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

With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

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 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

Sample data for exercises in Further Adventures in Data Cleaning.

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

A messy data for demonstrating "how to clean data using spreadsheet". This dataset was intentionally formatted to be messy, for the purpose of demonstration. It was collated from here - https://openafrica.net/dataset/historic-and-projected-rainfall-and-runoff-for-4-lake-victoria-sub-regions

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.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available using the link in the 'Related Links' section below. Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it July take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

Download Employee Vehicle Personal Use Excel SheetThis dataset lists the employee name and taxable benefit for personal use of City of Greater Sudbury Vehicle as travel expenses for the year 2020. Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Data for other years is available in separate datasets. Updated quarterly when expenses are prepared.

This is a spreadsheet of 1 of 10 companies in the shoe industry. Highlighting COGS, Total Revenue, Market share and Industry share.

Tabulated excel sheet

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.

ObjectivesThe aim of the present study was to analyze the association between sports participation in childhood and adolescence and the practice of physical activity at different intensities in adulthood, and to verify if some sports participation characteristics such as number of sports; type of sport (individual, collective or a combination of both) and total estimated sports participation time are associated with the different physical activity intensities in adulthood.DesignThis is a cross-sectional study.MethodsThis study included 129 young adults of both sexes aged 18–25 years. Sports participation in childhood (7–10 years) and adolescence (11–17 years) was retrospectively estimated through specific questionnaire. Light, moderate, vigorous and moderate to vigorous intensity physical activity was objectively estimated by accelerometers. To verify the association between SP in childhood and adolescence and BP intensities in adults, multiple linear regression was adopted, with 5% significance.ResultsAnalyses showed that, in females, sports participation in childhood (β = 0.315; R2 = 0.14; P = 0.020) and persistence in sports participation (β = 0.364; R2 = 0.18; P = 0.007) were positive predictors of vigorous physical activity in adulthood. In addition, the comparison according to the specificities of the sport practice, indicated that participation in two or more sports in childhood, one sport and collective sports in adolescence and at least one year of sports participation throughout childhood and adolescence were associated with longer time in vigorous physical activity intensity and MVPA (minutes/day) in adult females (P < 0.05).ConclusionsIt could be concluded that sports participation indicators in childhood and adolescence were considered predictors of vigorous physical activity in adult females. In addition, number of sports, type of sport and practice time in childhood and adolescence seem to predict vigorous and moderate to vigorous levels of physical activity for adult females.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

The Excel spreadsheet contains, in separate sheets, data on respondents’ characteristics, knowledge and practices, as well as data for Figures 2, 3 and 4.

The General Practice Workforce series of Official Statistics presents a snapshot of the primary care general practice workforce. A snapshot statistic relates to the situation at a specific date, which for these workforce statistics is now the last calendar day each month. This monthly snapshot reflects the general practice workforce at 28 February 2025. These statistics present full-time equivalent (FTE) and headcount figures by four staff groups, (GPs, Nurses, Direct Patient Care (DPC) and administrative staff), with breakdowns of individual job roles within these high-level groups. For the purposes of NHS workforce statistics, we define full-time working to be 37.5 hours per week. Full-time equivalent is a standardised measure of the workload of an employed person. Using FTE, we can convert part-time and additional working hours into an equivalent number of full-time staff. For example, an individual working 37.5 hours would be classed as 1.0 FTE while a colleague working 30 hours would be 0.8 FTE. The term “headcount” relates to distinct individuals, and as the same person may hold more than one role, care should be taken when interpreting headcount figures. Please refer to the Using this Publication section for information and guidance about the contents of this publication and how it can and cannot be used. England-level time series figures for all job roles are available in the Excel bulletin tables back to September 2015 when this series of Official Statistics began. The Excel file also includes Sub-ICB Location-level FTE and headcount breakdowns for the current reporting period. CSVs containing practice-level summaries and Sub-ICB Location-level counts of individuals are also available. Please refer to the Publication content, analysis, and release schedule in the Using this publication section for more details of what’s available. We are continually working to improve our publications to ensure their contents are as useful and relevant as possible for our users. We welcome feedback from all users to PrimaryCareWorkforce@nhs.net.

Background and objectivesAim of the current study was to assess the perception, preference, and practice of endodontists and restorative dentists at different locations around the world about dental magnification instruments.Materials and methodsA multicenter, cross-sectional study was ethically approved from the local committee of bioethics. After thorough literature search, a questionnaire was designed and validated. Later, the questionnaire was distributed to 10% (53 participants) of the total planned participants to conduct a pilot study. Based on the feedback from these participants, any ambiguities or discrepancies observed in the items and content of the questionnaire was modified. The questionnaire was assessed for its internal consistency as part of validating the items with Cronbach’s alpha of 0.80. The completed questionnaire with an informed consent form for the participant was administered to the endodontists and restorative dentists in three different geographical regions namely MENA (Middle East and Northern Africa), British-Isles, and Indian Sub-continent using WhatsApp through the snowball convenience sampling technique.ResultsMajority of the participants were male (56.5%) and in the age group of 25–35 years (30.3%). About 68.9% were from Indian sub-continent, followed by the British-Isles (16.5%) and the least (14.6%) were from the MENA region. By large, the participants of the present study, strongly agreed that dental magnification devices improved ergonomics, quality of work, and should be considered as standard of care in modern endodontic. Flip-up magnifiers (51.1%) and medium (8x-16x) magnification were preferred by majority of the participants. About 46.3% of specialist reported that they always used devices for all operative and endodontic procedures, especially while locating hidden and canals and negotiating calcified canals. Participants practicing in British-Isles have 2.42 times (P<0.05) higher adequate perception with reference participants in Indian sub-continent. Additionally, participants with fellowship have 2.77 times more (P<0.01) adequate perception with reference to their counterparts with a master’s degree.ConclusionsMost of the participants believe that dental magnification devices enhance the prognosis and quality of treatment of possibly all operative and endodontics procedures. Thus, emphasized on the inclusion of devices in the postgraduate curriculum and signifies the role of continuing dental education for specialist and dental assistant handling devices. However, multicenter studies with larger sample is required for generalizing the results.