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).

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

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.

Sample data for exercises in Further Adventures in Data Cleaning.

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.

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%.

While there have been ongoing calls to reform child welfare so that it better meets children’s and families’ needs, to date there have been no comprehensive summaries of child welfare reform strategies. For this systematic scoping review, we summarized authors’ recommendations for improving child welfare. We conducted a systematic search (2010 to 2021) and included published reviews that addressed authors’ recommendations for improving child welfare for children, youth, and families coming into contact with child welfare in high-income countries. A total of 4,758 records was identified by the systematic search, 685 full-text articles were screened for eligibility, and 433 reviews were found to be eligible for this scoping review. Reviews were theoretically divided, with some review authors arguing for reform efforts at the macro level (e.g., addressing poverty) and others arguing for reform efforts at the practice level (e.g., implementing evidence-based parenting programs). Reform efforts across socioecological levels were summarized in this scoping review. An important next step is to formulate what policy solutions are likely to lead to the greatest improvement in safety and well-being for children and families involved in child welfare. Methods For this systematic scoping review we: 1) conducted a systematic search, 2) summarized the number of reviews published, type of review, and populations included in reviews and 3) thematically analyzed the primary theme and author recommendations for child welfare reform. The Excel data coding file counts the number of reviews in each of these areas.

The appendix file includes a simplified coding scheme, a checklist for future reporting practice, and a full list of reference of studies meta-analyzed. The excel spreadsheet shows the forrest plot of the overall effect size analysis.

The Nuclear Medicine National HQ System database is a series of MS Excel spreadsheets and Access Database Tables by fiscal year. They consist of information from all Veterans Affairs Medical Centers (VAMCs) performing or contracting nuclear medicine services in Veterans Affairs medical facilities. The medical centers are required to complete questionnaires annually (RCS 10-0010-Nuclear Medicine Service Annual Report). The information is then manually entered into the Access Tables, which includes: * Distribution and cost of in-house VA - Contract Physician Services, whether contracted services are made via sharing agreement (with another VA medical facility or other government medical providers) or with private providers. * Workload data for the performance and/or purchase of PET/CT studies. * Organizational structure of services. * Updated changes in key imaging service personnel (chiefs, chief technicians, radiation safety officers). * Workload data on the number and type of studies (scans) performed, including Medicare Relative Value Units (RVUs), also referred to as Weighted Work Units (WWUs). WWUs are a workload measure calculated as the product of a study's Current Procedural Terminology (CPT) code, which consists of total work costs (the cost of physician medical expertise and time), and total practice costs (the costs of running a practice, such as equipment, supplies, salaries, utilities etc). Medicare combines WWUs together with one other parameter to derive RVUs, a workload measure widely used in the health care industry. WWUs allow Nuclear Medicine to account for the complexity of each study in assessing workload, that some studies are more time consuming and require higher levels of expertise. This gives a more accurate picture of workload; productivity etc than using just 'total studies' would yield. * A detailed Full-Time Equivalent Employee (FTEE) grid, and staffing distributions of FTEEs across nuclear medicine services. * Information on Radiation Safety Committees and Radiation Safety Officers (RSOs). Beginning in 2011 this will include data collection on part-time and non VA (contract) RSOs; other affiliations they may have and if so to whom they report (supervision) at their VA medical center.Collection of data on nuclear medicine services' progress in meeting the special needs of our female veterans. Revolving documentation of all major VA-owned gamma cameras (by type) and computer systems, their specifications and ages. * Revolving data collection for PET/CT cameras owned or leased by VA; and the numbers and types of PET/CT studies performed on VA patients whether produced on-site, via mobile PET/CT contract or from non-VA providers in the community.* Types of educational training/certification programs available at VA sites * Ongoing funded research projects by Nuclear Medicine (NM) staff, identified by source of funding and research purpose. * Data on physician-specific quality indicators at each nuclear medicine service.* Academic achievements by NM staff, including published books/chapters, journals and abstracts. * Information from polling field sites re: relevant issues and programs Headquarters needs to address. * Results of a Congressionally mandated contracted quality assessment exercise, also known as a Proficiency study. Study results are analyzed for comparison within VA facilities (for example by mission or size), and against participating private sector health care groups. * Information collected on current issues in nuclear medicine as they arise. Radiation Safety Committee structures and membership, Radiation Safety Officer information and information on how nuclear medicine services provided for female Veterans are examples of current issues.The database is now stored completely within MS Access Database Tables with output still presented in the form of Excel graphs and tables.

Data is the analysis of the data outputs of 240 randomly selected research papers from 12 top-ranked journals published in early 2023. We investigate author compliance with recommended (but not compulsory) data policies, whether there is evidence to suggest that authors apply FAIR data guidance in their data publishing, and if the existence of specific recommendations for publishing NMR data by some journals encourages compliance. Files in the data package have been provided in both human and machine-readable forms. The main dataset is available in the Excel file Data worksheet.XLSX, the contents of which can also be found in Main_dataset.CSV, Data_types.CSV, and Article_selection.CSV with explanations of the variable coding used in the studies in Variable_names.CSV, Codes.CSV, and FAIR_variable_coding.CSV. The R code used for the article selection can be found in Article_selection.R. Data about article types from the journals that contain original research data is in Article_types.CSV. Data collected for analysis in our sister paper[4] can be found in Extended_Adherence.CSV, Extended_Crystallography.CSV, Extended_DAS.CSV, Extended_File_Types.CSV, and Extended_Submission_Process.CSV. A full list of files in the data package and a short description for each is given in README.TXT.

The Nuclear Medicine National HQ System database is a series of MS Excel spreadsheets and Access Database Tables by fiscal year. They consist of information from all Veterans Affairs Medical Centers (VAMCs) performing or contracting nuclear medicine services in Veterans Affairs medical facilities. The medical centers are required to complete questionnaires annually (RCS 10-0010-Nuclear Medicine Service Annual Report). The information is then manually entered into the Access Tables, which includes: * Distribution and cost of in-house VA - Contract Physician Services, whether contracted services are made via sharing agreement (with another VA medical facility or other government medical providers) or with private providers. * Workload data for the performance and/or purchase of PET/CT studies. * Organizational structure of services. * Updated changes in key imaging service personnel (chiefs, chief technicians, radiation safety officers). * Workload data on the number and type of studies (scans) performed, including Medicare Relative Value Units (RVUs), also referred to as Weighted Work Units (WWUs). WWUs are a workload measure calculated as the product of a study's Current Procedural Terminology (CPT) code, which consists of total work costs (the cost of physician medical expertise and time), and total practice costs (the costs of running a practice, such as equipment, supplies, salaries, utilities etc). Medicare combines WWUs together with one other parameter to derive RVUs, a workload measure widely used in the health care industry. WWUs allow Nuclear Medicine to account for the complexity of each study in assessing workload, that some studies are more time consuming and require higher levels of expertise. This gives a more accurate picture of workload; productivity etc than using just 'total studies' would yield. * A detailed Full-Time Equivalent Employee (FTEE) grid, and staffing distributions of FTEEs across nuclear medicine services. * Information on Radiation Safety Committees and Radiation Safety Officers (RSOs). Beginning in 2011 this will include data collection on part-time and non VA (contract) RSOs; other affiliations they may have and if so to whom they report (supervision) at their VA medical center.Collection of data on nuclear medicine services' progress in meeting the special needs of our female veterans. Revolving documentation of all major VA-owned gamma cameras (by type) and computer systems, their specifications and ages. * Revolving data collection for PET/CT cameras owned or leased by VA; and the numbers and types of PET/CT studies performed on VA patients whether produced on-site, via mobile PET/CT contract or from non-VA providers in the community.* Types of educational training/certification programs available at VA sites * Ongoing funded research projects by Nuclear Medicine (NM) staff, identified by source of funding and research purpose. * Data on physician-specific quality indicators at each nuclear medicine service.* Academic achievements by NM staff, including published books/chapters, journals and abstracts. * Information from polling field sites re: relevant issues and programs Headquarters needs to address. * Results of a Congressionally mandated contracted quality assessment exercise, also known as a Proficiency study. Study results are analyzed for comparison within VA facilities (for example by mission or size), and against participating private sector health care groups. * Information collected on current issues in nuclear medicine as they arise. Radiation Safety Committee structures and membership, Radiation Safety Officer information and information on how nuclear medicine services provided for female Veterans are examples of current issues.The database is now stored completely within MS Access Database Tables with output still presented in the form of Excel graphs and tables.

Contents: The data shows the total number of unique patients at each reporting level, who: were prescribed 3 or more, 6 or more, 9 or more and 12 or more SABA inhalers (see below) in a practice / cost centre under the organisational unit shown in the 12 months to the reporting month (the column "Numerator patient count"); and those prescribed a preventer but not an antimuscarinic, in the same practice in which the numerator is measured, in the same 12-month period (the column "Denominator patient count"). Organisational structures: The data is shown for items prescribed in GP practices and cost centres in sub ICB locations (SICBLs) in England, and dispensed anywhere within the community. Medicines: In the numerator, data is shown for prescribing of short-acting beta-agonist (SABA) inhalers; in the denominator, data is shown for prescribing of preventer inhalers and antimuscarinic products. A full list of medicines considered for inclusion can be found alongside the datasets, in the Excel spreadsheet. Patient data: Data is shown for patients who could be identified by means of a valid NHS number on the prescription form, and those for whom a valid age was captured on the form. Prescribing for patients who do not meet this criteria has been excluded. No personal identifiable data has been included in this release. Patient totals are calculated uniquely for each reporting period and organisational unit; patients may appear under more than one category, so patient totals must not be combined or double counting will occur. Data coverage: For all prescription items issued during the reporting periods included in the data, 99.7% of items can be attributed to an identifiable patient. For medicines included in this analysis, the rate is 99.8%. Background: NHSBSA Prescription Services process prescriptions and information is then used to make payments to pharmacists and appliance contractors (in England) for prescriptions dispensed in primary care settings. This involves processing approximately 1 billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health users. Exclusions:

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

Abstract

The 2003 Agriculture Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmer organisations, etc. As a result the dataset is both more numerous in its sample and detailed in its scope compared to previous censuses and surveys. To date this is the most detailed Agricultural Census carried out in Africa.

The census was carried out in order to: · Identify structural changes if any, in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in rural infrastructure and in the level of agriculture household living conditions; · Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stake holders. · Establish baseline data for the measurement of the impact of high level objectives of the Agriculture Sector Development Programme (ASDP), National Strategy for Growth and Reduction of Poverty (NSGRP) and other rural development programs and projects. · Obtain benchmark data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing, service delivery, etc.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

• Households
• Individuals

Universe

Large scale, small scale and community farms.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Mainland sample consisted of 3,221 villages. These villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the 2002 Population and Housing Census. The total Mainland sample was 48,315 agricultural households. In Zanzibar a total of 317 enumeration areas (EAs) were selected and 4,755 agriculture households were covered. Nationwide, all regions and districts were sampled with the exception of three urban districts (two from Mainland and one from Zanzibar).

In both Mainland and Zanzibar, a stratified two stage sample was used. The number of villages/EAs selected for the first stage was based on a probability proportional to the number of villages in each district. In the second stage, 15 households were selected from a list of farming households in each selected Village/EA, using systematic random sampling, with the village chairpersons assisting to locate the selected households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census covered agriculture in detail as well as many other aspects of rural development and was conducted using three different questionnaires: • Small scale questionnaire • Community level questionnaire • Large scale farm questionnaire

The small scale farm questionnaire was the main census instrument and it includes questions related to crop and livestock production and practices; population demographics; access to services, resources and infrastructure; and issues on poverty, gender and subsistence versus profit making production unit.

The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices.

The large scale farm questionnaire was administered to large farms either privately or corporately managed.

Questionnaire Design The questionnaires were designed following user meetings to ensure that the questions asked were in line with users data needs. Several features were incorporated into the design of the questionnaires to increase the accuracy of the data: • Where feasible all variables were extensively coded to reduce post enumeration coding error. • The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions whilst interviewing the farmer. • The responses to all questions were placed in boxes printed on the questionnaire, with one box per character. This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data entry. • Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent. • Each section was clearly numbered, which facilitated the use of skip patterns and provided a reference for data type coding for the programming of CSPro, SPSS and the dissemination applications.

Cleaning operations

Data processing consisted of the following processes: · Data entry · Data structure formatting · Batch validation · Tabulation

Data Entry Scanning and ICR data capture technology for the small holder questionnaire were used on the Mainland. This not only increased the speed of data entry, it also increased the accuracy due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to track errors during the verification process. The scanning operation was so successful that it is highly recommended for adoption in future censuses/surveys. In Zanzibar all data was entered manually using CSPro.

Prior to scanning, all questionnaires underwent a manual cleaning exercise. This involved checking that the questionnaire had a full set of pages, correct identification and good handwriting. A score was given to each questionnaire based on the legibility and the completeness of enumeration. This score will be used to assess the quality of enumeration and supervision in order to select the best field staff for future censuses/surveys.

CSPro was used for data entry of all Large Scale Farm and community based questionnaires due to the relatively small number of questionnaires. It was also used to enter data from the 2,880 small holder questionnaires that were rejected by the ICR extraction application.

Data Structure Formatting A program was developed in visual basic to automatically alter the structure of the output from the scanning/extraction process in order to harmonise it with the manually entered data. The program automatically checked and changed the number of digits for each variable, the record type code, the number of questionnaires in the village, the consistency of the Village ID Code and saved the data of one village in a file named after the village code.

Batch Validation A batch validation program was developed in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to the more complex checking between variables. It took six months to screen, edit and validate the data from the smallholder questionnaires. After the long process of data cleaning, tabulations were prepared based on a pre-designed tabulation plan.

Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while ArcView and Freehand were used for the maps.

Analysis and Report Preparation The analysis in this report focuses on regional comparisons, time series and national production estimates. Microsoft Excel was used to produce charts; ArcView and Freehand were used for maps, whereas Microsoft Word was used to compile the report.

Data Quality A great deal of emphasis was placed on data quality throughout the whole exercise from planning, questionnaire design, training, supervision, data entry, validation and cleaning/editing. As a result of this, it is believed that the census is highly accurate and representative of what was experienced at field level during the Census year. With very few exceptions, the variables in the questionnaire are within the norms for Tanzania and they follow expected time series trends when compared to historical data. Standard Errors and Coefficients of Variation for the main variables are presented in the Technical Report (Volume I).

Sampling error estimates

The Sampling Error found on page (21) up to page (22) in the Technical Report for Agriculture Sample Census Survey 2002-2003

Anonymised responses from university academics to a questionnaire about their experiences teaching Akkadian language at the elementary level in university courses. The questionnaire was produced on the Microsoft Forms online platform. The researcher distributed the link to the form via email to a total of 120 academics in 27 countries, of whom 62 responded.

This data was used to inform a review of current practice in university teaching of Akkadian, as part of the researcher's MA thesis at Leiden University.

The uploaded files comprise an Excel spreadsheet with the full set of responses, and a PDF copy of the blank questionnaire form.