1,721,181 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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Controls on Bacterial Functional Trait Expression with Carbon and Nutrient Cycling Consequences
Get PDFBacteria are key drivers of global biogeochemical cycling. By producing extracellular enzymes they are able to turnover organic matter so that it can be assimilated for new biomass or energy production. However, enzymes are metabolically expensive, resulting in decreased fitness with their production. How bacteria allocate their resources determines not only the rate at which carbon is cycled, but its fate in an ecosystem. Therefore, the central role that enzymes play in this process makes them an important research target. Understanding the mechanisms that impact the expression of extracellular enzymes is fundamental to predicting ecosystem carbon and nutrient cycling. In this dissertation, we began with a literature review that explores the role that microbial interactions play in soil carbon cycling dynamics through phenotypic plasticity and evolutionary processes. Next, to determine the physiological limitation of extracellular enzyme production and how bacteria allocate resources, we assessed the trade-offs between extracellular enzyme production and growth rate relative to resources across several strains of bacteria. Bacteria do trade off these traits, though selectively, and with a stronger effect in nutrient-poor media. Finally, we examined the functional capacity in a river system to determine if enzyme expression is determined by community structure, nutrients, or environmental parameters. Enzyme expression was most strongly determined by biofilm productivity, and had no relationship to alpha or beta diversity. While there is some evidence to suggest a phylogenetic signal of extracellular enzyme production, from the empirical results presented here, bacterial expression of extracellular enzymes, in both populations and communities, appears to be predominantly determined by nutrient parameters
Methods for Bayesian Inference and Data Assimilation of Soil Biogeochemical Models
Get PDFImproving mechanistic understanding and prediction capabilities of long-term organic soil system dynamics is a high priority for biogeochemists, soil scientists, and climate policy researchers who aim to reduce uncertainty regarding changes in the global trajectory of soil carbon sequestration and emissions. While popular "black box" machine learning and classical statistics approaches including XGBoost, LSTM, and ARIMA have been demonstrated to be effective and efficient for time series forecasting, they are not designed to inform on the physical processes underlying a data generating process. Instead, we can turn to soil biogeochemical models, also known as soil carbon models, to jointly predict and falsify soil dynamics. Soil biogeochemical models are formulated to simulate the microbe-driven movement of organic elements between terrestrial pools of soil organic matter. As dynamical systems, they provide an avenue to mathematically translate and formalize hypotheses about soil system mechanics into parameterized differential equations.If we assume that soil biogeochemical models superior at describing empirical soil measurements more closely represent the actual data generating processes, we can surmise that models better at fitting data under biologically realistic parameter regimes are more useful for forecasting purposes; mismatch to data can suggest a need to reparameterize or restructure a model. However, the determination of statistical frameworks that can rigorously assess the capability of models to assimilate observations under compute time and resource limitations remains an open and unsettled issue. On this note, we will first expound on soil biogeochemical models in greater detail and motivate the use of Bayesian statistical methods as a means of model fitting and parameter inference while incorporating expert uncertainty and beliefs across the first two chapters of this interdisciplinary dissertation. Subsequently, we will demonstrate the use of a contemporary inference algorithm to assimilate two models with the same data set and then compare their goodness-of-fit quantified with Bayesian information criteria and cross-validation metrics. Finally, in the remaining chapters, we will trial the ability of two novel Bayesian soil biogeochemical model inference schemes offering improved computational efficiency to recover observations and parameter values of known synthetic data generating processes and evidence algorithm functionality worthy of future exploration
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Impacts of Urbanization and Drought on Soil Microbial Communities
Get PDFSoils support many vital ecosystem services including water filtration, pollution remediation, carbon sequestration, nutrient cycling, and increased biodiversity. Microbial communities are key regulators of these soil processes and are functionally responsive to shifts in environmental conditions. Global changes including climate change and urbanization are altering soil properties and soil microbial activity. The resulting feedbacks could increase GHG emissions and nitrogen leaching into water systems from both urbanized and natural soils. The aim of this dissertation is to investigate the impact of global changes on the soil microbiome. First, I addressed the impacts of urbanization on soil ecosystems by synthesizing prior literature and developed a framework to assist researchers in answering key questions about the urban soil microbiome. I argue that urban soils offer an excellent opportunity to study fundamental questions about microbial community structure and function under different environmental conditions, with the additional benefit finding methods to improve urban sustainability. Next, I conducted a field experiment applying this framework to soils in a local neighborhood. I constructed a chrono-sequence of yards built across four decades, and characterized the soil and microbial community to provide insight into how urban soils recover from disturbance, and how irrigation, fertilization, and plant type may alter microbial processes compared to an adjacent undeveloped ecosystem. I found that these yard soils, particularly under turfgrass, are wetter and more nutrient-rich compared to undeveloped soils. The chrono-sequence also revealed that urban soils gain more abundant and active microbial communities over time which may result in accumulated soil carbon.
Finally, my last chapter explores the impact of drought and nitrogen addition, as may result from fossil fuel burning, on a natural grassland ecosystem. I characterized the microbial community of bulk soils across experimental treatments down to 30cm, and explored the effects of depth, drought, and fertilization on microbial community composition and potential function. I found that depth was the most consistent driver of microbial function, while microbial functions were more resilient to drought and fertilization. An interesting finding from this work was that community composition did not respond to treatments while potential function did. This suggests a need for a trait-based approach to describe microbial communities and predict function from their structure
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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