The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (Zea mays) / rye cover-crop (Secale cereale) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP, described below. The experiment included ten different treatments: (1) broadcast manure with disk-harrow incorporation, (2) broadcast manure with no tillage incorporation, (3) manure application with “strip-tillage” which was sweep injection ridged with paired disks, (4) aerator band manure application, (5) low-disturbance sweep injection of manure, (6) Coulter injection of manure with sweep tillage, (7) no manure with urea to supply 60 lb N/acre (67 kg N/ha), (8) no manure with urea to supply 120 lb N/acre (135 kg N/ha), (9) no manure with urea to supply 180 lb N/acre (202 kg N/ha), (10) no manure / no fertilizer control. Manure was applied in the fall; fertilizer was applied in the spring. These ten treatments were replicated four times in a randomized complete block design. The LDMI experiment was conducted at the Marshfield Research Station of the University of Wisconsin and the USDA Agricultural Research Service (ARS) in Stratford, WI (Marathon County, Latitude 44.7627, Longitude -90.0938). Soils at the research station are from the Withee soil series, fine-loamy, mixed, superactive, frigid Aquic Glossudalf. Each experimental plot was approximately 70 square meters. A weather station was located at the south edge of field site. A secondary weather station (MARS South), for snow and snow water equivalence data and for backup of the main weather station, was located at Latitude 44.641445 and Longitude -90.133526 (16,093 meters southwest of the field site). The experiment was initiated on November 28, 2011 with fall tillage and manure application in each plot according to its treatment type. Each spring, corn silage was planted in rows at a rate of 87500 plants per hectare. The cultivar was Pioneer P8906HR. The LDMI experiment ended on November 30, 2015. The manure applied in this experiment was from the dairy herd at the Marshfield Research Station. Cows were fed a diet of 48% dry matter, 17.45% protein, and 72.8% total digestible nutrients. Liquid slurry manure, including feces, urine, and rain, was collected and stored in a lagoon on the site. Manure was withdrawn from the lagoon, spread on the plots and sampled for analysis all on the same day, once per year. Manure samples were analyzed at the University of Wisconsin Soil and Forage Lab in Marshfield (NH4-N, total P and total K) and at the Marshfield ARS (pH, dry matter, volatile solids, total N and total C). GHG fluxes from soil (CO2, CH4, N2O) were measured using static chambers as described in Parkin and Venterea (2010). Measurements were made with the chambers placed across the rows of corn. I Additional soil chemical and physical characteristics were measured as noted in the data dictionary and other metadata of the LDMI data set, included here. This experiment was part of “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP), funded by the United States Department of Agriculture - National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP was to improve understanding of the magnitudes and controlling factors over GHG emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP has improved life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities. Resources in this dataset:Resource Title: Data_dictionary_DairyCAP_LDMI. File Name: Data_dictionary_DairyCAP_LDMI.xlsxResource Description: This is the data dictionary for the Low-Disturbance Manure Incorporation (LDMI) experiment, conducted at the USDA-ARS research station in Marshfield, WI. (Separate spreadsheet tabs)Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: DairyCAP_LDMI. File Name: DairyCAP_LDMI.xlsxResource Description: This is the data from the Low-Disturbance Manure Incorporation (LDMI) experiment, conducted at the USDA-ARS research station in Marshfield, WI.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary DairyCAP LDMI. File Name: Data_dictionary_DairyCAP_LDMI.csvResource Description: This is the data dictionary for the Low-Disturbance Manure Incorporation (LDMI) experiment, conducted at the USDA-ARS research station in Marshfield, WI.

Resource Title: Biomass Data. File Name: LDMI_Biomass.csvResource Title: Experimental Set-up Data. File Name: LDMI_Exp_setup.csvResource Title: Gas Flux Data. File Name: LDMI_Gas_Fluxes.csvResource Title: Management History Data. File Name: LDMI_Management_History.csvResource Title: Manure Analysis Data. File Name: LDMI_Manure_Analysis.csvResource Title: Soil Chemical Data. File Name: LDMI_Soil_Chem.csvResource Title: Soil Physical Data. File Name: LDMI_Soil_Phys.csvResource Title: Weather Data. File Name: LDMI_Weather.csv

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.

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

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

Rank and count of the top names for baby boys, changes in rank since the previous year and breakdown by country, region, mother's age and month of birth.

This dataset was generated from soybean (Glycine max) field trials conducted at the West Tennessee Research and Education Center in Jackson, TN and at the Research and Education Center at Milan in Milan, TN as well as from molecular marker screening conducted at the West Tennessee Research and Education Center in Jackson, TN. Table 3 includes measured data for height, yield, and seed size, and rating data for lodging and seed quality for JTN-5110, 5601T, and select other released germplasm lines and cultivars tested in replicated breeder yield trials in Jackson and Milan, TN from 2010-2016, excluding 2014. This data may be useful in measuring yield gain in future releases of soybean germplasm or cultivars with broad resistance to soybean cyst nematode (SCN; Heterodera glycines). This data should not be used to measure yield gain for elite high-yielding cultivars that do not have broad cyst nematode resistance. Table 5 includes rating data for JTN-5110 and soybeans with established SCN resistance from simple sequence repeat (SSR) markers: Satt309 and Sat_168, associated with rhg1 on chromosome 18; Sat_162, associated with Rhg4 on chromosome 8; and Satt574, associated with cqSCN-005 on chromosome 17. This data may be useful in understanding the role of these molecular regions in SCN resistance for JTN-5110 and parent line Anand. This data should not be used to draw broad conclusions about cyst nematode resistance, in general. Table 7 includes rating data for JTN-5110 and check cultivars from frogeye leafspot (caused by Cercospora sojina) field disease screenings conducted in Milan, TN from 2010-2012. This data may be useful in measuring changes in frogeye leafspot incidence and severity in West Tennessee. This data should not be used to draw broad conclusions or represent different geographic areas. Resources in this dataset:Resource Title: Data dictionary. File Name: data dictionary.csvResource Description: A data dictionary defining the fields in Tables 3, 5, and 7Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 3 - JTN-5110 compared to 5601T. File Name: Table 3 - JTN-5110 compared to 5601T.csvResource Description: Breeder yield trial data from Jackson and Milan, TN from 2010-2016, excluding 2014Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 5 - compiled marker data. File Name: Table 5 - compiled marker data.csvResource Description: Genetic marker data for SSR markers associated with soybean cyst nematode resistance. Screening conducted in Jackson, TN from 2005-2020.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 7 - frogeye leafspot evaluation. File Name: Table 7 - frogeye leafspot evaluation.csvResource Description: Data from frogeye leafspot field screening conducted in Milan, TN from 2010-2012.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel