To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Civil and geological engineers have used field variable-head permeability tests (VH tests or slug tests) for over one century to assess the local hydraulic conductivity of tested soils and rocks. The water level in the pipe or riser casing reaches, after some rest time, a static position or elevation, z2. Then, the water level position is changed rapidly, by adding or removing some water volume, or by inserting or removing a solid slug. Afterward, the water level position or elevation z1(t) is recorded vs. time t, yielding a difference in hydraulic head or water column defined as Z(t) = z1(t) - z2. The water level at rest is assumed to be the piezometric level or PL for the tested zone, before drilling a hole and installing test equipment. All equations use Z(t) or Z*(t) = Z(t) / Z(t=0). The water-level response vs. time may be a slow return to equilibrium (overdamped test), or an oscillation back to equilibrium (underdamped test). This document deals exclusively with overdamped tests. Their data may be analyzed using several methods, known to yield different results for the hydraulic conductivity. The methods fit in three groups: group 1 neglects the influence of the solid matrix strain, group 2 is for tests in aquitards with delayed strain caused by consolidation, and group 3 takes into account some elastic and instant solid matrix strain. This document briefly explains what is wrong with certain theories and why. It shows three ways to plot the data, which are the three diagnostic graphs. According to experience with thousands of tests, most test data are biased by an incorrect estimate z2 of the piezometric level at rest. The derivative or velocity plot does not depend upon this assumed piezometric level, but can verify its correctness. The document presents experimental results and explains the three-diagnostic graphs approach, which unifies the theories and, most important, yields a user-independent result. Two free spreadsheet files are provided. The spreadsheet "Lefranc-Test-English-Model" follows the Canadian standards and is used to explain how to treat correctly the test data to reach a user-independent result. The user does not modify this model spreadsheet but can make as many copies as needed, with different names. The user can treat any other data set in a copy, and can also modify any copy if needed. The second Excel spreadsheet contains several sets of data that can be used to practice with the copies of the model spreadsheet. En génie civil et géologique, on a utilisé depuis plus d'un siècle les essais in situ de perméabilité à niveau variable (essais VH ou slug tests), afin d'évaluer la conductivité hydraulique locale des sols et rocs testés. Le niveau d'eau dans le tuyau ou le tubage prend, après une période de repos, une position ou élévation statique, z2. Ensuite, on modifie rapidement la position du niveau d'eau, en ajoutant ou en enlevant rapi-dement un volume d'eau, ou en insérant ou retirant un objet solide. La position ou l'élévation du niveau d'eau, z1(t), est alors notée en fonction du temps, t, ce qui donne une différence de charge hydraulique définie par Z(t) = z1(t) - z2. Le niveau d'eau au repos est supposé être le niveau piézométrique pour la zone testée, avant de forer un trou et d'installer l'équipement pour un essai. Toutes les équations utilisent Z(t) ou Z*(t) = Z(t) / Z(t=0). La réponse du niveau d'eau avec le temps peut être soit un lent retour à l'équilibre (cas suramorti) soit une oscillation amortie retournant à l'équilibre (cas sous-amorti). Ce document ne traite que des cas suramortis. Leurs données peuvent être analysées à l'aide de plusieurs méthodes, connues pour donner des résultats différents pour la conductivité hydraulique. Les méthodes appartiennent à trois groupes : le groupe 1 néglige l'influence de la déformation de la matrice solide, le groupe 2 est pour les essais dans des aquitards avec une déformation différée causée par la consolidation, et le groupe 3 prend en compte une certaine déformation élastique et instantanée de la matrice solide. Ce document explique brièvement ce qui est incorrect dans les théories et pourquoi. Il montre trois façons de tracer les données, qui sont les trois graphiques de diagnostic. Selon l'expérience de milliers d'essais, la plupart des données sont biaisées par un estimé incorrect de z2, le niveau piézométrique supposé. Le graphe de la dérivée ou graphe des vitesses ne dépend pas de la valeur supposée pour le niveau piézomé-trique, mais peut vérifier son exactitude. Le document présente des résultats expérimentaux et explique le diagnostic à trois graphiques, qui unifie les théories et donne un résultat indépendant de l'utilisateur, ce qui est important. Deux fichiers Excel gratuits sont fournis. Le fichier"Lefranc-Test-English-Model" suit les normes canadiennes : il sert à expliquer comment traiter correctement les données d'essai pour avoir un résultat indépendant de l'utilisateur. Celui-ci ne modifie pas ce...

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

The Hadith Isnad narrators data may be useful for the researcher to better adapt these techniques for particular problems. This data is developed for future research on the public repository for all the Research Institutes, Scientific and Islamic communities who want to work on Hadith's domain. This dataset contains two types of excel documents: Hadith_SahihMuslim_CoreInfo.xlsx file (7748 records) and Hadith_SahihMuslim_DetailsInfo_Sanad_Narrators.xlsx document (77797 records). The data contains 7748 Hadiths and 2092 unique records of Narrators of All Sahih Muslim Hadith

The data set for the article [Bahl, C. R., Engelbrecht, K., Gideon, A., Levy, M. A. V., Marcussen, J. B., Imbaquingo, C., & Bjørk, R. (2024). Design, optimization and operation of a high power thermomagnetic harvester. Applied Energy, 376, 124304.]. DOI for the publication: 10.1016/j.apenergy.2024.124304The data are stored in four zip files, each containing a single folder, according to the different sections in the paper.The folder "Simulation_design" contains three Origin plot files, which contains the data for Figs. 3-5, Fig. 6 and Fig. 7. Both plots and data are contained in the Origin files.The folder "Experiment_thin_coils" contains an Origin file with the data and plot for Fig. 9. Furthermore it contains Matlab scripts for plotting Fig. 10 and Fig. 11 as well as the supporting file "lvm_import.m" for importing the lvm files which contains the raw experimental data. The RMS voltage plotted in Fig. 11 is given in the file "Voltage_RMS.txt".The folder "Experiment_big_coils" contains an Excel sheet with the data shown in Fig. 13, as well as the raw data, Raw_data.xslx, from the experiments needed to produce the average data in the Fig_13_data.xslx file. The data is described within the Excel files.The folder "Experiment_big_coils" contains the raw data in lvm format for the experiments with and without foam. The files are named according to the frequency, flow rate and temperature span and can be read with the "lvm_import.m" file in Matlab.

This MS Excel data has been processed into line graphs to create time series line graphs and data tables which give insight into changing physiochemical water quality characteristics and influences. The study sets out to determine if climate change has had an influence on physiochemical water quality characteristics both within and between the Breede and Olifants estuaries over a nine year monitoring period. The data represents changes and comparisons between salinity, temperature and rainfall within and between the Olifants and Breede river estuaries in the Wester Cape Province of South Africa.

Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?:

This data set contains all the experimental raw data, analysis and source files for the final figures reported in the manuscript: "Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?". It is divided into five (1-5) zipped folders, named as the technique used to obtain the data. Each of them, where applicable, consists of three different subfolders (raw data, analysed data, final graph). Read below for more details.

1) ConfocalMicroscopy

1a) Raw_Data: the raw images are reported as .dat and .tif formats, divided into folders (according to date first yymmdd, and within the same day according to composition). Each folder contains a .txt file reporting the experimental details

1b) GUVs_Statistics - GUVs_Statistics.txt explains how we generated the bar plot shown in Fig. 1E

1c) Final_Graph - Figure_1B_1D.png is the figure representing figure 1B and 1D - Figure1E_%ofGUVswithCaMAdsorbptions.csv is the source file x-y of the bar plot shown in figure 1E (% of GUVs which showed adsorption of CaM over the total amount of measured GUVs) - Where_To_Find_Representative_Images.txt states the folders where the raw images chosen for figure 1 can be found

2) FCS 2a) Raw_Data: - 1_points: .ptu files - 2_points: .ht3 files - Raw_Data_Description.docx which compositions and conditions correspond to which point in the two data sets 2b) Final_Graphs: - Figure_2A.xlsx contains the x-y source file for figure 2A

2c) Analysis: - FCS_Fits.xlsx outcome of the global fitting procedure described in the .docx below (each group of points represents a certain composition and calcium concentration, read the Raw_Data_Description.docx in the FCS > Raw_Data) - Notes_for_FCS_Analysis.docx contains a brief description of the analysis of the autocorrelation curves

3) GPLaurdan 3a) Raw Data: all the spectra are stored in folders named by date (yymmdd_lipidcomposition_Laurdan) and are in both .FS and .txt formats

3b) GP calculations: contains all the .xlsx files calculating the GP values from the raw emission and excitation spectra

3c) Final_Graphs - Data_Processing_For_Fig_2D.csv contains the data processing from the GP values calculated from the spectra to the DeltaGP (GP with- GP without CaM) reported in fig. 2D - Figure_2C_2D.xlsx contains the x-y source file for the figure 2C and 2D

4) LiveCellsImaging

3a) Intensity_Protrusions_vs_Cell_Body: - contains all the .xlsx files calculating the intensity of the various images. File renamed by date (yymmdd) - All data in all excel sheets gathered in another Excel file to create a final graph

3b) Final_Graphs - Figure_S2B.xlsx contains the x-y source file for the figure S2B

5) LiveCellImaging_Raw_Data: it contains some of the images, which are given in .tif. They are divided by date (yymmdd) and each contains subfolders renamed by sample name, concentration of ionomycin. Within the subfolders, the images are divided into folders distinguishing the data acquired before and after the ionomycin treatment and the incubation time.

6) 211124_BioCev_Imaging_1 folder has the .jpg files of the time laps, these are shown in fig 1A and S2.

7) 211124_BioCev_Imaging_2 and 8) 211124_BioCev_Imaging_3 contain the images of HeLa cells expressing EGFP-CaM after treatment with ionomycin 200 nM (A1) and 1 uM (A2), respectively.

9) SPR

9a) Raw Data: - SPR_Raw_Data.xlsx x/y exported sensorgrams - the .jpg files of the software are also reported and named by lipid composition

9b) Final_Graph: - Fig.2B.xlsx contains the x-y source file for the figure 2B

9c) Analysis - SPR_Analysis.xlsx: excel file containing step-by-step (sheet by sheet) how we processed the raw data to obtain the final figure (details explained in the .docx below) - Analysis of SPR data_notes.docx: read me for detailed explanation

Graph Database Vector Search Market Outlook

According to our latest research, the global Graph Database Vector Search market size reached USD 2.35 billion in 2024, exhibiting robust growth driven by the increasing demand for advanced data analytics and AI-powered search capabilities. The market is expected to expand at a CAGR of 21.7% during the forecast period, propelling the market size to an anticipated USD 16.8 billion by 2033. This remarkable growth trajectory is primarily fueled by the proliferation of big data, the widespread adoption of AI and machine learning, and the growing necessity for real-time, context-aware search solutions across diverse industry verticals.

One of the primary growth factors for the Graph Database Vector Search market is the exponential increase in unstructured and semi-structured data generated by enterprises worldwide. Organizations are increasingly seeking efficient ways to extract meaningful insights from complex datasets, and graph databases paired with vector search capabilities are emerging as the preferred solution. These technologies enable organizations to model intricate relationships and perform semantic searches with unprecedented speed and accuracy. Additionally, the integration of AI and machine learning algorithms with graph databases is enhancing their ability to deliver context-rich, relevant results, thereby improving decision-making processes and business outcomes.

Another significant driver is the rising adoption of recommendation systems and fraud detection solutions across various sectors, particularly in BFSI, retail, and e-commerce. Graph database vector search platforms excel at identifying patterns, anomalies, and connections that traditional relational databases often miss. This capability is crucial for detecting fraudulent activities, building sophisticated recommendation engines, and powering knowledge graphs that underpin intelligent digital experiences. The growing need for personalized customer engagement and proactive risk mitigation is prompting organizations to invest heavily in these advanced technologies, further accelerating market growth.

Furthermore, the shift towards cloud-based deployment models is catalyzing the adoption of graph database vector search solutions. Cloud platforms offer scalability, flexibility, and cost-effectiveness, making it easier for organizations of all sizes to implement and scale graph-powered applications. The availability of managed services and API-driven architectures is reducing the complexity associated with deployment and maintenance, enabling faster time-to-value. As more enterprises migrate their data infrastructure to the cloud, the demand for cloud-native graph database vector search solutions is expected to surge, driving sustained market expansion.

Geographically, North America currently dominates the Graph Database Vector Search market, owing to its advanced IT infrastructure, high adoption rate of AI-driven technologies, and presence of leading technology vendors. However, rapid digital transformation initiatives across Europe and the Asia Pacific are positioning these regions as high-growth markets. The increasing focus on data-driven decision-making, coupled with supportive regulatory frameworks and government investments in AI and big data analytics, is expected to fuel robust growth in these regions over the forecast period.

Component Analysis

The Component segment of the Graph Database Vector Search market is broadly categorized into software and services. The software sub-segment commands the largest share, driven by the relentless innovation in graph database technologies and the integration of advanced vector search functionalities. Organizations are increasingly deploying graph database software to manage complex data relationships, power semantic search, and enhance the performance of AI and machine learning applications. The software market is characterized by the proliferation of both open-source and proprietary solutions, with vendors

Graph Neural Network Platform Market Outlook

According to our latest research, the global Graph Neural Network (GNN) Platform market size is valued at USD 1.08 billion in 2024, underscoring its rapid ascent in the artificial intelligence domain. The market is projected to expand at a robust CAGR of 32.4% from 2025 to 2033, reaching an estimated USD 13.5 billion by 2033. This remarkable growth trajectory is fueled by the increasing adoption of graph-based deep learning for complex data analytics, especially in sectors such as BFSI, healthcare, and IT & telecommunications, where traditional AI models fall short in capturing intricate data relationships.

One of the primary growth drivers for the Graph Neural Network Platform market is the exponential increase in connected data and the need for advanced analytics to derive actionable insights from it. With the proliferation of IoT devices, social networks, and enterprise systems, organizations are accumulating vast volumes of data with complex interdependencies. GNN platforms excel in analyzing these intricate networks, enabling businesses to uncover hidden patterns, detect anomalies, and optimize decision-making processes. The ability of GNNs to model relationships in data far surpasses conventional machine learning algorithms, making them indispensable for applications like fraud detection, recommendation systems, and knowledge graph construction.

Moreover, the growing emphasis on personalized customer experiences and targeted marketing strategies is accelerating the adoption of Graph Neural Network Platforms in retail, e-commerce, and financial services. Enterprises are leveraging GNNs to enhance recommendation engines, predict customer behavior, and deliver hyper-personalized offerings, thereby increasing customer engagement and retention. In the healthcare sector, GNNs are revolutionizing drug discovery and patient care by facilitating the analysis of biological networks, protein interactions, and disease pathways. This technological edge, combined with increasing investments in AI research and development, is propelling the market forward at an unprecedented pace.

Another significant factor contributing to the market’s growth is the rapid evolution of cloud computing and scalable infrastructure. Cloud-based deployment modes are making GNN platforms more accessible to organizations of all sizes, eliminating the need for heavy upfront investments in hardware and specialized personnel. The integration of GNNs with big data analytics, edge computing, and other AI technologies is further expanding their use cases across industries. As regulatory frameworks mature and data privacy concerns are addressed, adoption rates are expected to soar, particularly in regions with strong digital transformation initiatives.

From a regional perspective, North America currently dominates the Graph Neural Network Platform market due to its robust technological ecosystem, high concentration of AI startups, and significant R&D investments. However, the Asia Pacific region is emerging as a formidable contender, driven by rapid digitization, government support for AI initiatives, and the presence of large-scale enterprises in countries like China, India, and Japan. Europe also represents a substantial share, bolstered by stringent data regulations and a focus on innovation in healthcare and finance. Latin America and the Middle East & Africa are gradually catching up, fueled by growing awareness and adoption of advanced analytics solutions.

Component Analysis

The Component segment of the Graph Neural Network Platform market is bifurcated into Software and Services, each playing a pivotal role in the ecosystem. The Software sub-segment dominates the market, accounting for over 68% of the total revenue in 2024. This dominance is attributed to the increasing demand for robust, scalable, and easy-to-integrate GNN frameworks and libraries that can be tailored for diverse use cases. Software solutions are continuously evolving to offer greater flexibility, interoperability with existing data systems, and user-friendly interfaces that cater to both data scientists and business analysts. The proliferation of open-source GNN libraries and the integration of proprietary features by leading vendors are further enhancing the value proposition for enterprises.<br

Graph and download economic data for Canadian Dollars to U.S. Dollar Spot Exchange Rate (EXCAUS) from Jan 1971 to Nov 2025 about Canada, exchange rate, currency, rate, and USA.