AndreaBozzo/ceres-open-data-index huggingface dataset:
Panzea

<p>Panzea is an NSF-funded project called "Biology of Rare Alleles in Maize and its Wild Relatives". We are investigating the connection between phenotype (what we see) and genotype (the genes underlying the phenotype) - of complex traits in maize and its wild relative, teosinte, and specifically in how rare genetic variations contribute to overall plant function. These studies will enrich our knowledge of evolution, sustainable agriculture, and genetic diversity and conservation. Over the 10 years of the project, we have trained many new scientists at all levels and generated key resources for the public, teachers, and scientific researchers. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Panzea Data Sets and Genotype Search Tools.</p> <p>File Name: Web Page, url: <a href="https://www.panzea.org/data">https://www.panzea.org/data</a> </p><p>From here you can obtain the genotypic and phenotypic datasets produced by this project as flat files. You can also perform custom searches on our large genotypic data sets, to obtain smaller subsets of data for chromosomal regions or taxa subsets of interest. Two different genotype searches are available: one for GBS genotypes, and another for other types of genotypes (from maize HapMapV1 and V2, the MaizeSNP50 Bead Chip, and traditional SNPs).</p></li></ul><p></p>

Tags: ars, data-gov, np301
Organization: Department of Agriculture
Switchgrass ESTs and SNPs

<p>As part of our project, “Developing Association Mapping in Polyploid Perennial Biofuel Grasses” (DOE-USDA Plant Feedstock Genomics for Bioenergy Program grant DE-A102-07ER64454)*, two SNP discovery initiatives were carried out.  The earlier one (2009) was an approach based on EST sequences.  The latest initiative (2011-12) adopted a more powerful approach, based on GBS (Genotyping by Sequencing). We believe that the SNP markers identified in these studies will greatly enhance breeding efforts that target the improvement of key biofuel traits and the development of new switchgrass cultivars.</p>
<p>To enable genome-wide association study (GWAS) and genomic selection (GS) in switchgrass, we genotyped a full-sib population (n =130), a half-sib population (n =168) and association populations (66 pops, n =540). The parents of the linkage populations are upland tetraploids.  The association populations are primarily of the upland ecotype, both tetraploid and octoploid, with a few lowland tetraploids as well. A total of 350 GB of sequence was generated from 840 individuals using GBS. Over 1.2 million putative SNPs were discovered with the UNEAK pipeline.  In addition, ultra-high density paternal and maternal linkage maps, of 41K and 46K SNPs, respectively, were also constructed based on the conserved synteny between switchgrass and foxtail millet.</p>
<p>The data associated with this study are listed here:</p>
<ul>
<li>Genotype calls from the full-sib population [65 MB]</li>
<li>Genotype calls from the half-sib population [80 MB]</li>
<li>Genotype calls from the association populations [164 MB]</li>
<li>
<p>Paternal and maternal linkage maps of the full-sib population [8 MB]</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Switchgrass Data.</p> <p>File Name: Web Page, url: <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/panzea/genotypes/GBS/switchgrass/Lu_PLoSGenet_2013_switchgrassGenos20150612.zip" target="_blank">http://datacommons.cyverse.org/browse/iplant/home/shared/panzea/genotypes/GBS/switchgrass/Lu_PLoSGenet_2013_switchgrassGenos20150612.zip</a> </p><p>CyVerse Data Store - the current path to the data until the links on the main site are fixed</p></li></ul>
</li>
</ul>

Tags: ars, data-gov, np301
Organization: Department of Agriculture
2DSOIL version 03

<p>2D finite element water, solute, and heat mover model for plant models.</p>
<p>Most crops are grown in rows and this introduces spatial variability in soil processes with respect to the row. However, this variability can be exploited to reduce chemical transport to groundwater or improve management of irrigation water. Unless a model can account for variability perpendicular to crop rows as well as vertically into the soil profile it will not be able to fully evaluate all possible management practices that can be used to make agriculture more efficient and less harmful to the environment. To address this concern we developed 2DSOIL, the first comprehensive, modular, two-dimensional soil simulator that can simulate the major physical, chemical and biological processes in soil. Fully implemented, principles of modular modeling facilitate the addition and replacement of modules, as well as the reuse of existing code. The modularity of 2DSOIL has been designed to make it easy to modify the model and to make it easy to incorporate into plant models. 2DSOIL was used to simulate the effect of several water and nitrogen management practices and was incorporated into ARS potato and cotton models, into the Root Zone Water Quality Model, and into the USGS Modular Modeling System. </p>

Tags: computer-software, cotton, crop-models, crops, finite-element-analysis, groundwater, heat, interphase, irrigation-management, irrigation-water, models, nitrogen, potatoes, root-growth, root-zone-water-quality-model, simulation-models, soil, soil-profiles, solutes
Organization: Department of Agriculture
Gas Flux from Band Application

<p>Gas Flux from Band Application (GF-Band) is an MS Excel spreadsheet tool that calculates the effective gas flux from soil of a multiple-band area to which manure or fertilizer has been applied in bands.  One spreadsheet is for circular gas flux chambers and another is for rectangular chambers.</p>
<p>Greenhouse gases are emitted following application of manure and nitrogen‐containing fertilizers to soil. Manure and fertilizers are often applied in subsurface bands in the soil, or in bands on the soil surface. This article presents a method that has been developed for calculating the effective gas flux for a multiple‐band area to which manure or fertilizer has been applied in bands. The method has been developed for circular and rectangular flux chambers. In analyzing the method, a combination of CO2 gas fluxes from a field experiment that gave a relatively low whole‐plot effective flux and a combination that gave a relatively high whole‐plot effective flux were used. For the lower‐end flux situation, when the dimension of the flux chamber in the direction perpendicular to the band is considerably less than the band spacing, if the flux in a chamber that is centered on a band is assumed to be the whole‐plot effective flux, then this assumption would overestimate the actual whole‐plot effective flux by a considerable amount. The error of this type of assumption is reduced for the higher‐end flux situation, regardless of flux chamber dimensions, and is reduced when the lower‐end flux situation occurs and the dimension of the flux chamber in the direction perpendicular to the band is intermediate to nearly as large as the band spacing. The method in useful in calculating effective gas fluxes for whole plots to which manure or fertilizer has been band‐applied.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Gas Flux from Band Application.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=291&modecode=60-10-05-00">https://www.ars.usda.gov/research/software/download/?softwareid=291&modecode=60-10-05-00</a> </p><p>download page</p></li></ul><p></p>

Tags: ars, carbon-dioxide, data-gov, emissions, fertilizers, greenhouse-gases, manures, np212
Organization: Department of Agriculture
USDA LCA Commons Data Submission Guidelines

<p>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.</p>
<p>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. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: LCA Commons Submission Guidelines_12/09/2015.</p> <p>File Name: lcaCommonsSubmissionGuidelines_Final_2015-12-09.pdf</p></li></ul><p></p>

Tags: ars, data-gov, nal-ksd
Organization: Department of Agriculture
BBGD: an Online Database for Blueberry Genomic Data

<p>NOTE: This dataset is no longer publicly available.</p>
<p>BBGD (<a href="http://bioinformatics.towson.edu/BBGD/">http://bioinformatics.towson.edu/BBGD/</a>) was developed as a database for blueberry genomics. BBGD is both a sequence and gene expression database. It stores both EST and microarray data and allows scientists to correlate expression profiles with gene function. BBGD is a public online database. "Presently, the main focus of the database is the identification of genes in blueberry that are significantly induced or suppressed after low temperature exposure. "</p>
<p>To gain a better understanding of changes in gene expression associated with cold acclimation in blueberry, the Rowland laboratory (USDA-ARS, Beltsville, MD)  has undertaken a genomics approach based on the analysis of Expressed Sequence Tags (ESTs). Initially, two standard cDNA libraries were constructed using RNA from cold-acclimated and non-acclimated floral buds of the blueberry cultivar ‘Bluecrop’ (<em>Vaccinium corymbosum</em> L.) and about 1200 5’-end ESTs were generated from each of the libraries. About 100 3’-end ESTs were generated from the cold-acclimated library as well.</p>
<p>The Blueberry EST database contains EST sequences from a number blueberry libraries including cold acclimated and non-acclimated libraries. It also includes forward and reverse subtractive libraries.</p>
<p>You can query the sequence database by clone ID, accession number or gene (clone) name below. Or you can get a list (in tabular) format of all the clones in a particular library by clicking on the library name on the left side navigation bar.</p>
<p>Attribution for photo: <br><a href="http://www.ars.usda.gov/is/graphics/photos/sep12/d2601-1.htm">D2601-1 -  Blueberry plant:</a> Copyright free, public domain photo by Mark Ehlenfeldt</p>

Tags: ars, bbgd, blueberry, cyanococcus, data-gov, database, np301, np303, vaccinium
Organization: Department of Agriculture
Data from: Rotating perennial forages into annual wheat cropping systems: correlations between plant available soil and grain mineral concentrations

<p dir="ltr">Correlations between plant available soil and grain mineral concentrations are often assumed, yet few studies examine these associations. Here, soil and wheat grain samples were analyzed from a semi-arid dryland cropping study in the northern Great Plains conducted between 2006 and 2011. Continuous spring wheat (fertilized) (Triticum aestivum L; CSW) was compared with wheat following 5 yr of perennial forages of either alfalfa (Medicago sativa L.), intermediate wheatgrass (fertilized) (Thinopyrum intermedium (Host) Barkw. & D.R. Dewey sbsp. Intermedium; IWG), or an alfalfa/intermediate wheatgrass mixture (fertilized; MIX). Wheat performance (yield, 1,000 kernel weight [TKW], and crude protein [CP] concentration), and associations between 11 plant available soil mineral concentrations and 11 wheat grain mineral concentrations were assessed. Wheat following alfalfa had greater yield than all treatments, greater TKW than CSW, greater CP than IWG and CSW, but lower grain Zn concentration than IWG (p ≤ .05). Wheat grain following IWG had greater Fe and Mn concentration than MIX, greater Mg concentration than CSW, and lower S concentration than all treatments (p < .05). Multivariate correlation analysis showed positive correlations between plant available soil and grain B, Mg, Mn, and S concentrations (p ≤ .02), while plant available soil and grain Zn and Ca concentrations showed negative associations (p ≤ .05). Rotating perennial forage phases into wheat cropping systems increased wheat yield and CP but reduced certain plant available soil minerals. Although rotating perennials into annual cropping systems can benefit some soil quality parameters it may also diminish plant available soil minerals, influencing fertility recommendations. </p>

Tags: perennials, soil-quality, spring-wheat, wheat-grain-quality
Organization: Department of Agriculture
Cacao Genome Database

<p>Not only is cacao the basic ingredient in the world’s favorite confection, chocolate, but it provides a livelihood for over 6.5 million farmers in Africa, South America and Asia and ranks as one of the top ten agriculture commodities in the world. Historically, cocoa production has been plagued by serious losses due to pests and diseases. The release of the cacao genome sequence will provide researchers with access to the latest genomic tools, enabling more efficient research and accelerating the breeding process, thereby expediting the release of superior cacao cultivars. The sequenced genotype, Matina 1-6, is representative of the genetic background most commonly found in the cacao producing countries, enabling results to be applied immediately and broadly to current commercial cultivars.  Matina 1-6 is highly homozygous which greatly reduces the complexity of the sequence assembly process. While the sequence provided is a preliminary release, it already covers 92% of the genome, with approximately 35,000 genes. We will continue to refine the assembly and annotation, working toward a complete finished sequence. Updates will be made available via the main project website.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Cacao Genome Database.</p> <p>File Name: Web Page, url: <a href="http://www.cacaogenomedb.org/" target="_blank">http://www.cacaogenomedb.org/</a> </p></li></ul>

Tags: ars, data-gov, np301, online-database
Organization: Department of Agriculture
USDA Table of Cooking Yields for Meat and Poultry

<p>USDA Table of Cooking Yields for Meat and Poultry, Release 2, is an update of previously released USDA data. Data in the updated table, accessible below, are measures of changes in meat and poultry weights resulting from moisture and fat losses during cooking. The table includes percentages for cooking yield, moisture change, and fat change for specific cuts of meat and poultry prepared in USDA research studies according to specific cooking protocols.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: USDA Table of Cooking Yields for Meat and Poultry, Release 2.</p> <p>File Name: USDA_CookingYields_MeatPoultry02.csv</p><p>Resource Description: USDA Table of Cooking Yields for Meat and Poultry, Release 2, is an update of previously released USDA data. Data in the updated table, accessible below, are measures of changes in meat and poultry weights resulting from moisture and fat losses during cooking. The table includes percentages for cooking yield, moisture change, and fat change for specific cuts of meat and poultry prepared in USDA research studies according to specific cooking protocols.</p>
<p>This data was downloaded from the published online spreadsheet and converted to CSV format on November 15, 2017.</p></li><br><li><p>Resource Title: USDA Table of Cooking Yields for Meat and Poultry, Release 2 Data Dictionary.</p> <p>File Name: USDA_CookingYields_MeatPoultry02_DD.csv</p></li><br><li><p>Resource Title:   USDA Table of Cooking Yields for Meat and Poultry (2014).</p> <p>File Name: USDA_CookingYields_MeatPoultry02.pdf</p><p>Resource Description: USDA Table of Cooking Yields for Meat and Poultry, Release 2, is an update of previously released USDA data. Data in the updated table, accessible below, are measures of changes in meat and poultry weights resulting from moisture and fat losses during cooking. The table includes percentages for cooking yield, moisture change, and fat change for specific cuts of meat and poultry prepared in USDA research studies according to specific cooking protocols.</p></li></ul><p></p>

Tags: ars, data-gov, np107
Organization: Department of Agriculture
NAL Geodata

<p>The United States Department of Agriculture National Agricultural Library Geospatial Data catalog contains geographic location-based agricultural research data, imagery, research location context, and more. Users can search records representing a variety of datasets, maps and graphics, aerial and phenocam images, and other services. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: NAL Geodata.</p> <p>File Name: Web Page, url: <a href="https://geodata.nal.usda.gov/geonetwork/">https://geodata.nal.usda.gov/geonetwork/</a> </p></li></ul><p></p>

Tags: ars, data-gov, nal-ksd, online-database
Organization: Department of Agriculture
Farmers Markets Directory and Geographic Data

Longitude and latitude, state, address, name, and zip code of Farmers Markets in the United States

Tags: baked-goods, butter, cheese, crafts, dairy, direct-to-consumer, farmers-markets, flowers, fresh-fruit, herbs, honey, local-food, maple, meat, nuts, organic, plants, prepared-food, preserves, processed-food, seafood, vegetables, yogurt
Organization: Department of Agriculture
SWAGMAN-Whatif

<p>Effectively managing salt affected irrigated lands and judicially using irrigation water of marginal salinity quality requires understanding the interactions among many inputs. They include soil salinity, crop salt tolerances, soil physical properties, irrigation water quality, irrigation management, water table depth and quality, climatic factors and crop yield. Visualizing the simultaneous interactions among this many factors over a cropping season extends beyond the capacity of the human mind.</p>
<p>An interactive computer program was developed to simulate the interactions among the above factors. It shows how changing one factor impacts the outcome of the other factors for a single growing season. The user selects a climate, a crop, and soil characteristics from menu lists, and then sets the water table depth and quality, irrigation (river or well) water quality and then develops an irrigation schedule. On execution, the relative yield reductions due to over irrigation, under irrigation, and salinity, water table rise or fall and surface runoff are shown numerically for the growing season. Soil water content, soil salinity, water table depth changes and rain and irrigation events during the season are also shown graphically.</p>
<p>This is an educational tool designed to teach the concepts of salinity and irrigation management and IS NOT an irrigation scheduling program nor a management tool. Two versions have been developed, one using metric units, southern hemisphere growing seasons and Australian terminology, and a second using northern hemisphere growing seasons, with units and terminology currently used in US irrigated agriculture. An option in the US version also allows use of metric units.</p>
<p>The SWAGMAN acronym and the SWAGMAN-Whatif program copyrights are owned by Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia. The SWAGMAN acronym is not to be used for other programs.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SWAGMAN.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=207" target="_blank">https://www.ars.usda.gov/research/software/download/?softwareid=207</a> </p><p>download page</p></li></ul>

Tags: australia, climatic-factors, computer-software, crop-yield, educational-materials, growing-season, humans, irrigation-scheduling, irrigation-water, models, rain, rivers, runoff, salinity, soil-physical-properties, soil-salinity, soil-water, soil-water-content, terminology, united-states, water-quality, water-table
Organization: Department of Agriculture
Bushland ET Calculator

<p>The Bushland Reference ET calculator was developed at the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. Although it was designed and developed for use mainly by producers and crop consultants to manage irrigation scheduling, it can also be used in educational training, research, and other practical application. It uses the ASCE Standardized Reference Evapotranspiration (ET) Equation for calculating grass and alfalfa reference ET at hourly and daily time steps. This program uses the more complex equation for estimating clear-sky solar radiation provided in Appendix D of the ASCE-EWRI ET Manual. Users have the option of using single set or time series weather data to calculate reference ET. Daily reference ET can be calculated either by summing the hourly ET values for a given day or by using averages of the climatic data. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Bushland ET Calculator download page.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=Bushland+ET+Calculator&modecode=30-90-05-00">https://www.ars.usda.gov/research/software/download/?softwareid=Bushland+ET+Calculator&modecode=30-90-05-00</a> </p></li></ul><p></p>

Tags: ars, data-gov, np211
Organization: Department of Agriculture
Livestock and Grain Market News LMR Web Services

The primary function of the Livestock and Grain Market News Division of the Livestock and Seed Program (LSP) is to compile and disseminate information that will aid producers, consumers, and distributors in the sale and purchase of livestock, meat, grain, and their related products nationally and internationally.

Tags: beef, cattle, grain, hogs, lamb, livestock, market-news, meat, sheep
Organization: Department of Agriculture
myPhyloDB

<p>myPhyloDB is an open-source software package aimed at developing a user-friendly web-interface for accessing and analyzing all of your laboratory's microbial ecology data (currently supported project types: soil, air, water, microbial, and human-associated). The storage and handling capabilities of myPhyloDB archives users' raw sequencing files, and allows for easy selection of any combination of projects/samples from all of your projects using the built-in SQL database. The data processing capabilities of myPhyloDB are also flexible enough to allow the upload, storage, and analysis of pre-processed data or raw (454 or Illumina) data files using the built-in versions of Mothur and R. myPhyloDB is designed to run as a local web-server, which allows a single installation to be accessible to all of your laboratory members, regardless of their operating system or other hardware limitations. myPhyloDB includes an embedded copy of the popular Mothur program and uses a customizable batch file to perform sequence editing and processing.  This allows myPhyloDB to leverage the flexibility of Mothur and allow for greater standardization of data processing and handling across all of your sequencing projects.  </p>
<p>myPhyloDB also includes an embedded copy of the R software environment for a variety of statistical analyses and graphics.  Currently, myPhyloDB includes analysis for factor or regression-based ANcOVA, principal coordinates analysis (PCoA), differential abundance analysis (DESeq), and sparse partial least-squares regression (sPLS).  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website Pointer to myPhyloDB.</p> <p>File Name: Web Page, url: <a href="https://myphylodb.azurecloudgov.us/myPhyloDB/home/">https://myphylodb.azurecloudgov.us/myPhyloDB/home/</a> </p><p>Provides information and links to download latest version, release history, documentation, and tutorials including type of analysis you would like to perform (Univariate: ANCOVA/GLM; Multivariate: DiffAbund, PcoA, or sPLS). </p></li></ul><p></p>

Tags: agricultural-research-service, ars, data-gov, myphylodb, np211, np212, online-database
Organization: Department of Agriculture