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The Gunpowder Plot
\"Who tried to blow up the King of England? Read this book to find out the answer to this question and more! Discover who the plotters were, why they planned to blow up Parliament, how they were discovered and how we remember the plot today.\"--Back cover.
Book
Resurveying historical vegetation data — opportunities and challenges
Background: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. Methods: Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions: Re-sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses.
Journal Article
Through the forest
Mother Forest invites the reader to take a walk with her and choose different options along the way, such as following a squirrel or discovering the world of insects. Pages have numbered tabs to guide selection.
Book
Displaying Variation in Large Datasets: Plotting a Visual Summary of Effect Sizes
Displaying the component-wise between-group differences high-dimensional datasets is problematic because widely used plots such as Bland-Altman and Volcano plots do not show what they are colloquially believed to show. Thus, it is difficult for the experimentalist to grasp why the between-group difference of one component is \"significant\" while that of another component is not. Here, we propose a type of \"Effect Plot\" that displays between-group differences in relation to respective underlying variability for every component of a high-dimensional dataset. We use synthetic data to show that such a plot captures the essence of what determines \"significance\" for between-group differences in each component, and provide guidance in the interpretation of the plot. Supplementary online materials contain the code and data for this article and include simple R functions to produce an effect plot from suitable datasets.
Journal Article
Can you survive in the Special Forces? : an interactive survival adventure
\"Describes the fight for survival as a member of the U.S. Special Forces\"--Provided by publisher.
Book
Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews
The Bland–Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland–Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland–Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569–575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland–Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland–Altman agreement analyses.
Journal Article
The life of guy : Guy Fawkes, the Gunpowder Plot, and the unlikely history of an indispensable word
Had you said \"What a guy!\" in 17th-century England, anyone would have understood you were admiring a flaming effigy of Guy Fawkes of the Gunpowder Treason Plot. 0How times have changed! In America and, indeed, most of the English-speaking world, \"guy\" is so embedded in daily speech that we scarcely notice how odd it truly is: a singular \"guy\" referring to males only, a plural \"guys\" encompassing the entire human race. The journey from England's greatest villain to America's favorite second-person plural pronoun offers a story rich with surprising and unprecedented turns. 0Through his trademark breezy, highly readable style, acclaimed writer Allan Metcalf takes us deep into this history, uncovering the intrigue, murderous plots, and torture out of which the word emerged in 1605. From there, it's a thrilling run through 17th-century England, bloody religious controversies, and across the Atlantic to America, where the word took on a life of its own, exploding into popular culture and day-to-day conversation. From the disappearance of <\"thou,>\" to George Washington and the American Revolution, to the modern revival of Guy Fawkes in V for Vendetta, Metcalf explores the improbable history of a simple word so indispensable to our daily lives, and that evokes deep insights into the evolution of English itself.
Book
A Simple Step-by-Step Guide to the Design and Analysis of Unreplicated Split-Plot Experiments Through a Case Study on Molybdenum Recycling from CIGS Solar Cells
Considerable effort has been put over the last few decades into clarifying the correct design and analysis of split-plot factorial experiments. However, the information found in the literature is scattered and sometimes still not easy to grasp for non-experts. Because of the importance of split-plots for the industry and the fact that any experimenter may need to use them at some point, a detailed and step-by-step guide collecting all the available information on the fundamental methodology in one place was deemed necessary. More specifically, this paper discusses the simple case of an unreplicated split-plot factorial experiment with more than one whole-plot (WP) factors and all factors set at two levels each. Explanations on how to properly design the experiment, analyze the data, and assess the proposed model are provided. Special attention is given to clarifications on the calculations of contrasts, effects, sum of squares (SS), parameters, WP and sub-plot (SP) residuals, as well as the proper division of the proposed model into its sub-designs and sub-models for calculating measures of adequacy correctly. The application of the discussed theory is showcased by a case study on the recycling of molybdenum (Mo) from CIGS solar cells. Factors expected to affect Mo recovery were investigated and the analysis showed that all of them are significant, while the way they affect the response variable was also revealed. After reading this guide, the reader is expected to acquire a good understanding of how to work with split-plots smoothly and handle with confidence more complex split-plot types.
Journal Article
Fighting for independence : an interactive American Revolution adventure
\"Vivid storytelling and authentic dialogue bring American history to life and place readers in the shoes of real people who experienced some of the most pivotal moments of the American Revolution. Battles such as Bunker Hill have begun to take their toll on both sides in the war. The Second Continental Congress meets and names George Washington as the commander in chief of the Continental Army. John Adams and Benjamin Franklin work to secure European allies in the fight against British rule. Readers dive into this history and make choices throughout that affect the outcome of the story. Scenarios are developed and lead up to choices, which the readers take to control the direction of the character and story. This format creates a unique and powerful experience for readers as they face the challenges and decisions that real people encountered.\"--Provided by publisher.
Book
Modified Arrhenius Equation in Materials Science, Chemistry and Biology
The Arrhenius plot (logarithmic plot vs. inverse temperature) is represented by a straight line if the Arrhenius equation holds. A curved Arrhenius plot (mostly concave) is usually described phenomenologically, often using polynomials of T or 1/T. Many modifications of the Arrhenius equation based on different models have also been published, which fit the experimental data better or worse. This paper proposes two solutions for the concave-curved Arrhenius plot. The first is based on consecutive A→B→C reaction with rate constants k1 ≪ k2 at higher temperatures and k1 ≫ k2 (or at least k1 > k2) at lower temperatures. The second is based on the substitution of the temperature T the by temperature difference T − T0 in the Arrhenius equation, where T0 is the maximum temperature at which the Arrheniusprocess under study does not yet occur.
Journal Article