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 project includes a series of Excel files demonstrating key Excel functionalities, including:

• Conditional Formatting for data visualization.
• Pivot Tables for summarizing and analyzing data.
• Excel Charts for visual representation of key insights.
• Use of Formulas and XLOOKUP to automate calculations and data lookup.
• Data Cleaning techniques to prepare the dataset for analysis.
• Additionally, the project includes a final Excel file with bike sales data and an interactive dashboard.

You can download the original Excel file with all formatting here: https://www.kaggle.com/datasets/carinacruz/excel-project

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

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

The excel file contains time series data of flow rates, concentrations of alachlor , atrazine, ammonia, total phosphorus, and total suspended solids observed in two watersheds in Indiana from 2002 to 2007. The aggregate time series data corresponding or representative to all these parameters was obtained using a specialized, data-driven technique. The aggregate data is hypothesized in the published paper to represent the overall health of both watersheds with respect to various potential water quality impairments. The time series data for each of the individual water quality parameters were used to compute corresponding risk measures (Rel, Res, and Vul) that are reported in Table 4 and 5. The aggregation of the risk measures, which is computed from the aggregate time series and water quality standards in Table 1, is also reported in Table 4 and 5 of the published paper. Values under column heading "uncertainty" reports uncertainties associated with reconstruction of missing records of the water quality parameters. Long-term records of the water quality parameters were reconstructed in order to estimate the (R-R-V) and corresponding aggregate risk measures. This dataset is associated with the following publication: Hoque, Y., S. Tripathi, M. Hantush , and R. Govindaraju. Aggregate Measures of Watershed Health from Reconstructed Water Quality Data with Uncertainty. Ed Gregorich JOURNAL OF ENVIRONMENTAL QUALITY. American Society of Agronomy, MADISON, WI, USA, 45(2): 709-719, (2016).

This dataset is about book series. It has 1 row and is filtered where the books is Microsoft Excel 2000 : introductory concepts and techniques. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fccaafe1ac302c872362d9a979177359d%2Fsdsdsdsd.gif?generation=1705783217150079&alt=media" alt="">

In this Excel file, the time series prices of Imamieh coin, half toman, and quarter toman are presented. Additionally, the bubble levels of these assets are provided over time. The information is presented in six separate sheets.

Imamieh coin: Daily prices are recorded from the year 1389. Imamieh coin bubble: Daily data is available from the year 1379. Half toman: Daily prices are documented from the year 1392. Half toman bubble: Daily records are available from the year 1397. Quarter toman: Daily prices are recorded from the year 1392.

These sheets offer a comprehensive view of the temporal variation in prices and market bubbles of these assets. The organization into separate sheets allows for easy and specific analysis of each coin and its market dynamics over time. This detailed dataset provides users with the ability to examine trends, identify patterns, and understand fluctuations in the values of these coins, as well as correlations with their corresponding market bubbles.

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.

This dataset is about book series. It has 1 row and is filtered where the books is Excel at problem solving. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as statistical meaningfulness, which is a stricter quality criterion for trends than high statistical significance. The time series is divided into intervals and interval mean values are calculated. Thereafter, r2 and p values are calculated from regressions concerning time and interval mean values. If r2≥0.65 at p≤0.05 in any of these regressions, then the trend is regarded as statistically meaningful. Out of ten investigated time series from different scientific disciplines, five displayed statistically meaningful trends. A Microsoft Excel application (add-in) was developed which can perform statistical meaningfulness tests and which may increase the operationality of the test. The presented method for distinguishing statistically meaningful trends should be reasonably uncomplicated for researchers with basic statistics skills and may thus be useful for determining which trends are worth analysing further, for instance with respect to causal factors. The method can also be used for determining which segments of a time trend may be particularly worthwhile to focus on.

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

File 2 includes seven excels, named Data for Time-series regression in 2007 to Data for Time-series regression in 2013 respectively. In specific, we average all the 25 portfolios’ realized jump measures in 2007 as just one series from File 1 in the excel of “Data for Horse running regression in 2007”. The excels constitute jump variables and the other variables, among which value-weighted monthly returns of 25 portfolios are directly from the RESSET database.

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 sales data for the first two products (P1 and P2) are weekly and data was collected until November 10, 2019. Products P3 and P4 are daily and might be related. For product P4, the company has provided potential explanatory variables X1 (price) and X2 (weather forecast of temperature in °C) that may be helpful for forecasting these two products. The sales data for products P3 and P4 was collected until November 24, 2019. Data for product P5 is weekly and was collected until August 30, 2019.

Visualization - https://public.tableau.com/views/ProductSales_16457072047730/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

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.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

1. Citation

Please cite the following papers when using this dataset:

I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

1. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File

Period

Number of Samples (days)

product 1 2020.xlsx

01/01/2020–31/12/2020

363

product 1 2021.xlsx

01/01/2021–31/12/2021

364

product 1 2022.xlsx

01/01/2022–31/12/2022

365

product 2 2020.xlsx

01/01/2020–31/12/2020

363

product 2 2021.xlsx

01/01/2021–31/12/2021

364

product 2 2022.xlsx

01/01/2022–31/12/2022

365

product 3 2020.xlsx

01/01/2020–31/12/2020

363

product 3 2021.xlsx

01/01/2021–31/12/2021

364

product 3 2022.xlsx

01/01/2022–31/12/2022

365

product 4 2020.xlsx

01/01/2020–31/12/2020

363

product 4 2021.xlsx

01/01/2021–31/12/2021

364

product 4 2022.xlsx

01/01/2022–31/12/2022

364

product 5 2020.xlsx

01/01/2020–31/12/2020

363

product 5 2021.xlsx

01/01/2021–31/12/2021

364

product 5 2022.xlsx

01/01/2022–31/12/2022

365

product 6 2020.xlsx

01/01/2020–31/12/2020

362

product 6 2021.xlsx

01/01/2021–31/12/2021

364

product 6 2022.xlsx

01/01/2022–31/12/2022

365

product 7 2020.xlsx

01/01/2020–31/12/2020

362

product 7 2021.xlsx

01/01/2021–31/12/2021

364

product 7 2022.xlsx

01/01/2022–31/12/2022

365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature

Description

Unit

Day

day of the month

-

Month

Month

-

Year

Year

-

daily_unit_sales

Daily sales - the amount of products, measured in units, that during that specific day were sold

units

previous_year_daily_unit_sales

Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year

units

percentage_difference_daily_unit_sales

The percentage difference between the two above values

%

daily_unit_sales_kg

The amount of products, measured in kilograms, that during that specific day were sold

kg

previous_year_daily_unit_sales_kg

Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year

kg

percentage_difference_daily_unit_sales_kg

The percentage difference between the two above values

kg

daily_unit_returns_kg

The percentage of the products that were shipped to selling points and were returned

%

previous_year_daily_unit_returns_kg

The percentage of the products that were shipped to selling points and were returned the previous year

%

points_of_distribution

The amount of sales representatives through which the product was sold to the market for this year

previous_year_points_of_distribution

The amount of sales representatives through which the product was sold to the market for the same day for the previous year

Table 1 – Dataset Feature Description

1. Structure and Format

4.1 Dataset Structure

The provided dataset has the following structure:

Where:

Name

Type

Property

Readme.docx

Report

A File that contains the documentation of the Dataset.

product X

Folder

A folder containing the data of a product X.

product X YYYY.xlsx

Data file

An excel file containing the sales data of product X for year YYYY.

Table 2 - Dataset File Description

1. Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).

References

[1] MEVGAL is a Greek dairy production company