The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

HOW TO: - Hierarchy using the category, subcategory & product fields (columns “Product Category” “Product SubCategory”, & “Product Name”). - Group the values ​​of the column "Region" into 2 groups, alphabetically, based on the name of each region.

1. Display a table, which shows, for each value of the product hierarchy you created above, the total amount of sales ("Sales") and profitability ("Profit").
2. The same information as the previous point (2) in a bar chart illustration.
3. Display columns with the total sales amount ("Sales") for each value of the alphabetical grouping of the Region field you created. The color of each column should be derived from the corresponding total shipping cost (“Shipping Cost”). In the Tooltip of the illustration all numeric values ​​should have a currency format.
4. The same diagram as above (3), with the addition of a data filter at visual level filter that will display only the data subset related to sales with positive values ​​for the field "Profit".
5. The same diagram with the above point (3), with the addition of a data filter at visual level filter that will display only the subset of data related to sales with negative values ​​for the field "Profit".
6. Map showing the total amount of sales (size of each point), as well as the total profitability (color of each point). Change the dimensions of the image

This is a Dataset Regarding the games available on the Nintendo Switch Console. The data is sorted on the Total Sales of the games The data was scrapped using Microsoft Excel from www.vgchartz.com .

Column Description: - Position: This represents the position of the game based on total sales. - Game: Name of the Game. - Publisher, Developer: The names of the Developer and Publisher. - VGChartz Score, Critic Score, User Score: Ratings for the games based on various parameters. - Total Shipped: Shows the total no.of units of the game shipped across the Globe. - Release Date: Gives the release Date for the game. - Last Update: Shows when the game was last updated.

Attached is the Cobble App in Matlab developed by Erin Bray, for calculation of cobble shape parameters as reported in Bray et al "Influence of particle lithology, size, and angularity on rates and products of bedload wear: an experimental study" (In Review).

For the Cobble App to work, install Matlab version 2022b; Add the Image Processing Toolbox; Add the Computer Vision System Toolbox.Photo files must be saved in grayscale (no RGB embedded when saving photo files). Files of photos can be saved as .tif or .tiff (both should work in the cobble app) All extraneous white edges/borders or dots in files need to be removed (there were some stray white specks in the background of one of the photo files that was reduced to 75% resolution). The Photo ID string column such as "P1_A1_N_PT" needs to be consistently formatted, with no extra spaces or extra characters, in both the Excel spreadsheet and in the photo file names, with no changes to the file string name even if you reduce the photo resolution to 75%. To use the merge functionality within the Cobble App, which pairs image-based shape parameters with corresponding handheld measurements of mass, diameter of each particle, the Excel spreadsheet needs to always have the identical number of columns and name of columns.

The Agricultural Research Service of the US Department of Agriculture (USDA) in collaboration with other government agencies has a program to track changes in the sodium content of commercially processed and restaurant foods. This monitoring program includes these activities: Tracking sodium levels of ~125 popular foods, called "Sentinel Foods," by periodically sampling them at stores and restaurants around the country, followed by laboratory analyses. Tracking levels of "related" nutrients that could change when manufacturers reformulate their foods to reduce sodium; these related nutrients are potassium, total and saturated fat, total dietary fiber, and total sugar. Sharing the results of these monitoring activities to the public periodically in the Sodium Monitoring Dataset and USDA National Nutrient Database for Standard Reference and once every two years in the Food and Nutrient Database for Dietary Studies. The Sodium Monitoring Dataset is downloadable in Excel spreadsheet format. Resources in this dataset:Resource Title: Data Dictionary. File Name: SodiumMonitoringDataset_datadictionary.csvResource Description: Defines variables, descriptions, data types, character length, etc. for each of the spreadsheets in this Excel data file: Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority-2 Foods - Monitoring.Resource Title: Sodium Monitoring Dataset (MS Excel download). File Name: SodiumMonitoringDatasetUpdatedJuly2616.xlsxResource Description: Microsoft Excel : Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority Foods - Monitoring.

In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.

This data providers package provides the data collection configuration and supporting materials for reporting Chemical Contaminants in SSD1. These are to be used for the official data reporting phase.

The package includes:

The Standard Sample Description Version 2 XSD schema definition for CONTAMINANTS reporting.

The general and CONTAMINANTS SSD1 specific business rules applied for the automatic validation of the submitted datasets.

Excel Mapping tool to convert excel files after mapping into XML document.

Please follow the instructions below for the correct use of the mapping tool to avoid compromising its functionalities:

Download and save the MS Excel® Standard Sample Description file to your computer (do not open the file before saving and do not change the file name)

Download and save the file MS Excel® Simplified Reporting Format (do not open the file before saving)

Keep both Excel files in the same folder

Open both Excel files and enable the macros

Keep both files open in the same Excel instance when filling in the data

Guidance on how to run the validation report after submitting data to the DCF.

BackgroundConfusion between look-alike and sound-alike (LASA) medication names (such as mercaptamine and mercaptopurine) accounts for up to one in four medication errors, threatening patient safety. Error reduction strategies include computerized physician order entry interventions, and ‘Tall Man’ lettering. The purpose of this study is to explore the medication name designation process, to elucidate properties that may prime the risk of confusion.Methods and FindingsWe analysed the formal and semantic properties of 7,987 International Non-proprietary Names (INNs), in relation to naming guidelines of the World Health Organization (WHO) INN programme, and have identified potential for errors. We explored: their linguistic properties, the underlying taxonomy of stems to indicate pharmacological interrelationships, and similarities between INNs. We used Microsoft Excel for analysis, including calculation of Levenshtein edit distance (LED). Compliance with WHO naming guidelines was inconsistent. Since the 1970s there has been a trend towards compliance in formal properties, such as word length, but longer names published in the 1950s and 1960s are still in use. The stems used to show pharmacological interrelationships are not spelled consistently and the guidelines do not impose an unequivocal order on them, making the meanings of INNs difficult to understand. Pairs of INNs sharing a stem (appropriately or not) often have high levels of similarity (

Our goals were to 1) isolate, and culture two fungal morphotypes, 2) characterize the volatile emissions from grain inoculated by each fungal morphotype (Aspergillus flavus or Fusarium spp.) compared to uninoculated and sanitized grain, and 3) understand how MVOCs from each morphotype affects mobility, attraction, and preference by L. serricorne. Headspace collection revealed that the Fusarium- and A. flavus-inoculated grain produced significantly different volatiles compared to sanitized grain or the positive control. Changes in MVOC emissions affected close-range foraging during an Ethovision assay, with a greater frequency of entering and spending time in a small zone with kernels inoculated with A. flavus compared to other treatments. In the release-recapture assay, MVOCs were found to be attractive to L. serricorne at a longer distances in commercial pitfall traps. While there was no preference shown among semiochemical stimuli in a still-air, four-way olfactometer, it is possible that methodological limitations prevented robust interpretation from this assay. Overall, our study suggests that MVOCs are important for close- and long-range orientation of L.serricorne during foraging, and that MVOCs may have the potential for inclusion in behaviorally-based tactics for this species. Resources in this dataset:Resource Title: 4-way olfactometer assay. File Name: Sierra_2021_olfactometer_Ag_Data_commons.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Release-recapture Assay . File Name: sierra_release_recapture_exp_2021_fungal_volatiles_agdata_commons.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Ethovision Movement Assay . File Name: ethovision_sierra_2021_microbial_volatiles_agdatacommons.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Headspace volatile collection assay. File Name: headspace_compounds_sierra_2021_fungal_volatiles_final_agdatacommons.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Sequencing Data. File Name: sequencing_data.zipResource Title: File list. File Name: File_list_L_serricone_attraction_data.txt