1,627 research outputs found
Sciunits: Reusable Research Objects
Science is conducted collaboratively, often requiring knowledge sharing about
computational experiments. When experiments include only datasets, they can be
shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers
(DOIs). An experiment, however, seldom includes only datasets, but more often
includes software, its past execution, provenance, and associated
documentation. The Research Object has recently emerged as a comprehensive and
systematic method for aggregation and identification of diverse elements of
computational experiments. While a necessary method, mere aggregation is not
sufficient for the sharing of computational experiments. Other users must be
able to easily recompute on these shared research objects. In this paper, we
present the sciunit, a reusable research object in which aggregated content is
recomputable. We describe a Git-like client that efficiently creates, stores,
and repeats sciunits. We show through analysis that sciunits repeat
computational experiments with minimal storage and processing overhead.
Finally, we provide an overview of sharing and reproducible cyberinfrastructure
based on sciunits gaining adoption in the domain of geosciences
The lifecycle of provenance metadata and its associated challenges and opportunities
This chapter outlines some of the challenges and opportunities associated
with adopting provenance principles and standards in a variety of disciplines,
including data publication and reuse, and information sciences
The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web
Research in life sciences is increasingly being conducted in a digital and
online environment. In particular, life scientists have been pioneers in
embracing new computational tools to conduct their investigations. To support
the sharing of digital objects produced during such research investigations, we
have witnessed in the last few years the emergence of specialized repositories,
e.g., DataVerse and FigShare. Such repositories provide users with the means to
share and publish datasets that were used or generated in research
investigations. While these repositories have proven their usefulness,
interpreting and reusing evidence for most research results is a challenging
task. Additional contextual descriptions are needed to understand how those
results were generated and/or the circumstances under which they were
concluded. Because of this, scientists are calling for models that go beyond
the publication of datasets to systematically capture the life cycle of
scientific investigations and provide a single entry point to access the
information about the hypothesis investigated, the datasets used, the
experiments carried out, the results of the experiments, the people involved in
the research, etc. In this paper we present the Research Object (RO) suite of
ontologies, which provide a structured container to encapsulate research data
and methods along with essential metadata descriptions. Research Objects are
portable units that enable the sharing, preservation, interpretation and reuse
of research investigation results. The ontologies we present have been designed
in the light of requirements that we gathered from life scientists. They have
been built upon existing popular vocabularies to facilitate interoperability.
Furthermore, we have developed tools to support the creation and sharing of
Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page
Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning
OpenMOLE is a scientific workflow engine with a strong emphasis on workload
distribution. Workflows are designed using a high level Domain Specific
Language (DSL) built on top of Scala. It exposes natural parallelism constructs
to easily delegate the workload resulting from a workflow to a wide range of
distributed computing environments. In this work, we briefly expose the strong
assets of OpenMOLE and demonstrate its efficiency at exploring the parameter
set of an agent simulation model. We perform a multi-objective optimisation on
this model using computationally expensive Genetic Algorithms (GA). OpenMOLE
hides the complexity of designing such an experiment thanks to its DSL, and
transparently distributes the optimisation process. The example shows how an
initialisation of the GA with a population of 200,000 individuals can be
evaluated in one hour on the European Grid Infrastructure.Comment: IEEE High Performance Computing and Simulation conference 2015, Jun
2015, Amsterdam, Netherland
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