AndreaBozzo/ceres-open-data-index huggingface dataset:
CottonGen Sequence Retrieval

<p>Sequence Retrieval allows users to download nucleotide and protein sequences including chromosomes, scaffolds, genes, mRNAs, transcript coding sequences, protein, reftrans contigs and unigene contigs. For the sequences aligned to larger sequences, such as genes, mRNAs and transcript coding sequences, a numeric value specifying the number of upstream bases and downstream bases can be entered. A video and text tutorial are provided for additional help information. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website Pointer for Cottongen Sequence Retrieval.</p> <p>File Name: Web Page, url: <a href="https://www.cottongen.org/retrieve/sequences">https://www.cottongen.org/retrieve/sequences</a> </p><p>Sequence Retrieval allows users to download nucleotide and protein sequences including chromosomes, scaffolds, genes, mRNAs, transcript coding sequences, protein, reftrans contigs and unigene contigs. For the sequences aligned to larger sequences, such as genes, mRNAs and transcript coding sequences, a numeric value specifying the number of upstream bases and downstream bases can be entered. A video and text tutorial are provided for additional help information.</p>
<p>Available Datasets:</p>
<ul>
<li><em>Gossypioides kirkii</em> genome ISU Assembly v1.0 & Annotation v1.0</li>
<li><em>Gossypium arboreum</em> (A2) Genome BGI Assembly v2.0 & Annotation v1.0</li>
<li><em>Gossypium arboreum</em> (A2) genome CRI-Updated Assembly v1.0 & Annotation v1.0</li>
<li><em>Gossypium arboreum</em> CottonGen RefTrans V1</li>
<li><em>Gossypium barbadense</em> (AD2) Genome HAU Assembly v2.0 & Annotation v1.0</li>
<li><em>Gossypium barbadense</em> (AD2) Genome HAU-SGI Assembly v1.0 & Annotation v1.0</li>
<li><em>Gossypium barbadense</em> (AD2) Hai7124 genome ZJU Assembly v1.1 & Annotation a1</li>
<li><em>Gossypium barbadense</em> CottonGen RefTrans V1</li>
<li><em>Gossypium hirsutum</em> (AD1) Genome CGP-BGI Assembly v1.0 & Annotation v1.0</li>
<li><em>Gossypium hirsutum</em> (AD1) Genome HAU Assembly v1.0 & Annotation a1.0 / v1.1 a1.1</li>
<li><em>Gossypium hirsutum</em> (AD1) Genome NAU-NBI Assembly v1.1 & Annotation v1.1</li>
<li><em>Gossypium hirsutum</em> (AD1) Genome - Texas Interim release UTX-JGI v1.1</li>
<li><em>Gossypium hirsutum</em> (AD1) genome ZJU-Improved Assembly v2.1 & Annotation a1.0</li>
<li><em>Gossypium hirsutum</em> CottonGen RefTrans V1</li>
<li><em>Gossypium raimondii</em> CottonGen RefTrans V1</li>
<li><em>Gossypium raimondii</em> (D5) Draft Genome BGI-CGP v1.0 Assembly & Annotation</li>
<li><em>Gossypium raimondii</em> (D5) genome BYU Assembly v1 & Annotation a1</li>
<li><em>Gossypium raimondii</em> (D5) genome JGI assembly v2.0 (annot v2.1)</li>
<li><em>Gossypium thurberi</em> (D1) Genome ISU Assembly v1.0</li>
<li><em>Gossypium turneri</em> (D10) genome BYU Assembly v1 & Annotation a2</li>
</ul>
<p></p></li></ul><p></p>

Tags: ars, cottongen, data-gov, np301, online-database, sequence-retrieval
Organization: Department of Agriculture
<b>Data from: Evolution, diversity, and function of the disease susceptibility gene </b><b><i>Snn1</i></b><b> in wheat</b>

<p dir="ltr">The disease known as Septoria nodorum blotch, or SNB, is caused by a fungal pathogen that infects wheat plants and can cause substantial losses in grain yield. When a specific protein known as Tox1 is produced by the fungus and it is recognized by the wheat gene named Snn1, there is a compatible interaction that leads to disease in the wheat plant. Here, researchers conducted genetic, genomic, and bioinformatic analyses to determine how the Snn1 gene evolved, characterize the level of genetic diversity among wheat lines that carry the Snn1 gene, determine how the Snn1 gene functions to recognize the fungal SnTox1 protein, and to develop molecular markers that can be used by wheat breeders to track the Snn1 gene. The researchers found that some wheat lines carry two copies of Snn1, and the second copy resulted from a relatively recent genetic duplication of the first copy. Specific features in the DNA of the Snn1 genes were identified that dictate whether Snn1 can recognize SnTox1 thereby making the wheat plant either resistant or susceptible to SNB. These features were targeted for the development of several molecular markers that can be used in efficient DNA assays to determine if the Snn1 gene is present, and therefore if a given wheat plant will be resistant or susceptible to SNB. These marker assays will serve as useful tools to wheat breeders for the efficient development of SNB-resistant wheat varieties.</p>

Tags: disease-resistance, durum, host-pathogen-interaction, necrotrophic-effector, necrotrophic-fungus, parastagonospora-nodorum, septoria-nodorum-blotch, snn1, sntox1, triticum-aestivum, triticum-turgidum, wheat
Organization: Department of Agriculture
Data from: How Hives Collapse: Allee Effects, Ecological Resilience, and the Honey Bee

<p>A mathematical model is constructed to quantify the loss of resilience in collapsing honey bee colonies due to the presence of a strong Allee effect. In the model, recruitment and mortality of adult bees have substantial social components, with recruitment enhanced and mortality reduced by additional adult bee numbers. The result is an Allee effect, a net per-individual rate of hive increase that increases as a function of adult bee numbers. The Allee effect creates a critical minimum size in adult bee numbers, below which mortality is greater than recruitment, with ensuing loss of viability of the hive. Under ordinary and favorable environmental circumstances, the critical size is low, and hives remain large, sending off viably-sized swarms (naturally or through beekeeping management) when hive numbers approach an upper stable equilibrium size (carrying capacity). However, both the lower critical size and the upper stable size depend on many parameters related to demographic rates and their enhancement by bee sociality. Any environmental factors that increase mortality, decrease recruitment, or interfere with the social moderation of these rates has the effect of exacerbating the Allee effect by increasing the lower critical size and substantially decreasing the upper stable size. As well, the basin of attraction to the upper stable size, defined by the model potential function, becomes narrower and shallower, indicating the loss of resilience as the hive becomes subjected to increased risk of falling below the critical size. Environmental effects of greater severity can cause the two equilibria to merge and the basin of attraction to the upper stable size to disappear, resulting in collapse of the hive from any initial size. The model suggests that multiple proximate causes, among them pesticides, mites, pathogens, and climate change, working singly or in combinations, could trigger hive collapse. This data supplement provides a text file containing 7 scripts written in the R programming language for reproducing Figures 1–7. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: S1 R scripts for figures.</p> <p>File Name: Web Page, url: <a href="https://doi.org/10.1371/journal.pone.0150055.s001">https://doi.org/10.1371/journal.pone.0150055.s001</a> </p><p>Text file containing 7 scripts written in the R programming language for reproducing Figs 1–7 of this article.</p><p>Resource Software Recommended: R programming language,url: <a href="https://www.r-project.org/">https://www.r-project.org/</a> </p></li></ul><p></p>

Tags: ars, data-gov, hive-collapse
Organization: Department of Agriculture
Data from: Phenotypic Variation in Leaf Morphology of the USDA-ARS Sweetpotato Germplasm Collection

<p>For this study, 737 sweetpotato accessions were obtained from the USDA, Agricultural Research Service, <a href="https://www.ars.usda.gov/southeast-area/griffin-ga/pgrcu/">PGRCU</a> (Plant Genetic Resources Conservation Unit), Griffin, GA. Each PI was grown in the field in replicated plots at the U. S. Vegetable Laboratory, Charleston, SC. The mature leaves of each PI were evaluated for morphological characters. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Sweetpotato Leaf Descriptor - Raw Data.</p> <p>File Name: Sweetpotato-Leaf-Descriptor-Raw Data.xlsx</p></li><br><li><p>Resource Title: Sweetpotato Leaf Descriptors - Summary Table.</p> <p>File Name: Sweetpotato-Leaf-Descriptor-Summary Table.xlsx</p></li><br><li><p>Resource Title: Sweetpotato Leaf Petiole Length - Raw Data.</p> <p>File Name: Sweetpotato-Leaf Petiole Length-Raw Data.xlsx</p></li><br><li><p>Resource Title: Sweetpotato Leaf Petiole Length - Summary Table.</p> <p>File Name: Sweetpotato-Leaf Petiole Length-Summary.xlsx</p></li><br><li><p>Resource Title: Sweetpotato Vine Length, Canopy Coverage - Raw Data.</p> <p>File Name: Sweetpotato-Vine Length, Canopy Rating -Raw Data.xlsx</p></li><br><li><p>Resource Title: Sweetpotato Vine Length, Canopy Coverage - Summary Table.</p> <p>File Name: Sweetpotato-Vine Length, Canopy Rating - Summary.xlsx</p></li><br><li><p>Resource Title: Sweetpotato Vine Length, Canopy Coverage - Raw Data .</p> <p>File Name: Sweetpotato-Vine Length, Canopy Rating -Raw Data.csv</p></li><br><li><p>Resource Title: Sweetpotato Vine Length, Canopy Coverage - Summary Table.</p> <p>File Name: Sweetpotato-Vine Length, Canopy Rating - Summary.csv</p></li><br><li><p>Resource Title: Sweetpotato Leaf Petiole Length - Summary Table.</p> <p>File Name: Sweetpotato-Leaf Petiole Length-Summary.csv</p></li><br><li><p>Resource Title: Sweetpotato Leaf Petiole Length - Raw Data.</p> <p>File Name: Sweetpotato-Leaf Petiole Length-Raw Data.csv</p></li><br><li><p>Resource Title: Sweetpotato Leaf Descriptors - Summary Table.</p> <p>File Name: Sweetpotato-Leaf-Descriptor-Summary Table.csv</p></li><br><li><p>Resource Title: Sweetpotato Leaf Descriptor - Raw Data.</p> <p>File Name: Sweetpotato-Leaf-Descriptor-Raw Data.csv</p></li><br><li><p>Resource Title: Data Dictionary.</p> <p>File Name: Data Dictionary - Phenotypic Variation in Leaf Morphology of the USDA-ARS Sweetpotato Germplasm Collection.csv</p></li></ul><p></p>

Tags: ars, data-gov, np304, sweetpotato-phenotypes
Organization: Department of Agriculture
Data from: Transcriptomic and bioinformatics analysis of the early time-course of the response to prostaglandin F2 alpha in the bovine corpus luteum

<p>RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Supporting information as Excel spreadsheets and tables.</p> <p>File Name: Web Page, url: <a href="http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070">http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070</a> </p></li></ul><p></p>

Tags: 16s-rrna-gene, ars, bos-taurus, data-gov, miseq, otus, pacbio, reproductive-biology, rumen-microbiome
Organization: Department of Agriculture
Dairy production systems for six regions of the U.S. in 1971 and 2020

<p dir="ltr">Representative dairy farms were modeled using the Integrated Farm System Model with 20 farms in each of 6 regions of the United States for the years of 1971 and 2020 to determine improvements made in reducing environmental impacts over the 50-year period. Important data and information describing these farms are documented in these tables. These data include the farm location, number of cows and heifers maintained, milk produced, feeds and nutrient contents fed, crop areas, crop yields, fertilizer and lime application rates, irrigation water applied, milking and housing facilities, manure collection, storage and application methods used, and soil characteristics. These data are published as supplementary information for the article “Fifty years of environmental progress for United States dairy farms” published in the Journal of Dairy Science.</p>

Tags: dairy-farm-characteristics, dairy-farms-in-1970, integrated-farm-system-model, regional-differences-in-dairy-farms
Organization: Department of Agriculture
Food Patterns Equivalents Database (FPED)

<p>USDA’s Food Patterns Equivalents Database (FPED) converts the foods and beverages in the Food and Nutrient Database for Dietary Studies to the 37 USDA Food Patterns components. The FPED was formerly known as the MyPyramid Equivalents Database.<br></p>
<p>The FPED serves as a unique research tool to evaluate food and beverage intakes of Americans with respect to the 2015-2020 Dietary Guidelines for Americans recommendations.<br></p>
<p>The Food Patterns are measured as cup equivalents of Fruit, Vegetables, and Dairy; ounce equivalents of Grains and Protein Foods; teaspoon equivalents of Added Sugars; gram equivalents of Solid Fats and Oils; and the number of Alcoholic Drinks.<br></p>
<p>In addition to the SAS datasets, the FPED release includes: (1) the Food Patterns Equivalents Ingredient Database (FPID) that includes the 37 USDA Food Patterns components per 100 grams of each unique ingredient used in the FNDDS; and (2) listings of gram weights for one cup equivalents of fruits, vegetables, dairy, and legumes used in the FPED.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Food Patterns Equivalents Database.</p> <p>File Name: Web Page, url: <a href="https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-overview/" target="_blank">https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-overview/</a> </p><p>Food Patterns Equivalents Database (FPED) converts the foods and beverages in the Food and Nutrient Database for Dietary Studies to the 37 USDA Food Patterns components.  The FPED serves as a unique research tool to evaluate food and beverage intakes of Americans with respect to the 2015-2020 Dietary Guidelines for Americans recommendations.</p></li></ul>

Tags: ars, data-gov, dietary-guidelines, food-patterns, fped, np107, nutrients, wweia
Organization: Department of Agriculture
Data from: Long-Distance Transportation Causes Temperature Stress in the Honey Bee, Apis mellifera (Hymenoptera: Apidae)

<p>Pollination services provided by the honey bee, <em>Apis mellifera</em> (Hymenoptera: Apidae, Linnaeus, 1758) have broad economic impacts and are necessary for production of a diversity of important crops. Hives may be transported multiple times per year to provide pollination. To test how temperature may contribute to transportation stress, temperature sensors were placed in hives in different locations and orientations on the trailer during shipping. Colony size prior to shipping significantly contributed to loss of population immediately after shipping which contributed to colony failure with smaller colonies more likely to fail and fail faster. Colony size also affects thermoregulation and temperature stress. Internal hive temperature varies significantly based on location and orientation. While colonies near the front and rear of the trailer and those oriented toward the center aisle had significantly different average internal temperatures, colony size best predicts loss of thermoregulation. Additionally, we profiled gene expression at departure, on arrival, and after a recovery period to identify transcriptional responses to transportation. Functional and enrichment analysis identified increased methylation and decreased ribosomal and protein-folding activity. Pheromone and odorant-binding transcripts were up-regulated after transportation. After recovery, transcripts associated with defense response, immune activity, and heat shock decreased, while production of antibiotic peptides increased. We conclude that hives experience considerable temperature stress possibly caused by turbulent airflow in exposed locations. Transportation stress should be considered an important component of annual colony losses which can be mitigated with improved management strategies.</p>
<p>Supplementary Tables S1–S8 are presented. Sequence reads associated with the mRNA sequencing analysis are archived at NCBI under <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA495845">BioProject PRJNA495845</a>. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website pointer to Supplementary Data.</p> <p>File Name: Web Page, url: <a href="https://academic.oup.com/ee/article/48/3/691/5423020?login=true#supplementary-data">https://academic.oup.com/ee/article/48/3/691/5423020?login=true#supplementary-data</a> </p></li></ul><p></p>

Tags: ars, data-gov, np305, pollinator-hive-transportation
Organization: Department of Agriculture
U.S. Grain Inspections: U.S. Gulf and PNW

Figure 15: U.S. Grain Inspections: U.S. Gulf and PNW

Tags: barge, carload, domestic, export, fuel-surcharge, grain, grain-inspections, international, ocean-freight, ocean-vessel, port-region, railcar-auction, railroad, shipment, shuttle-train, truck, unit-train
Organization: Department of Agriculture
U.S. Department of Agriculture (USDA) Pomological Watercolor Collection

<p>The USDA Pomological Watercolor Collection is a collection of original watercolors, lithographs, and photographs documenting fruit and nut varieties developed by growers, including USDA plant explorers, around the turn of the 20th century. Technically accurate paintings were used to create lithographs illustrating USDA bulletins, yearbooks, and other series distributed to growers and gardeners across America. </p>
<p>Coverage: 1886 to 1942, with the majority created between 1894 and 1916.</p>
<p>The collection contains 7,584 watercolor paintings, lithographs and line drawings, including 3,807 images of apples. The plant specimens illustrated originated in 29 countries and 51 states and territories in the U.S.</p>
<p>The paintings were created by approximately twenty-one artists commissioned by USDA for this purpose. Some works are not signed.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: U.S. Department of Agriculture (USDA) Pomological Watercolor Collection (web site).</p> <p>File Name: Web Page, url: <a href="https://search.nal.usda.gov/discovery/collectionDiscovery?vid=01NAL_INST:MAIN&collectionId=81279629860007426" target="_blank">https://search.nal.usda.gov/discovery/collectionDiscovery?vid=01NAL_INST:MAIN&collectionId=81279629860007426</a> </p><p>This is the web site for the  USDA Pomological Watercolor Collection  digitized at the National Agricultural Library.
Content: 7,584 watercolor paintings, lithographs and line drawings, including 3,807 images of apples.</p></li></ul>

Tags: apples, ars, bananas, data-gov, illustration, pears, pomology, prints, strawberries, watercolor
Organization: Department of Agriculture
Data from: Development and Validation of KASP Markers for Wheat Streak Mosaic Virus Resistance Gene Wsm2

<p>Wheat streak mosaic virus (WSMV) can cause significant yield loss in wheat (<em>Triticum aestivum</em> L.) in the Great Plains of North America. A recently identified WSMV resistance gene, <em>Wsm2</em>, was mapped to chromosome 3BS in germplasm line 'CO960293–2'. Effective genetic markers tightly linked to the gene will enhance the selection of WSMV-resistant lines through marker-assisted selection. We have mapped Wsm2 using a high-density map developed from the wheat 90K Infinium iSelect single-nucleotide polymorphism (SNP) array with recombinant inbred lines from the cross between CO960293–2 and susceptible cultivar 'TAM 111'. Array-based SNPs that mapped within 4 cM of <em>Wsm2</em> on chromosome 3BS were converted to Kompetitive Allele Specific Polymerase Chain Reaction (KASP) assays in this study. Six KASP SNPs were validated in two doubled haploid populations developed from crosses of 'RonL' × 'Ripper' and 'Snowmass' × 'Antero'. RonL and Snowmass possess the <em>Wsm2</em> gene from CO960293–2. Three closely linked KASP SNPs, converted from IAAV6442, BS00018764_51, and wsnp_Ra_c16264_24873670, showed high sensitivity and specificity (0.83 ≤ sensitivity ≤ 0.97, 0.89 ≤ specificity ≤ 0.99). The latter two were also validated in six F2 breeding populations. These three KASP SNPs were effective in differentiating resistant and susceptible genotypes. Comparative mapping was performed using sequences of SNPs flanking <em>Wsm2</em> and identified candidate genes and regions in <em>Brachypodium</em> and rice (<em>Oryza sativa</em> L. ssp. <em>japonica</em>). The KASP SNPs developed in this study should be useful for marker-assisted selection of <em>Wsm2</em> in wheat breeding programs, and the newly constructed map will also facilitate map based cloning of <em>Wsm2</em>. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Supplemental Table S1: A list of candidate genes identified in the region between wheat markers GENE-1856_1005 and Tdurum_contig57914_1144 2 to Brachypodium and rice orthologoues - Download PDF.</p> <p>File Name: csc2cropsci2016040234-sup-0001.pdf</p><p>Resource Description: <em>Brachypodium</em> and rice candidates for <em>Wsm2</em> orthologous to the colinear region in wheat chromosome 3B that were identified are presented.</p></li></ul><p></p>

Tags: ars, data-gov, wsm2, wsmv-resistance-gene
Organization: Department of Agriculture
ARS Microbial Genomic Sequence Database Server

<p>This database server is supported in fulfilment of the research mission of the Mycotoxin Prevention and Applied Microbiology Research Unit at the National Center for Agricultural Utilization Research in Peoria, Illinois. The linked website provides access to gene sequence databases for various groups of microorganisms, such as Streptomyces species or Aspergillus species and their relatives, that are the product of ARS research programs. The sequence databases are organized in the BIGSdb (Bacterial Isolate Genomic Sequence Database) software package developed by Keith Jolley and Martin Maiden at Oxford University.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: ARS Microbial Genomic Sequence Database Server.</p> <p>File Name: Web Page, url: <a href="http://199.133.98.43" target="_blank">http://199.133.98.43</a> </p></li></ul>

Tags: ars, data-gov, np301, online-database
Organization: Department of Agriculture