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Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1
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These datasets contain:(i) interview transcripts(ii) interviewer script(iii) example diagrams to elicit opinions(iv) task list for card sorting.Semi-structured interviews are a well-established technique for collecting data. Prior to formal commencement of the study one pilot user was taken through using a preliminary interview script, which led to the refinement of the interview materials. The interview questions are available alongside the transcript data, the overarching questions being:- Can you describe how you use diagrams when communicating your research?- How do you use diagrams when consuming research?Following the graphic elicitation and card sorting exercises, additional follow-up questions were asked, about the role of diagrams more generally and exploring topics that came up during the interview. The entire interview session, including the two exercises, was audio recorded and documented in the transcript. Six participants were interviewed face-to-face, and six were interviewed over Skype video software. Interview resources were presented as printouts or as PDFs. The interviews took an average of just over 1 hour, resulting in 12 hours, 4 minutes, 54 seconds of audio recording. The recordings were transcribed with personally identifiable information and unnecessary non-words redacted, resulting in over 58,000 words of transcription. The interviews were conducted in English, and the majority of participants were non-native English speakers. The transcripts capture what was said, with the interviewer adding clarifications of understood meaning in square brackets where required.
RESEARCH BACKGROUND: `Explosion/implosion, exhale investigates lace techniques and the process of creating two-dimensional representations of three-dimensional forms that flatten and fragment spaces and landscape. The exhibition showcased innovative work that challenged traditional concepts of lace. McPherson's work is an investigation into spaces and self through a process of mapping and measuring landscapes with small grids to demonstrate how each fragment is a small component of the measured world. RESEARCH CONTRIBUTION This work is an example of circular paper lace, loosely based on the idea of the diagrammatic representation of crystalline formations. The crystalline forms are adapted from an 18th century representation of crystal rock formations that have been abstracted and remade into geometric shapes. The research has led to the development of new printing techniques; ink formulations; templates; and making processes to construct paper cut lace-like artwork. RESEARCH SIGNIFICANCE The exhibition was curated and commissioned by Lindie Ward through a competitive peer-review process. McPherson's work sits alongside 33 other art practitioners including sculptors, designers, textile artists and architects from 20 countries as part of the Sydney Design 2011, the major design festival of Sydney, Australia.
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Multispectral imaging (presented as false color based on composite images in S1 Fig) and XRF results indicate the presence of luminescence and key elements associated with EB production.
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The dataset supports the article: Radivojević, M., Pendić, J., Srejić, A., Korać, M., Davey, C., Benzonelli, A., Martinón-Torres, M., Jovanović, N., and Kamberović, Ž. (2017). Experimental design of the Cu-As-Sn ternary colour diagram. Journal of Archaeological Science, https://doi.org/10.1016/j.jas.2017.12.001. Abstract: The aesthetic appearance of metals has long been recognised in archaeometric studies as an important factor driving inventions and innovations in the evolution of metal production. Nevertheless, while the studies of ancient gold metallurgy are well supported by the modern research in colour characteristics of gold alloys, the colour properties of major prehistoric copper alloys, like arsenical copper and tin bronzes, remain either largely understudied or not easily accessible to the western scholarship. A few published studies have already indicated that alloying and heat treatment change the colours of copper alloys, although they are mainly based on the examples of prehistoric tin bronze objects and experimental casts. Here we present the procedure for building the Cu-As-Sn ternary colour diagram, starting with experimental casting of 64 binary and ternary alloys in this system. We used two types of information to produce two different ternary colour diagrams: one, based on photographs of the samples, and the other, established on the colorimetric measurements. Furthermore, we developed the procedure for creating a graphic representation of colours in the Cu-As-Sn ternary diagram using QGIS. As an initial case study, we plotted the composition of the world’s earliest tin bronze artefacts; the graphic representation further supports claims about the importance of golden hue for their invention and demand, c. 6,500 years ago. We argue that the presented colour diagrams will find wide use in future investigations of aesthetics of prehistoric copper alloys.
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The current chemical industry has been heavily optimized for the use of petroleum-derived base chemicals as its primary source of building blocks. However, incorporation of heteroatoms, absent in the base chemicals, is necessary to meet the different property requirements in the plethora of products the industry makes such as plastics, cosmetics, and pharmaceuticals. As global oil reserves deplete, a shift toward renewable bioderived building blocks, so-called platform molecules, will become a necessity. Bioderived platform molecules are typically rich in heteroatoms as a result of their biomass feedstock also being heteroatom rich, and it would therefore seem logical to carry these heteroatoms through to the aforementioned products. A tool was herein developed to assess the rationality of a synthetic route from feedstock to product, designed specifically to give a visual representation of the pathways and options available. BioLogicTool plots (%heteroatom by mass vs M) are an alternative to the conventional van Krevelen diagram, and are designed to better consider the diversity in heteroatom content encountered in biobased chemicals. The tool can rapidly help its user to design more logical multistep synthetic routes and enhance the mass efficiency of pathways. Several examples were selected to demonstrate the power and limitations of the BioLogicTool, but it was clear from these examples that removing heteroatoms from platform molecules to reincorporate them later in the final product is, in most cases, not logical in a mass efficiency sense.
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How authentic is your current assessment? How could you stretch or adapt it to make it more authentic? This radar diagram toolkit helps you and others to explore your assessment according to different dimensions of authenticity. This representation can also then help you to consider ways to make your assessment more authentic. The Excel spreadsheet gives you a worked example and an editable sheet to use. If you prefer to work on paper, the handout document can be printed and the final page used for drawing on the radar diagram. Credit: radar diagram adapted by Tunde Varga-Atkins, Centre for Innovation in Education, Univ of Liverpool from Ashford-Rowe et al., 2014; Gulikers et al., 2004; Osborne et al., 2013; Whitelock & Cross, 2012More information: https://www.liverpool.ac.uk/centre-for-innovation-in-education/curriculum-resources/authentic-assessment/
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Mechanics is a discipline of physics that deals with the motion and forces that govern object movement. To apply fundamental principles successfully, engineering students must have a thorough understanding of mechanics. The free-body diagrams, which are visual representations of forces, assist students in identifying and analyzing forces. The current research looks into how Free-body Diagrams affect engineering students' performance in physics courses. However, many engineering students struggle to understand free-body diagrams, which might impede problem-solving. A sample of undergraduate engineering students from the University of Rwanda College of Science and Technology participated in the study. The students are separated into two groups: one for control and one for experimentation. The control group is given standard instruction, whereas the experimental group is given free-body diagram training. Before and after the intervention, both groups were evaluated on their ability to solve mechanics problems. A pre-test and a post-test are used to assess the student's performance. The results revealed that the experimental group improved much more than the control group in problem-solving performance. Furthermore, students in the experimental group expressed a more favorable attitude toward employing free-body diagrams in problem-solving activities. The research findings revealed that adding them into mechanics problem-solving education can be an effective strategy to improve engineering students' performance in physics problem-solving. The findings could also have a considerable impact on engineering education if they are implemented into the physics curriculum and teaching methods. They could potentially also be used to build instructional strategies and learning materials that foster free-body diagrams and improve students' mechanics problem-solving abilities.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1