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.

This dataset contains sales transaction records used to create an interactive Excel Sales Performance Dashboard for business analytics practice.

It includes six columns capturing essential sales metrics such as date, region, product, quantity, sales revenue, and profit. The data is structured to help analysts and learners explore data visualization, PivotTable summarization, and dashboard design concepts in Excel.

The dataset was created for educational and demonstration purposes to help users:

1. Build dashboards that visualize total sales and profit trends
2. Identify top-performing products and high-profit regions
3. Practice Excel-based business analytics workflows

Columns: Date – Transaction date (daily sales record) Region – Geographic area of the sale (East, West, North, South) Product – Product category or item sold Sales – Total revenue generated from the sale (USD) Profit – Net profit made per transaction Quantity – Number of units sold

Typical uses include: Excel or Power BI dashboard projects PivotTable practice for business reporting Data cleaning and chart-building exercises Portfolio development for business analytics students Built and tested in Microsoft Excel using PivotTables, Charts, and Conditional Formatting.

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.

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 contains in-air hand-written numbers and shapes data used in the paper:B. Alwaely and C. Abhayaratne, "Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition," in IEEE Access, vol. 7, pp. 159661-159673, 2019, doi: 10.1109/ACCESS.2019.2950643.The dataset contains the following:-Readme.txt- InAirNumberShapeDataset.zip containing-Number Folder (With 2 sub folders for Matlab and Excel)-Shapes Folder (With 2 sub folders for Matlab and Excel)The datasets include the in-air drawn number and shape hand movement path captured by a Kinect sensor. The number sub dataset includes 500 instances per each number 0 to 9, resulting in a total of 5000 number data instances. Similarly, the shape sub dataset also includes 500 instances per each shape for 10 different arbitrary 2D shapes, resulting in a total of 5000 shape instances. The dataset provides X, Y, Z coordinates of the hand movement path data in Matlab (M-file) and Excel formats and their corresponding labels.This dataset creation has received The University of Sheffield ethics approval under application #023005 granted on 19/10/2018.

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...

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

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

📊 Bank Transaction Analytics Dashboard – SQL + Excel

🔹 Overview

This project focuses on Bank Transaction Analysis using a combination of SQL scripts and Excel dashboards. The goal is to provide insights into customer spending patterns, payment modes, suspicious transactions, and overall financial trends.

The dataset and analysis files can help learners and professionals understand how SQL and Excel can be used together for business decision-making, customer behavior tracking, and data-driven insights.

🔹 Contents

The dataset includes the following resources:

📂 SQL Scripts:

Create & Insert tables

15 Basic Queries

15 Advanced Queries

📂 CSV File:

Bank Transaction Analytics.csv (main dataset)

📂 Excel Charts:

Pie, Bar, Column, Line, Doughnut charts

Final Interactive Dashboard

📂 Screenshots:

Query outputs, Charts, and Final Dashboard visualization

📂 PDF Reports:

Project Report

Dashboard Report

📄 README.md:

Complete documentation and step-by-step explanation

🔹 Key Insights

26–35 age group spent the most across categories.

Amazon identified as the top merchant.

NetBanking showed the highest share compared to POS/UPI.

Travel & Shopping emerged as dominant categories.

🔹 Applications

Detecting suspicious transactions.

Understanding customer behavior.

Identifying top merchants and categories.

Building business intelligence dashboards.

🔹 How to Use

Download the dataset and SQL scripts.

Run Bank_Transaction_Analytics.SQL to create and insert data.

Execute the queries (Basic + Advanced) for insights.

Open Excel files to explore interactive charts and dashboards.

Refer to Project Report PDF for documentation.

🔹 Author

👩‍💻 Created by: Prachi Singh

GitHub: Bank Transaction Analytics Dashboard(https://github.com/prachi-singh-ds/Bank-Transaction-Analytics-Dashboard)

⚡This project is a complete SQL + Excel integration case study and is suitable for Data Science, Business Analytics, and Data Engineering portfolios.

UPDATED EVERY WEEK Last Update - 26th July 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

Context

• The NIFTY 50 is a well-diversified 50 stock index and it represents 13 important sectors of the economy.
• It is used for a variety of purposes such as benchmarking fund portfolios, index-based derivatives, and index funds.
• NIFTY 50 is owned and managed by NSE Indices Limited.
• The NIFTY 50 index has shaped up to be the largest single financial product in India.

This data contains all the indices of NSE. NIFTY 50, NIFTY BANK, NIFTY 100, NIFTY COMMODITIES, NIFTY CONSUMPTION, NIFTY FIN SERVICE, NIFTY IT, NIFTY INFRA, NIFTY ENERGY, NIFTY FMCG, NIFTY AUTO, NIFTY 200, NIFTY ALPHA 50, NIFTY 500, NIFTY CPSE, NIFTY GS COMPSITE, NIFTY HEALTHCARE, NIFTY CONSR DURBL, NIFTY LARGEMID250, NIFTY INDIA MFG, NIFTY IND DIGITAL, INDIA VIX

File Information and Column Descriptions.

Nifty 50 index data with 1 minute data. The dataset contains OHLC (Open, High, Low, and Close) prices from Jan 2015 to Aug 2024. - This dataset can be used for time series analysis, regression problems, and time series forecasting both for one step and multi-step ahead in the future. - Options data can be integrated with this minute data, to get more insight about this data. - Different backtesting strategies can be built on this data.

File Information

• This dataset contains 6 files, each file contains nifty 50 data with different intervals.
• Different intervals are - 1 min, 3 min, 5 min, 15 min, and 1 hour, Daily data from intervals of 2015 Jan to 2024 August.

Column Descriptors

• Each file contains OHLC (Open, High, Low, and Close) prices and Data time information. Since these are Nifty 50 index data, so volume is not present.

Inspiration

Time series forecasting - Predict stock price

• Predict future stock price one step ahead and multi-step ahead in time.
• Use different time series forecasting techniques for forecasting the future stock price. ### Machine learning and Deep learning techniques
• Possible ML and DL models include Neural networks, RNNs, LSTMs, Transformers, Attention networks, etc.
• Different error functions can be considered like RMSE, MAE, RMSEP etc. ### Feature engineering
• Different augmented features can be created and that can be used for forecasting.
• Correlation analysis, Feature importance to justify the important features.

This data publication is part of the 'P³-Petrophysical Property Database' project, which was developed within the EC funded project IMAGE (Integrated Methods for Advanced Geothermal Exploration, EU grant agreement No. 608553) and consists of a scientific paper, a full report on the database, the database as excel and .csv files and additional tables for a hierarchical classification of the petrography and stratigraphy of the investigated rock samples (see related references). This publication here provides a hierarchical interlinked stratigraphic classification according to the chronostratigraphical units of the international chronostratigraphic chart of the IUGS v2016/04 (Cohen et al. 2013, updated) according to international standardisation. As addition to this IUGS chart, which is also documented in GeoSciML, stratigraphic IDs and parent IDs were included to define the direct relationships between the stratigraphic terms. The P³ database aims at providing easily accessible, peer-reviewed information on physical rock properties relevant for geothermal exploration and reservoir characterization in one single compilation. Collected data include hydraulic, thermophysical and mechanical properties and, in addition, electrical resistivity and magnetic susceptibility. Each measured value is complemented by relevant meta-information such as the corresponding sample location, petrographic description, chronostratigraphic age and, most important, original citation. The original stratigraphic and petrographic descriptions are transferred to standardized catalogues following a hierarchical structure ensuring intercomparability for statistical analysis, of which the stratigraphic catalogue is presented here. These chronostratigraphic units are compiled to ensure that formations of a certain age are connected to the corresponding stratigraphic epoch, period or erathem. Thus, the chronostratigraphic units are directly correlated to each other by their stratigraphic ID and stratigraphic parent ID and can thus be used for interlinked data assessment of the petrophysical properties of samples of an according stratigraphic unit.

According to Cognitive Market Research, the global Graph Database market size was USD 7.3 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031. Market Dynamics of Graph Database Market

Key Drivers for Graph Database Market

Increasing demand for solutions with the capability to process low-latency queries-One of the main reasons the Graph Database market is extensively being used all over the globe, to the extent that numerous legacy database providers are endeavoring to assimilate graph database schemas into their main relational database infrastructures. Whereas, in theory, the strategy might save money, it might degrade and slow down the performance of queries run beside the database. A graph database is altering traditional brick-and-mortar trades into digital business powerhouses in terms of digital business activities.
Growing usage of graph database technology to drive the Graph Database market's expansion in the years ahead.

Key Restraints for Graph Database Market

Complex programming and standardization pose a serious threat to the Graph Database industry.
The market also faces significant difficulties related to low-cost clusters.

Introduction of the Graph Database Market

The graph database market has experienced significant growth due to the increasing need for efficient data management and complex relationship mapping in various industries. Unlike traditional relational databases, graph databases excel in handling interconnected data, making them ideal for applications such as social networks, fraud detection, recommendation engines, and supply chain management. Key drivers of this market include the rising adoption of big data analytics, advancements in artificial intelligence, and the proliferation of connected devices. Leading players, such as Neo4j, Amazon Web Services, and Microsoft, continue to innovate, offering scalable and robust graph database solutions. The growing demand for real-time, low-latency data processing capabilities further propels the market's expansion.

📊 Road Accident Data Analysis: Interactive Excel Dashboard 🚗

Excited to share my Kaggle project focusing on road accident data analysis. Leveraging Excel's power, I've developed an interactive dashboard offering comprehensive insights for safer roads.

Key Aspects:

Data Processing & Cleaning: Ensured data reliability through meticulous processing. KPIs: Primarily focused on Total Casualties, with detailed breakdowns for Fatal, Serious, Slight, and by Car type. Visualizations: Engaging charts - Doughnuts, Line, Bar, and Pie - offering a holistic view of accident trends. Interactivity: User-friendly features include Urban/Rural and Year filters for dynamic exploration. Unique Insights:

Monthly Trends: Line chart for a nuanced comparison of current vs. previous year casualties. Road Type Breakdown: Bar chart to showcase casualties distributed across different road types. Geospatial Analysis: Doughnut charts detailing casualties by location and area. Call for Collaboration: Seeking Kaggle community input for refinement and optimization. Let's collectively contribute to making our roads safer through data-driven insights!

DataAnalysis #RoadSafety #InteractiveDashboard #KaggleProject #Excel #DataVisualization #CollaborationOpportunity

Looking forward to your feedback and contributions! 🚀🌐

Article title should theoretically reflect the content to which it represents. But, there is a paucity of quantitative methods for measuring the relevance of a title to an article’s content. One approach for quantifying the relevance of a title with respect to an article’s content is through mining the frequency of occurrence of words in the article title in the main text of the article. Profiling of high frequency of occurrence of many words in the title would suggests higher degree of relevance of the title with respect to the article’s content. But this must be taken in light of the caveat that some titles are phrased to express the conclusion of a paper, and would thus suffer from a lower frequency of occurrence of words of the title in the main text. This work reports a dataset that has been obtained through analysing the frequency of occurrence of words in the title of journal article in the main text of the article. This process is aided by an in-house MATLAB software coded for the project. Articles from different fields of biology such as biochemistry, bioinformatics, biotechnology, cell biology, computational biology, genetics, genomics, microbiology, molecular biology, structural biology, synthetic biology, and systems biology were put through the same analysis, and an Excel file detailing the frequency of occurrence of words in the title of the article serve as the output. The data is also visualized as a bar chart in the same Excel file.

Geological events chart in excel, includes columns to input information on the plate tectonic setting, regional tectonic setting, depositional environment and lithostratigraphy. This list is not exhaustive and should be used to build on, adding your own (e.g. local datasets, petroleum systems, data by basin or quad).Age (Ma) based on the International Chronostratigraphic Chart v2018/08 by the International Commission on Stratigraphy. Use this spreadsheet as a basis for collating and cataloging information when starting a new project or when working in a new area. Allows tectonostratigraphic data to be laid out chronologically along the geological timescale.