Science of Scientific Team Science: A survey

Scientific teamwork collaboration is an integral element of the scientific process that often leads to significant findings. Systematic analysis of scientific teamwork collaboration continues to influence both the advance in science and knowledge production. This paper presents an overview of Science of Scientific Team Science (SSTS). SSTS explores the behaviors and attributes of teamwork and team-based collaboration specific to scientific teams from the perspective of quantitative analysis, which refers to a branch of science that analyzes and discovers scientific collaboration patterns inter- or extra-team. Aiming at assisting scientific team formation, improving collaboration environment, evaluating team performance, and fostering collaborative behaviors, this survey presents an overview in SSTS. Theoretical background of SSTS at different team development stages has been discussed. In addition, three classifications of SSTS, including interdisciplinary, multidisciplinary, and transdisciplinary research approaches have been investigated. Their associated similarities and differences, challenges and benefits, are also examined. This paper also summarizes web-based tools that enhance one’s understanding and opinion of SSTS. Key technologies and open issues are then discussed. The association among scientific collaboration, scientific teamwork, SSTS, and cross-disciplinary research gives rise to critical implications for scholars who wish to employ and invest in those issues.     

Service Science

This paper is a first exploration of the relationship between service science and Grid computing. Service science is the study of value co-creation interactions among entities, known as service systems. Within the emerging service science community, service is often defined as the application of competences (resources) for the benefit of another. Grid computing is the study of resource sharing among entities, known as virtual organizations, which solve complex business, societal, scientific, and engineering problems. Within the Grid computing community, service is sometimes defined as protocols plus behavior. Both Grid computing and service science are connecting academic, industry, government, and volunteer sector collaborators on a range of projects including eScience, healthcare, environmental sustainability, and more. This paper compares and contrasts the notions of resource, entity, service, interaction, and success criteria for the two areas of study. In conclusion, new areas for collaborative inquiry are proposed.     

Judging Science Fairs

Brilliant engineering students are as creative as experienced engineers in industry, but they lack the experience to know how to apply their creativity. All things considered, the best strategy is to judge submissions to a science fair effort in engineering at the high school or college level both relative to each other and absolutely, against the highest conceivable standards.     

Infrastructuring for Cross-Disciplinary Synthetic Science: Meta-Study Research in Land System Science

Traditionally infrastructure studies are post-hoc analyses of emergent phenomena. While acknowledging the contextual complexity of co-evolution, there has been a turn toward exploring these processes from a design perspective. In this paper we examine a new interdiscipline, Land System Science, whose scientific inquiry is predicated on a deep and ongoing integration of radically disparate data from across the natural, physical, and social sciences. We report the results of a three-and-a half year field study of meta-study practice. In doing so, we perform infrastructural inversion to foreground the backstage scientific work practice to identify points of infrastructure. We used these insights regarding breakdowns and workarounds to inform the design of GLOBE, infrastructural tools that support this community’s needs for communication, cooperation, and knowledge construction. Our insight comes from being embedded both with domain scientists and software developers. Through four cases, we highlight the scientists’ unique challenges, strategies developed to address them, and the system components designed to better support many of these tactics. Specifically, we address the difficulties of finding, standardizing, interpreting, and validating data. This advances the infrastructuring literature by illustrating how design can be used to engage a scientific community in active self-reflection.     

The DECIDE Science Gateway

The motivation of this work fits with the general vision to enable e-health for European citizens, irrespective of their social and financial status and their place of residence. Services to be provided include access to a high-quality early diagnostic and prognostic service for the Alzheimer Disease and other forms of dementia, based both on the European Research and Education Networks and the European Grid Infrastructure. The present paper reports on the architecture and services of a Science Gateway developed in the context of the DECIDE project, which aims to support the medical community in its daily duties of patients’ examination and diagnosis. The implementation of the Science Gateway is described with particular focus on the standard technologies adopted to ease the access by non IT-.expert users. The work leverages on an authentication and authorization infrastructure based on Identity Federations and robot certificates and on the adoption of the SAGA standard for middleware-independent Grid interaction. The architecture and the functionalities of the digital repository for medical image storage and analysis are also presented.     

神经计算科学的展望

神经计算科学是从信息科学的角度来研究如何加速神经网络模仿和延伸 人脑的高级精神活动,如联想,记忆,推理,思维及意识等智能行为,这涉及到脑科学,认知科学,神经生物学,非线性科学,计算机科学,数学,物理学诸学科的综合集成,它是综合研究和实现类脑智能信息系统的一个新思想和新策略,本文仅重点论述神经智能信息处理,盲信号处理技术,演化认知与进化计算,混沌信息技术及软科学的开发,由此展望神经计算科学的未来发展及潜力,可带动诸学科及信息技术的美好发展。     

Josephus问题分析与探讨

Josephus问题是一个经典的递归问题。本文从问题的编程解决入手，提出静态数组、环链表、递归等3种解题方案。本文还对问题作进一步分析，考虑解的稳定点,最后提出一个优化方案，将问题的解决归约到一个较小的规模。     

控制科学中的复杂性

首先讨论控制科学中的传统复杂性观点,复杂性主要根源于非线性,非定常性与不确 定性,高维数和系统模式的非单一性.其次对于大系统提出一些新问题,并且指出对于大系统 关联是产生复杂性又一主要根源.     

数据科学在国内管理学研究中的应用综述

基于大数据时代的新学科——数据科学的研究方法正在被包括管理学在内的其他学科应用。首先,探讨了以大数据为理论基础的数据科学研究范式与管理学研究的经典范式之间的联系和区别。其次,分析了国家自然科学基金委认定的A类重要管理学期刊文献及引证文献,对当前国内管理学领域为数不多的基于数据驱动的公共管理、基于复杂网络仿真的网络行为管理和基于多源数据融合的创新管理等热点领域进行了分类梳理。然后, 归纳总结了当前国内管理学领域采用数据科学研究方法的特征。最后,提出了数据科学在管理学科学研究应用中的趋势,即范式融合、大数据利用、场景融合、专家合作。     

Art and Science

Nexus Network Journal - .     

Mad Science in Art

Cover artist Mike Butler discusses how he's influenced by science fiction and artists to create his unique digital art.     

HB-1温度程序控制仪

介绍了一种用于低压铸造系统温度控制的程序温度控制仪，该程序温度控制仪具有操作简单、控温精度高的特点，可满足不同铸件的生产过程的温度控制要求。     

Software Science Applied to APL

Previous attempts to apply Halstead's software metrics to APL have led to inconsistent and counter-intuitive results. This work is a further investigation into the application of software metrics to APL to try to resolve some of the inconsistency. The effect of variations in the counting rules on values calculated for the software metrics was studied. These rules were used to analyze a set of programs from a previous study. In addition, a large number of APL programs from a university environment were analyzed. Evidence is presented that verifies that APL has a higher language level than any other common programming language previously studied. Counting monadic and dyadic uses of the same APL symbol as an instance of a different operator was found to have a significant effect on the language level calculated for APL. However, decomposing derived APL functions into separate operators did not seem to have a significant effect on language level.