13,604 research outputs found
Enabling collaborative numerical modeling in earth sciences using knowledge infrastructure
Knowledge Infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use Knowledge Infrastructure to address common barriers to entry to numerical modeling in Earth sciences: computational modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) a community-supported collaborative web platform; 3) distributed data storage; 4) a software environment; 5) a personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research demonstrating how to use Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools
Development of Grid e-Infrastructure in South-Eastern Europe
Over the period of 6 years and three phases, the SEE-GRID programme has
established a strong regional human network in the area of distributed
scientific computing and has set up a powerful regional Grid infrastructure. It
attracted a number of user communities and applications from diverse fields
from countries throughout the South-Eastern Europe. From the infrastructure
point view, the first project phase has established a pilot Grid infrastructure
with more than 20 resource centers in 11 countries. During the subsequent two
phases of the project, the infrastructure has grown to currently 55 resource
centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16
participating countries. Inclusion of new resource centers to the existing
infrastructure, as well as a support to new user communities, has demanded
setup of regionally distributed core services, development of new monitoring
and operational tools, and close collaboration of all partner institution in
managing such a complex infrastructure. In this paper we give an overview of
the development and current status of SEE-GRID regional infrastructure and
describe its transition to the NGI-based Grid model in EGI, with the strong SEE
regional collaboration.Comment: 22 pages, 12 figures, 4 table
Models of everywhere revisited: a technological perspective
The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software
This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
GTTC Future of Ground Testing Meta-Analysis of 20 Documents
National research, development, test, and evaluation ground testing capabilities in the United States are at risk. There is a lack of vision and consensus on what is and will be needed, contributing to a significant threat that ground test capabilities may not be able to meet the national security and industrial needs of the future. To support future decisions, the AIAA Ground Testing Technical Committees (GTTC) Future of Ground Test (FoGT) Working Group selected and reviewed 20 seminal documents related to the application and direction of ground testing. Each document was reviewed, with the content main points collected and organized into sections in the form of a gap analysis current state, future state, major challenges/gaps, and recommendations. This paper includes key findings and selected commentary by an editing team
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
D19 final plan for using and disseminating knowledge
This document presents the Final Plan for Using and Disseminating Knowledge acquired throughout the development of the CYCLOPS project as deliverable D19. It includes a description of the main achievements in disseminating knowledge, and the consortium and each participant’s plans for the exploitation of the results for the consortium as a whole, or for individual participants or groups of participants. It updates the Plan for Using and Disseminating Knowledge that was presented as Deliverable D4 and describes the final dissemination plan of the CYCLOPS project.
This deliverable provides a strategy aimed at addressing various target communities in order to achieve the project dissemination and exploitation goals. After an update of the dissemination instruments employed, the deliverable focuses on the description of the dissemination activities carried out.
In addition to the normal dissemination and exploitation of the work through scientific journals and professional bodies, Civil Protection Community will be specifically targeted for dissemination of the CYCLOPS deliverables, and their future exploitation of the results.
Other written deliverables focus on presenting dissemination activities in specific subject areas. In particular deliverable D17 reports “the results of the dissemination of EGEE towards the Civil Protection community, and about the coordination between the EGEE and CYCLOPS activities”, deliverable D18 focuses on “collecting the CYCLOPS project results for dissemination towards different interested audiences such as Grid communities, other Civil protection agencies, but also national and international initiative and projects, SMEs, etc.” and deliverable D20 that reports “the extent to which actors beyond the research community have been involved to help spread awareness and to explore the wider societal implications of the proposed work
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