Analogies in science and science teaching

Analogies are often used in science, but students may not appreciate their significance, and so the analogies can be misunderstood or discounted. For this reason, educationalists often express concern about the use of analogies in teaching. Given the important place of analogies in the discourse of science, it is necessary that students are explicitly shown how they work, perhaps based on the structure-mapping theory we outline here. When using an analogy, the teacher should very clearly specify both its components and its limitations. Great care is required in developing an analogy to ensure that it is understood as intended and that misconceptions are minimized. This approach models the behavior of a scientist, which helps to develop student understanding of the practice of science.     

Data-intensive science applied to broad-scale citizen science

Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers ('citizen-scientists') to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.     

Master of laboratory animal science program: teaching the science of laboratory animal science

The administrators of the only program of its kind in the US explain the demand for a Master's degree in Laboratory Animal Science, the value of the degree, and plans to expand the program to make it available for distance learning.     

神经科学：脑研究的综合学科

目录一、神经科学对社会和科学发展的意义（一）神经科学对社会健康的意义（二）神经科学对社会发展的意义（三）神经科学发展的科学意义（四）国际社会和国际科技界对神经科学的重视二、神经科学前沿简介（一）脑的高级功能（二）脑和神经系统的疾病（三）脑发育的分子原...     

Niche science

Studies on stem cell aging are uncovering molecular mechanisms of regenerative decline, providing new insight into potential rejuvenating therapies.     

The art and science of multi-scale citizen science support

Citizen science and community-based monitoring programs are increasing in number and breadth, generating volumes of scientific data. Many programs are ill-equipped to effectively manage these data. We examined the art and science of multi-scale citizen science support, focusing on issues of integration and flexibility that arise for data management when programs span multiple spatial, temporal, and social scales across many domains. Our objectives were to: (1) briefly review existing citizen science approaches and data management needs; (2) propose a framework for multi-scale citizen science support; (3) develop a cyber-infrastructure to support citizen science program needs; and (4) describe lessons learned. We find that approaches differ in scope, scale, and activities and that the proposed framework situates programs while guiding cyber-infrastructure system development. We built a cyber-infrastructure support system for citizen science programs (www.citsci.org) and show that carefully designed systems can be adept enough to support programs at multiple spatial and temporal scales across many domains when built with a flexible architecture. The advantage of a flexible, yet controlled, cyber-infrastructure system lies in the ability of users with different levels of permission to easily customize the features themselves, while adhering to controlled vocabularies necessary for cross-discipline comparisons and meta-analyses. Program evaluation tied to this framework and integrated into cyber-infrastructure support systems will improve our ability to track effectiveness. We compare existing systems and discuss the importance of standards for interoperability and the challenges associated with system maintenance and long-term support. We conclude by offering a vision of the future of citizen science data management and cyber-infrastructure support.