The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping sub- communities within a research specialty and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we aim to increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set in order to adequately represent a research specialty, and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS

Report from the Tri-Agency Cosmological Simulation Task Force

The Tri-Agency Cosmological Simulations (TACS) Task Force was formed when Program Managers from the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF) expressed an interest in receiving input into the cosmological simulations landscape related to the upcoming DOE/NSF Vera Rubin Observatory (Rubin), NASA/ESA's Euclid, and NASA's Wide Field Infrared Survey Telescope (WFIRST). The Co-Chairs of TACS, Katrin Heitmann and Alina Kiessling, invited community scientists from the USA and Europe who are each subject matter experts and are also members of one or more of the surveys to contribute. The following report represents the input from TACS that was delivered to the Agencies in December 2018.Comment: 36 pages, 3 figures. Delivered to NASA, NSF, and DOE in Dec 201

Assessing Researcher Interdisciplinarity: A Case Study of the University of Hawaii NASA Astrobiology Institute

In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential Information Bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate ISI Web of Knowledge subject categories as descriptive labels for astrobiology documents, and to assess individual researcher interdisciplinarity to determine where collabo- ration opportunities might occur. We find that the majority of the UHNAI team is engaged in interdisciplinary research, and suggest that our method could be applied to additional NASA Astrobiology Institute teams to identify and facilitate collaboration opportunities

A comprehensive review of industrial symbiosis

Industrial symbiosis, which allows entities and companies that traditionally be separated, to cooperate among them in the sharing of resources, contributes to the increase of sustainability with environmental, economic and social benefits. Examples of industrial symbiosis have grown over the years with increasing geographic dispersion. Thus, through a comprehensive review of previous studies, this work aims to trace the trend of industrial symbiosis research and to map the existing case studies around the world, with a critical analysis of its impact. The analysis of the 584 selected publications allowed tracing the evolution of these according to their content and the type of article, as well as its distribution by journals. Based on the literature review, the main lines for research in industrial symbiosis are assessed, as well as an updated study of the published case studies is provided with emphasis on the location, type of industry and employed methodologies. Several challenges are then identified for future research. The results reveal the number of articles on industrial symbiosis has greatly increased since 2007 and China is the country with the largest number of publications and cases of industrial symbiosis, followed by the United States. The methods for quantifying impacts and analysing industrial symbiosis networks were the most widely used. The analysis of the published case studies allowed an overview of the industrial symbiosis in the world and showed that the potential for application is enormous, both in developed countries and in countries with developing economies, and although the most present economic activities in the synergies are associated with the manufacturing sector, the possibilities of industrial symbiosis are not restricted to these activities nor to the number of entities involved. The symbioses between industry and the surrounding community also have great potential for development with numerous advantages for both parties.publishe

Sustainability Assessment and Engineering of Emerging Aircraft Technologies: Challenges, Methods and Tools

Driven by concerns regarding the sustainability of aviation and the continued growth of air traffic, increasing interest is given to emerging aircraft technologies. Although new technologies, such as battery-electric propulsion systems, have the potential to minimise in-flight emissions and noise, environmental burdens are possibly shifted to other stages of the aircraft’s life cycle, and new socio-economic challenges may arise. Therefore, a life-cycle-oriented sustainability assessment is required to identify these hotspots and problem shifts and to derive recommendations for action for aircraft development at an early stage. This paper proposes a framework for the modelling and assessment of future aircraft technologies and provides an overview of the challenges and available methods and tools in this field. A structured search and screening process is used to determine which aspects of the proposed framework are already addressed in the scientific literature and in which areas research is still needed. For this purpose, a total of 66 related articles are identified and systematically analysed. Firstly, an overview of statistics of papers dealing with life-cycle-oriented analysis of conventional and emerging aircraft propulsion systems is given, classifying them according to the technologies considered, the sustainability dimensions and indicators investigated, and the assessment methods applied. Secondly, a detailed analysis of the articles is conducted to derive answers to the defined research questions. It illustrates that the assessment of environmental aspects of alternative fuels is a dominating research theme, while novel approaches that integrate socio-economic aspects and broaden the scope to battery-powered, fuel-cell-based, or hybrid-electric aircraft are emerging. It also provides insights by what extent future aviation technologies can contribute to more sustainable and energy-efficient aviation. The findings underline the need to harmonise existing methods into an integrated modelling and assessment approach that considers the specifics of upcoming technological developments in aviation

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

While it is well-known and acknowledged that the performance of graph algorithms is heavily dependent on the input data, there has been surprisingly little research to quantify and predict the impact the graph structure has on performance. Parallel graph algorithms, running on many-core systems such as GPUs, are no exception: most research has focused on how to efficiently implement and tune different graph operations on a specific GPU. However, the performance impact of the input graph has only been taken into account indirectly as a result of the graphs used to benchmark the system. In this work, we present a case study investigating how to use the properties of the input graph to improve the performance of the breadth-first search (BFS) graph traversal. To do so, we first study the performance variation of 15 different BFS implementations across 248 graphs. Using this performance data, we show that significant speed-up can be achieved by combining the best implementation for each level of the traversal. To make use of this data-dependent optimization, we must correctly predict the relative performance of algorithms per graph level, and enable dynamic switching to the optimal algorithm for each level at runtime. We use the collected performance data to train a binary decision tree, to enable high-accuracy predictions and fast switching. We demonstrate empirically that our decision tree is both fast enough to allow dynamic switching between implementations, without noticeable overhead, and accurate enough in its prediction to enable significant BFS speedup. We conclude that our model-driven approach (1) enables BFS to outperform state of the art GPU algorithms, and (2) can be adapted for other BFS variants, other algorithms, or more specific datasets

Assessing the bibliometric productivity of forest scientists in Italy

Since 2010, the Italian Ministry of University and Research issued new evaluation protocols to select candidates for University professorships and assess the bibliometric productivity of Universities and Research Institutes based on bibliometric indicators, i.e. scientific paper and citation numbers and the h-index. Under this framework, the objective of this study was to quantify the bibliometric productivity of the Italian forest research community during the 2002-2012 period. We examined the following productivity parameters: (i) the bibliometric productivity under the Forestry subject category at the global level; (ii) compared the aggregated bibliometric productivity of Italian forest scientists with scientists from other countries; (iii) analyzed publication and citation temporal trends of Italian forest scientists and their international collaborations; and (iv) characterized productivity distribution among Italian forest scientists at different career levels. Results indicated the following: (i) the UK is the most efficient country based on the ratio between Gross Domestic Spending (GDS) on Research and Development (R&D) and bibliometric productivity under the Forestry subject category, followed by Italy; (ii) Italian forest scientist productivity exhibited a significant positive time trend, but was characterized by high inequality across authors; (iii) one-half of the Italian forest scientist publications were written in collaboration with foreign scientists; (iv) a strong relationship exists between bibliometric indicators calculated by WOS and SCOPUS, suggesting these two databases have the same potential to evaluate the forestry research community; and (v) self-citations did not significantly affect the rank of Italian forest scientists