CMIP6 Data Citation of Evolving Data

Stockhause, Martina; Lautenschlager, Michael

doi:10.5334/dsj-2017-030

Cited by 26 publications

(22 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Datasets used by the model for the configuration of model inputs (Input Datasets for Model Intercomparison Projects) input4MIPs, see Durack et al, 2018) as well as observations for the comparison with models (Observations for Model Intercomparison Projects) obs4MIPs, see Teixeira et al, 2014;Ferraro et al, 2015) are both now organized in the same way, and share many of the naming and metadata conventions as the CMIP model output itself. The coherence of standards across model inputs, outputs, and observational datasets is a development that will enable the community to build a rich toolset across all of these datasets.…”

Section: Model Inputsmentioning

confidence: 99%

Requirements for a global data infrastructure in support of CMIP6

et al. 2018

Self Cite

View full text Add to dashboard Cite

The World Climate Research Programme (WCRP)'s Working Group on Climate Modelling (WGCM) Infrastructure Panel (WIP) was formed in 2014 in response to the explosive growth in size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005)(2006) and CMIP5 (2011CMIP5 ( -2012. This article presents the WIP recommendations for the global data infrastructure needed to support CMIP design, future growth, and evolution. Developed in close coordination with those who build and run the existing infrastructure (the Earth System Grid Federation; ESGF), the recommendations are based on several principles beginning with the need to separate requirements, implementation, and operations. Other important principles include the consideration of the diversity of community needs around data -a data ecosystem -the importance of provenance, the need for automation, and the obligation to measure costs and benefits.This paper concentrates on requirements, recognizing the diversity of communities involved (modelers, analysts, software developers, and downstream users). Such requirements include the need for scientific reproducibility and account-ability alongside the need to record and track data usage. One key element is to generate a dataset-centric rather than system-centric focus, with an aim to making the infrastructure less prone to systemic failure.With these overarching principles and requirements, the WIP has produced a set of position papers, which are summarized in the latter pages of this document. They provide specifications for managing and delivering model output, including strategies for replication and versioning, licensing, data quality assurance, citation, long-term archiving, and dataset tracking. They also describe a new and more formal approach for specifying what data, and associated metadata, should be saved, which enables future data volumes to be estimated, particularly for well-defined projects such as CMIP6.The paper concludes with a future facing consideration of the global data infrastructure evolution that follows from the blurring of boundaries between climate and weather, and the changing nature of published scientific results in the digital age.

show abstract

Section: Model Inputsmentioning

confidence: 99%

Requirements for a global data infrastructure in support of CMIP6

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Earth Observation domain is handling data curation in different principles than the climate model domain. Stockhause et al [16] give a detailed overview of the evolving data characteristics and compare the different approaches.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Data Citation Service—Subset Tool for Operational Data Management

Schubert¹,

Seyerl²,

Sack

2019

Data

View full text Add to dashboard Cite

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of “dynamic data citation”. The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.

show abstract

“…ESGF allows data access by scientists globally, promoting collaboration which is needed for comparison of the model output. These data are held across multiple data centres around the world, CEDA being the representative for the UK (Stockhause and Lautenschlager, 2017). As such it is crucial to have standards across the data formats, file naming conventions and metadata.…”

Section: Variety: Data Standards and Formatsmentioning

confidence: 99%