Microsoft Excel table containing six sound features across three sound types produced by Dactylopterus volitans, convenient for comparative inter-sound analyses.

Compare state-level estimates from the 2021-2022 National Surveys on Drug Use and Health (NSDUH) using p-values. The tables accompany the2021-2022 NSDUH State Estimates of Substance Use and Mental Disorders, and can be used to determine whether the difference in estimates between two geographic areas are statistically significant. Aguide to their useis also available.The tables are available in an Excel spreadsheet or a zip file containing CSV text files. Each tab or text file contains p-values for a particular measure and a particular age group.

Compare state-level estimates from the 2017-2018 National Surveys on Drug Use and Health (NSDUH) using p-values. The tables accompany the2017-2018 NSDUH State Estimates of Substance Use and Mental Disorders, and can be used to determine whether the difference in estimates between two geographic areas are statistically significant. A guide to their use is also included.The tables are available in an Excel spreadsheet or a zip file containing CSV text files. Each tab or text file contains p-values for a particular measure and a particular age group.

Final EFO-CLO alignment result. The 874 EFO-CLO mapped cell lines aligned and merged into CLO (Tab. 1 in the excel file) and 344 EFO unique immortalized permanent cell lines added to CLO (Tab. 2 in the excel file). File is stored in Microsoft Excel spreadsheet (xlsx) format. (XLSX 54Â kb)

The dataset includes a PDF file containing the results and an Excel file with the following tables:

Table S1 Results of comparing the performance of MetaFetcheR to MetaboAnalystR using Diamanti et al. Table S2 Results of comparing the performance of MetaFetcheR to MetaboAnalystR for Priolo et al. Table S3 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool using Diamanti et al. Table S4 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool for Priolo et al. Table S5 Data quality test results for running 100 iterations on HMDB database. Table S6 Data quality test results for running 100 iterations on KEGG database. Table S7 Data quality test results for running 100 iterations on ChEBI database. Table S8 Data quality test results for running 100 iterations on PubChem database. Table S9 Data quality test results for running 100 iterations on LIPID MAPS database. Table S10 The list of metabolites that were not mapped by MetaboAnalystR for Diamanti et al. Table S11 An example of an input matrix for MetaFetcheR. Table S12 Results of comparing the performance of MetaFetcheR to MS_targeted using Diamanti et al. Table S13 Data set from Diamanti et al. Table S14 Data set from Priolo et al. Table S15 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Diamanti et al. Table S16 Results of comparing the performance of MetaFetcheR to CTS using LIPID MAPS identifiers available in Diamanti et al. Table S17 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. Table S18 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. (See the "index" tab in the Excel file for more information)

Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. Lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power and large delays in delivery of results.

We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets.

The dataset was originally published in DiVA and moved to SND in 2024.

Spreadsheet showing physical information associated with habitats in the shallow section of the Marine Habitat Classification for Britain and Ireland (published in 2004). The physical comparative tables enable a rapid comparison of the principal physical characteristics between user-defined sets of biotopes (and other classification units). There are no tables for the deep-sea section added in 2015; therefore they are only relevant to littoral and sublittoral zones - equivalent to version 04.05 of the classification. The tables are provided in the form of a downloadable Excel document.

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

Excel file containing Tables S1–S3, each provided in a separate tab. Tables S1 and S2 present differential gene expression analyses from bulk RNA sequencing of mouse hearts treated with isoproterenol (ISO) or lipopolysaccharide (LPS). Related RNA-seq data are available in GEO accession GSE307900. Table S3 presents differential chromatin accessibility analyses from bulk ATAC sequencing of bone marrow–derived hematopoietic stem and progenitor cells (HSPCs) from mice treated with ISO or LPS, focusing on loci associated with genes implicated in clonal hematopoiesis. Related ATAC-seq data are available in GEO accession GSE305274.

Spreadsheet showing species information associated with habitats in the shallow section of the Marine Habitat Classification for Britain and Ireland (published in 2004). The biological comparative tables enable a rapid comparison of the species composition between user-defined sets of biotopes (and other classification units). There are no tables for the deep-sea section added in 2015; therefore they are only relevant to littoral and sublittoral zones - equivalent to version 04.05 of the classification. The tables are provided in the form of a downloadable Excel document.

This dataset was generated from soybean (Glycine max) field trials conducted at the West Tennessee Research and Education Center in Jackson, TN and at the Research and Education Center at Milan in Milan, TN as well as from molecular marker screening conducted at the West Tennessee Research and Education Center in Jackson, TN. Table 3 includes measured data for height, yield, and seed size, and rating data for lodging and seed quality for JTN-5110, 5601T, and select other released germplasm lines and cultivars tested in replicated breeder yield trials in Jackson and Milan, TN from 2010-2016, excluding 2014. This data may be useful in measuring yield gain in future releases of soybean germplasm or cultivars with broad resistance to soybean cyst nematode (SCN; Heterodera glycines). This data should not be used to measure yield gain for elite high-yielding cultivars that do not have broad cyst nematode resistance. Table 5 includes rating data for JTN-5110 and soybeans with established SCN resistance from simple sequence repeat (SSR) markers: Satt309 and Sat_168, associated with rhg1 on chromosome 18; Sat_162, associated with Rhg4 on chromosome 8; and Satt574, associated with cqSCN-005 on chromosome 17. This data may be useful in understanding the role of these molecular regions in SCN resistance for JTN-5110 and parent line Anand. This data should not be used to draw broad conclusions about cyst nematode resistance, in general. Table 7 includes rating data for JTN-5110 and check cultivars from frogeye leafspot (caused by Cercospora sojina) field disease screenings conducted in Milan, TN from 2010-2012. This data may be useful in measuring changes in frogeye leafspot incidence and severity in West Tennessee. This data should not be used to draw broad conclusions or represent different geographic areas. Resources in this dataset:Resource Title: Data dictionary. File Name: data dictionary.csvResource Description: A data dictionary defining the fields in Tables 3, 5, and 7Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 3 - JTN-5110 compared to 5601T. File Name: Table 3 - JTN-5110 compared to 5601T.csvResource Description: Breeder yield trial data from Jackson and Milan, TN from 2010-2016, excluding 2014Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 5 - compiled marker data. File Name: Table 5 - compiled marker data.csvResource Description: Genetic marker data for SSR markers associated with soybean cyst nematode resistance. Screening conducted in Jackson, TN from 2005-2020.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 7 - frogeye leafspot evaluation. File Name: Table 7 - frogeye leafspot evaluation.csvResource Description: Data from frogeye leafspot field screening conducted in Milan, TN from 2010-2012.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

The attachment includes three folders:
The first folder, Data classification (testing and training), consists of two folders (crown_radius and height), the first crown_radius folder It contains excel data of three plant functional types (PFTs) - temperate needleleaf trees (MN), temperate broadleaf trees (MB) and tropical broadleaf trees (TB), these three excel data all contain 19 soil factors data, 22 climate factors data and information such as crown_radius_m, mask, stem_diameter_cm, etc. The information in the second height folder is similar, and it corresponds to Table 1.Data summary and Figure 3 for each PFT in the article;

The second folder, Feather importance, contains two excel spreadsheets (crown_radius-FI and height-FI), the first excel spreadsheet of crown_radius-FI Feather importance containing three plant functional types (PFTs) is temperate needleleaf trees (MN), temperate broadleaf trees (MB), and tropical broadleaf trees (TB); The excel table information of the second height-FI is similar, and its information corresponds to Figure 5 and Figure S3 in the article;

The third folder "program" contains two packages (make_model1 and make_model2) and a calling program "Source program". Among them, the make_model1 package is mainly used to obtain the best parameters for selecting the model; The make_model2 package is based on the selection of the make_model1 package to further analyze the specific FI values of the factors in the best model. The Source program is used to make specific calls to the package according to the requirements.

Cost comparison table showing community type costs by location

Additional File 1: Excel document collecting supplementary tables.Table S1: Comparison with the previously designed tools.Table S2: The 50 selected frequently altered genes and their lengths.Table S3: gRNA library for CAL-51-specific PIK3CA gene.Table S4: In silico annotation of the MYC screen.Table S5: BEstimate inputs.Additional File 2: gRNA library for 50 frequently mutated genes, retrieved from a cBioPortal pan-cancer study, for all coding and non-coding regions.Additional File 3: gRNA library for 50 frequently mutated genes, retrieved from a cBioPortal pan-cancer study, for all coding regions with protein level annotations.

The trematode parasite Schistosoma mansoni causes schistosomiasis, which affects over 200 million people worldwide. Schistosomes are dioecious, with egg laying depending on the females’ obligatory pairing with males. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with low or no protein-coding potential that have been involved in other species with reproduction, stem cell maintenance, and drug resistance. In S. mansoni, we recently showed that the knockdown of one lncRNA affects the pairing status of these parasites. Here, we re-analyzed public RNA-Seq data from paired and unpaired adult male and female worms and their gonads, obtained from mixed-sex or single-sex cercariae infections, and found thousands of differentially expressed pairing-dependent lncRNAs among the 23 biological samples that were compared. The expression levels of selected lncRNAs were validated by RT-qPCR using an in vitro unpairing model. In addition, the in vitro silencing of three selected lncRNAs showed that knockdown of these pairing-dependent lncRNAs reduced cell proliferation in adult worms and their gonads, and are essential for female vitellaria maintenance, reproduction, and/or egg development. Remarkably, in vivo silencing of each of the three selected lncRNAs significantly reduced worm burden in infected mice by 26 to 35%. Whole mount in situ hybridization experiments showed that these pairing-dependent lncRNAs are expressed in reproductive tissues. These results show that lncRNAs are key components intervening in S. mansoni adult worm homeostasis, which affects pairing status and survival in the mammalian host, thus presenting great potential as new therapeutic target candidates.

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...

Data from www.census.gov.in was downloaded and processed available in this link,

Used the table code :PC11_B07 which contains data regarding working population classified by industrial category, educational level and gender. From these 35 excel tables, data was extracted and transformed into required format using Excel and Power Query detailed here. Transformed it into two datasets of Urban and Rural.

There are two .csv files used here. One is Rural education data district wise and second one is Urban education data district wise.

An Excel table comparing taxonomic annotations to Mahnert et al. [26]. (XLSX 56 kb)

Microsoft Excel file containing the supplementary tables (TableS2 to TableS7) containing the results from our analyses, annexed table description and legends.

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