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

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

This dataset contains PAPP Excel Community data from the Latnjajaure site, Sweden in 1995, 1996, 1997 & 1998. The Press and Pulse Program (PAPP) experiment is comprised of four replicate blocks, each of which has four plots. This dataset is in excel format and includes data from all plots. For more information, please see the readme file.

These are a collection of XLSX sheets containing some of my favorite Excel tricks to reformat data to make analysis easier. I often use these to reformat column formatted data into plate layout or vice versa to better visualize and understand my data.

This data contains various types of formatting and custom formattings in excel, Basics Formula and Calculation.

The ITEX experiment at Audkuluheidi was started in 1996 when control and OTC plots 1-5 were set up. In 1997 Control and OTC plots 6-10 were set up in the protected area (No Graze). Also in 1997, 10 control plots were set up in the adjacent grazed area (Graze). In 2000, all plots were sampled again. This dataset is in excel format. For more information, please see the readme file.

The Alaska Geochemical Database Version 2.0 (AGDB2) contains new geochemical data compilations in which each geologic material sample has one "best value" determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database (AGDB) before it, the AGDB2 was created and designed to compile and integrate geochemical data from Alaska in order to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, environmental impact assessments, and studies in medical geology. This relational database, created from the Alaska Geochemical Database (AGDB) that was released in 2011, serves as a data archive in support of present and future Alaskan geologic and geochemical projects, and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 85 laboratory and field analytical methods on 264,095 rock, sediment, soil, mineral and heavy-mineral concentrate samples. Most samples were collected by U.S. Geological Survey (USGS) personnel and analyzed in USGS laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various USGS programs and projects from 1962 through 2009. In addition, mineralogical data from 18,138 nonmagnetic heavy mineral concentrate samples are included in this database. The AGDB2 includes historical geochemical data originally archived in the USGS Rock Analysis Storage System (RASS) database, used from the mid-1960s through the late 1980s and the USGS PLUTO database used from the mid-1970s through the mid-1990s. All of these data are currently maintained in the National Geochemical Database (NGDB). Retrievals from the NGDB were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. This arduous process of reviewing, verifying and, where necessary, editing all USGS geochemical data resulted in a significantly improved Alaska geochemical dataset. USGS data that were not previously in the NGDB because the data predate the earliest USGS geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB2 and will be added to the NGDB. The AGDB2 data provided here are the most accurate and complete to date, and should be useful for a wide variety of geochemical studies. The AGDB2 data provided in the linked database may be updated or changed periodically.

Patient (1–12) is the patient number. Cycle (1–3) is the cycle within patient. Pair labels the 36 pairs of data by patient (first number) and cycle (second number). Period (1–6) is the period within patient. Treatment (A or B) is the treatment given. FEV_1 is the outcome for forced expiratory volume in one second in mL. Missing is an indicator (1 = present, 2 = missing). (XLS)

This data set contains discrete temperature measurements from several hydrothermal vents (Biovent, Mvent, Bio9 vents, Pvent, Lvent) located along the East Pacific Rise (EPR) near 9°50'N. The compilation contains legacy data along with data from cruises AT42-06, AT42-21, RR2102, AT50-07, AT50-21, AT50-33, and AT50-36. The data file is in EXCEL spreadsheet format and were collected with temperature probes and autonomous temperature loggers. The data compilation was funded through awards OCE-1834797, OCE-1949485, OCE-1949938, OCE-1948936, ANR-24-CE56-6841 (Project OMENS), ERC-10117070619 (Project SeaSALT).

This dataset represents initial snow depths on the season-extension project study site on May 2nd or May 3rd for years 1995-2001. There were control snow depths, snow removal (May 2), snow removal and soil heating (May 4). There were 10 replicates of each treatment in randomized blocks located 3m apart. Plots 1-30 represent plots initiated in 1995. Plots 36-60 represent plots initiated in 1997. NOTE: This dataset contains the data in EXCEL format.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition.

Mean monthly flow (ML/month) and Annual flow (ML/yr) data at key gauges in the Macalister Irrigation District (MID) as monitored by SRW. The data are provided in MS Excel format in worksheets and charts.

Data used to produce Time-series drainage volume data provided by a third party. Site information and monitoring drainage flow data provided by the Southern Rural Water are specific to the Macalister Irrigation District.

Time specific data in the range 23/07/1997 to 31/12/2013

Dataset History

This dialogue has been copied from a draft of the BA-GIP report.

A total of 197 river gauges were identified within the model area representing all of the major rivers. Daily gauge level data was sourced from the Victorian Department of Environment, Land, Water and Planning Water Measurement Information System (WMIS, 2015). A list of the river gauges is provided in the report for key river basins

Only main stems of the major rivers were included in the model. These river reaches were identified using the DEPI hydro25 spatial data set (DEPI, 2014). The river classification was used to vary river incision depth (depth below the ground surface as defined by the digital elevation model) and width attributes. In the absence of recorded stage height information, river classification was used to estimate river stage heights. A total of 22,573 river cells are included in the model. Fifty-one gauges were selected to calibrate the catchment modelling framework in unregulated catchments based on Base Flow Indexes and observed stream flows.

Drainage channels and man-made drainage features in the Macalister Irrigation District (MID) were included in the model based on available drainage network mapping. This information was sourced from Southern Rural Water (SRW) and the DEPI Corporate Spatial Data library. Drainage cells are assigned to the uppermost cells within the model to capture groundwater discharge processes. Drain cells in Modflow can only act as groundwater discharge points and as such those cells outside drainage channels will be characterised as having a bed elevation equivalent to ground surface elevation. A total of 410,504 drainage cells are incorporated in the model. Apart from 3 river gauges sourced from the WMIS, SRW also has 15 gauges monitored drainage from the MID. The measurements commenced between 1997 and 2005. Of the 15 gauges, six were selected to calibrate the catchment modelling framework based on observed discharge.

Dataset Citation

Victorian Department of Economic Development, Jobs, Transport and Resources (2015) Mean monthly flow & annual flow data - Macalister Irrigation District. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/6ba89d78-1e42-4e02-bd5c-a435ee15bef4.

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.

Statistical Area Level of Education of Population Aged 15 and Over_ Secondary Release Area, Statistical Area Level of Education of Population Aged 15 and Over_ Primary Release Area, Statistical Area Level of Education of Population Aged 15 and Over_ Minimum Statistical AreaThe Ministry of the Interior's Statistics Department provides the latest annual statistical data for various counties and cities on the Government Open Data Platform in XML format. When viewed in a browser, it appears as a series of characters and numbers. Typically, this format is suitable for programmers to develop applications using the data, rather than being random characters. If you wish to download the data in CSV format (which can be viewed in Excel), please refer to the Social Economic Data Service Platform on the Land Information System website (segis.moi.gov.tw) for downloading.

The attached Excel spreadsheet is a codebook for our quantitative data analysis.

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 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.

PROJECT OBJECTIVE

We are a part of XYZ Co Pvt Ltd company who is in the business of organizing the sports events at international level. Countries nominate sportsmen from different departments and our team has been given the responsibility to systematize the membership roster and generate different reports as per business requirements.

Questions (KPIs)

TASK 1: STANDARDIZING THE DATASET

• Populate the FULLNAME consisting of the following fields ONLY, in the prescribed format: PREFIX FIRSTNAME LASTNAME.{Note: All UPPERCASE)
• Get the COUNTRY NAME to which these sportsmen belong to. Make use of LOCATION sheet to get the required data
• Populate the LANGUAGE_!poken by the sportsmen. Make use of LOCTION sheet to get the required data
• Generate the EMAIL ADDRESS for those members, who speak English, in the prescribed format :lastname.firstnamel@xyz .org {Note: All lowercase) and for all other members, format should be lastname.firstname@xyz.com (Note: All lowercase)
• Populate the SPORT LOCATION of the sport played by each player. Make use of SPORT sheet to get the required data

TASK 2: DATA FORMATING

• Display MEMBER IDas always 3 digit number {Note: 001,002 ...,D2D,..etc)
• Format the BIRTHDATE as dd mmm'yyyy (Prescribed format example: 09 May' 1986)
• Display the units for the WEIGHT column (Prescribed format example: 80 kg)
• Format the SALARY to show the data In thousands. If SALARY is less than 100,000 then display data with 2 decimal places else display data with one decimal place. In both cases units should be thousands (k) e.g. 87670 -> 87.67 k and 12 250 -> 123.2 k

TASK 3: SUMMARIZE DATA - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1) • Create a PIVOT table in the worksheet ANALYSIS, starting at cell B3,with the following details:

• In COLUMNS; Group : GENDER.
• In ROWS; Group : COUNTRY (Note: use COUNTRY NAMES).
• In VALUES; calculate the count of candidates from each COUNTRY and GENDER type, Remove GRAND TOTALs.

TASK 4: SUMMARIZE DATA - EXCEL FUNCTIONS (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a SUMMARY table in the worksheet ANALYSIS,starting at cell G4, with the following details:

• Starting from range RANGE H4; get the distinct GENDER. Use remove duplicates option and transpose the data.
• Starting from range RANGE GS; get the distinct COUNTRY (Note: use COUNTRY NAMES).
• In the cross table,get the count of candidates from each COUNTRY and GENDER type.

TASK 5: GENERATE REPORT - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a PIVOT table report in the worksheet REPORT, starting at cell A3, with the following information:

• Change the report layout to TABULAR form.
• Remove expand and collapse buttons.
• Remove GRAND TOTALs.
• Allow user to filter the data by SPORT LOCATION.

Process

• Verify data for any missing values and anomalies, and sort out the same.
• Made sure data is consistent and clean with respect to data type, data format and values used.
• Created pivot tables according to the questions asked.

All of these files are Microsoft Excel format files that contain Surface Elevation Table (SET) data. We installed deep rod surface elevation tables (SETs) to quantify the relative contributions of surface and subsurface processes to present-day elevation change (i.e., root growth, decomposition, compaction, water flux), shallow subsidence (accretion – elevation), and shallow subsidence between shallow (root zone) and deeper (to >10 m) portions of the soil profile. We installed four SETs at each marsh site, following methods described by Cahoon et al. 2002 and Webb et al., 2013. We established two SETs in low marsh and two in high marsh at each site after visual assessment of vegetation composition and distance from tidal source. We deployed each SET with three feldspar marker horizon plots. When the SET instrument is attached to the installed bench mark, the SET provides a constant reference plane in space from which the distance to the sediment surface can be measured by means of pins lowered to the sediment surface. SET measurements will be taken by reading the heights of nine pins lowered to the sediment surface using the SET instrumentation at 4 directions, which are 90 degrees from each other. Repeated measurements of elevation can be made with high precision because the orientation of the table in space remains fixed for each sampling. We are conducting on-going measurements every three months at all sites. We installed surface elevation tables (SETs) at our seven study sites between September and December 2013 (n = 24) to evaluate present-day changes in marsh surface elevation. As of the time of this report, we collected two to seven baseline readings at each site (Figure 24; Table 12). Early results suggest that the magnitude of marsh surface elevation change varied within sites and between low and high tidal marsh. Net marsh surface elevation change was positive in both high and low marsh in Bolinas and Mad River. In contrast, surface elevation declined in high marsh locations at Morro, Pt. Mugu, San Pablo, and Tijuana. These initial findings should be considered inconclusive until several additional years of data have been collected.