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415 results for “cow”
Genetic characterization of 24 Angus × Hereford cows from the Jornada Experimental Range, Las Cruces, NM, USA
The southwestern US is increasingly facing dry and variable climate conditions, requiring beef operations to adopt novel strategies to meet these emerging challenges. One potential approach is the use of locally adapted cattle breeds or biotypes. A distinctive Angus x Hereford (AH) research herd at the USDA Agricultural Research Service Jornada Experimental Range provides an opportunity to explore the genetic makeup of a desert-adapted cattle herd bred for over four decades under the extreme and harsh conditions of New Mexico’s Chihuahuan Desert. The objective of this study was to analyze the population structure, genetic diversity and signatures of selection of the AH research herd (n = 24). All cows were genotyped using a 64K SNP chip. Principal component and admixture analyses confirmed the mixed genetic background of the AH cows, predominantly of Angus ancestry. The heterozygosity level, effective population size, and inbreeding coefficient indicated that the AH cows maintain moderate genetic diversity and inbreeding levels. Genomic regions under positive selection revealed genes and Quantitative Trait Loci associated with beneficial carcass traits, milk composition, fertility, body homeostasis, antioxidant activity, immune response, and terrain utilization. This research herd could potentially serve as a valuable genetic resource for improving the adaptability and productivity of commercial beef cattle in harsh semi-arid and arid environments, balancing hardiness and performance.
Dataset of plasma non-esterified fatty acid concentrations response of a suckling cow exposed to a feed restriction
<p>The detaset describes the response of a suckler cow in terms of plasma non-esterified fatty-acids (NEFA) concentrations, that was exposed to a feed restriction that consisted in the reduction of net energy requirements by 50%. The dataset has two columns, one for time (t) in days (d) and another column for plasma NEFA concentrations (g·L<sup>-1</sup>). The feed restriction started at t = 1 d and lasted untill t = 4 d. Negative values for t represent the pre-challenge period. </p>
Dataset of milk yield response of a suckling cow exposed to feed restrictions
<p>The detaset describes the response of a suckler cow in terms of milk yield (MY, kg·d<sup>-1</sup>), which was exposed to two feed restrictions (FR) consisting in the reduction of net energy requirements by 50%. The dataset has three columns, the first one is time (t) in days (d), the second one is also time (t2, d) and the third column is MY. The first FR started at t = 0 d and lasted untill t = 3 d, the second FR started at t = 21 d until t = 31 d. The second column for time is obtained from the first column by omitting data points corresponding to restriction and recovery periods. Negative values for t and t2 represent the pre-challenge period. </p>
DPS Transmitter integrated in COW QKD system
<p>This dataset comprises characterization measurements from the DPS Transmitter used as an integrated QKD transmitter in the UNIQORN Project. The files include initial characterization data of the chip including SOA laser performance, stability, laser spectrum, wavelength tuning, RF response and initial visibility for the QKD application. The performance of the transmitter in a COW QKD system was also investigated.</p>
Data from: Cubicle design and dairy cow rising and lying down behaviours in free stalls with insufficient lunge space
<p>Original data from: "Cubicle design and dairy cow rising and lying down behaviours in free-stalls with insufficient lunge space" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.animal.2024.101314" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.animal.2024.101314</span></span></a></p>
Rumen_Microbial_Genomes_from_Cow_Fed_with_Red_Seaweed_Additives
<p>This dataset represents the 3180 non-redundant species-level rumen microbial genomes established through the integration of MAGs recovered from the rumen of cows that were fed with red seaweed or not, publicly available rumen MAGs and isolate genomes from Hungate collection. In order to match those old genome names with the new genome names used in the manuscript, please refer to supplementary table 3.</p>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
FIG. 5. — The serpent bova milking a cow. Illumination from manuscript MSL 11, fol. 137v in The comparative milk-suckling reptile
FIG. 5. — The serpent bova milking a cow. Illumination from manuscript MSL 11, fol. 137v, Archive of the Prague Castle, Library of the Metropolitan Chapter by St. Vitus.
FIG. 7. — A medallion with a snake milking a cow from a in The comparative milk-suckling reptile
FIG. 7. — A medallion with a snake milking a cow from a tapestry preserved in the Musée Royal des Beaux-Arts, Brussels. From Crick-Kuntziger (1948: 73).
Assessment of dairy cow welfare in small-scale farming systems dataset
<p>This database was created as preparatory work for the Scientific Opinion on the assessment of dairy cow welfare in small-scale farming systems (EFSA, 2015) to collect data for the description and the categorisation of European Small-Scale Dairy Farms (SSDF) based on size, farming system and husbandry practices and (ii) to analyse the feasibility in SSDF of animal-based measures usually used for intensive farming. The Scientific Opinion was necessary to address specific expectations of consumers on locally produced food and acceptable animal welfare conditions in the context of the EU Strategy for the protection and welfare of animals 2012-2015.</p> <p>The on-farm survey was run to collect data for welfare assessment covering Austria, France, Italy and Spain. A total of 124 farms with up to 75 cows were selected based on three criteria reflecting use of local resources or enrolment in a certification scheme: (1) the type of enterprise (ownership and workers), (2) the use of inputs in the production process, including the use of local feed and local breeds, and (3) the production type (certification schemes). From 124 dairy farms visited 119 were considered as SSDF. Among the 119 farms included in the survey as non-conventional, some of them had a very small herd size (44 had less than 25 cows and one had only 10 cows) and some of them had more animals (19 farms had between 51 and 75 dairy cows).</p> <p>The database includes 53 continuous and categorical farm descriptor variables, 23 continuous and categorical risk-factor variables and 47 animal-based measures in small-scale farms. The final data model used was based on data collection at farm/herd level, pen level and animal level.</p>
Dairy cow dry matter intake for multiverse analysis and Bradley-Terry modeling
<p>The palatability of feed for dairy cows is an important consideration but is difficult to measure, particularly when considering more than two feeds. We outline how a combination of multiverse analysis and Bradley-Terry modeling, two methodological tools that have rarely been applied in dairy science, can be adapted to address this problem. Specifically, we propose to apply multiverse analysis as a way to consider a range of thresholds for how much of a mixed grass-legume (MGL) silage had to be consumed (as a percent of the total DMI) to be designated as preferred. Each threshold gives rise to a separate dataset and a corresponding fitted Bradley-Terry model. Bradley-Terry models attribute to each feed what is commonly referred to as an "ability" in the context of sports or other competitions but can be interpreted as palatability when applied to feeds. This combined approach is a way of estimating palatabilities that appropriately reflect the degree of preference cows express through their feeding behavior. It has the advantages of being transparent and relatively easy to implement. A possible disadvantage is that this method is limited to a paired comparison approach and has difficulties with main-effects statistical inference. We demonstrate the use of this methodology on an example dataset comparing MGL silages under different ensiling conditions and exposed to oxygen for different durations.</p>
Cows Frontal Face Dataset
<p>The dataset is one of the biggest dataset of cows having 459 classes in total. It can be utilized for various purposes related to cow identification. Our work and dataset is limited to Muzzle Detection. The recognition part will be exploring in future using the noise removal and deep learning techniques in the next versions utilizing the same dataset. A limited number of data sets are accessible for analyzing cow muzzle, with only two publicly available datasets from Australia and United States. Additionally, there is no existing method for identifying cows using AI and computer vision, which means that there are no real-time photos available for training a model. The majority of the dataset used in our research has been collected by our team. We have collected the world’s largest dataset in number of subjects in Pakistan.</p>
Japanese Black Beef Cow Behavior Classification Dataset
<p>Licensed under:<br> Attribution-NonCommercial-NoDerivatives 4.0 International<br> <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</a></p> <p><strong>Japanese Black Beef Cow Behavior Classification Dataset</strong></p> <p>This dataset contains tri-axial accelerometer sensor data with thirteen different labeled cow behaviors. This data was gathered with a 16bit +/- 2g Kionix KX122-1037 accelerometer attached to the neck of six different Japanese Black Beef Cows (`cow1.csv`-`cow6.csv`) at a cow farm of Shinshu University in Nagano, Japan on the 12th of June, 2020.</p> <p>The data gathering took place over the course of one day in which the cows were allowed to roam freely in two different areas, namely, a grass field and farm pens, while being filmed with Sony FDR-X3000 4K video cameras.</p> <p>The timestamps of the video and accelerometer data were matched while human observers which included behavior experts and non-experts labeled the data from the video footage. The labeling and data gathering took a total of 69 person-hours.</p> <p>567 minutes of unlabeled data were parsed into 197 minutes of high-quality labeled data comprising thirteen behaviors by means of majority voting with three annotators. The time per behavior in number of samples (@25Hz) and their respective descriptions are shown in the following table:</p> <table> <thead> <tr> <th scope="col"> </th> <th scope="col">Cow 1</th> <th scope="col">Cow 2</th> <th scope="col">Cow 3</th> <th scope="col">Cow 4</th> <th scope="col">Cow 5</th> <th scope="col">Cow 6</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>RES</td> <td>35814</td> <td>47059</td> <td>20501</td> <td>15735</td> <td>11025</td> <td>19996</td> <td>Resting in standing position</td> </tr> <tr> <td>RUS</td> <td>1620</td> <td>25930</td> <td>11156</td> <td>14523</td> <td>0</td> <td>0</td> <td>Ruminating in standing position</td> </tr> <tr> <td>MOV</td> <td>6376</td> <td>8437</td> <td>7532</td> <td>17248</td> <td>4846</td> <td>5760</td> <td>Moving</td> </tr> <tr> <td>GRZ</td> <td>2416</td> <td>2199</td> <td>0</td> <td>2707</td> <td>2442</td> <td>7849</td> <td>Grazing</td> </tr> <tr> <td>SLT</td> <td>204</td> <td>0</td> <td>10654</td> <td>0</td> <td>0</td> <td>0</td> <td>Salt licking</td> </tr> <tr> <td>FES</td> <td>6809</td> <td>0</td> <td>0</td> <td>0</td> <td>1125</td> <td>0</td> <td>Feeding in stanchion</td> </tr> <tr> <td>DRN</td> <td>1176</td> <td>0</td> <td>1300</td> <td>0</td> <td>0</td> <td>0</td> <td>Drinking</td> </tr> <tr> <td>LCK</td> <td>0</td> <td>0</td> <td>649</td> <td>297</td> <td>0</td> <td>356</td> <td>Licking</td> </tr> <tr> <td>REL</td> <td>0 </td> <td>360</td> <td>0</td> <td>404</td> <td>0</td> <td>0</td> <td>Resting in lying position</td> </tr> <tr> <td>URI</td> <td>239</td> <td>0</td> <td>383</td> <td>0</td> <td>0</td> <td>0</td> <td>Urinating</td> </tr> <tr> <td>ATT</td> <td>57</td> <td>50</td> <td>0</td> <td>62</td> <td>0</td> <td>197</td> <td>Attacking</td> </tr> <tr> <td>ESC</td> <td>0</td> <td>0</td> <td>0</td> <td>128</td> <td>0</td> <td>0</td> <td>Escaping</td> </tr> <tr> <td>BMN</td> <td>0</td> <td>54</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>Being mounted</td> </tr> <tr> <td>ETC</td> <td>105917</td> <td>103084</td> <td>129297</td> <td>62064</td> <td>53922</td> <td>100571</td> <td>Other behaviors</td> </tr> <tr> <td>BLN</td> <td>151249</td> <td>82599</td> <td>88431</td> <td>111744</td> <td>61544</td> <td>45128</td> <td>Data without video, no label</td> </tr> <tr> <td>Sum</td> <td>311876</td> <td>269772</td> <td>269903</td> <td>224912</td> <td>134904</td> <td>179857</td> <td> </td> </tr> </tbody> </table> <p>Accelerometer sampling rate was set to 25Hz.</p> <p>The data is split into six .csv files which represents each of the 6 cows above. The columns of these files are defined as follows:</p> <table> <thead> <tr> <th scope="col">TimeStamp_UNIX [-]</th> <th scope="col">TimeStamp_JST [-]</th> <th scope="col">AccX [g]</th> <th scope="col">AccY [g]</th> <th scope="col">AccZ [g]</th> <th scope="col">Label [-]</th> </tr> </thead> <tbody> <tr> <td>GPS timestamp in UNIX</td> <td>GPS timestamp in JST</td> <td>X-axis acceleration</td> <td>Y-axis acceleration</td> <td>z-axis acceleration</td> <td>labeled behavior</td> </tr> </tbody> </table> <p>The gathering of this data with these cows was reviewed and approved by the Institutional Animal Care and Use Committee of Shinshu University.</p> <p><strong>Version History</strong></p> <p>v1.0.0: Release on 24th of September, 2021. First version.</p> <p>v2.0.0: This version. UNIX and Japan Standard Time (JST) time stamps are added for each .csv file of cow1-6. Added explanations of behaviors for ETC and BLN. More information on publications that use this dataset, data logger software that has been developed for this project.</p> <p><strong>Data logger open source software</strong></p> <p>Software developed for the data logger that was used to gather this dataset, Sony's IoT development board SPRESENSE, CXD5602PWBMAIN1. The function of this data logger is to write inertia sensor data along with timestamps. Timestamp data is corrected with GPS signal. Available in Arduino development environment.</p> <p><a href="https://zenodo.org/record/5848608#.YeFF9NHP3Z8">https://zenodo.org/record/5848608#.YeFF9NHP3Z8</a></p> <p><strong>Publications using this dataset</strong></p> <p><a href="https://ieeexplore.ieee.org/abstract/document/9566833">[1] Li, Chao, et al. "Data Augmentation for Inertial Sensor Data in CNNs for Cattle Behavior Classification." IEEE Sensors Letters 5.11 (2021): 1-4.</a></p> <p><a href="https://ieeexplore.ieee.org/abstract/document/9401342">[2] Bartels, Jim, et al. "A 216 microW, 87% Accurate Cow Behavior Classifying Decision Tree on FPGA With Interpolated Arctan2." 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021.</a></p>
Supporting dataset for: "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering"
<p>This dataset was used in the meta-analysis and hierarchical clustering published in "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering" in the Journal of Dairy Science. We searched Web of Science and Google Scholar databases through March 2020 with the terms “plasma EAA,” “milk urea” or “blood urea,” and “dairy” or lactating dairy”. To be included in our study, the papers must have met the following selection criteria: (1) been published in English in a peer-reviewed journal; (2) reported dietary ingredients on a DM basis and at minimum dietary CP concentration; (3) used treatments based on diet changes (e.g., no infusion trials were included); (4) reported DMI, lactation performance, and milk components yield; (5) reported all individual [EAA]p (excluding Trp); and (6) reported blood urea-N or plasma urea-N. Infusion studies were excluded to avoid possible effects of method of EAA supply (e.g., infusion vs. feeding) and to narrow the scope of application. The final dataset included 22 studies and 96 dietary treatments. For a more complete description of the methods, please refer to the published paper. </p>
METHICILLIN-RESISTANT STAPHYLOCOCCUS AUREUS AND ITS DETERMINANTS OF RAW COW MILK CONTAMINATION IN SELECTED GAMO ZONE DISTRICT OF SOUTHERN ETHIOPIA
<p>We conducted this study, titled "Methicillin-Resistant Staphylococcus aurous and its Determinants of Raw Cow Milk Contamination in a Selected Gamo Zone District of Southern Ethiopia," in order to disseminate our findings to the scientific community. The research has its own strengths, such as the work we did on milk quality and potential milk borne pathogens, as well as the determinants of contamination at the selling point, which is only on fresh milk after it has been checked for freshness. As a result, the authors initiated this research based on practical challenges encountered while providing professional support to various producers in the milk selling point. Because of the irrational use of antibiotics among human and animal health in the study area, this research aimed to estimate the microbial load, prevalence of methicillin resistant <em>S. aurous</em> (MRSA), and determinants of raw cow milk contamination at selling points. This could be a source of drug resistance development in Ethiopia and in the study area in particular. Furthermore, due to resource constraints, the study focuses on bacteriological analysis, risk factor association, and drug sensitivity testing, which can serve as baseline data for future characterization and intervention. Hoping that we authors are interested to publish our research output in your journal with your eminent support for the article gets published.</p>
Dataset: Amplify Cash Flow Dividend Leaders ETF (COWS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Pacer US Large Cap Cash Cows Growth Leaders ETF (COWG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Pacer US Small Cap Cash Cows Growth Leaders ETF (CAFG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Pacer Cash Cows Fund of Funds ETF (HERD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig.4 in A Review Of Latvian Blue (Lz) Cows From The List Of Animal Genetic Resources In Latvia
Fig.4. Association analysis between genotypes of A and B alleles of alpha – lactalbumin gene and protein content (%) at the first three lactations. Bar with a straight edge points to signs of an average group size with a standard deviation; * - refers to the association with the performance at a particular lactation; F – index of ANOVA; p F – statistical signification; η – index of correlation analyse.
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.