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

This is a computer exercise that takes you through retrieving multiple time series in CANSIM.

This database contains one hour oxygen saturation (SpO2) measurements of 36 patients, used for the analysis of oxygen saturation variability. The Ascii (.txt) files contain the raw data of SpO2 recorded with a sampling rate of 1/s. The attached excel file "Participant characteristics" contains anonymised participant information. Detailed analysis of this data is published on Frontiers Physiology.

The Nation is the series of basic data from the 1991 Census, providing national coverage. This series covers characteristics of the population, including demographic, social, cultural, labour force and income variables as well as details on dwellings, households and families. Generally the data are represented for Canada, provinces, territories and census metropolitan areas. Some tables include comparisons with data from earlier censuses. The aggregate data tables are presented in Beyond 20/20 Format (.ivt).

Using the User Manual as a guide and the Excel Graph Input Data Example file as a reference, the user enters the semantics of the graph model in this file.

This dataset is composed of 176 Excel files, downloaded via the Google Trends tool between June 11 and 15, 2025. The files include time series data on the relative frequency (in percentages) of Google searches conducted in European Union member states on four topics: corruption, immigration, security, and transexuality. They also include data related to right-wing, center-right, and center-left (used as a control group) political parties in each country. Each file contains one column with the date (on a weekly basis) and another with the series value (a percentage normalized by Google Trends). The time span covered in each file is five years.

Our analyses are based on 148×148 time- and frequency-domain correlation matrices. A correlation matrix covers all the possible use cases of every activity metric listed in the article. With these activity metrics and different preprocessing methods, we were able to calculate 148 different activity signals from multiple datasets of a single measurement. Each cell of a correlation matrix contains the mean and standard deviation of the calculated Pearson’s correlation coefficients between two types of activity signals based on 42 different subjects’ 10-days-long motion. The small correlation matrices presented both in the article and in the appendixes are derived from these 148 × 148 correlation matrices. This published Excel workbook contains multiple sheets labelled according to their content. The mean and standard deviation values for both time- and frequency-domain correlations can be found on their own separate sheet. Moreover, we reproduced the correlation matrix with an alternatively parametrized digital filter, which doubled the number of sheets to 8. In the Excel workbook, we used the same notation for both the datasets and activity metrics as presented in this article with an extension to the PIM metric: PIMs denotes the PIM metric where we used Simpson’s 3/8 rule integration method, PIMr indicates the PIM metric where we calculated the integral by simple numerical integration (Riemann sum). (XLSX)

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.

A range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including …Show full descriptionA range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including seasonally adjusted and trend estimates. The datacubes contain more detailed and cross classified original data than the spreadsheets.

Interprovincial Trade Flows (15F0002XDB) The interprovincial and international trade flows for goods and services by province and territory are available at the S-level of commodity aggregation in EXCEL files. National Input - Output Tables (15F0041XDB) The Input-Output accounting system consists of three tables. The input tables (USE tables) detail the commodities that are consumed by various industries. Output tables (MAKE tables) detail the commodities that are prod uced by various industries. Final demand tables detail the commodities bought by many categories of buyers (consumers, industries and government) for both consumption and investment purposes. These tables allow users to track intersectional exchanges of goods and services between industries and final demand categories such as personal expenditures, capital expenditures and public sector expenditures. There are four levels of detail: the "W" or Worksheet level with 303 industries, 727 commodities and 170 final demand categories, the "L" or Link level (the most detailed level that allows the construction of consistent time series of annual data from 1961 to 2002) with 117 industries, 469 commodities and 123 final demand categories, the "M" or Medium level with 62 industries, 111 commodities and 39 final demand categories, and the "S" or Small level with 25 industries, 59 commodities a nd 16 final demand categories. In 2009, several changes were made to the accounting system: there is a new level "D" that is the Detailed level, there are no "M" or "W" level tables, and there are two "L" level tables representing 1961 and 1997 aggregations. Provincial Input-Output Tables (15F0042XDB) The provincial input-output tables are constructed every year. The tables are available at the "S" level only. National and Provincial Multipliers (15F0046XDB) These are a series of Input-Output multipliers and ratios that allow users to quickly estimate the direct, indirect and total impacts of increases in industrial output or increases in an industry's labour force. These are the GDP, labour income, employment and gross output multipliers and ratios. Capital income multipliers and ratios can be calculated by subtracting the labour income figures from the GDP figures. National Symmetric Input-Output Tables - Aggregation Level S (15-207-XC B) The Industry Accounts Division of Statistics Canada publishes annual supply and use input-output (I-O) tables. While these rectangular, industry by commodity closely reflect actual economic transactions, certain analytical and modeling purposes, however, require symmetric industry-by-industry I-O tables. The symmetric industry by industry table shows the inter-industry transactions, that is, all purchases of an industry from all other industries including expenditures on imports and i nventory withdrawals as well as all expenditures on primary inputs. Similarly, the symmetric final demand table shows all purchases by a final demand category from all other industries, including expenditures on imports and inventory withdrawals as well as all expenditures on indirect taxes. National Symmetric Input-Output Tables - Aggregation Level L (15-208-XCB). The Industry Accounts Division of Statistics Canada publishes annual symmetric industry-by-industry I-O tables at the L level. The symmetric industry by industry table shows the inter-industry transactions, that is, all purchases of an industry from all other industries including expenditures on imports and inventory withdrawals as well as all expenditures on primary inputs. Similarly, the symmetric final demand table shows all purchases by a final demand category from all other industries, including expenditures on imports and inventory withdrawals as well as all expenditures on indirect taxes. Provincial GDP by Industry and Sector, at Basic Prices (15-209-XCB). This product presents estimates of Gross Domestic Product (GDP) by industry, in current dollars, evaluated at basic price for all provinces and territories. These estimates are derived from the provincial Input-Output tables. GDP measures the unduplicated value of production. The GDP by industry estimates are derived using a "value added" approach, that is, the value that a producer adds to their intermediate inputs before generating their own output. This allows not only for the computation of total economic production but also the industrial composition and origin of the economic production. When evaluated at basic prices, an industry's GDP is the sum of its factor incomes (wages and salaries, supplementary labour income, mixed income and other operating surplus) plus taxes less subsidies on production (labour and capital). Provincial Gross Output by Industry and Sector (15-210-XCB). This product presents estimates of gross output by industry, in current ... Visit https://dataone.org/datasets/sha256%3A6b83e9c4aed961c75dcb2e1cff6b3b937766e0a1c004f7d9236fa5ebdc7053d7 for complete metadata about this dataset.

The series consists of a list of files registered on the computer-based Records and Correspondence Management System (RCMS), under Registry 11 Correctional Services. It was created by exporting file data from the RCMS system into a Microsoft Excel spreadsheet. It is an artificial series, created by the Department of Justice at the request of Public Record Office Victoria, to provide access to VPRS 12700 General Correspondence Files, Registry 11 Correctional Services.

The list captured the file number, key-term classification, file title, and certain additional information for each file.

Organisation of the Data

The data is organised into 13 columns, or fields, presumably corresponding to discrete fields within the RCMS database.

The columns, from left to right, are as follows:

1. FILE.YEAR - The year the file was raised.

2. REGISTRY - The number of the registry in which the file has been registered on the RCMS system. The files referred to by this series were registered under Registry 11 Correctional Services.

3. FILE SEQUENCE - The sequential number allocated to each file as it is raised. Numbers start again from one each year.

4. FILE PART - The part number of the file.

The FILE.YEAR, REGISTRY, FILE SEQUENCE, and FILE PART fields, taken together, provide the file number.

5. KEY TERM - In theory, this is term used to describe the principle subject area of the file.

6. DESCRIPTOR.1, DESCRIPTOR.2 and DESCRIPTOR.3 (Columns 6 to 8) - In theory, these are narrower terms used to break the general subject area into smaller, more specific areas.

7. KWOC.1, KWOC.2, KWOC.3, and KWOC.4 (Key Word Out of Context) (Columns 9 to 12) - Provide for free text description of the file.

The KEY-TERM, DESCRIPTOR, and KWOC fields, taken together, provide the file title.

In practice, many different terms have been used in the key-term and descriptor fields. There appears to have been little control over the creation of new terms and the way in which the terms are used.

8. ADD.FILE.INFO (Additional File Information) - This field contains useful information about previous and subsequent files, related files, file closure, and so forth.

Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

The dataset included with this article contains three files describing and defining the sample and variables for VAT impact, and Excel file 1 consists of all raw and filtered data for the variables for the panel data sample. Excel file 2 depicts time-series and cross-sectional data for nonfinancial firms listed on the Saudi market for the second and third quarters of 2019 and the third and fourth quarters of 2020. Excel file 3 presents the raw material of variables used in measuring the company's profitability of the panel data sample

Subjective measurement data including participants' self-reported muscle fatigue rank, physiotherapist's palpation-based assessment of muscle stiffness during the 210-second experiment with 30-second intervals, and final assessment of muscle fatigue were summarised in Excel spreadsheet format (e.g., SelfReported_Subject01.xlsx and PhysioPalpation_Subject01.xlsx).readme.pdf with instructions about loading the dataset, running the code, and code execution.Subject: Each data file is named according to the participant number, which is an integer ranging from 1 to 30.Muscle stiffness measurements for 210 seconds with 30-second intervals: The subjective data records for each participant include the physiotherapist's palpation-based measurements taken at 0s and 30-second intervals for a total of 8 times across nine muscle locations.Physiotherapist's palpation-based muscle tightness Rank 1, Rank 2, Rank 3: Followed by the muscle stiffness measurement with 30-second intervals, the data records for physiotherapist-assessed muscle tightness rank 1, 2, and 3 contain the evaluations conducted by the physiotherapist to assess muscle tightness. Each record includes the participant number, the rank of muscle fatigue assigned by the physiotherapist (1, 2, or 3), and the associated muscle location. These records reflect the expert judgment of the physiotherapist regarding the severity and localization of muscle fatigue, providing valuable objective assessments of muscle condition during the experimental sessions.Self-reported perceived muscle fatigue Rank 1, Rank 2, Rank 3: The data records for self-reported muscle fatigue rank 1, 2, and 3 include information on the participants' subjective assessment of their muscle fatigue levels. Each record specifies the participant number, the rank of muscle fatigue (1, 2, or 3), and the corresponding muscle site. These records provide insights into the participants' individual perceptions of muscle fatigue and contribute to understanding the subjective experience of fatigue during the experimental sessions.Raw data contains sEMG data for all subjects with nine muscles. The sEMG time and signal data were collected via a Bluetooth module and an in-house data acquisition (DAQ) system. The recorded data was stored in Excel Spreadsheets in .xlsx format, with each participant's data saved in a separate file (e.g. Subject01.xlsx).Time: The sEMG raw time data consists of the time series measurements recorded from the sEMG sensors. These sensors captured the electrical activity generated by the muscles during the experimental sessions. Each data entry in the time series corresponds to a specific time point. The sEMG raw time data is stored in an Excel spreadsheet (.xlsx) using Time [s] format.Raw sEMG signal: The sEMG raw signal data contains the amplitude of the electrical signals recorded by the sEMG sensors. These signals represent the muscular electrical activity and provide insights into the muscle's activation levels during the experimental sessions. Each entry in the signal data corresponds to a specific time point, reflecting the magnitude of the electrical activity at that particular moment. The sEMG raw signal data is stored in an Excel spreadsheet (.xlsx) using Avanti sensor 5: EMG.A 5 [V] format.For any further information, please contact Jihoon Lim (jihoon.lim@student.unimelb.edu.au).

This data set is created to allow researchers to analyze the 2015 Canadian Federal Election using the Dyad Ratios Algorithm on Wcalc software created by James Stimson (http://stimson.web.unc.edu/software/). (See for example Stimson, J. A. (1991). Public opinion in America: Moods, cycles, and swings (Vol. 12). Boulder, CO: Westview Press. Or Stimson, J. A. (2004). Tides of consent: How opinion movements shape American politics. Cambridge University Press.) The software Wcalc can be found here: http://stimson.web.unc.edu/software/. Each major political party in 2015 has a separate excel file containing the party's level of support in surveys. Data for the party support in polls was gathered from Wikipedia at https://en.wikipedia.org/wiki/Opinion_polling_in_the_Canadian_federal_election,_2015Each file contains the date the survey was completed (month-day-year), the total sample size minus, percentage of the responses supporting the party in the table, and a recoded variable name. The recoded variable name corresponded with the survey firm and the question asked. The following firms are included in the datasets:Nanos Forum Leger Ipsos Innovative Angus Abacus EKOS The excel sheets for each party support are saved as a text file. Wcalc was used to run a time series of the data. When creating the time series, daily was selected and smooth on raw data series. Wcalc produced an excel file with an average of the days, and a log file interpreting the results (see Wcalc files for the results). This was repeated for each party. The resulting CSV files show party support overtime and the log files show the Iteration History and the factor loadings. Also included in this package is the code for the program R, which can also be used to run the Dyad Ratios Algorithm (see Extract.r.zip and ExtractDoc.pdf; Shared with permission of Dr. James Stimson).

raw data-ELISA

Data are transition probabilities of moving across full-time employment, voluntary part-time employment, involuntary part-time employment, unemployment, and nonparticipation. Data are calculated at the monthly frequency and cover U.S. workers over the period from 1976 until 2019. The content of each *_baseline MS Excel data file is as follows: time series of seasonally adjusted stocks (normalized by the corresponding population size), and time-series of seasonally adjusted transition probabilities, corrected for margin error and time aggregation bias. The content of each *_reclassified MS Excel data file is identical, but transition probabilities are in addition adjusted for potentially spurious transitions.

This data set was acquired with a DSPL HOBO HighTemp Temperature Probe and Major Fluid Sampler assembled as part of the 1991 EPR:9N_VonDamm data compilation (Chief Scientist: Dr. Karen Von Damm; Investigators: Dr. Julie Bryce, Florencia Prado, and Dr. Karen Von Damm). The data files are in Microsoft Excel format and include Fluid Chemistry and Temperature time series data and were processed after data collection. Funding was provided by NSF grant OCE03-27126. This data was cited by Oosting and Von Damm, 1996, Von Damm et al., 1997, Ravizza et al., 2001, Von Damm, 2000, Von Damm, 2004, Von Damm and Lilley, 2004, and Haymon et al., 1993.

Graph and download economic data for Canadian Dollars to U.S. Dollar Spot Exchange Rate (EXCAUS) from Jan 1971 to Jun 2025 about Canada, exchange rate, currency, rate, and USA.

Each of the monthly Gallup Poll Social Series (GPSS) micro-datasets has traditionally come with its own "codebook" (although it's really just a listing of variables, codes are not explained), in Microsoft Excel format. This combined variable listing, provided by Gallup Inc. on July 3, 2024, combines them into one file, potentially making it easier to discover what survey questions were asked in which month.