Data mashup with Microsoft Excel using Power Query and M : finding, transforming, and loading data from external sources is a book. It was written by Adam Aspin and published by Apress in 2020.

Contains the NodeJS code for data extraction, processing, and storage, a dump of the final data in a couchDB 1.6 file, and all excel files including the data used in the paper.See Readme.MD for dataprocessing details.Source code is currently in a private GIT repository, just copied here due to need for anonymization.

This dataset is about book subjects and is filtered where the books is Beginning big data with Power BI and Excel 2013 : big data processing and analysis using Power BI in Excel 2013, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

NaiveBayes_R.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given recidivism (P(x_ij│R)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|R): This tab contains probabilities of feature attributes among recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. Recidivated_Train: This tab contains re-coded features of recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|R) tab. NaiveBayes_NR.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given non-recidivism (P(x_ij│N)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|N): This tab contains probabilities of feature attributes among non-recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. NonRecidivated_Train: This tab contains re-coded features of non-recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given non-recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|N) tab. Training_LnTransformed.xlsx: Figures in each cell are log-transformed ratios of probabilities in NaiveBayes_R.xlsx (P(Xi|R)) to the probabilities in NaiveBayes_NR.xlsx (P(Xi|N)). TestData.xlsx: This Excel file includes the following tabs based on the test data: P(Xi|R), P(Xi|N), NIJ_Recoded, and Test_LnTransformed (log-transformed P(Xi|R)/ P(Xi|N)). Training_LnTransformed.dta: We transform Training_LnTransformed.xlsx to Stata data set. We use Stat/Transfer 13 software package to transfer the file format. StataLog.smcl: This file includes the results of the logistic regression analysis. Both estimated intercept and coefficient estimates in this Stata log correspond to the raw weights and standardized weights in Figure 1. Brier Score_Re-Check.xlsx: This Excel file recalculates Brier scores of Relaxed Naïve Bayes Classifier in Table 3, showing evidence that results displayed in Table 3 are correct. *****Full List***** NaiveBayes_R.xlsx NaiveBayes_NR.xlsx Training_LnTransformed.xlsx TestData.xlsx Training_LnTransformed.dta StataLog.smcl Brier Score_Re-Check.xlsx Data for Weka (Training Set): Bayes_2022_NoID Data for Weka (Test Set): BayesTest_2022_NoID Weka output for machine learning models (Conventional naïve Bayes, AdaBoost, Multilayer Perceptron, Logistic Regression, and Random Forest)

This covers data on production, processing and trading activities in Ghana's cocoa value chain that were used to populate a stock-and flow diagram representing the baseline model of the cocoa value chain. Data are retrieved from different secondary sources, collated in excel sheets

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: NM Food Retailers, 2022 - Microsoft Excel VersionItem Type: Microsoft ExcelSummary: Food Retailers by type (mobile, restaurant, etc.), as a Microsoft Excel fileNotes: Prepared by: Link uploaded by EMcRae_NMCDCSource: NM Environment Dept. - sent directlyFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=fdf6b9eeb01d4cd8bbc32d5b7da16f62UID: 7, 8, 38, 70Data Requested: Food trucks, Local cottage industry (commercial kitchens, etc), Food retailers, Grocery Stores - location, size, typeMethod of Acquisition: Contact made with NM Environment Dept. Date Acquired: May of 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 9, 7, 11, 6Tags: PENDING_ Title New Mexico Food Retailers 2022 - NMFoodRetailers2022

Summary List of licensed food retailers with categories as of April 2022

Notes

Source New Mexico Environment Department

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=69d62107fa3d49a18acb87a8a584ca03

Alias Definition

Name Name

License License Number

Status Status

Street1 Street 1

Street2 Street 2

City City

State State

Zip Zip

Retail Food Establishment (Retail)

Mobile Mobile Food Establisment

MobType MobileType

MobSup Mobile Support Unit

ServArea Servicing Area (Commissary)

FullServ Full Service Restaurant

Restrnt Restaurant

Deli Deli

Seafood Seafood Market

Meat Meat Market

ConvStore Convenience Store

Daycare Day Care

SchFood School Food Program

Bar Bar

Coffee Coffee Shop

Catering Catering Operation

Concess Concession Stand/Snack Bar

Snack Institution

Bakery Bakery

Grocery Market (Grocery)

Other Other

Lat Latitude

Long Longitude

AccScore Accuracy Score

AccType Accuracy Type

Number Number

Street Street

UnitType Unit Type

UnitNum Unit Number

GCCity City

GCState State

GCCounty County

GCZip Zip

GCCountry Country

GCSource Source

Hurricane Sandy, the largest storm of historical record in the Atlantic basin, severely impacted southern Long Island, New York in October 2012. In 2014, the U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers (USACE), conducted a high-resolution multibeam echosounder survey with Alpine Ocean Seismic Survey, Inc., offshore of Fire Island and western Long Island, New York to document the post-storm conditions of the inner continental shelf. The objectives of the survey were to determine the impact of Hurricane Sandy on the inner continental shelf morphology and modern sediment distribution, and provide additional geospatial data for sediment transport studies and coastal change model development. For more information about the WHCMSC Field Activity, see https://cmgds.marine.usgs.gov/fan_info.php?fan=2014-072-FA.

Digital Transformation Market size was valued at USD 695.88 Billion in 2023 and is poised to grow from USD 862.89 Billion in 2024 to USD 4823.1 Billion by 2032, growing at a CAGR of 24% during the forecast period (2025-2032).

The 2014 15 Budget is officially available at budget.gov.au as the authoritative source of Budget Papers (BPs) and Portfolio Budget Statement (PBS) documents. This dataset is a collection of data sources from the 2014 15 Budget, including:

• Selected Tables from the Budget Papers – Available after Budget Papers published on budget.gov.au (~7.30pm Budget night).
• Machine Readable Tables from Budget Papers – Available after Budget Papers published on budget.gov.au (~7.30pm Budget night).
• The Portfolio Budget Statement Excel spreadsheets – Available after PBSs tabled in the Senate (~8.30pm Budget night).
• A Machine Readable CSV of all PBS Excel Spreadsheet Line Items – Available after PBSs tabled in the Senate and translated (~8.30pm Budget night). First version published with 10 portfolios ~8:50pm Budget night. Work due to be competed by midday 14 May 2014.

Data from the 2014-15 Budget are provided to assist those who wish to analyse, visualise and programmatically access the 2014-15 Budget. It is the first time this has been done as per our announcement blog post. We intend to move further down the digital by default route to make the 2015-16 Budget more accessible and reusable in data form. We welcome your feedback and comments below. Data users should refer to footnotes and memoranda in the original files as these are not usually captured in machine readable CSVs.

This dataset was prepared by the Department of Finance and the Department of the Treasury.

Information about the PBS Excel files and CSV

The PBS Excel files published should include the following financial tables with headings and footnotes. Only the line item data (table 2.2) is available in CSV at this stage as we thought this would be the most useful PBS data to extract. Much of the other data is also available in the Budget Papers 1 and 4 in aggregate form:

• Table 1.1: Agency Resource Statement;
• Table 1.2: Agency 2014-15 Budget Measures;
• Table 2.1: Budgeted Expenses for Outcome X;
• Table 2.2: Programme Expenses and Programme Components.
• Table 3.1.1: Movement of Administered Funds Between Years;
• Table 3.1.2: Estimates of Special Account Flows and Balances;
• Table 3.1.3: Australian Government Indigenous Expenditure (AGIE);
• Tables 3.2.1 to 3.2.6: Departmental Budgeted Financial Statements; and
• Tables 3.2.7 to 3.2.11: Administered Budgeted Financial Statements.

Please note, total expenses reported in the csv file ‘2014-15 PBS line items dataset’ was prepared from individual agency programme expense tables. Totalling these figures does not produce the total expense figure in ‘Table1: estimates of general government expenses’ (Statement 6, Budget Paper 1). Differences relate to:

1. Intra agency charging for services which are eliminated for the reporting of general government financial statements;

2. Agency expenses that involve revaluation of assets and liabilities are reported as other economic flows in general government financial statements; and

3. Additional agencies’ expenses are included in general government sector expenses (e.g. Australian Strategic Policy Institute Limited and other entities) noting that only agencies that are directly government funded are required to prepare a PBS.

At this stage, the following Portfolios have contributed their PBS Excel files and are included in the line item CSV: 1.1 Agriculture Portfolio; 1.2 Attorney-General’s Portfolio; 1.3 Communications Portfolio; 1.4A Defence Portfolio; 1.4B Defence Portfolio (Department of Veterans’ Affairs); 1.5 Education Portfolio; 1.6 Employment Portfolio; 1.7 Environment Portfolio; 1.8 Finance Portfolio; 1.9 Foreign Affairs and Trade Portfolio; 1.10 Health Portfolio; 1.11 Immigration and Border Protection Portfolio; 1.12 Industry Portfolio; 1.13 Infrastructure and Regional Development Portfolio; 1.14 Prime Minister and Cabinet Portfolio; 1.15A Social Services Portfolio; 1.15B Social Services Portfolio (Department of Human Services); 1.16 Treasury Portfolio; 1.17A Department of the House of Representatives; 1.17B Department of the Senate; 1.17C Department of Parliamentary Services; and 1.17D Department of the Parliamentary Budget Office.

The original PBS Excel files and published documents include sub-totals and totals by agency and appropriation type which are not included in the line item CSV as these can be calculated programmatically. Where modifications are identified they will be updated as required. If a corrigendum to an agencies PBS is issued after budget night, tables will be updated as necessary.

Below is the CSV structure of the line item CSV. The data transformation is expected to be complete by midday 14 May, so we have put up the incomplete CSV which will be updated as additional PBSs are transformed into data form. Please keep refreshing for now.

Portfolio, Department/Agency, Outcome, Program, Expense type, Appropriation type, Description, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, Source document, Source table, URL

Budget Paper Tables

We have made a number of data tables from Budget Papers 1 and 4 available in their original format as Excel or XML files. We have transformed a number of these into machine readable format (as prioritised by several users of budget data) which will be published here as they are ready. Below is the list of the tables published and whether we’ve translated them into CSV form this year:

• Budget Paper 1 Overview Appendix C Major Intiatives (XLSX, CSV)
• Budget Paper 1 Overview Appendix D Major Savings (XLSX, CSV)
• Budget Paper 1 Statement 3 Table 5 Reconciliation of underlying cash balance estimates (XLSX, no CSV due to complexity)
• Budget Paper 1 Statement 5 Table 1 Australian Government general government receipts (XLSX, CSV)
• Budget Paper 1 Statement 5 Table 8 Australian Government general government (cash) receipts (XLSX, CSV)
• Budget Paper 1 Statement 5 (online tables) Australian Government (accrual) revenue (XLSX, CSV)
• Budget Paper 1 Statement 5 Table 11 Reconciliation of 2015-15 general government (accrual) revenue (XLSX, CSV)
• Budget Paper 1 Statement 6 Appendix A Table A1 Estimates of expenses by function and sub-function (XLSX, CSV)
• Budget Paper 1 Statement 10 Table 1 Australian Government general government sector receipts payments net Future Fund earnings and underlying cash balance (XLSX, CSV)
• Budget Paper 1 Statement 10 Table 4 Australian Government general government sector taxation receipts non taxation receipts and total receipts (XLSX, CSV)
• Budget Paper 1 Statement 10 Table 5 Australian Government general government sector net debt and net interest payments (XLSX, CSV)
• Budget Paper 4 Table 1.1 Agency Resourcing (xls, CSV coming)
• Budget Paper 4 Table 1.2 Special Appropriations (xlsx, CSV coming)
• Budget Paper 4 Table 1.3 Special Accounts (xlsx, CSV)
• Budget Paper 4 Table 2.2 Average Staffing Table (xlsx, CSV coming)
• Budget Paper 4 Table 3.1 Departmental Expenses (xlsx, CSV coming)
• Budget Paper 4 Table 3.2 Net Capital Investment (xlsx, CSV coming)

This data release consists of two directories: DepthToWater and WaterLevelModels. The DepthToWater directory contains five Microsoft Excel workbooks that present depth-to-groundwater data and drawdown analyses from five wells during an aquifer test at Well ER-6-1-2 Main (USGS site identification number 365901115593501). The WaterLevelModels directory contains 11 Microsoft Excel workbooks that present 10 archived SeriesSee (Halford and others, 2012) water-level models that were used to examine drawdown at 9 wells during the same aquifer test. An additional Microsoft Excel workbook (Continuous+TransformedData.xlsx) contains all raw and transformed data series used in the 10 water-level models.

The 2015-16 Budget is officially available at budget.gov.au as the authoritative source of Budget Papers and Portfolio Budget Statement (PBS) documents. This dataset is a collection of data sources from the 2015-16 Budget, including:

• The Portfolio Budget Statement Excel spreadsheets – Available after PBSs tabled in the Senate (~8.30pm Budget night).
• A Machine Readable CSV of all PBS Excel Spreadsheet Line Items – Available after PBSs tabled in the Senate and translated (~8.30pm Budget night).

Data from the 2015-16 Budget are provided to assist those who wish to analyse, visualise and programmatically access the 2015-16 Budget.

Data users should refer to footnotes and memoranda in the original files as these are not usually captured in machine readable CSVs.

We welcome your feedback and comments below.

This dataset was prepared by the Department of Finance and the Department of the Treasury.

Information about the PBS Excel files and CSV

The PBS Excel files published should include the following financial tables with headings and footnotes. Only the line item data (table 2.2) is available in CSV at this stage. Much of the other data is also available in the Budget Papers 1 and 4 in aggregate form:

• Table 1.1: Entity Resource Statement;
• Table 1.2: Entity 2015-16 Budget Measures;
• Table 2.1: Budgeted Expenses for Outcome X;
• Table 2.2: Programme Expenses and Programme Components.
• Table 3.1.1: Movement of Administered Funds Between Years;
• Table 3.1.2: Estimates of Special Account Flows and Balances;
• Table 3.1.3: Australian Government Indigenous Expenditure (AGIE);
• Tables 3.2.1 to 3.2.6: Departmental Budgeted Financial Statements; and
• Tables 3.2.7 to 3.2.11: Administered Budgeted Financial Statements.

Please note, total expenses reported in the CSV file ‘2015-16 PBS line items dataset’ was prepared from individual entity programme expense tables. Totalling these figures does not produce the total expense figure in ‘Table1: Estimates of General Government Expenses’ (Statement 6, Budget Paper 1).

Differences relate to:

1. Intra entity charging for services which are eliminated for the reporting of general government financial statements;
2. Entity expenses that involve revaluation of assets and liabilities are reported as other economic flows in general government financial statements; and
3. Additional entities’ expenses are included in general government sector expenses (e.g. Australian Strategic Policy Institute Limited and other entities) noting that only entities that are directly government funded are required to prepare a PBS.

The original PBS Excel files and published documents include sub-totals and totals by entity and appropriation type which are not included in the line item CSV. These can be calculated programmatically. Where modifications are identified they will be updated as required.

If a corrigendum to an entities PBS is issued after budget night, tables will be updated as necessary.

The structure of the line item CSV is;

• Portfolio
• Department/Entity
• Outcome
• Program
• Expense type
• Appropriation type
• Description
• 2014-15
• 2015-16
• 2016-17
• 2017-18
• 2018-19
• Source document
• Source table
• URL

The data transformation is expected to be complete by midday 13 May. We may put up an incomplete CSV which will continue to be updated as additional PBSs are transformed into data form.

The following Portfolios are included in the line item CSV:

• Agriculture
• Attorney General's
• Communications
• Defence
• Education and Training
• Employment
• Environment
• Finance
• Foreign Affairs and Trade
• Health
• Human Services
• Immigration and Border Protection
• Industry and Science
• Infrastructure and Regional Development
• Parliamentary Departments
• Prime Minister and Cabinet
• Social Services
• Treasury
• Veterans' Affairs

Budget Paper Tables

We have made a number of data tables from the Budget Papers available in Excel and CSV formats.

Below is the list of the tables published and whether we’ve translated them into CSV form this year:

• Budget Paper 1: Appendix A1 - Estimates of expenses by function and sub‑function
• Budget Paper 1: Overview - Appendix C Major Intiatives
• Budget Paper 1: Overview - Appendix D Major Savings
• Budget Paper 1: Statement 3 - Table 7: Reconciliation of underlying cash balance estimates
• Budget Paper 1: Statement 4 - Table 1: Australian Government general government receipts
• Budget Paper 1: Statement 4 - Table 7: Australian Government general government (cash) receipts
• Budget Paper 1: Statement 4 - Table 10: Reconciliation of 2015‑16 general government (accrual) revenue
• Budget Paper 1: Statement 4 - Supplementary table 3: Australian Government (accrual) revenue
• Budget Paper 1: Statement 10 - Table 1: Australian Government general government sector receipts, payments, net Future Fund earnings and underlying cash balance
• Budget Paper 1: Statement 10 - Table 4: Australian Government general government sector taxation receipts, non‑taxation receipts and total receipts
• Budget Paper 1: Statement 10 - Table 5: Australian Government general government sector net debt and net interest payments
• Budget Paper 4 Table 1.1 – Agency Resourcing
• Budget Paper 4 Table 1.2 – Special Appropriations
• Budget Paper 4 Table 1.3 – Special Accounts
• Budget Paper 4 Table 2.2 – Staffing Tables
• Budget Paper 4 Table 3.1 – Departmental Expenses
• Budget Paper 4 Table 3.2 – Net Capital Investment

Change Log

1. 22:30PM 12/05/2015 dataset first published
2. 09:00 13/05/2015 machine readable csv updated to include data from National Museum of Australia, Australian Aged Care Quality Agency & Australian Institute of Family Studies.
3. 15:30 13/05/2015 machine readable csv updated to include a column linking to resource file and fixed a few minor errors.
4. 16:00 14/05/2015 have added several selected transformed tables from Budget Paper 4.
5. 12:30 18/05/2015 several minor updates to CSV.
6. 16:00 03/06/2015 added several machine readable Budget Paper 1 tables prepared by Department of the Treasury.

The rapid advancement of additive manufacturing (AM) requires researchers to keep up with these advancements by continually improving the AM processes. Improving manufacturing processes involves evaluating the process outputs and their conformity to the required specifications. Process capability indices, calculated using critical quality characteristics (QCs), have long been used in the evaluation process due to their proven effectiveness. AM processes typically involve multi-correlated critical QCs, indicating the need to develop a multivariate process capability index (MPCI) rather than a univariate capability index, which may lead to misleading results. In this regard, this study proposes a general methodological framework for evaluating AM processes using MPCI. The proposed framework starts by identifying the AM process and product design. Fused Deposition Modeling (FDM) is chosen for this investigation. Then, the specification limits associated with critical QCs are established. To ensure that the MPCI assumptions are met, the critical QCs data are examined for normality, stability, and correlation. Additionally, the MPCI is estimated by simulating a large sample using the properties of the collected QCs data and determining the percent of nonconforming (PNC). Furthermore, the FDM process and its capable tolerance limits are then assessed using the proposed MPCI. Finally, the study presents a sensitivity analysis of the FDM process and suggestions for improvement based on the analysis of assignable causes of variation. The results revealed that the considered process mean is shifted for all QCs, and the most variation is associated with part diameter data. Moreover, the process data are not normally distributed, and the proposed transformation algorithm performs well in reducing data skewness. Also, the performance of the FDM process according to different designations of specification limits was estimated. The results showed that the FDM process is incapable of different designs except with very coarse specifications.

Additional table:The compositional variation of the Formula Diet fed to the experimental mice (Table 1), Differentially expressed genes by effect of RS, DJ, and DJ526 (Table 2), The most highly significant up-regulated and down-regulated pathways in the livers of mice on RS, DJ and DJ526 towards those on Ctrl groups (Table 3)Excel file: Globally normalized data (Fold change raw data), Z transformed data (Z-ratio raw data), and GSEA results

Arbuscular mycorrhizal (AM) fungi engage with land plants in a widespread, mutualistic endosymbiosis which provides their hosts with increased access to nutrients and enhanced biotic and abiotic stress resistance. The potential for reducing fertiliser use and improving crop resilience has resulted in rapidly increasing scientific interest. Microscopic quantification of the level of AM colonization is of fundamental importance to this research, however the methods for recording and processing these data are time-consuming and tedious. In order to streamline these processes, we have developed AMScorer, an easy-to-use Excel spreadsheet, which enables the user to record data rapidly during from microscopy-based assays, and instantly performs the subsequent data processing steps. In our hands, AMScorer has more than halved the time required for data collection compared to paper-based methods. Subsequently, we developed AMReader, a user-friendly R package, which enables easy visualization and statistical analyses of data from AMScorer. These tools require only limited skills in Excel and R, and can accelerate research into AM symbioses, help researchers with variable resources to conduct research, and facilitate the storage and sharing of data from AM colonization assays. They are available for download at https://github.com/EJarrattBarnham/AMReader, along with an extensive user manual.

In the Bangla language, sentiment analysis is becoming more and more significant. Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity on an aspect level. The data were collected from numerous individuals with a minimum of two aspects. Every comment is a complex or compound sentence. The datasets are organized in a folder named "BANGLA_ABSA dataset" which has four Excel files, one for each of the datasets: Car_ABSA, Mobile_phone_ABSA, Movie_ABSA, and Restaurant_ABSA. Each Excel file contains three columns namely Id, Comment, and {Aspect category, Sentiment Polarity}. Car_ABSA, Mobile_phone_ABSA, Movie_ABSA, and Restaurant_ABSA datasets have 1149, 975, 800, and 801 rows of data respectively.