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).

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

This worksheet allows you to see how IRT estimates the ML estimate of theta. Plus in the a and b for an item, as well as the response to each item (correct/incorrect).

The U.S. Geological Survey (USGS), in cooperation with Connecticut Department of Transportation, completed a study to improve flood-frequency estimates in Connecticut. This companion data release is a Microsoft Excel workbook for: (1) computing flood discharges for the 50- to 0.2-percent annual exceedance probabilities from peak-flow regression equations, and (2) computing additional prediction intervals, not available through the USGS StreamStats web application. The current StreamStats application (version 4) only computes the 90-percent prediction interval for stream sites in Connecticut. The Excel workbook can be used to compute the 70-, 80-, 90-, 95-, and 99-percent prediction intervals. The prediction interval provides upper and lower limits of the estimated flood discharge with a certain probability, or level of confidence in the accuracy of the estimate. The standard error of prediction for the Connecticut peak-flow regression equations ranged from 26.3 to 45.0 percent (Ahearn and Hodgkins, 2020). The Excel workbook consists of four worksheets. The worksheets provide an overview of how the application works; input and output tables of the explanatory variables and flood discharges, and graphical display of the results; and the computational formulas used to estimate the flood discharges and prediction intervals.

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

The data are organized into separate sheets corresponding to the following figure panels: 1C, 1G, 2B, 2D, 2F, 2H, 4C, 4D, 4F, 5B, 5C, S3B, S5C, S5E, S7B, S8B, S10B, S12A, S12B, and S21B. (XLSX)

This data file contains one worksheet containing raw data and descriptive statistics regarding static mechanical parameters obtained from force-distance curves of the spider silk fibres using SPIP software, and are described in its manual. The column descriptions for the raw data worksheet are as follows: COLUMN A – Sample Label COLUMN B – Maximum Load Force (Max Ld) COLUMN C – Maximum Pulling Force (Max Pull) COLUMN D – Snap In (Snap In) COLUMN E – Detachment Separation (Detach Sep) COLUMN F – Young’s Modulus (Youngs Mod) COLUMN G – Zero Indentation (Zero Ind) Column H – Dissipated Energy (Energy)

Worksheets for calculating items 9 and 10, simple mixtures and filtrations, extracts, and dietary supplements on TTB Form 5154.1.

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

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

Demonstration of basic applications of instrumental variable estimation (IVE), with applications of the Durbin-Wu-Hausman (DWH) test as an augmented regression.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey (USGS) have evaluated projections of future droughts for south Florida based on climate model output from the Multivariate Adaptive Constructed Analogs (MACA) downscaled climate dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The MACA dataset includes both Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5). A Microsoft Excel workbook is provided which tabulates model drought-evaluation statistics for the period 2056-95 based on drought characteristics derived from climate models downscaled by the MACA method assuming historical-standard stomatal resistance. Model drought-evaluation statistics based on 6-mo. and 12-mo. averaged balance anomaly timeseries are provided for four regions: (1) the entire South Florida Water Management District (SFWMD), (2) the Lower West Coast (LWC) water supply region, (3) the Lower East Coast (LEC) water supply region, and (4) ...

This file consists of the four original sheets included in the District Profile Report download, as described on the About FY18 Report tab, an additional five sheets created for GIS use and documentation, and an About File tab.The original four sheets are as follows:1. The “District Profile Report” provides a list of statistics for a given school district under seven groupings listed above (A-G). There are six columns of data in this worksheet. The first three columns provide statistics for each data element for the selected district, similar districts average and statewide average. There are three additional columns that enable the user to look at the statistics of three other school districts for comparison. By clicking in the cell below the Comparison Districts 1, 2 and 3 labels, the user can choose three additional school districts to review simultaneously.2. The worksheet labeled “District Data” provides a downloadable file of school district data.3. The worksheet labeled “Similar Districts Data” provides a downloadable file of similar districts averages.4. The worksheet labeled “Statewide Data” provides statewide averages for every statistic in the report.The five new sheets are as follows:1. FY18_Data_Dictionary - contains the abbreviated column names for GIS use and their definitions as found on the original download page. (The link is on the About FY18 Report sheet)2. FY18_Join - contains the District Data formatted for use in a GIS in the following way: the attribute/column titles were abbreviated for use in GIS, column C - UNSDLEA18 was added to facilitate joining to the Census School District geography file, column D - COUNTY was added for geographical filtering purposes, column E - COUNTY_GEOID was added for geographical filtering purposes. All columns from F to BM remain in the same order as the original dataset3. About FY18 Report - contains the report description as found on the original download page. The link to that original download page is also on this sheet.4. Mismatches - contains the school districts where the Census UNSDLEA cannot be derived from the Ohio IRN for quality control purposes5. No Data - contains the school districts that are not in the Profile Report but are in the Census geography data, for a quality control check when performing a join

Complete annotations for the tabular data are presented below. Tab Fig 1: (A) The heatmap data of G protein family members in the hippocampal tissue of 6-month-old Wildtype (n = 6) and 5xFAD (n = 6) mice; (B) The heatmap data of G protein family members in the cortical tissue of 6-month-old Wildtype (n = 6) and 5xFAD (n = 6) mice; (C) The data in the overlapping part of the Venn diagram (132 elements); (D) The data information for creating volcano plot; (E) The data information for creating heatmap of GPCR-related DEGs; (F) Expression of Gnb5 in the large sample dataset GSE44772; Control, n = 303; AD, n = 387; (H) Statistical analysis of Gnb5 protein levels from panel G; Wildtype, n = 4; 5xFAD, n = 4; (J) Statistical analysis of Gnb5 protein levels from panel I; Wildtype, n = 4; 5xFAD, n = 4; (L) Quantitative analysis of Gnb5 fluorescence intensity in 5xFAD and Wildtype groups; Wildtype, n = 4; 5xFAD, n = 4. Tab Fig 2: (D) qPCR data of Gnb5 knockout in hippocampal tissue; Gnb5F/F, n = 6; Gnb5-CCKO, n = 6; (E–I, L–N) Animal behavioral tests in mice, Gnb5F/F, n = 22; Gnb5-CCKO, n = 16; (E) Total distance traveled in the open field experiment; (F) Training curve in the Morris water maze (MWM); (F-day6) Data from the sixth day of MWM training; (G) Percentage of time spent by the mouse in the target quadrant in the MWM; (H) Statistical analysis of the number of times the mouse traverses the target quadrant in the MWM; (I) Latency to first reach the target quadrant in the MWM; (L) Baseline freezing percentage of mice in an identical testing context; (M) Percentage of freezing time of mice during the Context phase; (N) Percentage of freezing time of mice during the Cue phase. Tab Fig 3: (D–F, H) MWM tests in mice; Wildtype+AAV-GFP, n = 20; Wildtype+AAV-Gnb5-GFP, n = 23; 5xFAD + AAV-GFP, n = 23; 5xFAD + AAV-Gnb5-GFP, n = 26; (D) Training curve in the MWM; (D-day6) Data from the sixth day of MWM training; (E) Percentage of time spent in the target quadrant in the MWM; (F) Statistical analysis of the number of entries in the target quadrant in the MWM; (H) Movement speed of mice in the MWM; (I–K) The contextual fear conditioning test in mice; 5xFAD + AAV-GFP, n = 23; 5xFAD + AAV-Gnb5-GFP, n = 26; (I) Baseline freezing percentage of mice in an identical testing context; (J) Percentage of freezing time of mice during the Context phase; (K) Percentage of freezing time of mice during the Cue phase; (L) Total distance traveled in the open field test; (M) Percentage of time spent in the center area during the open field test. Tab Fig 4: (B, C) Quantification of Aβ plaques in the hippocampus sections from Wildtype and 5xFAD mice injected with either AAV-Gnb5 or AAV-GFP; Wildtype+AAV-GFP, n = 4; Wildtype+AAV-Gnb5-GFP, n = 4; 5xFAD + AAV-GFP, n = 4; 5xFAD + AAV-Gnb5-GFP, n = 4; (B) Quantification of Aβ plaques number; (C) Quantification of Aβ plaques size; (F, G) Quantification of Aβ pylaques from indicted mice lines; WT&Gnb5F/F&CamKIIa-CreERT+Vehicle, n = 4; 5xFAD&Gnb5F/F&CamKIIa-CreERT+Vehicle, n = 4; 5xFAD&Gnb5F/F&CamKIIa-CreERT+Tamoxifen, n = 4; (F) Quantification of Aβ plaque size; (G) Quantification of Aβ plaque number. Tab Fig 5: (B) Overexpression of Gnb5-AAV in 5xFAD mice affects the expression of proteins related to APP cleavage (BACE1, β-CTF, Nicastrin and APP); Statistical analysis of protein levels; n = 4, respectively; (D) Tamoxifen-induced Gnb5 knockdown in 5xFAD mice affects APP-cleaving proteins; Statistical analysis of protein levels; n = 4, respectively; (F) Gnb5-CCKO mice show altered expression of APP-cleaving proteins; Statistical analysis of protein levels; n = 6, respectively. Tab Fig 7: (C, D) Quantification of Aβ plaques in the overexpressed full-length Gnb5, truncated fragments, and mutant truncated fragment AAV in 5xFAD mice; n = 4, respectively; (C) Quantification of Aβ plaques size; (D) Quantification of Aβ plaques number; (F) Effect of overexpressing full-length Gnb5, truncated fragments, and mutant truncated fragment viruses on the expression of proteins related to APP cleavage process in 5xFAD; Statistical analysis of protein levels; n = 3, respectively. (XLSX)

Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for Figs 1B, 1C, 1E, 1F, 1G, 2A, 2B, 3, 4B, 4C, 4D, 4E, 4F, 4G, 5A, 5B, 5C, 5D, 5E, 6A, 6B, 6C, 6D, 8A, 8B, 8C, 8D, 9A, 9B, 9C, 9D, 10A, 10B, 10C, 10D, 10E, 10F, 10G, 10H, 11A, 11B, 11C, S1A, S1B, S1C, S1D, S2A, S2B, S2C, and S2D.

Excel spreadsheet of data and codes from articles.

The Geothermal Resource Portfolio Optimization and Reporting Tool (GeoRePORT) was developed as a way to distill large amounts of geothermal project data into an objective, reportable data set that can be used to communicate with experts and non-experts. GeoRePORT summarizes (1) resource grade and certainty and (2) project readiness. This Excel file allows users to easily navigate through the resource grade attributes, using drop-down menus to pick grades and project readiness, and then easily print and share the summary with others. This spreadsheet is the first draft, for which we are soliciting expert feedback. The spreadsheet will be updated based on this feedback to increase usability of the tool. If you have any comments, please feel free to contact us.