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1,648 results for “farm”
Inland-Offshore Wind Farm Dataset1
<p>Data included in these two datasets are 10-minute data generated from six wind turbines and three met masts and are arranged in six files, each of which is associated with a turbine. The six turbines are named WT1 through WT6, respectively. The layout of the turbines and the met masts is shown in Fig. 5.6 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book. On the offshore wind farm, all seven environmental variables as mentioned above are available, namely x =(V, D, rho, H, I, Sa, Sb), whereas on the inland wind farm, the humidity measurements are not available, nor is the above-hub wind shear, meaning that x =(V, D, rho, I, Sb). Variables in x were measured by sensors on the met mast, whereas y was measured at the wind turbines. Each met mast has two wind turbines associated with it, meaning that the x's measured at a met mast are paired with the y's of two associated turbines. For WT1 and WT2, the data were collected from July 30, 2010 through July 31, 2011 and for WT3 and WT4, the data were collected from April 29, 2010 through April 30, 2011. For WT5 and WT6, the data were collected from January 1, 2009 through December 31, 2009.</p> <p>Meaning of variables; V: wind speed; D: wind direction; rho: air density; H: humidity; I: turbulence intensity; S: vertical wind shear; Sa: above-hub height wind shear, Sb: below-hub height wind shear.</p>
A nocturnal jet flows over a wind farm in complex terrain
<p>A nocturnal jet simulated with the Weather Research and Forecasting model flows over topography where a wind farm is located. It produces downstream flow acceleration that enhances the performances of turbines in the back rows. This simulation is related to the case 3 (transect 1) of the paper <em>Nocturnal jets over wind farms in complex terrain </em>that will be submitted to <em>Applied Energy.</em></p>
Figure 2 from: Abdullah N (2016) Vertical-Horizontal Regulated Soilless Farming via Advanced Hydroponics for Domestic Food Production in Doha, Qatar. Research Ideas and Outcomes 2: e8134. https://doi.org/10.3897/rio.2.e8134
Figure 2 - Concept of upscaling the production of crops for large-scale food production. The red box indicates 1 floor. By upscaling upwards, the same amount of land area occupied by a single growhouse will enable an exponential increase in yields.
Figure 1 from: Abdullah N (2016) Vertical-Horizontal Regulated Soilless Farming via Advanced Hydroponics for Domestic Food Production in Doha, Qatar. Research Ideas and Outcomes 2: e8134. https://doi.org/10.3897/rio.2.e8134
Figure 1 - 24-24 Custom Vertical-Horizontal Hybrid NFT System - A concept of the proposed growing system which occupies as little as 9.2 m2, but could yield as much as 176 plants.
Fauna species collected in farmed and wild kelp in Norway April 2019
<p>This is the species list, with abundance of fauna, from the kelp farm, the wild kelp forests, and the water column. Collected as part of the KELPPRO project. KELPPRO was funded (2017-2021) by the Research Council of Norway (grant number 267536). </p>
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p>
FIG. 14 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 14. — Left mandible of Parapapio broomi Jones, 1937, specimen BPB 3: A, buccal view; B, lingual view; C, superior view; D, occlusal view of p/4-m/3. Scale bar: 10 mm.
FIG. 15 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 15. — Distal part of the right mandible of Parapapio broomi Jones, 1937, specimen BPB 4: A, buccal view; B, lingual view; C, superior view. Scale bar: 10 mm.
Fig. 2 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 2. IoT node prototype.
Fig. 6. T3 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 6. T3 test results.
Fig. 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 4. Organization diagram of tests T1 (left above), T2 (left below) and T3 (right).
Fig. 7. Packets received during testing using 4 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 7. Packets received during testing using 4 nodes.
Fig. 1 in A mesh network case study for digital audio signal processing in Smart Farm
Fig. 1. Mesh network topology (left) and star topology (right).
Data from: Can variation in standard metabolic rate explain context-dependent performance of farmed salmon offspring?
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Data from: Identification of quantitative genetic components of fitness variation in farmed, hybrid and native salmon in the wild
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Data from: Increased novel single nucleotide polymorphisms in weedy rice populations associated with the change of farming styles: Implications in adaptive mutation and evolution
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Data from: Bird services and disservices to strawberry farming in Californian agricultural landscapes
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Data from: Invest more and die faster: the life history of a parasite on intensive farms
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Data from: The assembly of ant-farmed gardens: mutualism specialization following host broadening
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Data from: Organic farming promotes biotic resistance to food-borne human pathogens
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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)
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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.