The Excel spreadsheet contains the quantitative questions (Questions 1, 3 and 4). Each question is analysed in the form of a frequency distribution table and a pie chart.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Vision and Change in Undergraduate Biology Education encouraged faculty to focus on core concepts and competencies in undergraduate curriculum. We created a sophomore-level course, Biologists' Toolkit, to focus on the competencies of quantitative reasoning and scientific communication. We introduce students to the statistical analysis of data using the open source statistical language and environment, R and R Studio, in the first two-thirds of the course. During this time the students learn to write basic computer commands to input data and conduct common statistical analysis. The students also learn to graphically represent their data using R. In a final project, we assign students unique data sets that require them to develop a hypothesis that can be explored with the data, analyze and graph the data, search literature related to their data set, and write a report that emulates a scientific paper. The final report includes publication quality graphs and proper reporting of data and statistical results. At the end of the course students reported greater confidence in their ability to read and make graphs, analyze data, and develop hypotheses. Although programming in R has a steep learning curve, we found that students who learned programming in R developed a robust strategy for data analyses and they retained and successfully applied those skills in other courses during their junior and senior years.

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

This contains CSV datasets used for composing the graphs in Nicholas Paige, Technologies of the Novel: Quantitative Data and the Evolution of Literary Systems (Cambridge UP, 2020). Each dataset is accompanied by a document explaining the tags used. An Electronic Graph Annex contains figures that Technologies of the Novel references but that were not included in the published version.

Use of vectors in financial graphs By using mathematical vectors calculations as financial modeling then further into a new form of quantitative analysis instrument for linear financial computation graphs. A new tool in financial data analysis as an indicator.

Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.

There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files).

The document Dataset_summary includes a detailed description of the dataset.

Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

Inhibitory effect of antisense TcCaNA2 oligonucleotides on T. cruzi cell invasion and proliferation. (XLSX)

We propose a novel approach to predict saturation vapor pressures using group contribution-assisted graph convolutional neural networks (GC2NN), which use both, molecular descriptors like molar mass and functional group counts, as well as molecular graphs containing atom and bond features, as representations of molecular structure. Molecular graphs allow the ML model to better infer molecular connectivity and spatial relations compared to methods using other, non-structural embeddings. We achieve best results with an adaptive-depth GC2NN, where the number of evaluated graph layers depends on molecular size. We apply the model to compounds relevant for the formation of SOA, achieving strong agreement between predicted and experimentally-determined vapor pressure. In this study, we present two models: a general model with broader scope, achieving a mean absolute error (MAE) of 0.69 log-units (R2 = 0.86), and a specialized model focused on atmospheric compounds (MAE = 0.37 log-units, R2 = 0.94).

Network of 29 papers and 43 citation links related to "Quantitative analysis of parallelism and data movement properties across the Berkeley computational motifs".

The "Dataset_Graph.zip" file contains the graph models of the trees in the dataset. These graph models are saved in the "pickle" format, which is a binary format used for serializing Python objects. The graph models capture the structural information and relationships of the cylinders in each tree, representing the hierarchical organization of the branches.

This repository contain datasets and results for the paper:

Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis

Github repository for the code:

Quantifying Language Confusion GitHub repo

DATA include the following datasets:

i) raw language graphs and

ii) the calculated language similarities from the language graphs,

iii) MTEI: the files from the experimental results of multilingual inversion attacks, and calculated language confusion entropy from the data;

iv) LCB: the files from the language confusion benchmark and calculated language confusion entropy from the data

Results include aggregated results for further analysis:

i) inversion_language_confusion: results from MTEI

ii) prompting_language_confusion: results from LCB

Network of 44 papers and 77 citation links related to "Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy".

Some data in the article "A Quantitative Monitoring Study of Environmental Factors Activating Caihua and Wooden Heritage Cracks in the Palace Museum". File name represents the monitoring date and time.

Excel files containing data shown on graphs and tables that appear in the research article evaluating the efficacy of disposable face coverings in containing aerosols from the wearer. This dataset is not publicly accessible because: EPA does not own these data. It can be accessed through the following means: Contact corresponding author of the published article, William_Bennett@med.unc.edu. Format: Excel files. This dataset is associated with the following publication: Bennett, W., S. Prince, K. Zeman, H. Chen, and J. Samet. A Novel Method for the Quantitative Assessment of Fitted Containment Efficiency of Face Coverings. INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY. Slack Incorporated, 13(1-4): 1481-1484, (2023).

Network of 44 papers and 70 citation links related to "Quantitative estimation of climatic parameters from vegetation data in North America by the mutual climatic range technique".

Network of 43 papers and 65 citation links related to "Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data".

RDF data graphs

The files include demographic data of all participants, as well as quantitative data used to create graphs and tables, provided in excel files. All qualitative data were analysed with NVIVO software and the files attached are nodes containing different information from participants. All the interview questions are also included as interview files.

Dataset containing supplemental material for the publication "2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualizations in Virtual Reality" This dataset contains: 1) archive containing all raw quantitative results, 2) archive containing all raw qualitative data, 3) archive containing the graphs used for the experiment (.graphml file format), 4) the code to generate the graph library (C++ files using OGDF), 5) a PDF document containing detailed results (with p-values and more charts), 6) a video showing the experimentation from a participant's point of view. 7) complete graph library generated by our graph generator for the experiment