DOI for geoscience data - how early practices shape present perceptions

Klump, Jens; Huber, Robert; Diepenbroek, Michael

doi:10.1007/s12145-015-0231-5

Cited by 25 publications

(31 citation statements)

References 47 publications

(49 reference statements)

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“…However science is innovative by nature and thus research data as a product of science is dynamic, especially high volume data released early in the project, like in the Coupled Model Intercomparison Project (CMIP, 2017). Klump et al (2016) suggest to characterize the kind of dynamic data changes as growing, evolving and fragmented datasets, where data is appended for growing datasets, data is revised and new versions are appended for evolving datasets, and data is partly replaced for fragmented datasets. Hereafter, only growing and evolving datasets will be discussed using the more general term evolving data.…”

Section: Citations Of Static and Evolving Datamentioning

confidence: 99%

CMIP6 Data Citation of Evolving Data

Stockhause

Lautenschlager

2017

Data Science Journal

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Data citations have become widely accepted. Technical infrastructures as well as principles and recommendations for data citation are in place but best practices or guidelines for their implementation are not yet available. On the other hand, the scientific climate community requests early citations on evolving data for credit, e.g. for CMIP6 (Coupled Model Intercomparison Project Phase 6). The data citation concept for CMIP6 is presented. The main challenges lie in limited resources, a strict project timeline and the dependency on changes of the data dissemination infrastructure ESGF (Earth System Grid Federation) to meet the data citation requirements. Therefore a pragmatic, flexible and extendible approach for the CMIP6 data citation service was developed, consisting of a citation for the full evolving data superset and a data cart approach for citing the concrete used data subset. This two citation approach can be implemented according to the RDA recommendations for evolving data. Because of resource constraints and missing project policies, the implementation of the second part of the citation concept is postponed to CMIP7.

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Section: Citations Of Static and Evolving Datamentioning

confidence: 99%

CMIP6 Data Citation of Evolving Data

Stockhause

Lautenschlager

2017

Data Science Journal

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“…Computational solutions for data citation often rely on persistent identifiers such as Digital Object Identifiers (DOI), Persistent Uniform Resource Locator (PURL) and the Archival Resource Key (ARK) [22, 32]. While persistent identifiers enable the data to be located and have associated guarantees of persistence (fixity), they do not constitute a full-fledged solution for data citation.…”

Section: Related Workmentioning

confidence: 99%

Automating Data Citation: The eagle-i Experience

Alawini¹,

Chen²,

Davidson³

et al. 2017

2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Data citation is of growing concern for owners of curated databases, who wish to give credit to the contributors and curators responsible for portions of the dataset and enable the data retrieved by a query to be later examined. While several databases specify how data should be cited, they leave it to users to manually construct the citations and do not generate them automatically. We report our experiences in automating data citation for an RDF dataset called eagle-i, and discuss how to generalize this to a citation framework that can work across a variety of different types of databases (e.g. relational, XML, and RDF). We also describe how a database administrator would use this framework to automate citation for a particular dataset.

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“…Fifthly, the persistence of identifiers for geoscientific data is especially problematic [42]. Geoscientific data can be highly dynamic and corrections or reprocessing is not unusual.…”

Section: Specifics Of Geographic Research Data Managementmentioning

confidence: 99%

Research Data Management Training for Geographers: First Impressions

Helbig

2016

IJGI

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Abstract:Sharing and secondary analysis of data have become increasingly important for research. Especially in geography, the collection of digital data has grown due to technological changes. Responsible handling and proper documentation of research data have therefore become essential for funders, publishers and higher education institutions. To achieve this goal, universities offer support and training in research data management. This article presents the experiences of a pilot workshop in research data management, especially for geographers. A discipline-specific approach to research data management training is recommended. The focus of this approach increases researchers' interest and allows for more specific guidance. The instructors identified problems and challenges of research data management for geographers. In regards to training, the communication of benefits and reaching the target groups seem to be the biggest challenges. Consequently, better incentive structures as well as communication channels have to be established.

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DOI for geoscience data - how early practices shape present perceptions

Cited by 25 publications

References 47 publications

CMIP6 Data Citation of Evolving Data

CMIP6 Data Citation of Evolving Data

Automating Data Citation: The eagle-i Experience

Research Data Management Training for Geographers: First Impressions

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