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.

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.

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.

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

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.

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

The folder named data-C-galaxies input contains the input data reported in the Sofue's database in Excel format for the 291 galaxies of this series, while the corresponding data-C-galaxies-output files present the detailed fitting results for each galaxy of the C-Series. The first plot is the global dispersion curve. The EXCEL file following this spread curve summarizes the fitting parameters, the estimated mass, the maximal velocity and the SNR results obtained for the individual best fitting plots for each galaxy. These plots are successively presented after the EXCEL files. Similarly, the files named data-P-galaxies input and data-P-galaxies-output as well as data-S-galaxies input and data-S-galaxies-output report the input data and the best fitting results of the 31 galaxies of the P-Series and of the 229 galaxies of the S-Series respectively. As seen in the last lines of the EXCEL files, overall, the mean SNR and its standard deviation is 25.2 (3.8) dB for the C-series, 23.6 (5.2) dB for the P-series and 22.1 (5.9) for the S-series, which can be considered as very good for a two-parameter fitting.

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.

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.

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.

Zooplankton samples were collected from the United States Coast Guard Cutter (USCGC) Healy between 22 July and 24 August 2004 (cruise designation HLY0403). Sampling focused on the shelf, slope, and basin regions in the northeastern Chukchi Sea and western Beaufort Sea. Samples were collected with a Hydro-Bios MultiNet plankton sampler with a 0.25 m2 net mouth fitted with 150 um mesh nets, pressure sensor and flowmeter. These data are provided in Excel spreadsheet format.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).