Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “Beef Cattle”
Baseline survey for beef cattle producers in the Southwest and Southern Plains
This data package includes survey questions from beef cattle producers collectively operating in at least 31 counties in at least 7 states (California, Illinois, Missouri, Nebraska, New Mexico, Oklahoma, Texas) - "at least" because there were some respondents who chose not to provide the location of their operation. Responses were collected between January 22, 2020 and May 31, 2021. Most of the surveys were administered in person at the 2020 Southwest Beef Symposium in Amarillo, TX. The survey was also placed online and an additional few responses were collected through the online survey. These data represent a sample of convenience as no formal sampling scheme was employed in soliciting responses. Survey responses are summarized in the publication, Snapshot of Rancher Perspectives on Creative Cattle Management Options (Elias et. al, 2020). The purpose of gathering these data was to learn more about the characteristics of beef cattle producers in the region and to gauge producer interest in precision livestock ranching technologies and heritage cattle – both strategies being researched by the Sustainable Southwest Beef Project to support sustainability of ranching operations in the Southwest and Southern Plains regions of the US.
Data From: Powerful detection of polygenic selection and environmental adaptation in US beef cattle
<p>GEMMA output containing summary statistics for generation proxy selection mapping (GPSM) and environmental GWAS (envGWAS) selection analyses from <br> Rowan et al. "Powerful detection of polygenic selection and environmental adaptation in US beef cattle" 2021<br> https://doi.org/10.1101/2020.03.11.988121 </p> <p>File names identify the analysis run, for example<br> "Gelbvieh_envgwas_desert_summary_stats.txt.gz"<br> Is the Gelbvieh dataset analyzed using the Desert ecoregion as the dependent variable <br> in a univariate envGWAS model. </p> <p>Files are formated according to GEMMA output.</p>
Smartcow EU project database on feed efficiency in beef cattle and analysis of natural 15N abundance and plasma urea concentration
<p>This is a database built in the frame of the Smartcow H2020 Eu project (N°730924) and gathering individual raw data for animal performances obtained in thirteen beef cattle trials conducted in France, UK and Switzerland as well as animal values for two biomakers of feed efficiency : the natural 15N abundance in animal proteins (plasma or muscle) and plasma urea concentration. </p>
Fig. 1 in Fluke abundance versus host age for an invasive trematode (Dicrocoelium dendriticum) of sympatric elk and beef cattle in southeastern Alberta, Canada
Fig. 1. Age–abundance profiles for the trematode, D. dendriticum in a population of elk sampled from 2009 to 2011 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 intervals.
Fig. 2 in Fluke abundance versus host age for an invasive trematode (Dicrocoelium dendriticum) of sympatric elk and beef cattle in southeastern Alberta, Canada
Fig. 2. Stacked frequency distribution of adult D. dendriticum in calf, juvenile, and adult elk collected between 1997 and 2011 from Cypress Hills Park, Alberta.
Fig. 4. Relationship between liver weight and host age for elk sampled from 2009 in Fluke abundance versus host age for an invasive trematode (Dicrocoelium dendriticum) of sympatric elk and beef cattle in southeastern Alberta, Canada
Fig. 4. Relationship between liver weight and host age for elk sampled from 2009 to 2011 from Cypress Hills Park, Alberta. Regression lines are maximum likelihood estimates.
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.
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>
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>
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>
Data from: Measuring behavior patterns and evaluating time sampling methodology to characterize brush use in weaned beef cattle
Open the record for dataset details and reuse information.
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.
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>
Genomic prediction with non-additive effects in beef cattle: Stability of variance component and genetic effect estimates against population size
Open the record for dataset details and reuse information.
Figure 2 in Meat quality of different beef cattle breeds fed high energy forage
Figure 2. The toughness of meat from cattle of different breeds, mg cm2
Figure 1 in Meat quality of different beef cattle breeds fed high energy forage
Figure 1. The pH of meat from cattle of different breeds
Data from: The impact of variable degrees of freedom and scale parameters in Bayesian methods for genomic prediction in Chinese Simmental beef cattle
Three conventional Bayesian approaches (BayesA, BayesB and BayesCπ) have been demonstrated to be powerful in predicting genomic merit for complex traits in livestock. A priori, these Bayesian models assume that the non-zero SNP effects (marginally) follow a t-distribution depending on two fixed hyperparameters, degrees of freedom and scale parameters. In this study, we performed genomic prediction in Chinese Simmental beef cattle and treated degrees of freedom and scale parameters as unknown with inappropriate priors. Furthermore, we compared the modified methods (BayesFA, BayesFB and BayesFCπ) with their corresponding counterparts using simulation datasets. We found that the modified methods with distribution assumed to the two hyperparameters were beneficial for improving the predictive accuracy. Our results showed that the predictive accuracies of the modified methods were slightly higher than those of their counterparts especially for traits with low heritability and a small number of QTLs. Moreover, cross-validation analysis for three traits, namely carcass weight, live weight and tenderloin weight, in 1136 Simmental beef cattle suggested that predictive accuracy of BayesFCπ noticeably outperformed BayesCπ with the highest increase (3.8%) for live weight using the cohort masking cross-validation.
Relate-estimated coalescence rates and allele ages for European beef cattle
<h1>Overview</h1> <p>Coalescence rates and allele ages calculated for five beef cattle breeds (Charolais, Simmental, Limousin, Hereford, Angus) using Relate.</p> <p>We estimated the joint genealogy of 684 individuals, including both <em>Bos taurus</em> and <em>Bos indicus</em>, then extracted the embedded genealogy for the five cattle breeds and re-estimated the population size history and branch lengths.</p> <p>We extracted the embedded genealogy for each of the five beef cattle breeds, jointly estimated the population size history, and re-estimated the branch lengths.</p> <p>Please email bft990914@163.com for any queries.</p> <h1>Coalescence Rates and Allele Ages</h1> <p>The *.coal files record coalescence rates for each of the five cattle breeds.</p> <p>The gzipped files allele_ages_*.gz record allele ages for each of the five cattle breeds.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.