AndreaBozzo/ceres-open-data-index huggingface dataset:
Data from: Life history changes in Trogoderma variabile and T. inclusum due to mating delay with implications for mating disruption as a management tactic

<p>Egg and progeny counts for Trogoderma variabile and Trogoderma inclusum adults with delays in mating. These data were generated to examine the effect of mating delay on life history and reproductive capacity as a cue to the use of mating disruption tactics such as pheromone lures. Survivorship was calculated as the last day egg counts were recorded for an individual. Blocks were adults that were all mated on the same day. Reps are an individual female. Control beetles are coded with either an "f" or an "m" for female and male and were never mated. Male control data was examined for similarity to female control data but was not used in further analysis and comparison. The experiment was all done at 30C, 65% relative humidity and a 16 light/8 dark photoperiod. Adults were transferred every 2 days to new vials and eggs were then counted and save for progeny counts. Trogoderma variabile populations were laboratory colonies for over 20 years. T. inclusum populations were collected in Kansas in August of 2012. We did not transform these data.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Progeny and egg counts for Trogoderma inclusum.</p> <p>File Name: inclusum_mating_full_long.csv</p><p>Resource Description: Data for Trogoderma inclusum. Egg and progeny counts. Column headers: female<em>age = day at which first mating occurred; species= T. inclusum; block=groups that were all mated on the same day; rep=an individual female; count=either eggs or progeny; Day</em> = number of eggs of progeny counted on that given day. Adults were moved to new vials every two days, so egg and progeny counts are on a 2 day basis. Survivorship can be calculated as the last day that egg counts were taken. Day_ columns are the days post-mating.</p></li><br><li><p>Resource Title: Progeny and egg counts for Trogoderma variabile.</p> <p>File Name: variabile_mating_full_long.csv</p><p>Resource Description: Data for Trogoderma variabile. Egg and progeny counts. Column headers: female<em>age = day at which first mating occurred; species= T. inclusum; block=groups that were all mated on the same day; rep=an individual female; count=either eggs or progeny; Day</em> = number of eggs of progeny counted on that given day. Adults were moved to new vials every two days, so egg and progeny counts are on a 2 day basis. Survivorship can be calculated as the last day that egg counts were taken. Day_ columns are the days post-mating.</p></li><br><li><p>Resource Title: Data Dictionary.</p> <p>File Name: Data_Dictionary-Life_history_changes_Trogoderma.csv</p></li></ul><p></p>

Tags: ars, data-gov, delayed-mating, demography, dermestid-beetle, larger-cabinet-beetle, life-history, mating-disruption, np304, pest-control, stored-product-pests, warehouse-beetle
Organization: Department of Agriculture
Data from: Temporal and spatial variation in switchgrass biomass composition and theoretical ethanol yield

<p dir="ltr">Information on temporal and spatial variation in switchgrass (<i>Panicum virgatum</i> L.) biomass composition as it affects ethanol yield (L Mg<sup>–1</sup>) at a biorefinery and ethanol production (L ha<sup>–1</sup>) at the field-scale. Switchgrass biomass samples were collected from a regional, on-farm trial and biomass composition was determined using developed near-infrared reflectance spectroscopy (NIRS) prediction equations and theoretical ethanol yield (100% conversion efficiency). Data comprises of forage quality components and cell wall composition parameters specific to the bioenergy sector. Switchgrass was harvested during the reproductive growth stage. </p>

Tags: bioenergy, biomass, cell-wall-composition, ethanol, hexose-content, near-infrared-reflectance-spectroscopy-nirs, nebraska, north-dakota, panicum-virgatum-switchgrass, pentose-content, south-dakota, xylose
Organization: Department of Agriculture
MaizeGDB

<p>MaizeGDB is a community-oriented, long-term, federally funded informatics service to researchers focused on the crop plant and model organism Zea mays. Genomic, genetic, sequence, germplasm, gene product, metabolic pathways, functional characterization, literature reference, diversity, and expression are among the datatypes stored at MaizeGDB. At the project's website are custom interfaces enabling researchers to browse data and to seek out specific information matching explicit search criteria.  First released in 1991 with the name MaizeDB, the Maize Genetics and Genomics Database, now MaizeGDB (since 2003), is funded, developed, and hosted by the USDA-ARS located at Ames, Iowa.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: MaizeGDB, the community database for maize genetics and genomics..</p> <p>File Name: Web Page, url: <a href="https://maizegdb.org/">https://maizegdb.org/</a> </p><p>MaizeGDB is a community-oriented, long-term, federally funded informatics service to researchers focused on the crop plant and model organism Zea mays.  Established as a USDA-ARS resource in 2003, MaizeGDB supplies data and resources related to maize.  The types of data include genomic, genetic, sequence, germplasm, gene product, metabolic pathways, functional characterization, literature reference, diversity, and expression.<br>
</p></li></ul><p></p>

Tags: ars, data-gov, database, genetics, genome-assembly, genome-sequences, genomics, germplasm, maize, metadata, np301, online-database, phenotype
Organization: Department of Agriculture
GrainGenes- A Global Data Repository for Small Grains

<p dir="ltr">GrainGenes is an international, centralized crop database for peer-reviewed small grains data and information portal that serves the small grains research and breeding communities (wheat, barley, oat, and rye). The GrainGenes project ensures long-term data curation, accessibility, and sustainability so that small grains researchers can develop new, more nutritious, disease and pest resistant, high yielding cultivars. As a digital platform, GrainGenes houses peer-reviewed and curated genetic, genomic, and protein data. It has been hard-funded by the U.S. Department of Agriculture-Agricultural Research Service to ensure long-term data sustainability through a functional and integrated web interface for wheat, barley, oat, and rye.</p><p dir="ltr"><br></p>

Tags: ars, avena, avena-sativa, computational-biology, data-gov, fair, genetic-maps, hordeum-vulgare-l, machine-learning, np301, online-database, pangenomes, secale-cereale, triticeae, triticum
Organization: Department of Agriculture
Pulse Crop Database Resources

<p>Genomic, Genetic and Breeding Resources for Pulse Crop Improvement. Crops supported include Adzuki bean, Bambara bean, Chickpea, Common bean, Cowpea, Faba bean, Lentil, Lupin, Pea, Pigeon pea, Vetch, and others. The Pulse Crop Database (PCD), formerly the Cool Season Food Legume Database (CSFL), is being developed by the Main Bioinformatics Laboratory at Washington State University in collaboration with the USDA-ARS Grain Legume Genetics and Physiology Research Unit, the USDA-ARS Plant Germplasm Introduction and Testing Unit, the USA Dry Pea and Lentil Council, Northern Pulse Growers and allied scientists in the US and across the world, to serve as a resource for Genomics-Assisted Breeding (GAB). GAB offers tools to identify genes related to traits of interest among other methods to optimize plant breeding efficiency and research, by providing relevant genomic, genetic and breeding information and analysis. Therefore, tools such as JBrowse and MapViewer can be found in this database, as well as key resources to provide the access to the annotation of available transcriptome data, helping pulse breeders and researchers to succeed in their programs.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Pulse Crop Database Resources.</p> <p>File Name: Web Page, url: <a href="https://www.pulsedb.org/" target="_blank">https://www.pulsedb.org/</a> </p><p>Resources include data submission and download, and search by gene and transcript, germplasm, map, marker, publication, QTL, sequence, megasearch, and trait/descriptor. A User Manual describes how to access data and use the tools on the Pulse Crop Database. Tools supported: BLAST, JBrowse, PathwayCyc, MapViewer, and Synteny Viewer</p></li></ul>

Tags: ars, data-gov, np301, online-database, pcd, pulse-crop-database, pulsedb
Organization: Department of Agriculture
CottonGen BLAST

<p>CottonGen offers BLAST with genome, transcriptome, peptide and marker sequence databases from <em>Gossypium</em> species. This can be done using nucleotide sequences or peptide sequences. BLAST functionality is similar to that on NCBI.</p>
<p>BLAST Programs:</p>
<ul>
<li>blastn: Search a nucleotide database using a nucleotide query.</li>
<li>blastx: Search protein database using a translated nucleotide query.</li>
<li>tblastn: Search translated nucleotide database using a protein query.</li>
<li>
<p>blastp: Search protein database using a protein query.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website Pointer for CottonGen BLAST Search.</p> <p>File Name: Web Page, url: <a href="https://www.cottongen.org/blast" target="_blank">https://www.cottongen.org/blast</a> </p><p>CottonGen offers BLAST with genome, transcriptome, peptide and marker sequence databases from *Gossypium* species. This can be done using nucleotide sequences or peptide sequences. BLAST functionality is similar to that on NCBI. Enter or upload FASTA sequence(s) to query and select BLAST database.
</p></li>
</ul>
<p>BLAST Programs:</p>
<ul>
<li>blastn: Search a nucleotide database using a nucleotide query.</li>
<li>blastx: Search protein database using a translated nucleotide query.</li>
<li>tblastn: Search translated nucleotide database using a protein query.</li>
<li>blastp: Search protein database using a protein query.</li></ul><p></p></li>
</ul>

Tags: ars, cottongen, data-gov, ncbi-blast, np301, online-database
Organization: Department of Agriculture
Stewardship Contracting: Point (Feature Layer)

Depicts the locations of activities within Stewardship Contracting Project Boundary. Activities are implemented through stewardship contracts or agreements and are self-reported by Forest Service Units through the FACTS database. <a href='https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.Activity_StwrdshpCntrctng_PT.xml' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata</a>

Tags: activities, environment, forest-management, open-data, recovery, resiliency, safety, stewardship-contracting
Organization: Department of Agriculture
Data from: An In Vitro Combined Antibiotic-Antibody Treatment Eliminates Toxicity from Shiga Toxin-Producing Escherichia coli

<p>Treating Shiga toxin-producing <em>Escherichia coli</em> (STEC) gastrointestinal infections is difficult. The utility of antibiotics for STEC treatment is controversial, since antibiotic resistance among STEC isolates is widespread and certain antibiotics dramatically increase the expression of Shiga toxins (Stxs), which are some of the most important virulence factors in STEC. Stxs contribute to life-threatening hemolytic uremic syndrome (HUS), which develops in considerable proportions of patients with STEC infections. Understanding the antibiotic resistance profiles of STEC isolates and the Stx induction potential of promising antibiotics is essential for evaluating any antibiotic treatment of STEC. In this study, 42 O157:H7 or non-O157 STEC isolates (including the “big six” serotypes) were evaluated for their resistance against 22 antibiotics by using an antibiotic array. Tigecycline inhibited the growth of all of the tested STEC isolates and also inhibited the production of Stxs (Stx2 in particular). In combination with neutralizing antibodies to Stx1 and Stx2, the tigecycline-antibody treatment fully protected Vero cells from Stx toxicity, even when the STEC bacteria and the Vero cells were cultured together. The combination of an antibiotic such as tigecycline with neutralizing antibodies presents a promising strategy for future STEC treatments. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Supplemental Materials.</p> <p>File Name: Web Page, url: <a href="https://aac.asm.org/content/59/9/5435/figures-only#fig-data-additional-files">https://aac.asm.org/content/59/9/5435/figures-only#fig-data-additional-files</a> </p><p>Table S1: Descriptions and characteristics of antibiotics used in this study. XLS, 35K</p>
<p>Table S2: MICs of antibiotics for STEC serotypes and probiotics. XLS, 30K</p>
<p>Table S3: MICs of antibiotics for individual isolates. XLS, 47K</p>
<p>Table S4: Induction of Stx1 and Stx2 by selected antibiotics. XLS, 35K</p></li></ul><p></p>

Tags: ars, data-gov, np108
Organization: Department of Agriculture
USDA Agricultural Research Service- Patented Materials and Methods Technologies

<p>Patented technologies on materials and methods that are available for licensing. Updated June 2018. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Materials and Methods - Available Technologies, June 2018.</p> <p>File Name: Materials and Methods.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: Materials and Methods - June 2018.</p> <p>File Name: Materials_and_Methods.csv</p><p>Resource Description: Listing of technologies and methods for the production of goods from raw materials. 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, animals, ars, composites, data-gov, glycerides, heat, nanoparticles, plants
Organization: Department of Agriculture
Redapol

<p>Developed by Gloria DeGrandi-Hoffman et al. A computer-based model simulating the interactions of weather, bloom and honey bee foraging activity that culminate in 'Delicious' apple fruit-set.  The model predicts the percentage of blossoms setting fruit based upon weather conditions, orchard design, tree characteistics, and honey bee colonies per hectare. Now available in a complete archive (.ZIP file). </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Redapol.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/research/software/download/?softwareid=74&modecode=20-22-05-00">https://www.ars.usda.gov/research/software/download/?softwareid=74&modecode=20-22-05-00</a> </p></li></ul><p></p>

Tags: apples, foraging, fruit-set, honey-bee-colonies, honey-bees, models, orchards, simulation-models, trees, weather
Organization: Department of Agriculture
Data from: Classical biological control and apparent competition: evaluating a waterhyacinth invaded community module

<p>The scope and complexity of interactions within community food webs necessitates their simplification to a community module scale for conducting empirical studies. An outdoor mesocosm study in the USA quantified the strengths of direct and indirect interactions between two herbivore congeners that fed on two aquatic plant species while sharing a parasitoid. <em>Kalopolynema ema</em> (Hymenoptera: Mymaridae) is a stenophagous native egg parasitoid that attacks the hemipteran species in this study, <em>Megamelus davisi</em> (Hemiptera: Delphacidae), a native herbivore that feeds on the native aquatic species <em>Nuphar advena</em>, and the introduced biological control agent <em>Megamelus scutellaris</em> (Hemiptera: Delphacidae) that feeds on the non-native aquatic species waterhyacinth, <em>Pontederia crassipes</em>. The presence of <em>M. scutellaris</em> did not significantly increase parasitism of <em>M. davisi</em> indicating that apparent competition was not a factor in this community module. There was no evidence of any trophic cascades caused by these interactions based on the relative growth rates of biomass and leaf area for both plant species. The relative strengths of interactions varied with herbivore densities suggesting that should negative indirect effects occur they would likely be transient and closely linked to population dynamics as influenced by abiotic factors like temperature.  The primary negative interaction was biotic resistance to <em>Megamelus scutellaris</em> from attack by <em>Kalopolynema ema</em>. Despite identifying potential negative interactions using path analysis, we saw no apparent competition following the insertion of a weed biological control agent into a community that contains an ecological analog. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Indirect Effects SS mesocosm .</p> <p>File Name: MS Indirect Data Commons version.xlsx</p><p>Resource Description: Raw data from MS Indirect study in mesocosms.  NA = Nuphar advena, WH = Pontederia crassipes, MD = Megamelus davisi, MS = M. scutellaris., KE = Kalopolynema ema, BGBM = below ground biomass.</p></li></ul><p></p>

Tags: ars, biological-control, community-food-webs, data-gov, mesocosm-studies
Organization: Department of Agriculture
CottonGen Breeding Information Management System (BIMS)

<p>BIMS, the Breeding Information Management System, is a secure and comprehensive online breeding management system developed for the generic Tripal Database Platform which allows breeders to store, manage, archive and analyze their private breeding program. Breeders can load data in templates provided as well as output from the Field Book App, an android app for collecting phenotype data. In addition to the private breeders BIMS, users without accounts can also view the publicly available breeding data. The fully developed version will allow users to:</p>
<ul>
<li>
<p>Fully integrate their data with publicly available genomic, genetic and breeding data in the community database.</p>
</li>
<li>
<p>Utilize their integrated pedigree, phenotype and genotype data in performing genomic analysis and making breeding decisions.</p>
</li>
<li>
<p>Use open-source new genomics tool and breeding decision tools with seamless access to HPC.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website Pointer for CottonGen BIMS (Breeding Information Management System).</p> <p>File Name: Web Page, url: <a href="https://www.cottongen.org/bims" target="_blank">https://www.cottongen.org/bims</a> </p><p>BIMS, the Breeding Information Management System, is a secure and comprehensive online breeding management system developed for the generic Tripal Database Platform which allows breeders to store, manage, archive and analyze their private breeding program. Breeders can load data in templates provided as well as output from the Field Book App, an android app for collecting phenotype data. In addition to the private breeders BIMS users without accounts can also view the publicly available breeding data. The fully developed version will allow users to:
</p></li>
<li>
<p>Fully integrate their data with publicly available genomic, genetic and breeding data in the community database</p>
</li>
<li>
<p>Utilize their integrated pedigree, phenotype and genotype data in performing genomic analysis and making breeding decisions.</p>
</li>
<li>
<p>Use open-source new genomics tool and breeding decision tools with seamless access to HPC.</p>
</li>
</ul>
<p></p></li></ul><p></p>

Tags: ars, cmap, cotton-marker-database, cottondb, cottongen, data-gov, gbrowse, ncbi-blast, np301, online-database, tripal-database
Organization: Department of Agriculture
Data from: Association mapping of agronomic and quality traits in USDA pea single-plant collection

<p>Association mapping is an efficient approach for the identification of the molecular basis of agronomic traits in crop plants. For this purpose in pea (<em>Pisum sativum</em> L.), we genotyped and phenotyped individual lines of the single-plant-derived core collection of the USDA pea collection including accessions from 330 landraces and cultivars of <em>Pisum sativum</em> subsp. <em>sativum</em> var. <em>sativum</em>, 28 <em>P. sativum</em> subsp. <em>elatius</em> var. <em>elatius</em>, 16 <em>P. sativum</em> subsp. <em>sativum</em> var. <em>arvense</em>, four <em>P. sativum</em> subsp. <em>elatius</em> var. <em>pumilio</em>, three <em>P. abyssinicum</em>, two <em>P. fulvum</em>, and one <em>P. sativum</em> subsp. <em>transcaucasicum</em>. These 384 accessions were collected or donated from a total of 64 countries. The accessions were genotyped with 256 informative SNPs using a primer extension chemistry and matrix-assisted laser desorption/ionization (MALDI–TOF) mass spectrometry assay. Genetic structure analysis showed that the collection was structured into two main groups, corresponding roughly to the cultivated types/landraces and the more primitive form species and subspecies, with some intermediates. Linkage disequilibrium of pairwise loci and population structure of the collection were analyzed, and an association analysis between SNP genotypes and 25 valuable traits such as disease resistance, seed type/color, flower color, seed low molecular weight carbohydrate concentration, and seed mineral nutrient concentration was performed using a mixed linear model. A total of 71 marker–trait associations were detected as significant with 1–34 markers per trait based on the false discovery rate (FDR < 0.05). This study demonstrates the potential of using association mapping to identify markers for pea breeding. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Supplementary material.</p> <p>File Name: Web Page, url: <a href="https://link.springer.com/article/10.1007/s11032-015-0277-6#Sec16">https://link.springer.com/article/10.1007/s11032-015-0277-6#Sec16</a> </p></li></ul><p></p>

Tags: ars, association-mapping, data-gov, np301
Organization: Department of Agriculture
Automated Water Supply Model (AWSM)

<p>Automated Water Supply Model (AWSM) was developed at the USDA Agricultural Research Service (ARS) in Boise, ID. AWSM was designed to streamline the work flow used by the ARS to forecast the water supply of multiple water basins. AWSM standardizes the steps needed to distribute weather station data with SMRF, run an energy and mass balance with iSnobal, and process the results, while maintaining the flexibility of each program. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: AWSM GitHub Repository.</p> <p>File Name: Web Page, url: <a href="https://github.com/USDA-ARS-NWRC/awsm">https://github.com/USDA-ARS-NWRC/awsm</a> </p><p>Automated Water Supply Model (AWSM) was developed at the USDA Agricultural Research Service. AWSM was designed to streamline the workflow used to forecast the water supply of multiple water basins.</p></li></ul><p></p>

Tags: ars, automated-water-supply-model, awsm, data-gov, np211
Organization: Department of Agriculture