18,292 research outputs found
Integrating Emerging Areas of Nursing Science into PhD Programs
The Council for the Advancement of Nursing Science aims to āfacilitate and recognize life-long nursing science career developmentā as an important part of its mission. In light of fast-paced advances in science and technology that are inspiring new questions and methods of investigation in the health sciences, the Council for the Advancement of Nursing Science convened the Idea Festival for Nursing Science Education and appointed the Idea Festival Advisory Committee to stimulate dialogue about linking PhD education with a renewed vision for preparation of the next generation of nursing scientists. Building on the 2010 American Association of Colleges of Nursing Position Statement āThe Research-Focused Doctoral Program in Nursing: Pathways to Excellence,ā Idea Festival Advisory Committee members focused on emerging areas of science and technology that impact the ability of research-focused doctoral programs to prepare graduates for competitive and sustained programs of nursing research using scientific advances in emerging areas of science and technology. The purpose of this article is to describe the educational and scientific contexts for the Idea Festival, which will serve as the foundation for recommendations for incorporating emerging areas of science and technology into research-focused doctoral programs in nursing
Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices
Integrative biological simulations have a varied and controversial history in
the biological sciences. From computational models of organelles, cells, and
simple organisms, to physiological models of tissues, organ systems, and
ecosystems, a diverse array of biological systems have been the target of
large-scale computational modeling efforts. Nonetheless, these research agendas
have yet to prove decisively their value among the broader community of
theoretical and experimental biologists. In this commentary, we examine a range
of philosophical and practical issues relevant to understanding the potential
of integrative simulations. We discuss the role of theory and modeling in
different areas of physics and suggest that certain sub-disciplines of physics
provide useful cultural analogies for imagining the future role of simulations
in biological research. We examine philosophical issues related to modeling
which consistently arise in discussions about integrative simulations and
suggest a pragmatic viewpoint that balances a belief in philosophy with the
recognition of the relative infancy of our state of philosophical
understanding. Finally, we discuss community workflow and publication practices
to allow research to be readily discoverable and amenable to incorporation into
simulations. We argue that there are aligned incentives in widespread adoption
of practices which will both advance the needs of integrative simulation
efforts as well as other contemporary trends in the biological sciences,
ranging from open science and data sharing to improving reproducibility.Comment: 10 page
2011 Strategic roadmap for Australian research infrastructure
The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australiaās research capacity and improve innovation and
research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups
It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation.
The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a āProcess to identify and prioritise Australian Government landmark research infrastructure investmentsā which is currently under consideration by the government as part of broader deliberations relating to research infrastructure.
NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure
Bridging the biodiversity data gaps: Recommendations to meet usersā data needs
A strong case has been made for freely available, high quality data on species occurrence, in order to track changes in biodiversity. However, one of the main issues surrounding the provision of such data is that sources vary in quality, scope, and accuracy. Therefore publishers of such data must face the challenge of maximizing quality, utility and breadth of data coverage, in order to make such data useful to users. Here, we report a number of recommendations that stem from a content need assessment survey conducted by the Global Biodiversity Information Facility (GBIF). Through this survey, we aimed to distil the main user needs regarding biodiversity data. We find a broad range of recommendations from the survey respondents, principally concerning issues such as data quality, bias, and coverage, and extending ease of access. We recommend a candidate set of actions for the GBIF that fall into three classes: 1) addressing data gaps, data volume, and data quality, 2) aggregating new kinds of data for new applications, and 3) promoting ease-of-use and providing incentives for wider use. Addressing the challenge of providing high quality primary biodiversity data can potentially serve the needs of many international biodiversity initiatives, including the new 2020 biodiversity targets of the Convention on Biological Diversity, the emerging global biodiversity observation network (GEO BON), and the new Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES)
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Phylogenetic comparative analysis is an approach to inferring evolutionary
process from a combination of phylogenetic and phenotypic data. The last few
years have seen increasingly sophisticated models employed in the evaluation of
more and more detailed evolutionary hypotheses, including adaptive hypotheses
with multiple selective optima and hypotheses with rate variation within and
across lineages. The statistical performance of these sophisticated models has
received relatively little systematic attention, however. We conducted an
extensive simulation study to quantify the statistical properties of a class of
models toward the simpler end of the spectrum that model phenotypic evolution
using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and
why these methods break down so that users can apply them with greater
understanding of their strengths and weaknesses. Our analysis identifies three
key determinants of performance: a discriminability ratio, a signal-to-noise
ratio, and the number of taxa sampled. Interestingly, we find that
model-selection power can be high even in regions that were previously thought
to be difficult, such as when tree size is small. On the other hand, we find
that model parameters are in many circumstances difficult to estimate
accurately, indicating a relative paucity of information in the data relative
to these parameters. Nevertheless, we note that accurate model selection is
often possible when parameters are only weakly identified. Our results have
implications for more sophisticated methods inasmuch as the latter are
generalizations of the case we study.Comment: 38 pages, in press at Systematic Biolog
Identifying features predictive of faculty integrating computation into physics courses
Computation is a central aspect of 21st century physics practice; it is used
to model complicated systems, to simulate impossible experiments, and to
analyze mountains of data. Physics departments and their faculty are
increasingly recognizing the importance of teaching computation to their
students. We recently completed a national survey of faculty in physics
departments to understand the state of computational instruction and the
factors that underlie that instruction. The data collected from the faculty
responding to the survey included a variety of scales, binary questions, and
numerical responses. We then used Random Forest, a supervised learning
technique, to explore the factors that are most predictive of whether a faculty
member decides to include computation in their physics courses. We find that
experience using computation with students in their research, or lack thereof
and various personal beliefs to be most predictive of a faculty member having
experience teaching computation. Interestingly, we find demographic and
departmental factors to be less useful factors in our model. The results of
this study inform future efforts to promote greater integration of computation
into the physics curriculum as well as comment on the current state of
computational instruction across the United States
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