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 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

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.

Distribution of doses of a volatile organic compound from inhalation of one consumer product, other near -field sources, far-field sources, and aggregate (total) exposure. In this instance, far-field scenarios account for several orders of magnitude of less of the predicted dose compared to near-field scenarios. This dataset is associated with the following publication: Vallero, D. Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus. Sustainability. MDPI AG, Basel, SWITZERLAND, 8(12): 1216, (2016).

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

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

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.

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 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.

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).

The series consists of a list of files registered on the computer-based Records and Correspondence Management System (RCMS), under Registry 01 Corporate Management Division. 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 PROV, to provide access to VPRS 12607 General Correspondence Files, Registry 01 Corporate Management Division.

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 01 Corporate Management Division.

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.

Identifying Top-numbered Files:

This series also records the original file numbers for files that have been top-numbered into VPRS 12607 from other correspondence registries that operated in the Law Department in the 1980's. The details are as follows:

Files top-numbered from the Central Correspondence Registry (VPRS 266 Inward Registered Correspondence 1857-1986) - the original file number is recorded in the field "ADD.FILE.INFO".

Files top-numbered from the Courts Management Division Registry (VPRS 12705 General Correspondence Files, Courts Management Division) - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Files top-numbered from the Buildings and Property Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from the Human Resource Management Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from RCMS Registry 02 Courts and Tribunals Division - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Researchers should not discount the possibility that file numbers may be recorded in fields other than those specified above.

Supplementary data for chapter 2 in excel format.

Dataset

This dataset was created by Shiva Vashishtha

Contents

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

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.

The dataset for the article "The current utilization status of wearable devices in clinical research".Analyses were performed by utilizing the JMP Pro 16.10, Microsoft Excel for Mac version 16 (Microsoft).The file extension "jrp" is a file of the statistical analysis software JMP, which contains both the analysis code and the data set.In case JMP is not available, a "csv" file as a data set and JMP script, the analysis code, are prepared in "rtf" format.The "xlsx" file is a Microsoft Excel file that contains the data set and the data plotted or tabulated using Microsoft Excel functions.Supplementary Figure 1. NCT number duplication frequencyIncludes Excel file used to create the figure (Supplemental Figure 1).・Sfig1_NCT number duplication frequency.xlsxSupplementary Figure 2-5　Simple and annual time series aggregationIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 2-5).・Sfig2-5 Annual time series aggregation.xlsx・Sfig2 Study Type.jrp・Sfig4device type.jrp・Sfig3 Interventions Type.jrp・Sfig5Conditions type.jrp・Sfig2, 3 ,5_database.csv・Sfig2_JMP script_Study type.rtf・Sfig3_JMP script Interventions type.rtf・Sfig5_JMP script Conditions type.rtf・Sfig4_dataset.csv・Sfig4_JMP script_device type.rtfSupplementary Figures 6-11 Mosaic diagram of intervention by conditionSupplementary tables 4-9 Analysis of contingency table for intervention by condition JMP repot files used to create the figures(Supplementary Figures 6-11 ) and tables(Supplementary Tablea 4-9) , including the csv dataset of JMP repot files and JMP scripts.・Sfig6-11 Stable4-9 Intervention devicetype_conditions.jrp・Sfig6-11_Stable4-9_dataset.csv・Sfig6-11_Stable4-9_JMP script.rtfSupplementary Figure 12. Distribution of enrollmentIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 12).・Sfig12_Distribution of enrollment.jrp・Sfig12_Distribution of enrollment.csv・Sfig12_JMP script.rtf

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.

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).