Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

The data is in the form of an Excel sheet. In that booklet, the first page is the raw data collected from google forms after data collection. The second page has the self-esteem scores. The third has the basic psychological needs scores. The fourth has the depression scores. The fifth has the compilation of all scores with the demographic data. Finally, the sixth page is the key to the demographics.

Dataset

This dataset was created by Shiva Vashishtha

Contents

Distribution of doses of a volatile organic compound from inhalation of one consumer product, other near -field sources, far-field sources, and aggregate (total) exposure. In this instance, far-field scenarios account for several orders of magnitude of less of the predicted dose compared to near-field scenarios. This dataset is associated with the following publication: Vallero, D. Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus. Sustainability. MDPI AG, Basel, SWITZERLAND, 8(12): 1216, (2016).

This dataset contains two Excel files, one Word file and one Text file. They are my work in the capstone project of a 21-day Excel Challenge that I took part of.

A description of the files is as follows: 1. raw_data: the raw version that was provided to be in the beginning of the project. 2. insights: where I noted down some insights that answer the stakeholders' business questions, as well as some notes about my data cleaning process for the data team manager. 3. project_requirements: the original requirements from the manager and other stakeholders, in the form of two emails addressed to the data team. 4. finished_project: my finished work. The completed version includes: - Tasks sheet: where I copied all the tasks given for more convenience. - PivotTables sheet: where I placed the pivot tables that were used to create visualizations for a dashboard. - Dashboard sheet: where I built a dashboard with data visualizations that answers the stakeholders' questions and some brief insights from the vizzes. - Forecast Exponential sheet: where I created a forecasted sales chart using the Forecast Sheet feature in Excel (exponential method). - Forecast Linear sheet: where I did a linear regression on the cleaned data to predict future sales (this is the method that the stakeholders required). - The remaining 4 sheets are data sheets that have been cleaned by me.

I hold no copyrights over the raw dataset and offer my sincere thanks to our Mentors in the Discord group for bringing to us a great course/challenge and a chance to show what we had learned during the course through this final project. I also thank my challenge partner for sharing great insights and tips along our course journey.

Excel sheet of the collected data

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2021. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset: Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided). Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Excel sheet with data of the original research 'Evaluation of simple and cost-effective hematological inflammatory biomarkers in type 2 diabetes and their correlation with glycemic control'

Sample data for exercises in Further Adventures in Data Cleaning.

The Orders database contains information on the following variables. • Continuous variables: Row ID, Order ID, Order Date, Ship Date, Customer ID, Product ID, Sales, Quantity, Discount, Profit, Shipping Cost
• Categorical variables: Ship Mode, Customer Name, Segment, Postal Code, City, State, Country, Region, Market, Category, Subcategory, Product Name, Order Priority

The purpose of this project: 1. To use descriptive statistics methods to assess the sales performance across various segments, markets, product categories and subcategories; 2. To use diagnostic analytics methods to understand the statistical significance of the factors that influence sales; 3. Use predictive analytics (regression) to understand the strengths of the relationship between sales and sales drivers and generate a regression formula to predict sales 4. develop a sales forecasting model based on the insights.

Descriptive analytics Descriptive statistics for sales https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F848f47b38b7f2360163bb2221703c658%2FPicture2.png?generation=1715109635788424&alt=media" alt="">

Frequency distribution for sales Around 44,500 transactions of value >=USD 500. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F39cfd8ffd8fdf296300bb9f1fa5243e2%2FPicture3.png?generation=1715109667755923&alt=media" alt="">

Sales values across markets https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F3385959d11b6daafae24c848b4b00f13%2FPicture4.png?generation=1715109744629587&alt=media" alt="">

We see an increase in sales across all markets and throughout 2012-2015. We have high sales volumes in the USCA and LATAM markets:
• USCA: USD 757,108 in 2015; • LATAM: USD 706,632 in 2015.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F4aa59b5a5b980aad6873c8a4af4cd223%2FPicture1.png?generation=1715109770510368&alt=media" alt="">

Sales across product categories https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F867cbe622bf94d25a25a1c4b9281656d%2FPicture5.png?generation=1715109794950614&alt=media" alt="">

Office supplies were the largely sold product category in 2012-2015. Technology was the least sold product category by quantity. However, the Technology category yields high sales. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F5c74664f77cce2bc2f7c77c7b01e9890%2FPicture6.png?generation=1715109834309500&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2Fd3bb766183e9f58fbf009a998c01adf6%2FPicture7.png?generation=1715109872961254&alt=media" alt="">

Further analysis of profitable products reveals that phones and copiers demonstrate high sales. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F109c4c3eab81fa581c19a5c09beff839%2FPicture9.png?generation=1715109914590660&alt=media" alt="">

Sales across segments The data reveals that there are high sales in the Consumer segment across all product categories. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F65075cc20028a37a1aff6932fa89d3d5%2FPicture10.png?generation=1715109992655572&alt=media" alt="">

Diagnostic analytics

Two sample T-test Using a t-test, we can evaluate how sales differ across different segments, regions, and product types. T-test allows us to evaluate the statistical significance of sales samples. The two-sample t-test of sales numbers across markets resulted in the statistical significance of sales in USCA and LATAM markets with p-values >0.05. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F7b7264d5f44a9a79b352028b28d1c618%2FPicture11.png?generation=1715110082746375&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F4061ef38ea83d7e3bbd252a802863e8f%2FPicture12.png?generation=1715110097203251&alt=media" alt="">

The two-sample t-test of sales numbers across product categories resulted in the statistical significance of sales in Office supplies and Technology categories with p-values >0.05. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2Fd9994377d605222d77ef67af3e273771%2FPicture13.png?generation=1715110126112322&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F669779e9aad19d51a28fb44e7c484bc7%2FPicture14.png?generation=1715110140543290&alt=media" alt="">

Pearson correlation The correlation of continuous values in the dataset allows us to see the relationship between sales, quantity sold, shipping costs and profit. ![](https://www.googleapis.com/download/sto...

Analytical and field sampling data for each 2018-2019 NRSA Fish Tissue Study chemical contaminant are provided, along with a data dictionary that describes the contents of each data file. All results for the fillet tissue concentrations are reported on a wet weight basis. All the fish fillet samples analyzed contained detectable levels of mercury and PCBs, and PFAS were detected in 95% of the fillet samples. This dataset is associated with the following publication: Stahl, L., B.D. Snyder, H.B. McCarty, T. Kincaid, A. Olsen, T.R. Cohen, and J. Healey. Contaminants in Fish from U.S. Rivers: Probability-Based National Assessments. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 861(25): 160557, (2023).

Dataset

This dataset was created by Shwetank Chaudhary

Released under CC0: Public Domain

Contents

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

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

The spreadsheet includes for each day and for each subject the sleep-related variables measured by sleep logs and wrist actigraphy, as well as compliance data. Sheet 1 has definitions for variable headings in the table and relevant descriptions. For reference, between-subjects variables are reported in Table 2. (XLSX)

Parcel file includes information such as current values, ownership information, situs address and abbreviated legal.Instructions for Opening in Microsoft Excel: When opening this file in Microsoft Excel, the ParcelNumber should be treated as a text field because parcel numbers can have leading zeros. Use the following procedure:1. Do not open the file directly, start Excel with an empty workbook.2. Click the "Data" tab, then click the "From Text" button in the ribbon.3. Navigate to the saved CSV file and click the Import button.4. The "Delimited" radio button should be selected so click "Next".5. Check only the "Comma" check box and click "Next".6. Click the ParcelNumber field to select it in the "Data Preview" pane then select the "Text" radio button to define that column as a text column.7. Change any other columns as desired and click the "Finish" button to import.When the Text driver is used to open a file, the format of the text file is determined by using a schema information file (schema.ini) which is included with this download to correctly identify the column types.

Circadian clock and chromatin-remodeling complexes are tightly intertwined systems that regulate rhythmic gene expression. The circadian clock promotes rhythmic expression, timely recruitment, and/or activation of chromatin remodelers, while chromatin remodelers regulate accessibility of clock transcription factors to the DNA to influence expression of clock genes. We previously reported that the BRAHMA (BRM) chromatin-remodeling complex promotes the repression of circadian gene expression in Drosophila. In this study, we investigated the mechanisms by which the circadian clock feeds back to modulate daily BRM activity. Using chromatin immunoprecipitation, we observed rhythmic BRM binding to clock gene promoters despite constitutive BRM protein expression, suggesting that factors other than protein abundance are responsible for rhythmic BRM occupancy at clock-controlled loci. Since we previously reported that BRM interacts with two key clock proteins, CLOCK (CLK) and TIMELESS (TIM), we examined their effect on BRM occupancy to the period (per) promoter. We observed reduced BRM binding to the DNA in clk null flies, suggesting that CLK is involved in enhancing BRM occupancy to initiate transcriptional repression at the conclusion of the activation phase. Additionally, we observed reduced BRM binding to the per promoter in flies overexpressing TIM, suggesting that TIM promotes BRM removal from DNA. These conclusions are further supported by elevated BRM binding to the per promoter in flies subjected to constant light and experiments in Drosophila tissue culture in which the levels of CLK and TIM are manipulated. In summary, this study provides new insights into the reciprocal regulation between the circadian clock and the BRM chromatin-remodeling complex.

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

Access updated Excel import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Excel buyers in India.