This document provides instructions for editing and submitting unit process or product system models to the USDA LCA Commons life cycle inventory (LCI) database. The LCA Commons LCI database uses the openLCA life cycle modeling tool's database schema. Therefore, this document describes how to import and edit data in openLCA and name and classify flows such that they properly import into and operate in the database. This document also describes metadata or documentation requirements for posting models to the LCA Commons. This document is an evolving standard for LCA Commons data. As USDA-NAL continues to gain experience in managing a general purpose LCI database and global conventions continue to evolve, so too will the LCA Commons Submission Guidelines. Resources in this dataset:Resource Title: LCA Commons Submission Guidelines_12/09/2015. File Name: lcaCommonsSubmissionGuidelines_Final_2015-12-09.pdf

description:

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data.

Purpose

As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data.

An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

• establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals
• compare how much data is in institutional vs. domain-specific vs. federal platforms
• determine which repositories are recommended by top journals that require or recommend the publication of supporting data
• ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach

The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.

Search methods

We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of agricultural data /ag data / scientific data + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects.

We then used search engines such as Bing and Google to find top agricultural university repositories using variations of agriculture, ag data and university to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University International Research Institute for Climate and Society, UC Davis Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories.

Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo.

Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories.

Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals.

Evaluation

We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results.

We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind.

Results

A summary of the major findings from our data review:

• Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors.
• There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
• Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data.

Purpose

As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data.

An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

• establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals
• compare how much data is in institutional vs. domain-specific vs. federal platforms
• determine which repositories are recommended by top journals that require or recommend the publication of supporting data
• ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach

The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.

Search methods

We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K

This data set includes rasters of tidal marsh soil properties in the Northeast US for the purpose of blue carbon accounting. Mapping products cover estuarine and emergent wetland classes in the National Wetlands Inventory.Resources in this dataset:Resource Title: Northeast Blue Carbon Raster Map - LOI File Name: Northeast_marsh_LOI.7z Resource Description: This raster file provides soil organic matter (% LOI) estimates for tidal marshes in the Northeast US.Resource Title: Northeast Blue Carbon Raster Map - BD File Name: Northeast_marsh_BD.7z Resource Description: This raster based data product provides soil dry bulk density estimates for tidal marshes in the Northeast US.Resource Title: README file for Northeast Blue Carbon Rasters File Name: README_Northeast_blue_carbon_rasters_LOI_BD.txt Resource Description: Brief description of the raster properties and methods by which they were created to model soil organic matter, soil dry bulk density, and carbon density for the Northeast US. See Teng et al (in prep., 2023) for more details on the model development.

In support of nutrition research, concentrations of compounds from different parts of the watermelon plant are provided. The parts of the plant for which data are tabulated include (red) flesh, heart tissue, juice, seed, rind, peel, yellow flesh, seedling, leaf, root, other parts of the plant, and detected but plant part undeclared. The collected data include the low value in the range, the high value in the range, deviation from those values, and units (assumed to be fresh or wet weight unless noted). This table also provides for all compounds the citations to the literature and database sources. The “AFC” identifier represents the Agricultural Research Service (ARS) Food Compound; PubChem refers to the identifier from this resource of chemical compounds. Resources in this dataset:Resource Title: Catalog of natural products occurring in watermelon. File Name: Watermelon_NP_catalog_20210623.tsvResource Description: This is a table of chemical compounds found in watermelonResource Title: Data dictionary. File Name: Data_dictionary_Watermelon_compounds_NAL_20210623.xlsxResource Description: This is the data dictionaryResource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/access

Data dictionary for Gridded National Soil Survey Geographic Database (gNATSGO). https://data.nal.usda.gov/node/23067gNATSGO has a schema that is very similar to that of SSURGO and STATSGO2. A CSV version of the data dictionary is presented.A data dictionary typically provides a detailed description for each element or variable in a dataset or data model. Data dictionaries are used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description.Dataset citation: (dataset) Soil Survey Staff. Gridded National Soil Survey Geographic (gNATSGO) Database for [State name -or- the Conterminous United States]. United States Department of Agriculture, Natural Resources Conservation Service. Available online at https://nrcs.app.box.com/v/soils. Month, day, year.

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 dictionary for "Agricultural Collaborative Research Outcomes System (AgCROS)". https://data.nal.usda.gov/node/5643/ Data dictionary for the growing AgCROS family of products as of 04/2019.A data dictionary typically provides a detailed description for each element or variable in a dataset or data model. Data dictionaries are used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description.Dataset citation: (dataset) USDA Agricultural Research Service. (2017). Agricultural Collaborative Research Outcomes System (AgCROS). USDA Agricultural Research Service. https://data.nal.usda.gov/dataset/agricultural-collaborative-research-outcomes-system-agcros.

This parent dataset (collection of datasets) describes the general organization of data in the datasets for each growing season (year) when alfalfa (Medicago sativa L.) was grown as a reference evapotranspiration (ETr) crop at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Alfalfa was grown on two large, precision weighing lysimeters, calibrated to NIST standards (Howell et al., 1995). Each lysimeter was in the center of a 4.44 ha square field on which alfalfa was also grown (Evett et al., 2000). The two fields were contiguous and arranged with one (labeled northeast, NE) directly north of the other (labeled southeast, SE). See the resource "Geographic Coordinates, USDA, ARS, Bushland, Texas" for UTM geographic coordinates for field and lysimeter locations. Alfalfa was planted in Autumn 1995 and grown for hay in 1996, 1997, 1998, and 1999. The resource "Agronomic Calendar for the Bushland, Texas Alfalfa Datasets", gives a calendar listing by date the agronomic practices applied, severe weather, and activities (e.g. planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest) in and on lysimeters that could influence crop growth, water use, and lysimeter data. These include fertilizer and pesticide applications. There is one calendar, from before planting in autumn 1995 to after final harvest in 1999, for the NE and SE lysimeters and fields. There were 4 harvests each year except 1998 when 5 harvests were taken. Irrigation was by linear move sprinkler system equipped with pressure regulated low pressure sprays (mid-elevation spray application, MESA). Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings via field-calibrated (Evett and Steiner, 1995) neutron probe from 0.10- to 2.4-m depth in the field. Lysimeters and fields were planted to the same plant density, row spacing, tillage depth (by hand on the lysimeters and by machine in the fields), and fertilizer and pesticide applications. Weighing lysimeters measured relative soil water storage to 0.05 mm accuracy at 5-min intervals, and the 5-min change in soil water storage was used along with precipitation, dew and frost accumulation, and irrigation amounts to calculate crop evapotranspiration (ET), reported at 15-min intervals. Each lysimeter was instrumented to sense wind speed, air temperature and humidity, radiant energy (incoming and reflected, typically both shortwave and longwave), surface temperature, soil heat flux, and soil temperature, all at 15-min intervals. Instruments used changed from season to season, thus subsidiary datasets and data dictionaries for each season are required. The Bushland weighing lysimeter research program is described by Evett et al. (2016), and lysimeter design is described by Marek et al. (1988). Important conventions concerning the data-time correspondence, sign conventions, and terminology specific to the USDA ARS, Bushland, TX, field operations are given in the resource "Conventions for Bushland, TX, Weighing Lysimeter Datasets". There are 5 datasets in this collection. Common symbols and abbreviations used are defined in the resource "Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets". Datasets consist of Excel (xlsx) files. Each xlsx file contains an Introductory tab that explains the other tabs, lists the authors, describes conventions and symbols used, and lists instruments used. The remaining tabs in a file consist of dictionary and data tabs. The 5 datasets are:

Growth and Yield Data for the Bushland, Texas Alfalfa Datasets Weighing Lysimeter Data for The Bushland, Texas Alfalfa Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Alfalfa Datasets Standard Quality Controlled Research Weather Data – USDA-ARS, Bushland, Texas

See README for descriptions of each dataset. The soil is a Pullman series fine, mixed, superactive, thermic Torrertic Paleustoll. Soil properties are given in the resource titled "Soil Properties for the Bushland, TX, Weighing Lysimeter Datasets". Land slope in the lysimeter fields is

The USDA Branded Food Database was integrated as part of FoodData Central on April 2019. For more information on FoodData Central and the USDA Branded Food Database: Website: https://fdc.nal.usda.gov/ Ag Data Commons link: https://data.nal.usda.gov/dataset/fooddata-central

This dataset contains research data compiled by the “Managing Water for Increased Resiliency of Drained Agricultural Landscapes” project a.k.a. Transforming Drainage. This project was funded from 2015-2021 by the United States Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA, Award No. 2015-68007-23193). Data are also available from a separate web-accessible application (drainagedata.org). At drainagedata.org, users can visualize the data with customized tools, query based on specific sites and measurements of interest, and access site photographs, maps, summaries, and publications. Additional data or edits made following the publication of this data here at USDA NAL Ag Data Commons will be posted under the Versions tab on drainagedata.org. These data began in 1996 and include plot- and field-level measurements for 39 experiments across the Midwest and North Carolina. Practices studied include controlled drainage, drainage water recycling, and saturated buffers. In total, 219 variables are reported and span 207 site-years for tile drainage, 154 for nitrate-N load, 181 for water quality, 92 for water table, and 201 for crop yield. The Transforming Drainage Project worked to advance the process of designing and implementing agricultural drainage systems for storing water in the landscape to improve the resiliency and productivity of agricultural systems. At each site, a control plot was paired with a plot with one of the following three practices to assess impacts. Controlled Drainage (CD) is the practice of using a water control structure to raise the depth of the drainage outlet, holding water in the field during periods when drainage is not needed. Drainage Water Recycling (DWR) diverts subsurface drainage water into on-farm ponds or reservoirs, where it is stored until it can be used by the crop later in the season through supplemental irrigation. Saturated Buffers (SB) remove nitrate from subsurface drainage water by diverting it into the buffer where it can be taken up by growing vegetation or removed by denitrification. Resources in this dataset:Resource Title: Field management - tillage. File Name: mngt_tillage_data.csvResource Description: Information about tillage operations performed in the research fields during the study periodResource Title: Field management – notes. File Name: mngt_notes_data.csvResource Description: General information about field conditions during the study periodResource Title: Field management – residue. File Name: mngt_residue_data.csvResource Description: Information about residue management in the research fields during the study periodResource Title: Field management – fertilizing. File Name: mngt_fertilizing_data.csvResource Description: Information about fertilizer application and soil amendments performed in the research fields during the study periodResource Title: Field management – harvesting. File Name: mngt_harvesting_data.csvResource Description: Information about harvesting operations performed in the research fields during the study periodResource Title: Field management – planting. File Name: mngt_planting_data.csvResource Description: Information about planting operations performed in the research fields during the study periodResource Title: Field management – irrigation. File Name: mngt_irrigation_data.csvResource Description: Information about irrigation operations performed in the research fields during the study periodResource Title: Field management – drainage water management. File Name: mngt_dwm_data.csvResource Description: Information about drainage water management in the research fields during the study periodResource Title: Weather data. File Name: weather_data.csvResource Description: On-site weather data collected in the research fields during the study periodResource Title: Soil physicochemical properties data. File Name: soil_properties_data.csvResource Description: Soil physicochemical measurements collected in the research fields during the study periodResource Title: Soil moisture data. File Name: soil_moisture_data.csvResource Description: Soil moisture, temperature and bulk EC measurements collected in the research fields during the study periodResource Title: Irrigation data. File Name: irrigation_data.csvResource Description: Amount of irrigation water applied to the research fields during the study periodResource Title: Stage data. File Name: water_stage_data.csvResource Description: Stage measurements in the wetlands during the study periodResource Title: Water table data. File Name: water_table_data.csvResource Description: Water table measurements collected in the research fields during the study periodResource Title: Water quality data. File Name: water_quality_data.csvResource Description: Water quality measurements collected from the research fields during the study periodResource Title: Methodology. File Name: meta_methods.csvResource Description: Description of the drainage system set up, sampling procedures, and other protocols used at each research site during the study periodResource Title: Plot treatment. File Name: meta_treatment_identifier.csvResource Description: List of treatments used across the research sites during the study periodResource Title: Plot description. File Name: meta_plot_characteristics.csvResource Description: Description of plots and corresponding drainage systems for each research siteResource Title: Agronomic data. File Name: agronomic_data.csvResource Description: Agronomic measurements collected in the research fields during the study periodResource Title: Site description. File Name: meta_site_characteristics.csvResource Description: Description of the research sitesResource Title: Drainage data. File Name: drain_flow_and_N_loads_data.csvResource Description: Drain flow and nitrate load measurements collected from the research fields during the study periodResource Title: Data dictionary. File Name: data_dictionary.csv

Data dictionary and brochure for REAP (Resilient Economic Agricultural Practices). https://data.nal.usda.gov/node/5594

Data Entry Template 2017 includes

Excel templates for Experiment description worksheets, Site characterization worksheets, Management worksheets, Measurement worksheets where experimental unit data are reported, and Information that may be useful to the user, including drop down lists of treatment specific information and ranges of expected values. General and introductory instructions, as well as a Data Validation check are also included.A data dictionary typically provides a detailed description for each element or variable in a dataset or data model. Data dictionaries are used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description.Dataset citation: (dataset) USDA Agricultural Research Service. (2017). REAP (Resilient Economic Agricultural Practices). Agricultural Research Service. https://doi.org/10.15482/USDA.ADC/1372394.

This model was originally trained for use in a recommendation system to the Ag Data Commons that will automatically link viewers of one dataset to other directly relevant datasets and research papers that they may be interested in. It was also used to determine the similarities and differences between projects within ARS’ National Programs and create a visualization layer to allow leaders to explore and manage their programs easily.

This model was generated using the Word2Vec model, starting with a set of word vectors trained on Google News articles, and further training it on the titles+abstracts from PubAg and the titles+descriptions from Ag Data Commons. This model was trained using a vector length of 300 and the Continuous Bag of Words version of the algorithm with negative sampling.

This word vector model could be used for any Natural-Language Processing applications involving text with a large amount of agricultural research vocabulary.

• Resource Title: Agricultural Word Vectors.

  File Name: AgWordVectors-300.zip

  Resource Description: Word vectors trained on the full titles/abstracts in PubAg and titles/abstracts in Ag Data Commons. (Part A)




• Resource Title: Agricultural Word Vectors Trainables.

  File Name: AgWordVectors-300.model.trainables.syn1neg.zip

  Resource Description: Word vectors trained on the full titles/abstracts in PubAg and titles/abstracts in Ag Data Commons. (Part B)




• Resource Title: Agricultural Word Vector Model.

  File Name: AgWordVectors-300.model.wv_.vectors.zip

  Resource Description: Word vectors trained on the full titles/abstracts in PubAg and titles/abstracts in Ag Data Commons. (Part C)

The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information (https://coagmet.colostate.edu/ ; GLY04 station). Additional soil data can be found in a previous dataset (USDA-ARS Colorado Maize Water Productivity Dataset 2008-2011) also available from the Ag Data Commons. This previous dataset included six targeted treatments that were generally uniform through the season. This new dataset can be used to further validate and refine maize crop models. The data are presented in a spreadsheet format in individual sheets within one workbook. The first sheet in the work book provides a list of data descriptions. Two sheets (one sheet for each of the two years) provide the hourly weather data, with the exception of the precipitation data, which is included in the sheet with daily data per treatment. The weather data is from a weather station on site. Another sheet provides plot level data (harvest index, yield, annual ET, maximum LAI, stand density, total aboveground biomass) taken annually by plot (four plots per treatment). Another sheet provides LAI measured four times over each season per plot. The final sheet provides daily data per treatment over each season, including data needed to compute daily water balance. This sheet has LAI, crop growth stage, plant height, estimated root depth, interpolated canopy cover, ET coefficients, precipitation, and estimated deep percolation, evaporation, and soil water deficit at four soil depths. List of files: LIRF small plots map 2012-2013 LIRF maize annual_daily_hourly data 2012-2013 Resources in this dataset:Resource Title: LIRF 2012-2013 Maize database. File Name: 2012-2013_Maize_Compiled database 06012018.xlsxResource Title: LIRF 2012-2013 Data Description. File Name: Data Description 06012018.xlsxResource Title: LIRF 2012-2013 Plot Map. File Name: Plot map 2012 2013.pdfResource Title: LIRF Data Dictionary. File Name: Data_Dictionary_Water_Prod_2012.csv

The data are derived from the field monitoring of irrigated furrows from 1998 to 2016 at the research farm of the USDA/ARS-Northwest Irrigation and Water Research Laboratory in Kimberly, Idaho, USA (south-central Idaho). For each monitored furrow, irrigation inflow rates, outflow rates, and sediment concentrations were recorded periodically during the irrigation. A gated pipe conveyed irrigation water across the plots at the head, or inflow-end, of the furrows and adjustable spigots supplied water to each irrigated furrow. The methodology used to obtain the field data is described by Lentz and Sojka (2009). Inflows were measured by timing the filling rate of a known volume, and runoff were measured with long-throated, v-notch flumes. Outflows were measured and runoff samples collected at 30 min intervals during the first 1-3 hr of an irrigation, and every hour or two for the next 3 to 5 hr. If the set was continued for an additional 12 hr, two to four additional measurements were made. Immediately after each flume reading, sediment concentration in furrow streams were measured by collecting one-liter runoff samples from free-flowing flume discharge. The weight of sediment per liter of runoff was determined from the settled volume of sediment using the Imhoff-cone technique. Three Imhoff-cone sediment samples were collected from each treatment in each irrigation. These were filtered, and the papers dried and weighed. A calibration function relating the 30-min, settled-sediment volume to sediment mass-per-unit-volume of runoff was then calculated and used to convert settled sediment volume in cones to sediment mass. The field data for each study or year were analyzed using the WASHOUT program (Lentz and Sojka, 1995). The WASHOUT program produces an output file (filename.out), which become components of this Ag Data Commons data set. For many years and irrigations, furrows were monitored at one or more locations along the furrow, as well as at the end (bottom) of the furrow. In these cases, data for each position within the furrow are listed in the data set, labelled for example as "Top", "Middle", and "Bottom" (See Data Dictionary tab). For each furrow position the data represent the flow, infiltration, and runoff information for the length of furrow, which begins at its inflow end (top of the field) and ends at the defined furrow position. This distance is listed in the field data file for each furrow and irrigation. An Irrigation Data Summary is included as a tab in the data set spreadsheet. This is a summary list of the studies and irrigations that are included in the data set. Also included is a PAM-Application-Codes tab that lists description of the polyacrylamide (PAM) treatments that were employed in some of the included studies. Resources in this dataset:Resource Title: Furrow Infiltration and Erosion Data, 1998 to 2016. File Name: IrrigationData.xlsxResource Description: Furrow irrigation inflow, outflow, infiltration, and sediment load data Summary of studies and irrigations included in the data Data Dictionary Description of specific polyacrylamide treatments included in some of the studiesResource Title: Data Dictionary - Kimberly, ID - Furrow Infiltration and Erosion Data, 1998 to 2016. File Name: Kimberly-ID-Furrow-Inf-Erosion-DataDictionary1998-2016.csv

House flies (Musca domestica L.) are vectors of human and animal pathogens at livestock operations. Microbial communities in flies are acquired from, and correlate with, their local environment. However, variation among microbial communities carried by flies from farms in different geographical areas is not well understood. We characterized bacterial communities of female house flies collected from beef and dairy farms in Oklahoma, Kansas, and Nebraska and further evaluated the prevalence of antibiotic resistance genes in bacteria within flies. We evaluated the influence of farm type and farm location on bacterial communities, diversity, pathogenic bacteria strains and prevalence of antibiotic resistance genes. These data can be used for better understanding of abundance and prevalence of bacterial communities in house flies associated with livestock operations. These data were collected in September 2019. Abbreviations used include Operational Taxonomic Units(OTUs), Canonical Correspondence analysis (CCA), Infectious Bovine Keratoconjunctivitis (IBK), Anti Microbial Resistance (AMR), and Antibiotic Resistance Genes (ARGs).
The raw Illumina MiSeq sequence data for this project can be found here: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA863664 Resources in this dataset:

Resource title: Metadata for Microbiome of House Fly Associated with Cattle Farms File name: Metadata for Microbiome of House Fly Associated with Cattle Farms.xlsx Resource description: This spreadsheet links the raw sequence reads on NCBI with data on farm type, farm location and sample type.

An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals

List of Arctic accessions as of 31 July 2015 for Germplasm Resources Information Network (GRIN). https://data.nal.usda.gov/node/140

The USDA National Plant Germplasm System has more than 500,000 accessions (distinct varieties of plants) representing more than 2,000 unique species. Accessions are described in the GRIN database (Germplasm Resource Information Network). Germplasm accessions are available to support research and education objectives. This resource provides direct hyperlinks to 614 accessions related to Alaska. Search Parameters

Search Type: Accession Area Queries Location: United States of America; State: Alaska

Additional Criteria:

ALL – All Repositories Improvement Status – Any Status Reproductive uniformity – Any Status Any Time Taxonomic sort

Dataset citation: (dataset) USDA Agricultural Research Service (2015). Germplasm Resources Information Network (GRIN). USDA Agricultural Research Service. https://doi.org/10.15482/USDA.ADC/1212393.

For this study, 737 sweetpotato accessions were obtained from the USDA, ARS, PGRCU, Griffin, GA. Each PI was grown in the field in replicated plots at the U. S. Vegetable Laboratory, Charleston, SC. The mature leaves of each PI was collected and measured using a Konica Minolta Chroma Meter (CR 400). Data were recorded using CIE 1976 Lab and CIE LCh color spaces. Data from this study is contained in a manuscript that will be submitted to Genetic Resources and Crop Evolution under the title "Color Analysis of Sweetpotato Leaves from the USDA, ARS Germplasm Collection." Data parameters collected were lightness (L), red-green coordinate (a), yellow-blue coordinate (b), color intensity or chroma (C), and hue angle (h*). Resources in this dataset:Resource Title: Sweetpotato Leaf Color - Raw Data (Mid-Season). File Name: Sweetpotato-Leaf Color (Mid-Season)-Raw Field Data.xlsxResource Description: Raw colorimetry data from 737 sweetpotato PIsResource Title: Sweetpotato Leaf Color Data Summary (Mid-Season). File Name: Sweetpotato-Leaf Color (Mid-Season)-Summary Table.xlsxResource Description: Summary table of sweetpotato leaf color data from mid-seasonResource Title: Sweetpotato Leaf Color - Raw Data (Late-Season). File Name: Sweetpotato-Leaf Color (Late-Season Purple)-Raw Field Data.xlsxResource Description: Raw colorimetry data from late-season sweetpotato leavesResource Title: Sweetpotato Leaf Color Data Summary (Late-Season). File Name: Sweetpotato-Leaf Color (Late-Season Purple)-Summary Table.xlsxResource Description: Summary table of late-season colorimetry data from sweetpotato leavesResource Title: Sweetpotato Leaf Color - Raw Data (Late Season). File Name: Sweetpotato-Leaf Color (Late-Season Purple)-Raw Field Data.csvResource Description: Raw colorimetry data from late-season sweetpotato leavesResource Title: Sweetpotato Leaf Color Data Summary (Mid Season). File Name: Sweetpotato-Leaf Color (Mid-Season)-Summary Table.csvResource Description: Summary table of sweetpotato leaf color data from mid-seasonResource Title: Sweetpotato Leaf Color Data Summary (Late Season). File Name: Sweetpotato-Leaf Color (Late-Season Purple)-Summary Table.csvResource Description: Summary table of late-season colorimetry data from sweetpotato leaves Resource Title: Sweetpotato Leaf Color - Raw Data (Mid Season) . File Name: Sweetpotato-Leaf Color (Mid-Season)-Raw Field Data.csvResource Description: Raw colorimetry data from 737 sweetpotato PIs Resource Title: Data Dictionary. File Name: Data Dictionary.csvResource Title: Sweetpotato Clomazone Injury- Raw Data. File Name: Sweetpotato-Clomazone Injury-Raw Data.xlsxResource Description: Injury (rated 1-7) caused by applications of clomazone to 564 sweetpotato accessions. (Note: This information was added to the publication after the review process had begun and after the Ag Data Commons dataset had been published and DOI assigned)Resource Title: Sweetpotato Clomazone Injury- Summary. File Name: Sweetpotato-Clomazone Injury-Summary.xlsxResource Description: Summary of clomazone injury on 564 sweetpotato accessions. (Note: This information was added to the publication after the review process had begun and after the Ag Data Commons dataset had been published and DOI assigned)Resource Title: Sweetpotato Clomazone Injury- Raw Data . File Name: Sweetpotato-Clomazone Injury-Raw Data.csvResource Description: Injury (rated 1-7) caused by applications of clomazone to 564 sweetpotato accessions. (Note: This information was added to the publication after the review process had begun and after the Ag Data Commons dataset had been published and DOI assigned)Resource Title: Sweetpotato Clomazone Injury- Summary. File Name: Sweetpotato-Clomazone Injury-Summary.csvResource Description: Summary of clomazone injury on 564 sweetpotato accessions. (Note: This information was added to the publication after the review process had begun and after the Ag Data Commons dataset had been published and DOI assigned)

Analysis of ‘Current and projected research data storage needs of Agricultural Research Service researchers in 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e2b7daf0-c8fe-4c68-b62d-891360ba8f96 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

Surface agronomic P budgets for 61 cropping systems using field-scale P flux data across 24 research sites in the United States and Canada. Data are representative of P inputs and outputs associated with the production of each crop in a respective rotation year, ranging from 1 to 10 rotation years. This dataset provides a comparison of field-scale soil surface P fluxes and phosphorus budgets across sites and cropping systems. Resources in this dataset:Resource Title: LTAR Phosphorus Budget Summary Data Sources and References. File Name: DataSourcesAndReferences.csvResource Description: This file includes data sources and references relevant to calculated P budgets. Affiliated numerical data can be found in the LTAR Phosphorus Budget Summary Data file.Resource Title: LTAR Phosphorus Budget Summary Data. File Name: PBudgetData.csvResource Description: Agronomic annual and system data for calculated P budgets for cropping systems throughout the United States and Canada.