The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

These datasets contains all the data used to make the figures in the associated paper. The excel files are self-explanatory and can be directly used. While the other files in netcdf format, need a visualization tool (such as VERDI) or statistical software (such as R) to make statistical summary or plots. Portions of this dataset are inaccessible because: data will be uploaded when paper will be accepted by journal. They can be accessed through the following means: For excel files, the data can be directly used to make summary or plots. For netcdf files, another visualization tool or statistical package (such as R) can be used. All the netcdf files can be visualized using VERDI. Format: Two types of data formats. One is the excel files which are self-explanatory. The other type is netcdf files which are used to make the spatial plots in the paper. This dataset is associated with the following publication: Kang, D., J. Willison, G. Sarwar, M. Madden, C. Hogrefe, R. Mathur, B. Gantt, and S. Alfonso. Improving the Characterization of the Natural Emissions in CMAQ. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, (10): 1-7, (2021).

This repository contains a collection of data about 454 value chains from 23 rural European areas of 16 countries. This data is obtained through a semi-automatic workflow that transforms raw textual data from an unstructured MS Excel sheet into semantic knowledge graphs.In particular, the repository contains:MS Excel sheet containing different value chains details provided by MOuntain Valorisation through INterconnectedness and Green growth (MOVING) European project;454 CSV files containing events, titles, entities and coordinates of narratives of each value chain, obtained by pre-processing the MS Excel sheet454 Web Ontology Language (OWL) files. This collection of files is the result of the semi-automatic workflow, and is organized as a semantic knowledge graph of narratives, where each narrative is a sub-graph explaining one among the 454 value chains and its territory aspects. The knowledge graph is based on the Narrative Ontology, an ontology developed by Institute of Information Science and Technologies (ISTI-CNR) as an extension of CIDOC CRM, FRBRoo, and OWL Time.Two CSV files that compile all the possible available information extracted from 454 Web Ontology Language (OWL) files.GeoPackage files with the geographic coordinates related to the narratives.The HTML files that show all the different SPARQL and GeoSPARQL queries.The HTML files that show the story maps about the 454 value chains.An image showing how the various components of the dataset interact with each other.

Graph Data Integration Platform Market Outlook

According to our latest research, the global graph data integration platform market size reached USD 2.1 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 18.4% from 2025 to 2033, reaching approximately USD 10.7 billion by 2033. This significant growth is fueled by the increasing need for advanced data management and analytics solutions that can handle complex, interconnected data across diverse organizational ecosystems. The rapid digital transformation and the proliferation of big data have further accelerated the demand for graph-based data integration platforms.

The primary growth factor driving the graph data integration platform market is the exponential increase in data complexity and volume within enterprises. As organizations collect vast amounts of structured and unstructured data from multiple sources, traditional relational databases often struggle to efficiently process and analyze these data sets. Graph data integration platforms, with their ability to map, connect, and analyze relationships between data points, offer a more intuitive and scalable solution. This capability is particularly valuable in sectors such as BFSI, healthcare, and telecommunications, where real-time data insights and dynamic relationship mapping are crucial for decision-making and operational efficiency.

Another significant driver is the growing emphasis on advanced analytics and artificial intelligence. Modern enterprises are increasingly leveraging AI and machine learning to extract actionable insights from their data. Graph data integration platforms enable the creation of knowledge graphs and support complex analytics, such as fraud detection, recommendation engines, and risk assessment. These platforms facilitate seamless integration of disparate data sources, enabling organizations to gain a holistic view of their operations and customers. As a result, investment in graph data integration solutions is rising, particularly among large enterprises seeking to enhance their analytics capabilities and maintain a competitive edge.

The surge in regulatory requirements and compliance mandates across various industries also contributes to the expansion of the graph data integration platform market. Organizations are under increasing pressure to ensure data accuracy, lineage, and transparency, especially in highly regulated sectors like finance and healthcare. Graph-based platforms excel in tracking data provenance and relationships, making it easier for companies to comply with regulations such as GDPR, HIPAA, and others. Additionally, the shift towards hybrid and multi-cloud environments further underscores the need for robust data integration tools capable of operating seamlessly across different infrastructures, further boosting market growth.

From a regional perspective, North America currently dominates the graph data integration platform market, accounting for the largest share due to early adoption of advanced data technologies, a strong presence of key market players, and significant investments in digital transformation initiatives. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid industrialization, expanding IT infrastructure, and increasing adoption of cloud-based solutions among enterprises in countries like China, India, and Japan. Europe also remains a significant contributor, supported by stringent data privacy regulations and a mature digital economy.

Component Analysis

The component segment of the graph data integration platform market is bifurcated into software and services. The software segment currently commands the largest market share, reflecting the critical role of robust graph database engines, visualization tools, and integration frameworks in managing and analyzing complex data relationships. These software solutions are designed to deliver high scalability, flexibility, and real-time proces

Project Priority Matrix (Dynamic Excel Tool)

Overview

This dataset provides a dynamic Excel model for prioritizing projects based on Feasibility, Impact, and Size.
It visualizes project data on a Bubble Chart that updates automatically when new projects are added.

Use this tool to make data-driven prioritization decisions by identifying which projects are most feasible and high-impact.

Goal

Organizations often struggle to compare multiple initiatives objectively.
This matrix helps teams quickly determine which projects to pursue first by visualizing:

• Feasibility → How achievable a project is
• Impact → The potential benefit or value it delivers
• Size → The level of effort or resources required

How It Works

1. Each project is rated on a 1–10 scale for:
   • Feasibility
   • Impact
   • Size
2. The Excel file uses a Bubble Chart:
   • X-axis: Feasibility
   • Y-axis: Impact
   • Bubble size: Project Size
3. The chart automatically updates when new projects or scores are added.

Example (partial data):

Interpretation Guide

Excel Features

• Dynamic Bubble Chart (updates with new data)
• Named Ranges for auto-expanding data
• Optional Conditional Formatting
• Data Validation for consistent scoring

How to Use

1. Download and open Project_Priority_Matrix.xlsx.
2. Go to the Data sheet.
3. Add your project names and scores (1–10).
4. Watch the chart update instantly to reflect your data.

You can use this for: - Portfolio management
- Product or feature prioritization
- Strategy planning workshops

File Information

• File: Project_Priority_Matrix.xlsx
• Format: Excel (.xlsx)
• Version: 1.0
• Last Updated: October 2025

License

Free for personal and organizational use.
Attribution is appreciated if you share or adapt this file.

Author: [Asjad]
Contact: [m.asjad2000@gmail.com]
Compatible With: Microsoft Excel 2019+ / Office 365

Hello Everyone, I made this Finance Dashboard in Power BI with the Finance Excel Workbook provided by Microsoft on their Website. Problem Statement The goal of this Power BI Dashboard is to analyze the financial performance of a company using the provided Microsoft Sample Data. To create a visually appealing dashboard that provides an overview of the company's financial metrics enabling stakeholders to make informed business decisions. Sections in the Report Report has multiple section's from where you can manage the data, like : • Report data can be sliced by Segments, Country and Year to show particular data. - Report Contain Two Navigation Page one is overview and other is sales dashboard page for better visualisation of data. - Report Contain all the important data. - Report Contain different chart and bar garph for different section .

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The data consists of long term diameter growth observations of uneven-sized Norway spruce dominated stands. The site has been managed with continuous individual tree selection for at least 100 years. There are two plots with different timing of cutting treatments. Height and age measurements of sample trees. One of the plots have coordinate set tree positions. Initial revision is 1989. The first plot consists of 209 tree observations and the second plot consists of 257 tree observations (at first revision point).

The Excel file [Romperöd uniform data copy of version 3.xlsx] contains all data from all revisions between 1989 and 2015. This data is without coordinates of the tree positions. The data file contains information that links the tree identities between the two data files.

The Excel file [Romperöd level 1b copy.xlsx] contains data from an extended revision of the thinned plot where the trees were also coordinate set. 74 of the trees have extended information on annual growth ring, root incidence, crown shape and height.

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/

This dataset includes two excel sheets. The first contains vegetation data ("species_data", a matrix of 668 plots x 213 species) and the second contains plant functional traits data ("traits_data") that were used to evaluate temporal changes in taxonomic and functional diversity of Mediterranean coastal dune habitats.

As to the first sheet ("species_data"): vegetation data were collected at two points in time (Time 0, hereafter T₀: 2002-2007, and Time 1, herafter T₁: 2017-2018) in 334 randomly-sampled, georeferenced, standardized (4 m²) plots. Historical data used for the resurveying study were extracted from RanVegDunes (Sperandii et al. 2017). Details on the resurveying protocol can be found in Sperandii et al. (2019), but in short: resampling activities took place during the same months in which the original sampling was done, and plot positions were relocated using a GPS unit on which historical geographic coordinates were stored. Plots are located in coastal dune sites along the Tyrrhenian and Adriatic coasts of Central Italy, and belong to herbaceous communities classified into the following EU Habitats (sensu Annex I 92/43/EEC): upper beach (Habitat 1210), embryo dunes (Habitat 2110), shifting dunes (Habitat 2120), fixed dunes (Habitat 2210), and dune grasslands (Habitat 2230). A subset of plots could not be classified into an EU Habitat because they were highly disturbed or invaded by alien species (“NC-plots”). The matrix includes cover data, expressed as percentage (%) cover.

As to the second sheet ("traits_data"): this sheet includes data on 3 plant functional traits, two of them quantitative (plant height, specific leaf area - SLA) and one qualitative (plant lifespan). Data for the quantitative traits represent species-level average trait values and were extracted from “TraitDunes”, a database registered on the global platform TRY (Kattge et al., 2020). Functional trait data were collected in the same sites covered by the resurveying study. Functional trait data were originally measured on the most abundant species, and are available for a varying number of species depending on the trait.

References:

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., ... & Wirth, C. (2020). TRY plant trait database–enhanced coverage and open access. Global Change Biology.

Sperandii, M.G., Prisco, I., Stanisci, A., & Acosta, A.T.R (2017). RanVegDunes-A random plot database of Italian coastal dunes. Phytocoenologia, 47(2), 231-232.

Sperandii, M.G., Bazzichetto, M., Gatti, F., & Acosta, A.T.R. (2019). Back into the past: Resurveying random plots to track community changes in Italian coastal dunes. Ecological Indicators, 96, 572-578.

A study was conducted in four different environments of Ghana. The aim was to optimize maize by developing maize hybrids tolerant of high plant density. The hybrids were evaluated under three plant densities, namely, high (88,888 plants/ha), medium (66,666 plants/ha), and low (53,333 plants/ha). The experimental design was 8 x 6 alpha lattice with split plot. The experiment was replicated two times in each of the four environments. Data on different phenotypic traits were collected either by measuring or counting., The data are collected from field experiments. Most of them were directly entered into Excel sheets using a tablet in the field, but some were recorded into a hard copy of data collecting sheets. Some data were transformed, but both the original and the transformed data are all available in the Excel file. , , ## Optimizing_maize_yield_in_West_and_Central_Africa

https://doi.org/10.5061/dryad.sbcc2frj9

Description of the data and file structure

The data are phenotypic data of maize hybrids.  The data are collected from four environments. The environments are written in full in the excel, except “Legon minor season†which is written as Legon_Mi, and “Legon off season†which is written as Legon_off. The data are put in one Excel file, but two separate sheets. The first excel sheet has data which were collected during harvesting and after harvest, and the second excel sheet has data which were collected before harvesting. All the data were collected from four environments, except days to maturity, chlorophyll content, and tassel size which were determined only from one, two, and three environments, respectively. For the environments where these parameters (traits) were not collected, the Excel cells are filled with "n/a". Plant Density (PD), Enviro...,

Service Topology Graph Database Market Outlook

According to our latest research, the global service topology graph database market size reached USD 1.42 billion in 2024, demonstrating robust momentum with a compound annual growth rate (CAGR) of 21.8%. The market is expected to achieve a value of USD 10.62 billion by 2033. This impressive growth is primarily driven by the increasing demand for advanced data management solutions, the proliferation of complex IT infrastructures, and the rising necessity for real-time analytics and visualization across diverse industries. The market’s rapid expansion is further bolstered by technological advancements in graph database architectures and the growing adoption of cloud-based deployment models.

One of the most significant growth factors in the service topology graph database market is the escalating complexity of modern IT environments. As organizations transition toward hybrid and multi-cloud infrastructures, the need for solutions that can accurately map and manage intricate service relationships has become paramount. Graph databases excel at representing highly interconnected data, making them ideal for modeling service topologies. This capability enables enterprises to visualize dependencies, identify bottlenecks, and optimize resource allocation, thereby enhancing operational efficiency and minimizing downtime. Additionally, the growing integration of artificial intelligence and machine learning with graph databases allows for predictive analytics and automated anomaly detection, further fueling market growth.

Another key driver is the surge in demand for enhanced network management and security. With the increasing frequency and sophistication of cyber threats, organizations are seeking comprehensive solutions to monitor and secure their networks. Service topology graph databases provide unparalleled visibility into network structures, enabling proactive identification of vulnerabilities and facilitating rapid incident response. These databases support real-time monitoring and compliance tracking, which are critical for industries with stringent regulatory requirements such as BFSI and healthcare. The ability to correlate data from multiple sources and uncover hidden patterns is proving invaluable for security teams, making graph databases an essential component of modern cybersecurity strategies.

The expanding adoption of digital transformation initiatives across various sectors also contributes to the market’s growth. Enterprises are leveraging service topology graph databases to streamline asset management, optimize IT operations, and improve customer experiences. In the retail sector, for example, these databases help map customer journeys and personalize interactions by analyzing relationships between products, users, and transactions. In manufacturing, they facilitate predictive maintenance and supply chain optimization by modeling equipment dependencies and process flows. As organizations continue to prioritize data-driven decision-making, the demand for graph-based solutions is expected to rise significantly, further propelling the market forward.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of major technology vendors, early adoption of advanced IT solutions, and significant investments in research and development. Europe follows closely, driven by stringent data privacy regulations and the need for efficient compliance management. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud computing. Latin America and the Middle East & Africa are also experiencing steady growth, supported by government initiatives to modernize public services and enhance cybersecurity capabilities.

Component Analysis

The component segment of the service topology graph database market is bifurcated into software and services, each playing a pivotal role in driving overall market expansion. The software sub-segment dominates the market, owing to the continuous evolution of graph database platforms that offer enhanced scalability, flexibility, and integration capabilities. Modern graph database software solutions are equipped with advanced visualization tools, intuitive user interfaces, and robust APIs, enabling seamless in

In this project, I analysed the employees of an organization located in two distinct countries using Excel. This project covers:

1) How to approach a data analysis project 2) How to systematically clean data 3) Doing EDA with Excel formulas & tables 4) How to use Power Query to combine two datasets 5) Statistical Analysis of data 6) Using formulas like COUNTIFS, SUMIFS, XLOOKUP 7) Making an information finder with your data 8) Male vs. Female Analysis with Pivot tables 9) Calculating Bonuses based on business rules 10) Visual analytics of data with 4 topics 11) Analysing the salary spread (Histograms & Box plots) 12) Relationship between Salary & Rating 13) Staff growth over time - trend analysis 14) Regional Scorecard to compare NZ with India

Including various Excel features such as: 1) Using Tables 2) Working with Power Query 3) Formulas 4) Pivot Tables 5) Conditional formatting 6) Charts 7) Data Validation 8) Keyboard Shortcuts & tricks 9) Dashboard Design

Master Data Graph Platforms Market Outlook

According to our latest research, the global Master Data Graph Platforms market size reached USD 2.14 billion in 2024, reflecting robust demand across diverse industries. The market is projected to expand at a CAGR of 18.2% from 2025 to 2033, culminating in a forecasted market size of USD 10.53 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of graph-based master data management solutions to address complex data relationships, enhance decision-making, and ensure regulatory compliance in an era defined by digital transformation and data-centric business models.

The primary growth factor fueling the Master Data Graph Platforms market is the explosive surge in enterprise data volumes and complexity. As organizations accumulate vast amounts of structured and unstructured data from multiple sources, the limitations of traditional relational databases have become increasingly apparent. Businesses are recognizing that graph platforms offer a highly flexible and scalable approach to modeling, integrating, and querying interconnected data. This capability is especially critical for organizations seeking to derive actionable insights from complex relationships, such as customer journeys, supply chain dependencies, and risk exposure. The market is further propelled by the need for real-time data integration and management, as enterprises strive to achieve a single, unified view of their master data across disparate systems and geographies.

Another significant driver is the growing emphasis on data governance, compliance, and risk management across regulated industries such as BFSI, healthcare, and government. Regulatory mandates like GDPR, HIPAA, and CCPA have heightened the importance of data lineage, transparency, and traceability. Master Data Graph Platforms excel at mapping data relationships and tracking data flows, enabling organizations to meet stringent compliance requirements while minimizing operational risk. The ability to visualize and audit data connections in real-time is a compelling value proposition, prompting enterprises to invest in advanced graph-based solutions that can adapt to evolving regulatory landscapes and safeguard sensitive information.

The proliferation of digital transformation initiatives, cloud migration, and the adoption of advanced analytics and artificial intelligence are also fueling market expansion. As organizations modernize their IT infrastructure and transition to cloud-native architectures, the demand for scalable, cloud-based master data management solutions is accelerating. The integration of Master Data Graph Platforms with AI and machine learning tools enhances the ability to uncover hidden patterns, automate data quality processes, and deliver personalized customer experiences. This convergence of technologies is creating new opportunities for innovation and competitive differentiation, further amplifying the market's growth trajectory.

From a regional perspective, North America continues to dominate the Master Data Graph Platforms market, accounting for the largest share in 2024, driven by early technology adoption, a mature digital ecosystem, and significant investments in data-driven initiatives. Europe is witnessing robust growth due to stringent data privacy regulations and the widespread adoption of advanced analytics in sectors such as finance, healthcare, and manufacturing. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding IT infrastructure, and increasing demand for data management solutions among enterprises in China, India, Japan, and Southeast Asia. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as organizations in these regions embark on digital transformation journeys and seek to enhance operational efficiency through better data management.

Component Analysis

The Component segment of the Master Data Graph Platforms market is primarily categorized into software and services, both of which play pivotal roles in driving the adoption and effectiveness of graph-based master data management solutions. The software component encompasses graph databases, data modeling tools, integration frameworks, and analytics engines that form the backbone of modern master data platforms. These software solutions are designed to facilitate the ingestion, storage, querying, and

his project involves the creation of an interactive Excel dashboard for SwiftAuto Traders to analyze and visualize car sales data. The dashboard includes several visualizations to provide insights into car sales, profits, and performance across different models and manufacturers. The project makes use of various charts and slicers in Excel for the analysis.

Objective: The primary goal of this project is to showcase the ability to manipulate and visualize car sales data effectively using Excel. The dashboard aims to provide:

Profit and Sales Analysis for each dealer. Sales Performance across various car models and manufacturers. Resale Value Analysis comparing prices and resale values. Insights into Retention Percentage by car models. Files in this Project: Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx: The original dataset used to create the dashboard. dashboards.xlsx: The final Excel file that contains the complete dashboard with interactive charts and slicers. Key Visualizations: Average Price and Year Resale Value: A bar chart comparing the average price and resale value of various car models. Power Performance Factor: A column chart displaying the performance across different car models. Unit Sales by Model: A donut chart showcasing unit sales by car model. Retention Percentage: A pie chart illustrating customer retention by car model. Tools Used: Microsoft Excel for creating and organizing the visualizations and dashboard. Excel Slicers for interactive filtering. Charts: Bar charts, pie charts, column charts, and sunburst charts. How to Use: Download the Dataset: You can download the Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx file from Kaggle and follow the steps to create a similar dashboard in Excel. Open the Dashboard: The dashboards.xlsx file contains the final version of the dashboard. Simply open it in Excel and start exploring the interactive charts and slicers.

Graph Database-as-a-Service Market Outlook

According to our latest research, the global Graph Database-as-a-Service market size reached USD 2.1 billion in 2024, reflecting a robust expansion across multiple industries. The market is exhibiting a strong compound annual growth rate (CAGR) of 25.6%, and is projected to attain a value of USD 15.2 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for highly scalable, flexible, and cloud-native data management solutions that can efficiently handle complex, interconnected datasets. The proliferation of digital transformation initiatives, surging adoption of advanced analytics, and the critical need for real-time data insights are further propelling the market forward, as organizations across sectors strive to optimize operations and unlock new business opportunities through graph-based technologies.

A significant factor fueling the expansion of the Graph Database-as-a-Service market is the escalating complexity of enterprise data environments. Traditional relational databases are often ill-equipped to manage the intricate relationships and dynamic data structures prevalent in modern business contexts. As a result, organizations are turning to graph databases for their ability to model, store, and analyze highly connected data efficiently. The rise of artificial intelligence, machine learning, and big data analytics has also intensified the need for data platforms that can seamlessly integrate with these technologies. Graph Database-as-a-Service solutions, with their cloud-native architecture and managed service offerings, enable businesses to rapidly deploy, scale, and maintain graph databases without the overhead of on-premises infrastructure, thus accelerating innovation and reducing operational costs.

Another key growth driver is the surge in demand for real-time analytics and personalized customer experiences across industries such as BFSI, retail, healthcare, and telecommunications. Graph databases excel at uncovering hidden patterns, detecting fraud, and enabling recommendation engines, which are critical for delivering tailored services and mitigating risks. Enterprises are leveraging Graph Database-as-a-Service platforms to enhance customer analytics, streamline risk and compliance management, and optimize network and IT operations. The flexibility of deployment models—including public, private, and hybrid cloud—further amplifies adoption, as organizations can select the architecture that best aligns with their security, scalability, and regulatory requirements. The integration of graph databases with existing IT ecosystems and the availability of robust APIs and developer tools are making it increasingly accessible for businesses of all sizes to harness the power of connected data.

From a regional perspective, North America continues to dominate the Graph Database-as-a-Service market, owing to its advanced technological infrastructure, early adoption of cloud computing, and a vibrant ecosystem of innovative startups and established enterprises. Europe is witnessing rapid growth, driven by stringent data privacy regulations and the increasing digitalization of industries. The Asia Pacific region is emerging as a significant growth engine, propelled by the expansion of e-commerce, financial services, and healthcare sectors, coupled with substantial investments in digital transformation initiatives. As organizations worldwide recognize the strategic value of graph data management, the market is expected to experience widespread adoption across both developed and emerging economies, with tailored solutions catering to diverse industry verticals and regulatory landscapes.

Component Analysis

The Graph Database-as-a-Service market is segmented by component into software and services, each playing a pivotal role in shaping the overall market dynamics. The software segment encompasses the core graph database platforms and associated tools that facilitate data modeling, querying, visualization, and integration. These platforms are designed to deliver high performance, scalability, and ease of use, enabling organizations to manage complex relationships and large volumes of interconnected data seamlessly. Leading vendors are continuously innovating, introducing advanced features such as multi-model support, enhanced security, and automated scaling, which are driving widespread adoption across various industry verticals. The software component is particularly critical for enterprise

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.