Victoria Bennett

Storing and manipulating environmental big data with JASMIN

JASMIN is a super-data-cluster designed to provide a high-performance high-volume data analysis environment for the UK environmental science community. Thus far JASMIN has been used primarily by the atmospheric science and earth observation communities, both to support their direct scientific workflow, and the curation of data products in the STFC Centre for Environmental Data Archival (CEDA). Initial JASMIN configuration and first experiences are reported here. Useful improvements in scientific workflow are presented. It is clear from the explosive growth in stored data and use that there was a pent up demand for a suitable big-data analysis environment. This demand is not yet satisfied, in part because JASMIN does not yet have enough compute, the storage is fully allocated, and not all software needs are met. Plans to address these constraints are introduced.

Understanding Climate Data Through Commentary Metadata: The CHARMe Project

We describe the CHARMe project, which aims to link climate datasets with publications, user feedback and other items of “commentary metadata”. The system will help users learn from previous community experience and select datasets that best suit their needs, as well as providing direct traceability between conclusions and the data that supported them. The project applies the principles of Linked Data and adopts the Open Annotation standard to record and publish commentary information. CHARMe contributes to the emerging landscape of “climate services”, which will provide climate data and information to influence policy and decision-making. Although the project focuses on climate science, the technologies and concepts are very general and could be applied to other fields.

Capturing and sharing our collective expertise on climate data: the CHARMe project

AbstractFor users of climate services, the ability to quickly determine the datasets that best fit one’s needs would be invaluable. The volume, variety, and complexity of climate data makes this judgment difficult. The ambition of CHARMe (Characterization of metadata to enable high-quality climate services) is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports, or feedback on previous applications of the data. The capture and discovery of this “commentary” information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search, and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems ...

On the Use of In-Memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets

The need to apply complex algorithms on large volumes of data is boosting the development of technological solutions able to satisfy big data analytics needs in Cloud and HPC environments. In this context Ophidia represents a big data analytics framework for eScience offering a cross-domain solution for managing scientific, multi-dimensional data. It also exploits an in-memory-based distributed data storage and provides support for the submission of complex workflows by means of various interfaces compliant to well-known standards. This paper presents some applications of Ophidia for the computation of climate indicators defined in the CLIPC project, the WPS interface used for the submission and the workflow based approach employed.