Spreadsheets targeted at the analysis of GHS safety fingerprints.AbstractOver a 20-year period, the UN developed the Globally Harmonized System (GHS) to address international variation in chemical safety information standards. By 2014, the GHS became widely accepted internationally and has become the cornerstone of OSHA’s Hazard Communication Standard. Despite this progress, today we observe that there are inconsistent results when different sources apply the GHS to specific chemicals, in terms of the GHS pictograms, hazard statements, precautionary statements, and signal words assigned to those chemicals. In order to assess the magnitude of this problem, this research uses an extension of the “chemical fingerprints” used in 2D chemical structure similarity analysis to GHS classifications. By generating a chemical safety fingerprint, the consistency of the GHS information for specific chemicals can be assessed. The problem is the sources for GHS information can differ. For example, the SDS for sodium hydroxide pellets found on Fisher Scientific’s website displays two pictograms, while the GHS information for sodium hydroxide pellets on Sigma Aldrich’s website has only one pictogram. A chemical information tool, which identifies such discrepancies within a specific chemical inventory, can assist in maintaining the quality of the safety information needed to support safe work in the laboratory. The tools for this analysis will be scaled to the size of a moderate large research lab or small chemistry department as a whole (between 1000 and 3000 chemical entities) so that labelling expectations within these universes can be established as consistently as possible.Most chemists are familiar with programs such as excel and google sheets which are spreadsheet programs that are used by many chemists daily. Though a monadal programming approach with these tools, the analysis of GHS information can be made possible for non-programmers. This monadal approach employs single spreadsheet functions to analyze the data collected rather than long programs, which can be difficult to debug and maintain. Another advantage of this approach is that the single monadal functions can be mixed and matched to meet new goals as information needs about the chemical inventory evolve over time. These monadal functions will be used to converts GHS information into binary strings of data called “bitstrings”. This approach is also used when comparing chemical structures. The binary approach make data analysis more manageable, as GHS information comes in a variety of formats such as pictures or alphanumeric strings which are difficult to compare on their face. Bitstrings generated using the GHS information can be compared using an operator such as the tanimoto coefficent to yield values from 0 for strings that have no similarity to 1 for strings that are the same. Once a particular set of information is analyzed the hope is the same techniques could be extended to more information. For example, if GHS hazard statements are analyzed through a spreadsheet approach the same techniques with minor modifications could be used to tackle more GHS information such as pictograms.Intellectual Merit. This research indicates that the use of the cheminformatic technique of structural fingerprints can be used to create safety fingerprints. Structural fingerprints are binary bit strings that are obtained from the non-numeric entity of 2D structure. This structural fingerprint allows comparison of 2D structure through the use of the tanimoto coefficient. The use of this structural fingerprint can be extended to safety fingerprints, which can be created by converting a non-numeric entity such as GHS information into a binary bit string and comparing data through the use of the tanimoto coefficient.Broader Impact. Extension of this research can be applied to many aspects of GHS information. This research focused on comparing GHS hazard statements, but could be further applied to other bits of GHS information such as pictograms and GHS precautionary statements. Another facet of this research is allowing the chemist who uses the data to be able to compare large dataset using spreadsheet programs such as excel and not need a large programming background. Development of this technique will also benefit the Chemical Health and Safety community and Chemical Information communities by better defining the quality of GHS information available and providing a scalable and transferable tool to manipulate this information to meet a variety of other organizational needs.

The Delta Produce Sources Study was an observational study designed to measure and compare food environments of farmers markets (n=3) and grocery stores (n=12) in 5 rural towns located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys from June 2019 to March 2020 using a modified version of the Nutrition Environment Measures Survey (NEMS) Farmers Market Audit tool. The tool was modified to collect information pertaining to source of fresh produce and also for use with both farmers markets and grocery stores. Availability, source, quality, and price information were collected and compared between farmers markets and grocery stores for 13 fresh fruits and 32 fresh vegetables via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Resources in this dataset:Resource Title: Delta Produce Sources Study dataset . File Name: DPS Data Public.csvResource Description: The dataset contains variables corresponding to availability, source (country, state and town if country is the United States), quality, and price (by weight or volume) of 13 fresh fruits and 32 fresh vegetables sold in farmers markets and grocery stores located in 5 Lower Mississippi Delta towns.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Delta Produce Sources Study data dictionary. File Name: DPS Data Dictionary Public.csvResource Description: This file is the data dictionary corresponding to the Delta Produce Sources Study dataset.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

This project delves into the sales data of Maven Coffee Shop to uncover valuable insights and trends. Using Excel, we meticulously cleaned, analyzed, and visualized the data to help understand the business's performance across various dimensions. The original data set was posted on Kaggle.

Key Objectives:

Analyze Sales Trends: Identify sales patterns over time to understand peak periods and sales growth. Evaluate Product Performance: Determine which products and categories drive the most revenue. Assess Store Performance: Compare sales across different store locations to highlight top-performing stores. Interactive Insights: Create an interactive dashboard that allows stakeholders to explore the data without the risk of unintentional edits.

Features:

Comprehensive Data Cleaning: Ensured the data is accurate and ready for analysis. Detailed Revenue Analysis: Explored total sales, average transaction values, and unit sales. Product and Store Analysis: Investigated sales by product category, type, and store location. Interactive Dashboard: Designed a user-friendly Excel dashboard for dynamic data interaction.

This project serves as a practical example of using Excel for data analysis and visualization, providing actionable insights into the operational and financial aspects of a coffee shop business.

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, after collecting a number of revenues and expenses over the months. Needed to know the answers to a number of questions to make important decisions based on intuition-free data. The Questions:- About Rev. & Exp.
- What is the total sales and profit for the whole period? And What Total products sold? And What is Net profit? - In which month was the highest percentage of revenue achieved? And in the same month, what is the largest day have amount of revenue? - In which month was the highest percentage of expenses achieved? And in the same month, what is the largest day have amount of exp.? - What is the extent of the change in expenditures for each month? Percentage change in net profit over the months? About Distribution - What is the number of products sold each month in the largest state? -The top 3 largest states buying products during the two years? Comparison - Between Sales Method by Sales? - Between Men and Women’s Product by Sales? - Between Retailer by Profit?

What I did? - Understanding the data - preprocessing and clean the data - Solve The problems in the cleaning like missing data or false type data - querying the data and make some calculations like "COGS" with power query "Excel". - Modeling and make some measures on the data with power pivot "Excel" - After finishing processing and preparation, I made Some Pivot tables to answers the questions. - Last, I made a dashboard with Power BI to visualize The Results.

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

Chemistry is increasingly data centric and the undergraduate curriculum needs to adjust to keep up. To address this, we created the Air Quality Activity, a new first-year undergraduate activity where students use Microsoft Excel to analyze a unique subset of atmospheric ozone (O3) and nitrogen dioxide (NO2) measurements from the Canadian National Air Pollution Surveillance (NAPS) program. Through this activity students develop their numeracy, graphicacy, and proficiency with Excel. Moreover, students are equipped with a foundational approach to data analysis they can leverage throughout their studies. To make this activity possible, we developed an open-source webbook detailing pertinent Excel operations for first-year students, and an interactive web-app for the generation, distribution, and exploration of NAPS data. Students were excited by the analysis of real-world chemical phenomena in comparison to traditional first-year lab exercises and appreciated their acquired Excel skills. The Air Quality Activity is readily adaptable for both virtual and in-person implementation, entirely open-source, and readily deployable at any institution wishing to teach data analysis in a chemistry context.

Excel spreadsheet of raw data used to generate Fig 29 (a)–(b).

Data set represents the average bill of materials for 25 common categories of consumer electronics products collected via product disassembly and physical material identification and measurement. These data records are compiled in two excel workbooks containing BOM data collected and organized at different levels of aggregation. First, the “Disassembly Detail” workbook provides resolved material and component data at the level of each major assembly and sub-assembly. Each worksheet represents a single product category, and most categories contain detailed data for multiple product samples.Second, the “Product Bill of Materials” workbook provides total mass and mass percent of each separable material and component for all products studied and a mean, maximum, and minimum mass (g) and mass percent (%) for each product category calculated using the lab data points. The workbooks also includes literature values, and evaluated using three parameters: traceability, level of detail and category consistency. This qualitative analysis of data from published literature is indicated next to each data point. Third, the "Uncertainty Analysis" workbook provides information on mass of the product prior to disassembly, post disassembly, and manufacturer reported mass, when available for the same make, model, and year of the product studied in the lab. Percentage difference between mass of the product prior and post disassembly, and mass of the product post disassembly and reported mass by manufacturer is provided. This workbook also includes side by side comparison of our lab BOM data for iPod and Amazon kindle to the high quality literature data.