Key components of data publishing: using current best practices to develop a reference model for data publishing

Austin, Claire C.; Bloom, Theodora; Dallmeier-Tiessen, Sünje; Khodiyar, Varsha; Murphy, Fiona; Nurnberger, Amy; Raymond, Lisa; Stockhause, Martina; Tedds, J. A.; Vardigan, Mary; Whyte, Angus

doi:10.1007/s00799-016-0178-2

Cited by 35 publications

(39 citation statements)

References 27 publications

(48 reference statements)

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“…The FORCE11 FAIR Data Publishing Group built on these the FAIR (Findable, Accessible, Interoperable, Re-usable) principles for data sharing (FORCE11, 2017). Additional standardization initiatives investigate the data publishing workflows and develop reference models, like the RDA/WDS Workflows Working Group (Austin et al, 2016). Others focus on data review and its integration in the scholarly publishing processing (Mayernik et al, 2015).…”

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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“…The group identified reference models that could be adopted by those venturing into data publishing for the first time and highlighted important gaps and challenges worthy of further consideration. [15] One of these gaps is the issue of identifying and exposing links between articles and datasets. This has been the focus of another RDA/WDS working group that has defined a high-level interoperability framework for scholarly link exchange called Scholix, [16] as well as a proof of concept Data-Literature Interlinking Service.…”

Section: Connecting Chemistry With Global Challenges Through Data Stamentioning

confidence: 99%

Connecting Chemistry with Global Challenges through Data Standards

Bruno¹,

Frey²

2017

Chemistry International

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The new millennium, now almost 20 years old, has been characterised by a recognition within the research community of the importance of the free flow of research data; not simply in the ability to access the data, but also in the understanding that this valuable resource needs to be reused and built upon. We believe there have been at least two main drivers for this. First, those who pay for the research want to know it is leading to useful outcomes with impact–the transparency and accountability agenda. Second is an appreciation that the major global concerns (food, health, climate, economy) are extraordinarily complex (‘wicked’) problems, [

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“…The release of research data, associated metadata, accompanying documentation, and software code -known as research data publication (Austin et al, 2016) -should be done in such a manner that these items can be discovered on the Web and referred to in a unique and persistent way (Austin et al, 2016). Currently, we have proven techniques for data publication, but lack connections between repositories and other platforms used "downstream" in the research cycle (Dallmeier-Tiessen et al, 2017).…”

Section: Motivating Examplementioning

confidence: 99%

Semantics all the way down: the Semantic Web and open science in big earth data

Narock

Shepherd

2017

Big Earth Data

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Semantic technologies have emerged as a prominent research area within Big Earth Data. These technologies have provided significant benefits for data discovery and integration. Yet, the formality of the Semantic Web, in languages such as the Web Ontology Language (OWL), does not always integrate well with the numerical, statistical, and geometric methods of the geosciences. Two prominent challenges in this area are how to semantically model individual measurements and what to do when geoscience needs are not addressed by languages such as OWL. This has led to a fragmented Big Earth Data community with either no solution or incompatible semantic solutions. We use an oceanographic example to highlight the limitations and challenges surrounding the semantic encoding of observations and the use of semantics during analysis. We then present potential solutions to each challenge showing that a full endto-end application of semantic technologies is not only feasible, but beneficial to Big Earth Data.

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Key components of data publishing: using current best practices to develop a reference model for data publishing

Cited by 35 publications

References 27 publications

CMIP6 Data Citation of Evolving Data

CMIP6 Data Citation of Evolving Data

Connecting Chemistry with Global Challenges through Data Standards

Semantics all the way down: the Semantic Web and open science in big earth data

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