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171 results for “beef”
Fig. 3 in Fluke abundance versus host age for an invasive trematode (Dicrocoelium dendriticum) of sympatric elk and beef cattle in southeastern Alberta, Canada
Fig. 3. Age–abundance profile of infection for the invasive trematode, D. dendriticum in beef cattle sampled from 2003 to 2013 from Cypress Hills Park, Alberta. The solid line represents the negative binomial distribution model fit using maximum likelihood; the dashed lines represent the 95% confidence interval.
Consumer's purchase intention and willingness to pay (WTP) for circular beef
<p>Data correspond to 5246 consumers responsible for buying food in their homes in 5 EU countries (Germany, Netherlands, Italy, Czech Republic, and Spain). The collected information corresponds to 1) socio-demographic information, 2) t consumer’s purchase intention and willingness to pay for beef obtained by circular farming (DCE), 3) and variables related to the components of the planned behaviour theory (social norms, environmental attitudes, consumer´ perceived behavioural control and frequency of consumption, and general sustainable behaviour).</p> <p>Data was collected online using a structured survey </p> <p>Data allow us to know consumers' preferences and WTP for beef obtained by circular farming and some sustainable behaviours</p>
Data from: Development and application of a scoring system for septum injuries in beef calves with and without a nose flap
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Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Genotypes for the Spanish Autochthonous Beef Cattle Populations
<p>A total of 171 triplets (sire/dam/offspring) were collected from seven Spanish beef cattle populations (Asturiana de los Valles, n=25; Avileña-Negra Ibérica; Bruna dels Pirineus, n=25; Morucha, n=25; Pirenaica, n=24; Retinta, n=24; Rubia Gallega, n=24). The parents were chosen by minimizing the genealogical coancestry among them with the aim of capturing the existing variability within the populations.</p> <p> Individuals were genotyped using the BovineHD 777K BeadChip (Illumina Inc., USA), The SNPs present in the data file belonged to the autosomal chromosomes and those that were in repeated positions were excluded. Additional requirements were Mendelian error rates below 0.05 and call rates over 95% for both, individuals and SNPs. The quality control was made using the PLINK 1.0.7 software. Phasing was performed with BEAGLE with the "trio" option.</p> <p>Files are on PLINK format with the following name:</p> <p>Beagle.'Pop'.cromo'Cr'.pre_phase.bgl.phased.</p> <p>where Pop is from 1 (Asturiana de los Valles) to 7 (Rubia Gallega) and Cr is from 1 to 29.</p> <p> </p> <p> </p>
Ise Udon and Beef bawl at Wakakusa-Dou
伊勢うどん+牛丼 伊勢外宮参道 若草堂 Ise Udon and Beef bawl at Wakakusa-Dou along approaching road to the Gegu (exterior shrine) of Ise Jingu, Mie Pref. Ise Udon is the popular local snack food in Ise region, with thick soft Udon noodle and blackish sauce made of Tamari Soy-sauce and kelp soup stock. 📍 [34.49016, 136.70867](https://scaniver.se/L34.49016,136.70867) Source: Objaverse 1.0 / Sketchfab
Beef Cattle Muzzle/Noseprint database for individual identification
<p>This dataset contains muzzle/noseprint images for beef cattle. A total of 4923 muzzle images for 268 feedyard yearlings in the Midwest US were collected from March to July 2021, using a mirrorless digital camera (26 MP maximum resolution) and a 70-300 mm F4-5.6 focal lens. All images were taken outside of the pen did not create any contact or interference with the animals. These images covered three common US feedyard cattle breeds, including Angus, Angus x Hereford, and Continental x British cross). This database only contains the clean and cropped images showing the cattle muzzle area. </p> <p>All images are housed in individual folders in the <strong>.zip file: </strong>“BeefCattle_Muzzle_database.zip”. Each folder contains pictures from the same animal. On average, there were more than 12 images collected for each animal. </p>
Beef Processing Microbiome Furbeck et al. FASTQ
<p>FASTQ Files for the Manuscript "Investigating the Impact of Manufacturing Steps During Meat Processing of the Beef Microbiome". To use, download, and place in a folder called "fastq" within a directory containing .Rmd and .RDS from code linked described in manuscript.</p>
Data from: Genome-wide scans reveal selection signatures and cross-population variation in South African and European beef cattle breeds
<p>In genetics and evolutionary biology, the concept of selection signatures is used to describe specific patterns in the genome that are associated with the process of natural selection. These selection signatures provide insights into how evolutionary forces have shaped a population over time.In this study, a total of 96 samples were collected in several farms from four different cattle breeds, namely South African indigenous Nguni (n = 28) and Bonsmara (n = 21), Scottish Angus (n = 22), and Swedish Simmental (n = 25). Genotyped samples were subjected to quality control, and a total of 105,675 SNPs from 78 individuals remained for further analysis. Genomic signatures of positive selection within each breed were identified using the Integrated Haplotype Score (iHS) method, and cross-population comparison analysis using cross-population extended haplotype homozygosity ( XP-EHH), relative extended haplotype homozygosity (Rsb), and fixation index (Fst) methods, to assess the genetic differences between breeds. The results from the iHS method revealed selection signatures in two genomic regions for Bonsmara, six for Simmental, four for Nguni, and one for Angus cattle. Ten regions were found to be under selection, with BTA 12 being shared between Nguni and Bonsmara. Comparisons across populations using Rsb, and Fst methods performed better and revealed the most specific genomic regions that varied in selection between breeds. Gene annotation analyses linked candidate genes to several Quantitative Trait Loci (QTL). For example, in Simmental cattle's FAM110B gene was linked to carcass weight and body confirmation score. Bonsmara showed fewer candidate genes, such as CDK8 and FLT1, whereas Angus had none on BTA 18. Nguni identified potential genes such as CRB1, PLAG2GA, and VASH2, with CDK8 shared by Bonsmara and Nguni on BTA 12. Further cross-population studies revealed candidate genes associated with certain traits, genes including as PLCXD3, FAM149B1, and GRIK2 for Bonsmara versus Nguni, and SLIT2 and TSPAN9 for Simmental vs Angus. The study also emphasised gene related to meat quality, reproduction, health, illnesses, fertility, and body conformation score. Gene interaction study with the STRING database revealed a network of 63 candidate genes, demonstrating the structure of genetic connections, some biological processes. The study found that iHS performed well in population analysis with Nguni cattle, having exhibited the highest number of signatures across the genome, and significant signatures were also seen in comparisons between Nguni and Bonsmara using the Fst and Rsb methods. Furthermore, the study discovered that a bigger number of genes were connected with various traits, including sperm count and insemination per conception, sensitivity to bovine respiratory disease, and ease of calving. This genomic analysis underlined the relevance of the genetic relying which distinguishes distinct breeds. This understanding has the potential to significantly enhance selective breeding and increase desirable traits in cattle herds. This genomic analysis underlined the significance of the genetic basis for breed-specific traits. This understanding has the potential to drastically improve selective breeding and increase desirable traits in cattle herds.</p>
Data of "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?"
<p>Data for article "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?" Dataset of 64 crossbred beef heifers which were subjected to three ACTH challenges. Both experimental independent variables (animal id, horn status, replicate, time of day of the ACTH challenge, ACTH challenge number) and post-ACTH salivary cortisol concentrations (at the seven sampling timepoints and area under the curve values) are presented.</p>
The Beer, Bread, and Beef that Bind Us at 17th Century Harvard College thesis Datasets
<p>Datasets for 2022 senior honors thesis titled, "The Beer, Bread, and Beef that Bind Us at 17<sup>th</sup> Century Harvard College"</p> <p>Dataset includes results of Steward Record analysis (S1), Zooarchaeology (S2), ZooMS (S3), and Combined Analysis (S4)</p>
Investigating the Effects of Beef Consumption on Cognitive and Brain Health
ClinicalTrials.gov study NCT06690892. IPD Sharing: YES. Countries: 1. Publications: 2.
Role of the Protein Matrix in the Anabolic Response to a Ground Beef Patty as Opposed to the Impossible (Vegi-) Burger
ClinicalTrials.gov study NCT05197140. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Measuring behavior patterns and evaluating time sampling methodology to characterize brush use in weaned beef cattle
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Data from: Genome-wide scans reveal selection signatures and cross-population variation in South African and European beef cattle breeds
Open the record for dataset details and reuse information.
Data of "Grazing intensity and horn status influence activity on pasture, physiological pre-slaughter reactions and meat quality in beef heifers"
<p>Raw data of the article of "Grazing intensity and horn status influence activity on pasture, physiological pre-slaughter reactions and meat quality in beef heifers". Dataset of 64 crossbred beef heifers. Both experimental independent variables (animal id, horn status, grazing intensity, replicate) and dependent variables (related to physical activity on pasture, behaviour tests, stress physiology and meat quality) are presented.</p>
Subspecies-specific BayesR posterior probabilities of inclusion for a composite population of tropically adapted beef heifers
<p>Many of the world's agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for example, originate from two subspecies, <em>Bos taurus indicus </em>(<em>Bos indicus</em>)<em> </em>and <em>Bos taurus taurus (Bos taurus). </em>Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign SNP alleles to ancestral subspecies of origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies differing effects. Applying this method in a composite population of <em>B. indicus</em> and <em>B. taurus </em> hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies of origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies of origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies of origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of QTL association mapping in genetically diverse populations.</p>
Distribution. SW Asia from Iraq and Iran to Afghanistan, Pakistan, India, Nepal, and Bhutan; also Bangladesh, Myanmar and S China (including Hainan I). Introduced to Antigua, Barbados, Beef Island, Buck Island, Carriacou, Croatia, Cuba, Fiji, French Guiana, Goat Island, Grenada, Guadeloupe, Guyana, Hawaii, Hispaniola, Jamaica, Japan, Jost Van Dyke, La Desirade, Lavango, Mafia (Tanzania), Marie Galante, Martinique, Maui, Mauritius, Molokai, Nevis, Oahu, Puerto Rico, St. Croix, St. John, St. Kitts, St. Lucia, St. Martin, St. Thomas, St. Vincent, Surinam, Tortola, Trinidad, Vieques, and Water Island. Introduction was unsuccessful in the Dominican Republic. The Small Indian Mongoose or the Javan Mongoose is said to occur on Hong Kong since the 1980s, and to have been also introduced to some Indonesian islands (particularly Ambon). in Herpestidae
Distribution. SW Asia from Iraq and Iran to Afghanistan, Pakistan, India, Nepal, and Bhutan; also Bangladesh, Myanmar and S China (including Hainan I). Introduced to Antigua, Barbados, Beef Island, Buck Island, Carriacou, Croatia, Cuba, Fiji, French Guiana, Goat Island, Grenada, Guadeloupe, Guyana, Hawaii, Hispaniola, Jamaica, Japan, Jost Van Dyke, La Desirade, Lavango, Mafia (Tanzania), Marie Galante, Martinique, Maui, Mauritius, Molokai, Nevis, Oahu, Puerto Rico, St. Croix, St. John, St. Kitts, St. Lucia, St. Martin, St. Thomas, St. Vincent, Surinam, Tortola, Trinidad, Vieques, and Water Island. Introduction was unsuccessful in the Dominican Republic. The Small Indian Mongoose or the Javan Mongoose is said to occur on Hong Kong since the 1980s, and to have been also introduced to some Indonesian islands (particularly Ambon).
Supplementary files for manuscript "Effects of deoxynivalenol and fumonisins fed in combination on beef cattle: Immunotoxicity and gene expression "
<p>Supplementary data files for manuscript "Effects of deoxynivalenol and fumonisins fed in combination on beef cattle: Immunotoxicity and gene expression" submitted to the journal <em>Toxins</em>.</p>
The Frequency of Beef Allergy in Children With Cow Milk Allergy
ClinicalTrials.gov study NCT05943704. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.