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

<p>WISDEM simulates the variation in multi-species weed populations over time in response to crop rotation, tillage system, and specific weed management tactics and the consequent crop yield loss due to weed competition. Population dynamics of individual weed species are predicted from a limited number of parameters that can be derived from literature sources and expert opinion.</p>
<p>Data to model the population dynamics and crop yield loss of multi-species weed populations is extremely limited as well as expensive and time-consuming to obtain. WISDEM simulates population dynamics of multi-species weed populations in response to crop rotation, tillage system, and specific weed management tactics as well as the resulting crop yield reduction from weed competition.  The model uses an innovative structure for modeling weed population dynamics that requires only a small number of parameters and these can be readily derived from literature sources and regional surveys of weed experts. The structure is based on the general theory of density dependence of plant productivity and the extensive use of rectangular hyperbolic equations for describing crop yield as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, seedling emergence and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence and differences among species in competitive ability. WISDEM has been parameterized for 4 crops and 15 weeds of the Great Plains. A preliminary, limited evaluation provides evidence that predictions of yield loss from single species of weeds and the short term trajectories of changes in weed populations are biologically reasonable. We think the accuracy is sufficient for the goal of modeling general trends in population density accurately enough to highlight potential weed problems and solutions when comparing alternative crop management options for a field. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: WISDEM.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=182&modecode=30-12-30-20">https://www.ars.usda.gov/research/software/download/?softwareid=182&modecode=30-12-30-20</a> </p><p>download page</p></li></ul><p></p>

Tags: alternative-crops, computer-software, crop-management, crop-weed-competition, crop-yield, equations, expert-opinion, experts, great-plains-region, models, mortality, population-density, population-dynamics, prediction, regional-surveys, seedling-emergence, weed-control
Organization: Department of Agriculture
Modified Langmuir Equation Spreadsheet

<p>Spreadsheet from the paper entitled: Revisiting a Statistical Shortcoming when Fitting the Langmuir Model to Sorption Data by C.H. Bolster, Journal of Environmental Quality, 2008, 37:1986-1992. Spreadsheet has been modified to make a correction to the calculation of E for weighted data. (3/18/2010).</p>
<p>Sorption models are commonly used for describing solute and metal sorption to soils. When fitting sorption models to sorption data, however, the user must be aware that certain statistical limitations exist with both linear and nonlinear versions of the models. Ongoing research at the Animal Waste Management Research Unit of the USDA-ARS addresses the effect of these statistical limitations on fitting phosphorus sorption data with various sorption models.</p>
<p>This research was originally part of the former USDA-ARS National Program 206: Manure and By-product Utilization.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Modified Langmuir Equation Spreadsheet.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=205&modecode=50-40-05-00">https://www.ars.usda.gov/research/software/download/?softwareid=205&modecode=50-40-05-00</a> </p><p>download page</p></li></ul><p></p>

Tags: ars, data-gov, np206
Organization: Department of Agriculture
Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) Software

<p>Evapotranspiration (ET) is a major component of the hydrologic cycle. ET data are used for a variety of water management and research purposes such as irrigation scheduling, water and crop modeling, streamflow, water availability, and many more. Remote sensing products have been widely used to create spatially representative ET data sets which provide important information from field to regional scales. As UAV capabilities increase, remote sensing use is likely to also increase. For that purpose, scientists at the USDA-ARS research laboratory in Bushland, TX developed the Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software. The BEARS software is a Java based software that allows users to process remote sensing data to generate ET outputs using predefined models, or enter custom equations and models. The capability to define new equations and build new models expands the applicability of the BEARS software beyond ET mapping to any remote sensing application. The software also includes an image viewing tool that allows users to visualize outputs, as well as draw an area of interest using various shapes. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: BEARS Software download page (Windows) .</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=BEARS+Software+(Windows)&modecode=30-90-05-00">https://www.ars.usda.gov/research/software/download/?softwareid=BEARS+Software+(Windows)&modecode=30-90-05-00</a> </p><p>This version of the software is designed for Windows operating systems. There is also a <a href="https://www.ars.usda.gov/research/software/download/?softwareid=BEARS+Software+(Mac)&modecode=30-90-05-00">Mac version of the BEARS Software</a> available for download.</p></li></ul><p></p>

Tags: ars, data-gov, np211
Organization: Department of Agriculture
Treehopper Immatures Project

<p>The attached, linked files provide the characters and character matrix for publications describing immatures of the New World treehoppers (Membracidae, Melizoderdae, and Aetalionidae) by McKamey, Wallner, Porter, Wallace, and Phillips, or some combination thereof. </p>
<p>Characters for each body part are assigned a number, and each character within each body part are assigned a letter, because additional characters (and states) are expected to be discovered with each newly examined tribe or subfamily and it is desirable to keep all characters pertaining to a body part together.  With each publication, both of these linked files will be updated. </p>
<p>Presently, the characters and matrix cover the tribes Amastrini and Thuridini (Smiliinae). </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: 2015 Characters.</p> <p>File Name: 2015 Characters.pdf</p><p>Resource Description: Characters and states for treehopper nymphs. Partly because character states were appended as discovered, and partly because of their plasticity, we recommend that, if these characters are used in a cladistic analysis, they be treated as unordered states.</p></li><br><li><p>Resource Title: May 2015 Matrix.</p> <p>File Name: May 2015 matrix.pdf</p></li></ul><p></p>

Tags: ars, data-gov, np304
Organization: Department of Agriculture
UAS User Log

<p>The UAS User Log is a server-based, digital logbook that is accessible through any web browser on internet-connected devices.​ It is an outcome of multi-state teams working together to develop a common protocol for unmanned aircraft systems (UAS, or drones) operation and bring standardization to flight data collection for purposes such as research/production, spray application, and any other activity of interest. It ​relies on simple user interactions to develop a record of UAS mission and can also serve to enhance flight and maintenance experience.​  The logbook provides options to interactively record the date, time and location of a flight, the make, model and registration information of the device, status of battery charge, type of flight (autonomous or manual), types of sensors used and data collected, safety precautions taken, weather during the flight and other related information. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website Pointer to UAS User Log.</p> <p>File Name: Web Page, url: <a href="https://www.uasuserlog.org/">https://www.uasuserlog.org/</a> </p><p>Generates a web form to log details of a specific UAS mission.</p></li></ul><p></p>

Tags: ars, data-gov, digital-logbook, np304, np305, uas-user-log
Organization: Department of Agriculture
USDA Agricultural Research Service- Patented Available Plant Cultivars

<p>Recent USDA/ARS patent- and PVP-protected plant cultivars that are available for licensing are described, including summary, contact, and patent number/status. Updated June 2018. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Available Plant Cultivars - June 2018.</p> <p>File Name: June Avail Plants.pptx</p><p>Resource Description: Slides presenting title, patent no./protection status, contact, docket number(s), description, and USPTO patent database URL of each new cultivar.</p></li><br><li><p>Resource Title: Available Plant Cultivars - June 2018.</p> <p>File Name: Available_Plants_2018-06.csv</p><p>Resource Description: Listing of patent- and PVP-protected cultivars. This CSV file provides the title, patent no./protection status, contact, docket number(s), description, and USPTO patent database URL of each new cultivar. Machine-readable content extracted from corresponding slides accompanying this dataset.</p></li><br><li><p>Resource Title: Available Plants Data Dictionary.</p> <p>File Name: available-plants-data-dictionary.csv</p><p>Resource Description: Defines fields, data type, allowed values etc. in available patented plants tables.</p></li></ul><p></p>

Tags: ars, clones, data-gov, genes, genotypes, infection, markers, virus
Organization: Department of Agriculture
Phytophthora Database

<p>NOTE: This resource, previously available at www.phytophthoradb.org, is no longer publicly accessible.</p>
<p>This database aims to include Phytophthora collections throughout the world in order to create a global atlas of the diversity and distribution of Phytophthora species. </p>
<p>Phytophthora, an oomycete plant pathogen, is more closely related to brown algae and diatoms than true fungi and has been placed in a separate kingdom, the Stramenopiles (Gunderson et al., 1987;   Förster et al., 1990; Leipe et al., 1994). Due to their high virulence and ability to spread rapidly throughout the world, Phytophthora is one of the most important groups of plant pathogens. The destructive potential of Phytophthora diseases is well illustrated by late blight (P. infestans), which was responsible for the Irish potato famine and has again become globally problematic due to the introduction of new, fungicide-resistant lineages (Fry and Goodwin, 1997). Sudden oak death in the US (Rizzo et al., 2002) and diseases on ornamental plants in Europe (Werres et al., 2001), caused by P. ramorum, are examples of the threat to forest ecosystems and the nursery industry.

Toward the goal of enhancing our ability to detect, diagnose, monitor, and manage Phytophthora diseases, we have been systematically cataloging genotypic and phenotypic data of Phytophthora spp. in a web-based database that can be easily accessed and utilized by the global community of plant health professionals. Although we are currently focusing on the genotypic characterization of the isolates archived in the World Phytophthora Collection (WPC; Phytophthora.ucr.edu) at UC-Riverside and in the Pennsylvania Department of Agriculture (PDA), we plan to include Phytophthora collections throughout the world in order to create a global atlas of the diversity and distribution of Phytophthora species. 

This project has been mainly supported by the NRI-Plant Biosecurity program (2005-35605-15393 and 2008-55605-18773) and currently involves the following principal investigators, including Seogchan Kang, David Geiser, and Scott Isard (Penn State), Mike Coffey (UC-Riverside), Joe Russo (ZedX, Inc.), Kelly Ivors (NC State), Frank Martin and Nik Grunwald (USDA-ARS). Grants from the Pennsylvania Department of Agriculture (ME442316 and ME 445580) and a cooperative agreement with USDA-ARS (59-1920-3-304) have also been used to support parts of this project.</p>
<p>Partially supported by Grant from the USDA-AFRI Plant Biosecurity program supported the
establishment and improvement of the PD (2005-35605-15393 and 2008-55605-18773).</p>

Tags: ars, basic-local-alignment-search-tool, blast, data-gov, invasive-pathogen, molds, molecular-diagnosis, np303, online-database, phytophthora, plant-disease, plant-pathogens
Organization: Department of Agriculture
USDA National Fluoride Database of Selected Beverages and Foods - Release 2 (2005)

<p>Assessment of fluoride intake is paramount in understanding the mechanisms of fluoride metabolism, specifically the prevention of dental caries, dental fluorosis, and skeletal fluorosis. A database for fluoride is needed for epidemiologists and health researchers to estimate the intakes, and to investigate the relationships between intakes and human health.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: READ ME - USDA National Fluoride Database of Selected  Beverages and Foods, Release 2.</p> <p>File Name: F02.pdf</p><p>Resource Description: </p><p>Describes methods and procedures, data generation and evaluation, formats of tables, data dissemination, references cited in the documentation and database, and other miscellaneous information about this database.</p>
<p></p></li><br><li><p>Resource Title: Fluoride Database of Selected Beverages and Foods (Release 2).</p> <p>File Name: F02.xls</p><p>Resource Description: This file contains the Fluoride Database imported into a Microsoft Excel spreadsheet. You need Excel 2000 or later to use this file. </p></li></ul>

Tags: ars, data-gov, fluoride, food-composition, np107, nutrient-database
Organization: Department of Agriculture
BESTPRED: Best Prediction Programs

<p>A set of programs for best prediction of lactation yields.</p>
<p>Lactation records of any reasonable length now can be processed with the selection index method known as best prediction (BP). Previous prediction programs were limited to the 305-d standard used since 1935. Best prediction was implemented in 1998 to calculate lactation records in USDA genetic evaluations, replacing the test interval method used since 1969 to calculate lactation records. Best prediction is more complex but also more accurate, particularly when testing is less frequent. Programs were reorganized to output better graphics, give users simpler access to options, and provide additional output, such as BP of daily yields. Test-day data for 6 breeds were extracted from the national dairy database, and lactation lengths were required to be ≥500 d (Ayrshire, Milking Shorthorn) or ≥800 d (all others). Average yield and SD at any day in milk (DIM) were estimated by fitting 3-parameter Wood’s curves (milk, fat, protein) and 4-parameter exponential functions (somatic cell score) to means and SD of 15- (≤300 DIM) and 30-d (>300 DIM) intervals. Correlations among TD yields were estimated using an autoregressive matrix to account for biological changes and an identity matrix to model daily measurement error. Autoregressive parameters (r) were estimated separately for first (r = 0.998) and later parities (r = 0.995). These r values were slightly larger than previous estimates due to the inclusion of the identity matrix. Correlations between traits were modified so that correlations between somatic cell score and other traits may be nonzero. The new lactation curves and correlation functions were validated by extracting TD data from the national database, estimating 305-d yields using the original and new programs, and correlating those results. Daily BP of yield were validated using daily milk weights from on-farm meters in university research herds. Correlations ranged from 0.900 to 0.988 for 305-d milk yield. High correlations ranged from 0.844 to 0.988 for daily yields, although correlations were as low as 0.015 on d 1 of lactation, which may be due to calving-related disorders that are not accounted for by BP. Correlations between 305-d yield calculated using 50-d intervals from 50 to 250 DIM and 305-yield calculated using all TD to 500 DIM increased as TD data accumulated. Many cows can profitably produce for >305 DIM, and the revised program provides a flexible tool to model these records.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: BESTPRED.</p> <p>File Name: Web Page, url: <a href="https://aipl.arsusda.gov/software/bestpred/">https://aipl.arsusda.gov/software/bestpred/</a> </p><p>download and information page</p></li></ul><p></p>

Tags: breeds, computer-software, cows, herds, lactation, lactation-curve, milk, milk-yield, milking, milking-shorthorn, models, prediction, selection-index, somatic-cells
Organization: Department of Agriculture
Yield Editor 2.0.7

<p>Yield Editor is a tool which allows the user to select, apply and analyze a variety of automated filters and editing techniques used to process and clean yield data.  The software imports either AgLeader advanced or Greenstar text file formats, and exports data in a delimited ASCII format.</p>
<p>Yield Editor 2.0.7 includes some of the improvements and updates that users of the software have asked to be included.  It provides three major improvements over version 1.0.2.  The most important of these is the inclusion of a module for automated selection of many yield filter values, as well as a couple of additional automated filter types.  A legend tool has been added which allows for the viewing of multiple data streams. Finally, a command line interface language under development allows for automated batch mode processing of large yield datasets.</p>
<p>Yield maps provide important information for developing and evaluating precision management strategies. The high-quality yield maps needed for decision-making require screening raw yield monitor datasets for errors and removing them before maps are made. To facilitate this process, we developed the Yield Editor interactive software which has been widely used by producers, consultants and researchers. Some of the most difficult and time consuming issues involved in cleaning yield maps include determination of combine delay times, and the removal of “overlapped” data, especially near end rows. Our new Yield Editor 2.0 automates these and other tasks, significantly increasing the reliability and reducing the difficulty of creating accurate yield maps. This paper describes this new software, with emphasis on the Automated Yield Cleaning Expert (AYCE) module. Application of Yield Editor 2.0 is illustrated through comparison of automated AYCE cleaning to the interactive approach available in Yield Editor 1.x. On a test set of fifty grain yield maps, AYCE cleaning was not significantly different than interactive cleaning by an expert user when examining field mean yield, yield standard deviation, and number of yield observations remaining after cleaning. Yield Editor 2.0 provides greatly improved efficiency and equivalent accuracy compared to the interactive methods available in Yield Editor 1.x.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Yield Editor 2.0.7.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=370&modecode=50-70-10-00">https://www.ars.usda.gov/research/software/download/?softwareid=370&modecode=50-70-10-00</a> </p><p>download page: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=370&modecode=50-70-10-00">https://www.ars.usda.gov/research/software/download/?softwareid=370&modecode=50-70-10-00</a></p></li></ul><p></p>

Tags: ars, combine-delay-time, data-gov, grain-yield, precision-agriculture, yield-map-errors
Organization: Department of Agriculture
<b>Data from: Coffee berry borer (Coleoptera: Scolytidae) population dynamics across Hawaii Island’s diverse coffee-growing landscape: Optimizing location-specific pesticide applications</b>

<p dir="ltr">A major challenge to area-wide management of coffee berry borer (CBB, <i>Hypothenemus hampei</i> Ferrari) (Coleoptera: Scolytidae) is understanding how a heterogeneous coffee-growing landscape affects CBB population dynamics across temporal and spatial scales. We examined coffee phenology, weather, CBB flight activity, infestation, CBB position within the fruit, and management across 14 commercial coffee farms from 2016–2018 on Hawaii Island to characterize variation among districts and elevations. This data was used to develop an optimized pesticide spray schedule for each growing location. Implementing a coordinated area-wide approach refined by small-scale optimization will lead to improved management of CBB on individual farms and a reduction in pest pressure across the coffee-growing landscape. </p>

Tags: coffea-arabica, crop-infestation, elevation-gradient, integrated-pest-management, plant-phenology, trap-catch, weather-variables
Organization: Department of Agriculture
BBGD454: an Online Database for Blueberry Genomic Data Transcriptome analysis of Blueberry using 454 EST sequencing

<p>NOTE: This dataset is no longer publicly available.</p>
<p>This database houses over 500,000 sequences that were generated and assembled into approximately 15,000 contigs, annotated and functionally mapped to Gene Ontology (GO) terms.</p>
<p>Blueberry (<em>Vaccinium corymbosum</em>) is a major berry crop in the United States. Next generation sequencing methodologies, such as 454, have been demonstrated to be successful and efficient in producing a snap-shot of transcriptional activities during an organism’s developmental stage(s) or its response to biotic or abiotic stresses. Such application of this new sequencing technique allows for high-throughput, genome-wide experimental verification of known and novel transcripts. We have applied a high-throughput pyrosequencing technology (454 EST sequencing) for transcriptome profiling of blueberry during different stages of fruit development to gain an understanding of the genes that are up or down regulated during this process. We have also sequenced flower buds at four different stages of cold acclimation to gain a better understanding of the genes and biochemical pathways that are up- or down-regulated during cold acclimation, since extreme low temperatures are known to reduce crop yield and cause major losses to US farmers. We have also sequenced a leaf sample to compare its transcriptome profile with that of bud and fruit samples. Over 500,000 sequences were generated and assembled into approximately 15,000 contigs and were annotated and functionally mapped to Gene Ontology (GO) terms. A database was developed to house these sequences and their annotations. A web based interface was also developed to allow collaborators to search\browse the data and aid in the analysis and interpretation of the data. The availability of these sequences will allow for future advances, such as the development of a blueberry microarray to study gene expression, and will aid in the blueberry genome sequencing effort that is underway.
This work was supported by grant 2008-51180-04861 from the USDA - Cooperative State Research, Education, and Extension Service (CSREES) Specialty Crop Research Initiative program.</p>

Tags: ars, blueberry, data-gov, genomics, np301, np303, online-database, vaccinium-corymbosum
Organization: Department of Agriculture