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).

Dataset

This dataset was created by Pinky Verma

Contents

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2020. 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.

Dataset

This dataset was created by Aziza Afrin

Contents

Sample data for exercises in Further Adventures in Data Cleaning.

Dataset

This dataset was created by Shiva Vashishtha

Contents

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%.

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.

The dataset includes customer id,Martial Status,Gender,Income,Children,Education,Occupation,Home Owner,Cars,Commute Distance,Region,Age,Purchased Bike. Blog

The link for the Excel project to download can be found on GitHub here. It includes the raw data, Pivot Tables, and an interactive dashboard with Pivot Charts and Slicers. The project also includes business questions and the formulas I used to answer. The image below is included for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2F61e460b5f6a1fa73cfaaa33aa8107bd5%2FBusinessQuestions.png?generation=1686190703261971&alt=media" alt=""> The link for the Tableau adjusted dashboard can be found here.

A screenshot of the interactive Excel dashboard is also included below for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2Fe581f1fce8afc732f7823904da9e4cce%2FScooter%20Dashboard%20Image.png?generation=1686190815608343&alt=media" alt="">

This cross-sectional study aimed to determine the prevalence of obesity and perceived barriers to weight loss in 1453 Bahraini adults who had used any intervention to lose weight in the past year. We found a high prevalence (78.2%) of overweight and obesity. Females were more likely to have obesity compared to males (81.4% vs. 66.7%). Older individuals aged 36-45 were 3.37 times, and 45 or older were 3.56 times more likely to have obesity. Married participants had higher odds of obesity compared to single participants (OR=1.79). Participants with obesity were more likely to be unemployed compared to students (OR=1.49). The most common contributing factors to weight gain were lack of physical activity (29.5%) and unhealthy diet (29.2%). Participants with obesity were more likely to have relied on dieting (OR=2.53) or exercise (OR=1.47) for weight loss and used medication (OR=5.23). This study highlights the complex relationship between sociodemographic factors, lifestyle behaviors, and obesity and sustaining weight loss.

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 from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may 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.

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

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

The Superstore Sales Data dataset, available in an Excel format as "Superstore.xlsx," is a comprehensive collection of sales and customer-related information from a retail superstore. This dataset comprises* three distinct tables*, each providing specific insights into the store's operations and customer interactions.

This dataset illustrates customer data from bike sales. It contains information such as Income, Occupation, Age, Commute, Gender, Children, and more. This is fictional data, created and used for data exploration and cleaning.

The link for the Excel project to download can be found on GitHub here. It includes the raw data, the cleaned data, Pivot Tables, and a dashboard with Pivot Charts and Slicers for interaction. This allows the interactive dashboard to filter by Marital Status, Region, and Education.

Below is a screenshot of the dashboard for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2Fcbc9db6fe00f3201c64e4fdb668ce9d1%2FBikeBuyers%20Dashboard%20Image.png?generation=1686186378985936&alt=media" alt="">

Corpus consisting of 10,000 Facebook posts manually annotated on sentiment (2,587 positive, 5,174 neutral, 1,991 negative and 248 bipolar posts). The archive contains data and statistics in an Excel file (FBData.xlsx) and gold data in two text files with posts (gold-posts.txt) and labels (gols-labels.txt) on corresponding lines.

Adventure Works 2022 dataset

How this Dataset is created?

On the official website the dataset is available over SQL server (localhost) and CSVs to be used via Power BI Desktop running on Virtual Lab (Virtaul Machine). As per first two steps of Importing data are executed in the virtual lab and then resultant Power BI tables are copied in CSVs. Added records till year 2022 as required.

How this Dataset may help you?

this dataset will be helpful in case you want to work offline with Adventure Works data in Power BI desktop in order to carry lab instructions as per training material on official website. The dataset is useful in case you want to work on Power BI desktop Sales Analysis example from Microsoft website PL 300 learning.

How to use this Dataset?

Download the CSV file(s) and import in Power BI desktop as tables. The CSVs are named as tables created after first two steps of importing data as mentioned in the PL-300 Microsoft Power BI Data Analyst exam lab.

The dataset contains information of 213 cancer patients undergoing clinical or surgical treatment characterized on sociodemographic and clinical data as well as data from the Care Transition Measure (CTM 15-Brazil). Data collection was carried out 7 to 30 days after their discharge from hospital from June to August 2019. Understanding these data can contribute to improving quality of care transitions and avoiding hospital readmissions. To this end, this dataset contains a broad array of variables:

*gender

*age group

*place of residence

*race

*marital status

*schooling

*paid work activity

*type of treatment

*cancer staging

*metastasis

*comorbidities

*main complaint

*continue use medication

*diagnosis

*cancer type

*diagnostic year

*oncology treatment

*first hospitalization

*readmission in the last 30 days

*number of hospitalizations in the last 30 days

*readmission in the last 6 months

*number of hospitalizations in the last 6 months

*readmission in the last year

*number of hospitalizations in the last year

*questions 1-15 from CTM 15-Brazil

The data are presented as a single Excel XLSX file: cancer patient´s care transitions dataset.xlsx.

The analyses of the present dataset have the potential to generate hospital readmission prevention strategies to be implemented by the hospital team. Researchers who are interested in CTs of cancer patients can extensively explore the variables described here.

The project from which these data were extracted was approved by the institution’s research ethics committee (approval n. 3.266.259/2019) at Associação Hospital de Caridade Ijuí, Rio Grande do Sul, Brazil.

Materials and Methods The study was held in the Oral and Maxillofacial Surgery department and Kasturba Hospital, Manipal, from November 2019 to October 2021 after approval from the Institutional Ethics Committee (IEC: 924/2019). The study included patients between 18-70 years. Patients with associated diseases like cysts or tumors of the jaw bones, pregnant women, and those with underlying psychological issues were excluded from the study. The patients were assessed 8-12 weeks after surgical intervention. A data schedule was prepared to document age, sex, and fracture type. The study consisted of 182 subjects divided into two groups of 91 each (Group A: Mild to moderate facial injury and Group B: Severe facial injury) based on the severity of maxillofacial fractures and facial injury. Informed consent was obtained from each of the study participants. We followed Facial Injury Severity Scale (FISS) to determine the severity of facial fractures and injuries. The face is divided horizontally into the mandibular, mid-facial, and upper facial thirds. Fractures in these thirds are given points based on their type (Table 1). Injuries with a total score above 4.4 were considered severe facial injuries (Group A), and those with a total score below 4.4 were considered mild/ moderate facial injuries (Group B). The QOL was compared between the two groups. Meticulous management of hard and soft tissue injuries in our state-of-the-art tertiary care hospital was implemented. All elective cases were surgically treated at least 72 hours after the initial trauma. The facial fractures were adequately reduced and fixed with high–end Titanium miniplates and screws (AO Principles of Fracture Management). Soft tissue injuries were managed by wound debridement, removal of foreign bodies, and layered wound closure. Adequate pain-relieving medication was prescribed to the patients postoperatively for effective pain control. The QOL of the subjects was assessed using the 'Twenty-point Quality of life assessment in facial trauma patients in Indian population' assessment tool. This tool contains 20 questions and uses a five-point Likert response scale. The Twenty – point quality of life assessment tool included two zones: Zone 1 (Psychosocial impact) and Zone 2 (Functional and esthetic impact), with ten questions (domains) each (Table 2). The scores for each question ranged from 1- 5, the higher score denoting better Quality of life. Accordingly, the score in each zone for a patient ranged from 10 -50, and the total scores of both zones were recorded to determine the QOL. The sum of both zones determined the prognosis following surgery (Table 2). The data collected was entered into a Microsoft Excel spreadsheet and analyzed using IBM SPSS Statistics, Version 22(Armonk, NY: IBM Corp). Descriptive data were presented in the form of frequency and percentage for categorical variables and in the form of mean, median, standard deviation, and quartiles for continuous variables. Since the data were not following normal distribution, a non-parametric test was used. QOL scores were compared between the study groups using the Mann-Whitney U test. P value < 0.05 was considered statistically significant.