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171 results for “beef”
Optimization of Solid-State Anaerobic Digestion of Prairie Biomass and Beef Manure
This dataset supports the evaluation and optimization of solid-state anaerobic digestion (SSAD) using prairie biomass and beef manure mixtures under varying total solids (TS) contents, particle sizes, and percolation frequency. It includes raw and processed data on biogas and methane production, volatile solids composition, carbon-to-nitrogen ratios, theoretical biochemical methane potential (BMP), energy balances, and water activity.
Data to explore circular manureshed management in beef supply chains of the United States and western Canada
Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.
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.
Grass-fed beef producers and retailers map
This data package includes two shapefiles and their associated attribute tables. The two files, GFB_producers_2021-02-18.zip and GFB_retailers_2021-02-18.zip, contain all internet-discoverable (at the time of data collection, July-August 2020; with minor edits/additions circa June 2022) grass-fed beef producers and retailers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah), compiled through an internet search. The data were initially collected in August of 2020 using publicly available information from Google search engine and Google map searches with the intention of informing members of the Sustainable Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team about existing grass-fed beef producers and retailers in the study area.
Opportunities and barriers for promoting biodiversity in Danish beef production
<p>Code and data used in the paper "Opportunities and barriers for promoting biodiversity in Danish beef production" published in iScience.</p> <p>Pre-proof available at: <span>https://doi.org/10.1016/j.isci.2024.111422</span></p>
Phenotypes of beef-on-dairy calves
<p><strong>Phenotypes from three different cattle F1 crossbreds obtained during three fattening trials. </strong></p> <p>calfID: individual calf ID</p> <p>dnaID: individual calf ID of genomic data</p> <p>Trial: fattening trial</p> <p>RatLot: treatment</p> <p>Breed: breed of sire, Angus (AAN), Limousin (LIM), Simmental (SIM); breed of dam is always Brown Swiss</p> <p>Sex: bull (B), heifer (H), steer (S)</p>
Supplementary Data: Fungal biostarter and bacterial occurrence of dry-aged beef: the sensory quality and volatile aroma compounds after 21 days of aging
<p>This dataset contains data generated during realisation of the project Tango-IV-C/0005/2019: Biostarters development for dry aged beef production, funded by National Centre for Research and Development (Poland). These data were used to prepare paper entitled "Fungal biostarter and bacterial occurrence of dry-aged beef: the sensory quality and volatile aroma compounds after 21 days of aging" by Wiesław Przybylski , Danuta Jaworska, Paweł Kresa, Grzegorz Michał Ostrowski, Magdalena Płecha, Dorota Korsak, Dorota Derewiaka, Lech Adamczak, Urszula Siekierko, Julia Pawłowska.</p>
Supplementary Materials for Genomic insight into Pediococcus acidilactici HN9, a potential probiotic strain isolated from the traditional Thai-style fermented beef Nhang
<p>Figure S1: Subsystem information of the HN9 based on the RAST annotation server, <br> Figure S2: Phylogenetic analysis of Pediococcus acidilactici HN9 and other strains at the species level, Figure S3: Sequence alignment of Enterolysin A from Pediococcus pentosaceus ATCC 25745 with Enterolysin A identified in the HN9, <br> Figure S4: Identification of bacteriocin-encoding genes from all bacterial strains in the genus Pediococcus, <br> Table S1: The information of strain used in this study, <br> Table S2: Metadata of all strains used in this study.</p>
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>
Salmonella, Shiga toxin-producing Escherichia coli O157:H7 and Listeria monocytogenes numbers during dry-aging of beef loins
<p>This dataset contains bacterial count data and loin characteristics from an experimental study assessing the survival/growth of <em>Salmonella</em>, <em>Escherichia coli</em> O157:H7 and <em>Listeria monocytogenes</em> during dry-aging of beef loins, after artificial inoculation. </p> <p>Four different csv files are provided with tabular data. A detailed description of the data is provided in the readme file.</p> <p> </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>
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>
Figure 5 in Toxoplasma gondii in beef consumed in France: regional variation in seroprevalence and parasite isolation
Figure 5. Comparison of observed values versus predicted values by the final model according to age. The observed values are in green bars, while for the predicted values the red point represents the mean prediction and the blue segment the 95% confidence interval of the prediction. The number above the blue segment is the number of observations for this particular class of age.
Figure 2 in Toxoplasma gondii in beef consumed in France: regional variation in seroprevalence and parasite isolation
Figure 2. Geographical variation of Toxoplasma gondii seroprevalence of French bovine samples according to the area of slaughtering and to age categories: (A) calves; (B) adults; (C) bovines overall (calves and adults). The numbers represent the number of samples collected for each region.
Figure 1 in Toxoplasma gondii in beef consumed in France: regional variation in seroprevalence and parasite isolation
Figure 1. (A) Map of French beef production according to the Ministry of Agriculture database. The colour gradient represents the number of cattle slaughtered in 2007. (B) The numbers represent the number of slaughterhouses per region that were included in the cross-sectional survey of Toxoplasma gondii presence in beef produced in France.
Figure 4 in Toxoplasma gondii in beef consumed in France: regional variation in seroprevalence and parasite isolation
Figure 4. Seroprevalence of Toxoplasma gondii infection in bovines of French origin (adults + calves) accordingly to the age and the titer (6; 10; 25; 50; 100; 200).
Figure 3 in Toxoplasma gondii in beef consumed in France: regional variation in seroprevalence and parasite isolation
Figure 3. Terminal titer of the modified agglutination test (MAT) for French origin samples in relation to age (A) for all samples (n = 2348) (age in years); (B) only for bovines less than 1 year (n = 601) (age in months). The number of observations at each month of age is given at the top of the corresponding bar.
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.
ScienceDex guides
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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.