, we include in the JSON-LD context a @base value specifying the base URI (http://n2t.net/ark:/99152/p0) to use when interpreting the PeriodO dataset as Linked Data.…”

Section: Minting Long-term Urlsmentioning

confidence: 99%

Nanopublication beyond the sciences: the PeriodO period gazetteer

Golden

Shaw

2016

PeerJ Computer Science

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The information expressed in humanities datasets is inextricably tied to a wider discursive environment that is irreducible to complete formal representation. Humanities scholars must wrestle with this fact when they attempt to publish or consume structured data. The practice of "nanopublication," which originated in the e-science domain, offers a way to maintain the connection between formal representations of humanities data and its discursive basis. In this paper we describe nanopublication, its potential applicability to the humanities, and our experience curating humanities nanopublications in the PeriodO period gazetteer.

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Section: Minting Long-term Urlsmentioning

confidence: 99%

Nanopublication beyond the sciences: the PeriodO period gazetteer

Golden

Shaw

2016

PeerJ Computer Science

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“…We surveyed most DLC models found in the literature and realized that each only model addressed some specific data stages, such as data discovery [5], data quality [6,9], data sharing [17] and data security [18]. In addition, we also found that each DLC model was proposed to address the particular requirements for a particular scientific field, for example, library and information science in [19], geomorphology in [1920 ecology in [7,8], or to address specific scenarios, such as Smart Cities in [21,22], or support of sciences in [4,10]. In addition, we assessed the completeness of all existed DLC models with respect to the 6Vs challenges as a benchmark test.…”

Section: Related Workmentioning

confidence: 99%

Towards a comprehensive data lifecycle model for big data environments

Sinaeepourfard

García

Masip-Bruin

et al. 2016

Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource; however, organizing and managing data during their entire life cycle becomes a complex research topic. As part of this, Data LifeCycle (DLC) models have been proposed to efficiently organize large and complex data sets, from creation to consumption, in any field, and any scale, for an effective data usage and big data exploitation.Several DLC frameworks can be found in the literature, each one defined for specific environments and scenarios. However, we realized that there is no global and comprehensive DLC model to be easily adapted to different scientific areas. For this reason, in this paper we describe the Comprehensive Scenario Agnostic Data LifeCycle (COSA-DLC) model, a DLC model which: i) is proved to be comprehensive as it addresses the 6Vs challenges (namely Value, Volume, Variety, Velocity, Variability and Veracity; and ii), it can be easily adapted to any particular scenario and, therefore, fit the requirements of a specific scientific field. In this paper we also include two use cases to illustrate the ease of the adaptation in different scenarios. We conclude that the comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data management, organization and integration, easing the adaptation to any kind of scenario, guaranteeing good data quality levels and, therefore, saving design time and efforts for the scientific and industrial communities. CCS Concepts• Information systems➝Data management systems • Computer systems organization➝Architectures➝Other architectures➝ Data flow architectures • Software and its engineering➝ Software system structures➝ Data flow architectures

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“…The citation acknowledges the researchers who provided the data products. The SAuS workflow provides the metadata needed to register the data set with the DOI registry using EzID [18]. The metadata used in the workflow also facilitates the creation of a data set landing page.…”

Section: Ingest Dashboardmentioning

confidence: 99%

A Semi-Automated Workflow Solution for Data Set Publication

Vannan

Beaty

Cook

et al. 2016

IJGI

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Abstract:To address the need for published data, considerable effort has gone into formalizing the process of data publication. From funding agencies to publishers, data publication has rapidly become a requirement. Digital Object Identifiers (DOI) and data citations have enhanced the integration and availability of data. The challenge facing data publishers now is to deal with the increased number of publishable data products and most importantly the difficulties of publishing diverse data products into an online archive. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-funded data center, faces these challenges as it deals with data products created by individual investigators. This paper summarizes the challenges of curating data and provides a summary of a workflow solution that ORNL DAAC researcher and technical staffs have created to deal with publication of the diverse data products. The workflow solution presented here is generic and can be applied to data from any scientific domain and data located at any data center.

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A Collaborative Framework for Data Management Services: The Experience of the University of California

Abstract: doi:10.7191/jeslib.2012.101

Cited by 22 publications

References 2 publications

Nanopublication beyond the sciences: the PeriodO period gazetteer

Nanopublication beyond the sciences: the PeriodO period gazetteer

Towards a comprehensive data lifecycle model for big data environments

A Semi-Automated Workflow Solution for Data Set Publication

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