AndreaBozzo/ceres-open-data-index huggingface dataset:
Data from: Eating the metaphorical elephant: Meeting Nitrogen Reduction Goals in Upper Mississippi River Basin States

<p dir="ltr">The hypoxic zone in the Gulf of Mexico is the world's second largest.  In 2008, the Hypoxia Task Force directed states in the Mississippi River Basin to develop plans to reduce nitrogen and phosphorus loads to the Gulf.  The states of Iowa, Illinois, and Minnesota developed Nutrient Reduction Strategies (NRS) based on science assessments.  The NRS quantified the effectiveness of management, land use changes, and conservation practices to reduce nutrient losses.  The objective of this research was to estimate nutrient reductions for different levels of implementation of management and land use changes and edge-of-field practices.  Selected practices across the states were grouped as (1) in-field management, (2) changes in land use, and (3) edge-of-field.  Four increasingly challenging levels of implementation were identified:  benchmark, low, medium, and high.  Benchmark estimates were taken from the time the states' NRS were written (2012–13).  The high level of implementation was taken to be maximum achievable adoption with low and medium levels between the benchmark and high.  Land areas in row cropping in the states were obtained from the 2012 NASS Cropland Data Layer.</p>

Tags: gulf-of-mexico, hypoxia, nitrate, nutrient-reduction, subsurface-tile-drainage
Organization: Department of Agriculture
Data from: The Biology of IncI2 Plasmids Shown by Whole-Plasmid Multi-Locus Sequence Typing

<p dir="ltr">DNA sequences of each allele for 165 loci found in a population of 147 fully sequenced IncI2 plasmids with allelic identifiers.</p><p dir="ltr">Data is based on whole-genome sequences in the National Center for Biotechnology Information database as of July, 2017.</p><p dir="ltr">Resources in this dataset:</p><ul><li>Resource Title: IncI2 locus alleles database.File Name: IncI2_locus_alleles.fasResource Description: defining DNA sequences for locus alleles: loci with just one allele have long name; any locus with more than one allele have four or five-letter code followed by underscore (_) and one or two digit number that is the locus ID.</li></ul><p><br></p>

Tags: antimicrobial-resistance-genes, ars, bacterial-population-genetics, data-gov, dna-sequence, enterobacteriaceae, inci2-plasmids, np108
Organization: Department of Agriculture
Genomes To Fields 2014

<p>Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3">CyVerse</a>.</p>
<p>Data types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (<em>Zea mays</em>) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: CyVerse Genomes To Fields 2014 dataset download.</p> <p>File Name: Web Page, url: <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3">http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3</a> </p><p>Dataset (csv, h5, gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.</p></li></ul><p></p>

Tags: ars, data-gov, g2f, genomes-by-environment, genomes-to-fields, gxe, np301
Organization: Department of Agriculture
SPUR2

<p><strong><em> Please note: This software is no longer being updated or maintained, and is out of date. </em></strong></p>
<p>SPUR2 DOS ver. 2.2 is a general grassland ecosystem simulation model designed to determine beef cattle performance and production by simultaneously simulating production of up to 15 plant species on 36 heterogeneous grassland sites. SPUR2 simulates grassland hydrology, nitrogen cycling, and soil organic matter on grazed ecosystems as well as rangeland production under different climatic regimes, environmental conditions, and management alternatives. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SPUR2 download page.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25">https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25</a> </p></li></ul><p></p>

Tags: agricultural-research-service, animals, beef-cattle, biomass, climate, computer-software, databases, ecosystems, environmental-factors, equations, evapotranspiration, forage, geographic-information-systems, grasslands, grazing, great-plains-region, models, nitrogen-cycle, plant-communities, prediction, rainfall-simulation, rangelands, runoff, sediments, simulation-models, soil, soil-and-water-assessment-tool-model, soil-organic-matter, soil-water, storms, vegetation, watersheds
Organization: Department of Agriculture
Data from: <i>Wolbachia </i>infection modifies phloem feeding behavior but not plant virus transmission by a hemipteran host

<p dir="ltr"><i>Wolbachia</i>-infected and uninfected subpopulations of beet leafhoppers, <i>Circulifer tenellus</i> (Baker) (Hemiptera: Cicadellidae), co-occur in the Columbia Basin region of Washington and Oregon. While facultative endosymbionts such as <i>Hamiltonella defensa</i> have demonstrably altered feeding/probing behavior in hemipteran hosts, the behavioral phenotypes conferred by <i>Wolbachia</i> to its insect hosts, including feeding/probing, are largely understudied. We studied the feeding/probing behavior of beet leafhoppers from in-house colonies with and without <i>Wolbachia </i>on plants using electropenetrography, along with corresponding inoculation rates of beet curly top virus (BCTV), a phloem-limited plant pathogen vectored by beet leafhoppers. Insects carrying BCTV with and without <i>Wolbachia </i>were individually recorded for four hours while interacting with a potato plant, and wavelengths annotated following established conventions. Virus inoculation rates and the duration of phloem salivation events did not vary. <i>Wolbachia</i>-infected insects more than tripled the duration of phloem ingestion, but despite this, <i>Wolbachia</i> infection was linked with marginally lower, not enhanced, acquisition. Regardless, results suggest potential for <i>Wolbachia </i>to increase the acquisition rate of other phloem-limited plant pathogens.</p>

Tags: bctv, beet-curly-top-virus, beet-leafhopper, circulifer-tenellus, electropenetrograph, epg, potato, solanum-tuberosum
Organization: Department of Agriculture
USDA Agricultural Research Service- Patented Animal Production and Protection Technologies

<p>Recent USDA/ARS patented technologies on animal production and protection that are available for licensing are described, including summary, contact, benefits, and applications. Updated June 2018. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Animal Production and Protection - Available Technologies, June 2018.</p> <p>File Name: Animal Production and Protection.pptx</p><p>Resource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.</p></li><br><li><p>Resource Title: Patented Technologies Data Dictionary.</p> <p>File Name: patented-technologies-data-dictionary.csv</p><p>Resource Description: Defines fields, data type, allowed values etc. in patented technology tables.</p></li><br><li><p>Resource Title: Animal Production & Protection - June 2018.</p> <p>File Name: Animal_Production_and_Protection_2018-06.csv</p><p>Resource Description: Listing of technologies to improve the health, well-being, and efficiency of livestock, poultry, and aquatic food animals to ensure a productive and safe food supply.  This CSV file provides the title, technology type, docket number, contact, description, and category for each item. Machine-readable content extracted from corresponding slides accompanying this dataset.</p></li></ul><p></p>

Tags: animals, antibodies, ars, avian, bovine, data-gov, disease, infection, poultry, toxins, vaccine, vitamin
Organization: Department of Agriculture
USDA Agricultural Research Service- Patented Food Processing and Products Technologies

<p>Recent USDA/ARS patented technologies on food processing and products that are available for licensing are described, including summary, contact, benefits, and applications. Updated June 2018. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Food Processing and Products - Available Technologies, June 2018.</p> <p>File Name: Food Processing and Products.pptx</p><p>Resource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.</p></li><br><li><p>Resource Title: Patented Technologies Data Dictionary.</p> <p>File Name: patented-technologies-data-dictionary.csv</p><p>Resource Description: Defines fields, data type, allowed values etc. in patented technology tables.</p></li><br><li><p>Resource Title: Patented Food Processing and Products - June 2018.</p> <p>File Name: Food_Processing_and_Products.csv</p><p>Resource Description: Listing of technologies and processes to transfer raw ingredients into food or other food forms, and technologies to ensure that the food supply is safe for consumers. This CSV file provides the title, technology type, docket number, contact, description, and category for each item. Machine-readable content extracted from corresponding slides accompanying this dataset.</p></li></ul><p></p>

Tags: acid, animal, ars, clones, data-gov, fda, food, fruits, packaging, poultry, protein, vegetables
Organization: Department of Agriculture
Maize Genetics Cooperation Stock Center Catalog of Stocks

<p>The Maize Genetics Cooperation Stock Center is operated by USDA/ARS, located at the University of Illinois, Urbana/Champaign, and integrated with the National Plant Germplasm System (NPGS). The center serves the maize research community by collecting, maintaining and distributing seeds of maize genetic stocks, and providing information about maize stocks and the mutations they carry through the Maize Genetics and Genomics Database (MaizeGDB).  Users can browse to obtain detailed information about the following stocks: </p>
<ul>
<li>Chromosome 1 Markers</li>
<li>Chromosome 2 Markers</li>
<li>Chromosome 3 Markers</li>
<li>Chromosome 4 Markers</li>
<li>Chromosome 5 Markers</li>
<li>Chromosome 6 Markers</li>
<li>Chromosome 7 Markers</li>
<li>Chromosome 8 Markers</li>
<li>Chromosome 9 Markers</li>
<li>Chromosome 10 Markers</li>
<li>Unplaced Genes</li>
<li>Multiple Genes</li>
<li>Rare Isozyme</li>
<li>B-Chromosome</li>
<li>Alien Addition</li>
<li>Trisomic</li>
<li>Tetraploid</li>
<li>Cytoplasmic-Sterile / Restorer</li>
<li>Cytoplasmic Trait</li>
<li>Toolkit</li>
<li>B-A Translocations (Basic Set)</li>
<li>B-A Translocations (Others)</li>
<li>Inversion</li>
<li>Reciprocal Translocations (wx1 and Wx1 marked)</li>
</ul>
<p>Stock records include information on availability, annotations, related records (genotypic variations, phenotypes), GRIN (Germplasm Resources Information Network) information, and offsite resources.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Maize Genetics Cooperation Stock Center Catalog of Stocks web link.</p> <p>File Name: Web Page, url: <a href="https://www.maizegdb.org/stock_catalog" target="_blank">https://www.maizegdb.org/stock_catalog</a> </p><p>A tool in the MaizeGDB system.</p></li></ul>

Tags: ars, data-gov, database, genetics, genome-assembly, genome-sequences, genomics, germplasm, maize, metadata, np301, online-database, phenotype
Organization: Department of Agriculture
Genomes To Fields 2016

<p>Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018">CyVerse</a>.</p>
<p>Data types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (<em>Zea mays</em>) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: CyVerse Genomes To Fields 2016 dataset download.</p> <p>File Name: Web Page, url: <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018">http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018</a> </p><p>Dataset (csv) and metadata (BibTex, Endnote) data downloads. See _readme.txt for file contents.</p></li></ul><p></p>

Tags: ars, data-gov, g2f, genomes-by-environment, genomes-to-fields, gxe, np301
Organization: Department of Agriculture
pySnobal

<p>Python implementation of the Snobal model applied at either a point or over an image (iSnobal). This package contains the C libraries of Snobal in a python wrapper for more flexibility and ease of interaction. ipysnobal.py has functions for general interaction. More detailed and flexible functions can be found in the Automated Water Supply Model (AWSM). Features:</p>
<ul>
<li>cythonized version of iSnobal</li>
<li>packaged up</li>
<li>can take variables from SMRF memory or read in inputs to run iSnobal</li>
<li>
<p>designed to work within AWSM framework</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: pySnobal GitHub repository.</p> <p>File Name: Web Page, url: <a href="https://github.com/USDA-ARS-NWRC/pysnobal/tree/v0.2.0" target="_blank">https://github.com/USDA-ARS-NWRC/pysnobal/tree/v0.2.0</a> </p><p>Python wrapped implementation of the Snobal model.</p></li></ul>
</li>
</ul>

Tags: ars, data-gov, interpolation, isnobal, np211, pysnobal, smrf, snobal, spatial-modeling-for-resources-framework
Organization: Department of Agriculture
Environmental Policy Integrated Climate (EPIC) Model

<p>Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion as part of the Soil and Water Resources Conservation Act analysis for 1980, which revealed a significant need for improving technology for evaluating the impacts of soil erosion on soil productivity.  EPIC simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management strategies. It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management. </p>
<p>EPIC functions on a daily time step and can simulate hundreds of years.  Since the initial development, EPIC has been continually improving through the additions of algorithms to simulate water quality, nitrogen and carbon cycling, climate change, and the effects of atmospheric carbon dioxide.  The processes simulated include leaf interception of solar radiation; conversion to biomass; division of biomass into roots, above ground mass, and economic yield; root growth; water use; and nutrient uptake.  It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management practices.  The model can also assess the cost of erosion for determining optimal management strategies.   </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Environmental Policy Integrated Climate (EPIC) Model.</p> <p>File Name: Web Page, url: <a href="https://epicapex.tamu.edu/epic/">https://epicapex.tamu.edu/epic/</a> </p><p>Web site for the EPIC model: describes capabilities, examples of applications, and download links for executables, source code, and supporting tools.</p></li></ul><p></p>

Tags: ars, data-gov, environmental-policy-integrated-climate, epic, np211, np212, np215
Organization: Department of Agriculture
Resampling Validation of Sample Plans (RVSP)

<p>Reliable and cost-effective sampling methods are critical to the development of monitoring systems for pest management and can enhance research activities that address issues in population ecology and population dynamics. Validation and evaluation of these plans are central to development and implementation in the field. Sampling plans are often developed from a restricted range of observations from a small area, but are then used over a wide area representing a novel array of environmental and agronomic conditions.</p>
<p>Sets of tools for sample plan evaluation originally released in 1997, these Monte Carlo simulations can be used to evaluate sampling models during the developmental phase; however, they may not be adequate for testing model validity and performance under field conditions. This is primarily due to the assumption of an underlying statistical distribution (e.g., negative-binomial, normal) which may not adequately represent the actual distributions of insects in all instances. Here we present a method in which actual field data is resampled to evaluate sample plan performance. We originally developed DOS-based computer software for this purpose.  The full functionality of this original program is available as an Excel Add-in in RVSP V.2 compatible with current and past versions of Excel. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Resampling Validation of Sample Plans (RVSP).</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=129&modecode=20-20-05-05">https://www.ars.usda.gov/research/software/download/?softwareid=129&modecode=20-20-05-05</a> </p></li></ul><p></p>

Tags: agricultural-research-service, arid-lands, arizona, computer-software, cost-effectiveness, entomology, insects, model-validation, models, monitoring, monte-carlo-method, pest-management, population-dynamics, population-ecology, sampling
Organization: Department of Agriculture
STARFM

<p>Landsat 30m resolution observations provide sufficient spatial details for monitoring land surface and changes. However, the 16-day revisit cycle and cloud contamination have limited its use for studying global biophysical processes, which evolve rapidly during the growing season. Meanwhile, MODIS sensors aboard the NASA EOS Terra and Aqua satellites provide daily global observations valuable for capturing rapid surface changes. However, the spatial resolution of 250m to 1000m may not good enough for heterogeneous areas. To better utilize Landsat and MODIS data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed (Gao et al., 2006). The STARFM algorithm uses spatial information from fine-resolution Landsat imagery and temporal information from coarse-resolution MODIS imagery to produce estimates of surface reflectance that are high resolution in both space and time. In essence, the collection of daily MODIS imagery and seasonal Landsat imagery allows the generation of synthetic daily Landsat-like views of the Earth’s surface.</p>
<p>The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: STARFM.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=432&modecode=80-42-05-10" target="_blank">https://www.ars.usda.gov/research/software/download/?softwareid=432&modecode=80-42-05-10</a> </p><p>download page</p></li></ul>

Tags: algorithms, computer-software, crop-yield, evapotranspiration, forest-damage, growing-season, image-analysis, landsat, models, moderate-resolution-imaging-spectroradiometer, monitoring, national-agricultural-statistics-service, reflectance, remote-sensing, spatial-data, spectroradiometers, vegetation, water-utilization
Organization: Department of Agriculture