1,720,983 research outputs found
NEXT Plant data: Results of Quality Assurance and Quality Control - Supplementary material for publications based on this data set
No full textFormat: HTML document (bookdown format)
Purpose: This file provides a detailed description of the quality assurance and quality control (QA/QC) procedures applied to the plant concentration data collected during the study. It includes statistical analysis of reference materials, drift correction, uncertainty modeling, and evaluation of laboratory and field precision.
Description of the File Content
This file is part of a larger data publication and serves as a supplementary document to the main dataset. It outlines the QA/QC procedures used to ensure the accuracy, precision, and reliability of the plant element concentration data. The file includes:
Reference Material (RM) Analysis:
Statistical summaries of standard reference materials (SRMs) such as UPDEEP_SPRU_BARK_DRY, UPDEEP_SPRU_TWIG_DRY, and UPDEEP_SPRU_NEED_DRY.
Comparison of pre-analyzed SRM values with actual measurements.
X-charts showing the performance of SRMs over time and across different batches.
Drift and Offset Correction:
Visualizations of raw and corrected data for routine samples, laboratory, and field replicates.
Analysis of data trends and correction of analytical drift and offsets.
Uncertainty Modeling:
Calculation of relative standard deviation (RSD) from laboratory replicates.
Identification of elements with high uncertainty (RSD > 10%) that may be excluded from further analysis.
Tables and visualizations showing the distribution of uncertainties across different plant tissues.
Field Precision Assessment:
Evaluation of field replicate data to assess variability in field sampling.
Identification of elements with poor field precision (RSD > 20%).
Data Preparation and Processing:
R code for data loading, cleaning, and transformation.
Use of packages such as data.table, ggplot2, dplyr, and kableExtra for data manipulation and visualization.
Summary of Key Findings and Data Included
Reference Materials: The file provides statistical summaries (mean, median, SD, RMAD) of SRMs used to monitor analytical performance. These are compared with actual measurements to assess accuracy and precision.
Drift Correction: The data shows the effect of drift correction on plant concentration measurements, improving the consistency of results across different batches.
Uncertainty Analysis: The RSD of laboratory replicates is calculated, and elements with high variability are flagged for exclusion.
Field Precision: Field replicates are used to assess the variability of sampling and analysis in the field, with some elements showing poor precision.
Visualizations: The file includes numerous plots (e.g., X-charts, scatter plots) to illustrate data trends, comparisons, and uncertainty levels
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
NEXT Plant data: Results of Quality Assurance and Quality Control - Supplementary material for publications based on this data set
No full textFormat: HTML document (bookdown format)
Purpose: This file provides a detailed description of the quality assurance and quality control (QA/QC) procedures applied to the plant concentration data collected during the study. It includes statistical analysis of reference materials, drift correction, uncertainty modeling, and evaluation of laboratory and field precision.
Description of the File Content
This file is part of a larger data publication and serves as a supplementary document to the main dataset. It outlines the QA/QC procedures used to ensure the accuracy, precision, and reliability of the plant element concentration data. The file includes:
Reference Material (RM) Analysis:
Statistical summaries of standard reference materials (SRMs) such as UPDEEP_SPRU_BARK_DRY, UPDEEP_SPRU_TWIG_DRY, and UPDEEP_SPRU_NEED_DRY.
Comparison of pre-analyzed SRM values with actual measurements.
X-charts showing the performance of SRMs over time and across different batches.
Drift and Offset Correction:
Visualizations of raw and corrected data for routine samples, laboratory, and field replicates.
Analysis of data trends and correction of analytical drift and offsets.
Uncertainty Modeling:
Calculation of relative standard deviation (RSD) from laboratory replicates.
Identification of elements with high uncertainty (RSD > 10%) that may be excluded from further analysis.
Tables and visualizations showing the distribution of uncertainties across different plant tissues.
Field Precision Assessment:
Evaluation of field replicate data to assess variability in field sampling.
Identification of elements with poor field precision (RSD > 20%).
Data Preparation and Processing:
R code for data loading, cleaning, and transformation.
Use of packages such as data.table, ggplot2, dplyr, and kableExtra for data manipulation and visualization.
Summary of Key Findings and Data Included
Reference Materials: The file provides statistical summaries (mean, median, SD, RMAD) of SRMs used to monitor analytical performance. These are compared with actual measurements to assess accuracy and precision.
Drift Correction: The data shows the effect of drift correction on plant concentration measurements, improving the consistency of results across different batches.
Uncertainty Analysis: The RSD of laboratory replicates is calculated, and elements with high variability are flagged for exclusion.
Field Precision: Field replicates are used to assess the variability of sampling and analysis in the field, with some elements showing poor precision.
Visualizations: The file includes numerous plots (e.g., X-charts, scatter plots) to illustrate data trends, comparisons, and uncertainty levels
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
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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