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

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.

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.

In order to test hypotheses about groundwater flow under and into estuaries and the Atlantic Ocean, geophysical surveys, geophysical probing, submarine groundwater sampling, and sediment coring were conducted by U.S. Geological Survey (USGS) scientists at Cape Cod National Seashore (CCNS) from 2004 through 2006. Coastal resource managers at CCNS and elsewhere are concerned about nutrients that are entering coastal waters via submarine groundwater discharge, which are contributing to eutrophication and harmful algal blooms. The research carried out as part of the study described here was designed, in part, to help refine assumptions required by earlier versions of models about the nature of submarine groundwater flow and discharge at CCNS. This study was conducted in four phases, with a variety of field techniques and equipment employed in each phase. Phase 1 consisted of continuous resistivity profiling (CRP) surveys of the entire study area conducted in 2004. Phase 2 consisted of CRP ground-truthing via resistivity probe measurements and submarine groundwater sampling from hydraulically-drive piezometers using a barge in the Salt Pond/Nauset Marsh area in 2005. Phase 3 consisted of supplemental detailed CRP surveys in the Salt Pond/Nauset Marsh area in 2006. Finally, Phase 4 consisted of sediment coring and porewater extraction in the Salt Pond/Nauset Marsh area later in 2006 to supplement the 2005 sampling.

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

No description is available. Visit https://dataone.org/datasets/6ffb72520e80a412991cd50d38f324d6 for complete metadata about this dataset.

An excel file containing the following data for each lavaka:- Study area (SA) number- Perimeter [m] and area [m²] on satellite image- Year of the satellite image- Perimeter (Shape_Length, [m]) and area (Shape_Area, [m²]) on the 1949 and 1969 image- Total bare surface perimeter (peri, [m]) and area (area, [m²] for all images- Relief [m]: height difference between lavaka edge and outlet [m]- Vertical distance to stream (DZ_SL [m])- Horizontal distance to stream (DXY_SL [m])- Lavaka outlet coordinates (X_OUTL, Y_OUTL)- Flow accumulation at outlet (FA)- Distance to drainage divide (DD_DXY [m])- Drainage Divide order (DD_order)- Lavaka edge coordinates (X_LE, Y_LE)

In order to test hypotheses about groundwater flow under and into estuaries and the Atlantic Ocean, geophysical surveys, geophysical probing, submarine groundwater sampling, and sediment coring were conducted by U.S. Geological Survey (USGS) scientists at Cape Cod National Seashore (CCNS) from 2004 through 2006. Coastal resource managers at CCNS and elsewhere are concerned about nutrients that are entering coastal waters via submarine groundwater discharge, which are contributing to eutrophication and harmful algal blooms. The research carried out as part of the study described here was designed, in part, to help refine assumptions required by earlier versions of models about the nature of submarine groundwater flow and discharge at CCNS. This study was conducted in four phases, with a variety of field techniques and equipment employed in each phase. Phase 1 consisted of continuous resistivity profiling (CRP) surveys of the entire study area conducted in 2004. Phase 2 consisted of CRP ground-truthing via resistivity probe measurements and submarine groundwater sampling from hydraulically-drive piezometers using a barge in the Salt Pond/Nauset Marsh area in 2005. Phase 3 consisted of supplemental detailed CRP surveys in the Salt Pond/Nauset Marsh area in 2006. Finally, Phase 4 consisted of sediment coring and porewater extraction in the Salt Pond/Nauset Marsh area later in 2006 to supplement the 2005 sampling.

These are results of a series of laboratory experiments to determine if topical application of methoprene and 20-ecdysone can terminate reproductive diapause of the weevil, Ceratapion basicorne, which is a recently permitted biological control agent of yellow starthistle (Centaurea solstitialis). Adult weevils feed on leaves, creating pin holes, and lay eggs inside leaves. Diapausing weevils were treated with various doses of methoprene (0, 0.01, 0.1, 1.0 micrograms) dissolved in acetone in experiments 1 and 2. They were treated sequentially first with acetone or 20-ecdysone (1.0 microgram) and then with methoprene (1.0 microgram) in experiment 3 and were treated with 20-ecdysone followed by methoprene in experiment 4. Resources in this dataset:Resource Title: data dictionary. File Name: JH Data Dictionary.csvResource Description: description of data fieldsResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 1. File Name: JH expt1 data.csvResource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0, 0.01 and 0.1 and 1.0 μg per female weevil, and the number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 2. File Name: JH expt2 data.csvResource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0 and 1.0 μg to female weevils that did not produce eggs in experiment 1. The number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 3. File Name: JH expt3 data.csvResource Description: Three types of treatments were applied with sequential applications 2 days apart: 1) acetone + acetone [AA: control], 2) acetone + methoprene [AM], and 20-ecdysone + methoprene 174 [2M]. All doses were 1.0 μg. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: experiment 4. File Name: JH expt4 data.csvResource Description: Females from experiment 3 that did not oviposit consistently were treated with 1.0 μg of 20-ecdysone followed 2 days later by 1.0 μg of methoprene. The treatments AA, AM, 2M refer to experiment 3. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel

The open repository consists of two folders; Dataset and Picture. The dataset folder consists file “AWS Dataset Pangandaraan.xlsx”. There are 10 columns with three first columns as time attributes and the other six as atmosphere datasets. Each parameter has 8085 data, and Each parameter has a parameter index at the bottom of the column we added, including mMinimum, mMaximum, and Average values.

For further use, the user can choose one or more parameters for calculating or analyzing. For example, wind data (speed and direction) can be utilized to calculate Waves using the Hindcast method. Furthermore, the user can filter data by using the feature in Excel to extract the exact time range for analyzing various phenomena considered correlated to atmosphere data around Pangandaran, Indonesia.

The second folder, named “Picture,” contains three figures, including the monthly distribution of datasets, temporal data, and wind rose. Furthermore, the user can filter data by using the feature in Excel sheet to extract the exact time range for analyzing various phenomena considered correlated to atmosphere data around Pangandaran, Indonesia

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.

With this add in it is possible to create map templates from GIS files in KML format, and create choropleths with them. Providing you have access to KML format map boundary files, it is possible to create your own quick and easy choropleth maps in Excel. The KML format files can be converted from 'shape' files. Many shape files are available to download for free from the web, including from Ordnance Survey and the London Datastore. Standard mapping packages such as QGIS (free to download) and ArcGIS can convert the files to KML format. A sample of a KML file (London wards) can be downloaded from this page, so that users can easily test the tool out. Macros must be enabled for the tool to function. When creating the map using the Excel tool, the 'unique ID' should normally be the area code, the 'Name' should be the area name and then if required and there is additional data in the KML file, further 'data' fields can be added. These columns will appear below and to the right of the map. If not, data can be added later on next to the codes and names. In the add-in version of the tool the final control, 'Scale (% window)' should not normally be changed. With the default value 0.5, the height of the map is set to be half the total size of the user's Excel window. To run a choropleth, select the menu option 'Run Choropleth' to get this form. To specify the colour ramp for the choropleth, the user needs to enter the number of boxes into which the range is to be divided, and the colours for the high and low ends of the range, which is done by selecting coloured option boxes as appropriate. If wished, hit the 'Swap' button to change which colours are for the different ends of the range. Then hit the 'Choropleth' button. The default options for the colours of the ends of the choropleth colour range are saved in the add in, but different values can be selected but setting up a column range of up to twelve cells, anywhere in Excel, filled with the option colours wanted. Then use the 'Colour range' control to select this range, and hit apply, having selected high or low values as wished. The button 'Copy' sets up a sheet 'ColourRamp' in the active workbook with the default colours, which can just be extended or deleted with just a few cells, so saving the user time. The add-in was developed entirely within the Excel VBA IDE by Tim Lund. He is kindly distributing the tool for free on the Datastore but suggests that users who find the tool useful make a donation to the Shelter charity. It is not intended to keep the actively maintained, but if any users or developers would like to add more features, email the author. Acknowledgments Calculation of Excel freeform shapes from latitudes and longitudes is done using calculations from the Ordnance Survey.

Raw data from six species of raptors that evaluate the percentage of GPS fixes and the percentage of the home range area, or 95% kernel area, captured by the buffer circle. In each file, the first worksheet has details on the length of each year and season per each individual; the second worksheet counts the individual and average home range and the percentage of GPS fixes captured by the calculated buffer circle during each year and season; the third worksheet calculates the individual and average percentage of the area captured by the calculated buffer circle; while the fourth worksheet calculates the individual year and average annual percentage of the home range covered by the calculated buffer circle, and at what size does the buffer circle capture 95% of the species' home range.

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

The units and description of variables are in the first sheet of the excel file, "Metadata". There are 4 data sheets total.

BackgroundThe Qualitative aspect of health care delivery is one of the major factors in reducing morbidity and mortality in a health care setup. The expanding suburban secondary health care delivery facilities of the Municipal Corporation of Greater Mumbai are an important part of the healthcare backbone of Mumbai and therefore the quality of care delivered here needed standardization.Material and MethodsThe project was completed over a period of one year from Jan to Dec, 2013 and implemented in three phases. The framework with components and sub-components were developed and formats for data collection were standardized. The benchmarks were based on past performance in the same hospital and probability was used for development of normal range. An Excel spreadsheet was developed to facilitate data analysis.ResultsThe indicators comprise of 3 components - Statutory Requirements, Patient care & Cure and Administrative efficiency. The measurements made, pointed to the broad areas needing attention.ConclusionThe Indicators for patient care and monitoring standards can be used as a self assessment tool for health care setups for standardization and improvement of delivery of health care services.

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.

Identification of study species

We identified 40 marine species with documented shifts in range limits along the coastline (<15 km from shore) of North America, including plants, invertebrates, fish, a protist, and a bird. Of these, 26 species were compiled by Sorte et al. (2010), and we added 14 species from an updated literature review. We searched Google Scholar (on 08/20/2019) using this search string: marine "range expansion" species "range shift". We reviewed titles and, when appropriate, abstracts and text of the first 600 results, identifying 12 additional species from eight papers. We added two species (Brachidontes adamsianus and Mexacanthina lugubris) from our literature files and personal observations. We excluded migratory or pelagic species with large biogeographic ranges, for which it was difficult to confirm historical native ranges.

Review of published impacts

Evidence of species’ impacts was compiled from online database searches an...