10,566 research outputs found
Materials Cloud, a platform for open computational science
Materials Cloud is a platform designed to enable open and seamless sharing of
resources for computational science, driven by applications in materials
modelling. It hosts 1) archival and dissemination services for raw and curated
data, together with their provenance graph, 2) modelling services and virtual
machines, 3) tools for data analytics, and pre-/post-processing, and 4)
educational materials. Data is citable and archived persistently, providing a
comprehensive embodiment of the FAIR principles that extends to computational
workflows. Materials Cloud leverages the AiiDA framework to record the
provenance of entire simulation pipelines (calculations performed, codes used,
data generated) in the form of graphs that allow to retrace and reproduce any
computed result. When an AiiDA database is shared on Materials Cloud, peers can
browse the interconnected record of simulations, download individual files or
the full database, and start their research from the results of the original
authors. The infrastructure is agnostic to the specific simulation codes used
and can support diverse applications in computational science that transcend
its initial materials domain.Comment: 19 pages, 8 figure
Viewpoint | Personal Data and the Internet of Things: It is time to care about digital provenance
The Internet of Things promises a connected environment reacting to and
addressing our every need, but based on the assumption that all of our
movements and words can be recorded and analysed to achieve this end.
Ubiquitous surveillance is also a precondition for most dystopian societies,
both real and fictional. How our personal data is processed and consumed in an
ever more connected world must imperatively be made transparent, and more
effective technical solutions than those currently on offer, to manage personal
data must urgently be investigated.Comment: 3 pages, 0 figures, preprint for Communication of the AC
Designing Traceability into Big Data Systems
Providing an appropriate level of accessibility and traceability to data or
process elements (so-called Items) in large volumes of data, often
Cloud-resident, is an essential requirement in the Big Data era.
Enterprise-wide data systems need to be designed from the outset to support
usage of such Items across the spectrum of business use rather than from any
specific application view. The design philosophy advocated in this paper is to
drive the design process using a so-called description-driven approach which
enriches models with meta-data and description and focuses the design process
on Item re-use, thereby promoting traceability. Details are given of the
description-driven design of big data systems at CERN, in health informatics
and in business process management. Evidence is presented that the approach
leads to design simplicity and consequent ease of management thanks to loose
typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International
Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore
July 2015. arXiv admin note: text overlap with arXiv:1402.5764,
arXiv:1402.575
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