1,720,975 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Exploring the impact of the recent global warming on extreme weather events in Central Asia using the counterfactual climate data ATTRICI v1.1
No full textInternational audienceWe study the impact of recent global warming on extreme climatic events in Central Asia (CA) for 1901-2019 by comparing the composite representation of the observational climate with a hypothetical counterfactual one that does not include the long-term global warming trend. The counterfactual climate data are produced based on a simple detrending approach, using the global mean temperature (GMT) as the independent variable and removing the long-term trends from the climate variables of the observational data. This trend elimination is independent of causality, and the day-to-day variability in the counterfactual climate remains preserved. The analysis done in the paper shows that the increase in frequency and magnitude of extreme temperature and precipitation events can be attributed to global warming. Specifically, the probability of experiencing a +7 K temperature anomaly event in CA increases by up to a factor of seven in some areas due to global warming. The analysis reveals a significant increase in heatwave occurrences in Central Asia, with the observational climate dataset GSWP3-W5E5 (later called also factual) showing more frequent and prolonged extreme heat events than hypothetical scenarios without global warming. This trend, evident in the disparity between factual and counterfactual data, underscores the critical impact of recent climatic changes on weather patterns, highlighting the urgent need for robust adaptation and mitigation strategies. Additionally, using the self-calibrated Palmer drought severity index (scPDSI), the sensitivity of dry and wet events to the coupled precipitation and temperature changes is analyzed. The areas under dry and wet conditions are enhanced under the observational climate compared to a counterfactual scenario, especially over the largest deserts in CA. The expansion of the dry regions aligns well with the pattern of desert development observed in CA in recent decades
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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
Heatwave analysis
No full text<p># Heatwave Duration Index (HWDI) Calculation</p>
<p>This repository provides scripts for calculating the Heat Wave Duration Index (HWDI) using the ATTRICIV1.1 dataset, aiming to analyze heatwave occurrences and their intensity.</p>
<p>## Installation</p>
<p>Ensure Python is installed, then set up the environment:</p>
<p>```bash<br>chmod +x pre_process.sh run_hwmid.sh<br>```</p>
<p>## Usage<br>First, preprocess the data:</p>
<p>```bash<br>./pre_process.sh<br>```<br>Run the HWDI calculation script with the slurm on cluster:<br>```bash<br>./run_hwmid.sh<br>```</p>
<p>## Figures</p>
<p> <br>*a. HWDI difference map, n day = 5, T = 7K*</p>
<p> <br>*b. HWDI difference map, n day = 10, T = 7K*</p>
<p> <br>*c. HWDI difference map, n day = 15, T = 7K*</p>
<p> <br>*d. HWDI difference map, n day = 20, T = 7K*</p>
<p>*Caption: Heat Wave Duration Index (HWDI) difference maps for varying consecutive days to define a heatwave (n day = 5, 10, 15, 20) and 7K above the reference value (T = 7K).*</p>
<p>## Acknowledgments</p>
<p>Credit to the creators of the ATTRICIV1.1 dataset and all supporting resources.</p>
Köppen Climate Classification Analysis
No full text<p><strong>Köppen Climate Classification Analysis</strong></p>
<p><strong>Overview</strong><br>This repository contains Python code for analyzing global and Asia-specific Köppen-Geiger climate classification data. The analysis is based on the 1 km global Köppen–Geiger climate classification maps for present (1980-2016) and future scenarios. This work leverages the methodology outlined by Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007) in their seminal paper "Updated world map of the Köppen-Geiger climate classification" published in Hydrology and Earth System Sciences Discussions.</p>
<p><strong>Files and Modules</strong><br>The repository is structured into four primary Python files, each serving a unique role in the data analysis process:</p>
<ul>
<li>- `main.py`: The entry point of the analysis, orchestrating the workflow including data loading, processing, and visualization based on the Köppen-Geiger classification.</li>
<li>- `koeppen_colors.py`: Defines the color mappings for the different Köppen-Geiger climate types, facilitating visual representations of climate classifications.</li>
<li>- `koppen_mappings.py`: Contains mappings of climate codes to their respective Köppen-Geiger climate categories, enabling classification and analysis of climate data.</li>
<li>- `utilities.py`: Provides utility functions that support data manipulation and processing tasks, such as reading data files, interpolating missing values, and more.</li>
</ul>
<p><strong>How It Works</strong><br>The code analyzes climate data by first categorizing geographical areas according to the Köppen-Geiger climate classification system. It then uses these classifications to perform various analyses, including comparing present and future climate scenarios. Visual representations of the data are generated using color codes defined for each climate type, aiding in the intuitive understanding of climate shifts and variations.</p>
<p><strong>Usage</strong><br>To run the analysis, ensure that Python 3.6 or later is installed on your system. Follow these steps:</p>
<p>1. Clone the repository to your local machine.<br>2. Install the required Python dependencies listed in `requirements.txt` (if provided).<br>3. Execute the `main.py` script from the terminal: `python main.py`.</p>
<p><strong>Dependencies</strong><br>- Matplotlib (for data visualization)<br>- NumPy (for numerical computations)<br>- (Additional dependencies may be listed in a `requirements.txt` file.)</p>
<p><strong>Citation</strong></p>
<p><br>If you use this code or the Köppen-Geiger climate classification data in your research, please cite the following paper:</p>
<p>Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions, 4(2), 439-473.</p>
<p>Brun, Philipp; Zimmermann, Niklaus E.; Hari, Chantal; Pellissier, Loïc; Karger, Dirk<br>Nikolaus (2022). A novel set of global climate-related predictors at kilometreresolution. EnviDat. https://doi.org/10.5194/essd-2022-212<br><br>Brun, Philipp; Zimmermann, Niklaus E.; Hari, Chantal; Pellissier, Loïc; Karger, Dirk<br>Nikolaus (2022). CHELSA-BIOCLIM+ A novel set of global climate-related predictors<br>at kilometre-resolution. EnviDat. https://doi.org/10.16904/envidat.332</p>
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