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1,648 results for “farm”
Kelmarsh wind farm data
<p>This dataset contains:</p> <ul> <li>A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. <a href="https://www.google.com/intl/en-GB/earth/versions/#earth-pro">Google Earth</a>)</li> <li>Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)</li> <li>10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to end 2024, which was extracted from Cubico's secondary SCADA system (Greenbyte). Note not all signals are available for the entire period</li> <li>Data mappings from primary SCADA to csv signal names</li> <li>Site substation/PMU meter data where available for the same period</li> <li>Site fiscal/grid meter data where available for the same period</li> </ul> <p>The dataset has been released by <a href="https://www.cubicoinvest.com/">Cubico Sustainable Investments Ltd</a> under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a> open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data.</p> <p>Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.</p> <p>If you have any questions or want to discuss open data and this or other initiatives, please either:</p> <ol> <li>Contact me on <a href="https://www.linkedin.com/in/charlie-plumley-8b8b613b">LinkedIn</a>, and I will endeavour to help</li> <li>Or in the <a href="https://www.wedowind.ch/">WeDoWind</a> platform in the ODE space</li> </ol> <p>I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager and team for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.</p> <p>You can also access data from Penmanshiel wind farm <a href="https://doi.org/10.5281/zenodo.5946807">here</a>.</p>
Penmanshiel wind farm data
<p>This dataset contains:</p> <ul> <li>A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. <a href="https://www.google.com/intl/en-GB/earth/versions/#earth-pro">Google Earth</a>)</li> <li>Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)</li> <li>10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to end of 2024, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03</li> <li>Data mappings from primary SCADA to csv signal names</li> <li>Site substation/PMU meter data where available for the same period</li> <li>Site fiscal/grid meter data where available for the same period</li> </ul> <p>The dataset has been released by <a href="https://www.cubicoinvest.com/">Cubico Sustainable Investments Ltd</a> under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a> open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data.</p> <p>Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.</p> <p>If you have any questions or want to discuss open data and this or other initiatives, please either:</p> <ol> <li>Contact me on <a href="https://www.linkedin.com/in/charlie-plumley-8b8b613b">LinkedIn</a>, and I will endeavour to help</li> <li>Or in the <a href="https://www.wedowind.ch/">WeDoWind</a> platform in the ODE space</li> </ol> <p>I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager and team for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.</p> <p>You can also access data from Kelmarsh wind farm <a href="https://doi.org/10.5281/zenodo.5841833">here</a>.</p>
Landscape structure and farming management interacts to modulate pollination supply and crop production in blueberries
<p><span>Pollination services are affected by landscape context, farming management, and pollinator community structure, all of which impact flower visitation rates, pollen deposition and final production. We studied these processes in Argentina for Highbush Blueberry crops which depend on pollinators to produce marketable yields. </span></p> <p><span>We studied how land cover and honeybee stocking influence the abundance of wild and managed pollinators in blueberry crops, using structural equation modeling to disentangle the cascading effects through which pollinators contribute to blueberry fruit number, size, nutritional content and overall yield. </span></p> <p><span>All pollinator functional groups responded to landscape changes at a spatial scale under 1000 m, and the significance or direction of the effects were modulated by the field-level deployment of honeybee hives. </span></p> <p><span>Fruit diameter increased with pollen deposited, but decreased with honeybee abundance, which, had indirect effects on fruit acidity and sugar content. Honeybees had a positive effect on the number of fruit produced by the plants and also benefited the overall yield (kg plant</span><sup><span>-1</span></sup><span>) through independent effects on both the quality and quantity components of fruit production.</span></p> <p><span><em>Synthesis and applications:</em> </span></p> <p><span>Deployment of beehives in blueberry fields can buffer, but not compensate for the negative effects on honeybee abundance produced by surrounding large scale none-flowering crops. Such compensation would require high-quality beehives by monitoring their health and strength.</span> <span>The </span><span>contribution of honeybees to crop production is not equal across production metrics. That is, higher abundance of honeybees increases the number of berries produced, but at the cost of smaller and more acidic fruits, potentially reducing market value. Growers must consider this trade-off between fruit quantity and quality when actively managing honeybee abundance. </span></p>
A dataset of seabird collision and displacement vulnerability factors relatively to marine wind farms in Portugal
<p>The implementation of marine wind farms has grown considerably along northern European's northern Atlantic coasts (e.g. Baltic and North Sea) and a boom in these infrastructures is expected to take place along Europe's entire Atlantic and Mediterranean coasts. Accordingly, the Portuguese government has recently proposed priority sites for the construction of wind farms along the mainland coast. We used sensitivity mapping (Garthe & Hüppop, 2004) to assess which areas along the Portuguese coast are most sensitive for seabirds and to what extent the proposed sites for wind farm construction overlap with these areas.</p><p>This dataset contains the base data to estimate a seabird Species Sensitivity Index (SSI) (following Bradbury et al., 2014, Certain et al., 2015), including scores for 11 species-specific ecological and behavioural factors related with seabird species' (i) vulnerability to collision with wind farms (4 factors), (ii) vulnerability to displacement due to disturbance by wind farms and associated maintenance (3 factors), and (iii) conservation status (4 factors). </p><p>We reviewed the literature to mine and compile data on these factors for 34 seabird species that regularly occur along the Portuguese mainland coast. We updated factor scores, particularly for those factors that have been studied in greater detail in recent years using tracking technologies (Clairbaux & Jessopp, 2021). However, in many cases empirical data were unavailable and we used the scores presented in previous sensitivity mapping studies (Garthe & Hüppop, 2004; Bradbury et al., 2014; Certain et al., 2015; Wade et al., 2016; Serratosa & Allinson, 2022).</p>
Farm Calendar Data
<p>This dataset contains recordings on the applied farming practices for one cultivation period. All sensitive information that could allowing to deduce the location and the type of the cultivation have been pseudonymized. The datasets contain in the original chronological order information about: growth stages, irrigations, pesticides, harvest, fertilization and land management. <br>The dataset is expressed with the use of two different data models: <br>a) The <a href="https://zenodo.org/api/records/10158603/draft/files/ploutos_farm_calendar_data_2022.json/content">ploutos_farm_calendar_data_2022.json</a> is modeled with the use of custom data types and expressed in json format<br>b) The <a href="https://zenodo.org/api/records/10158603/draft/files/Farm%20calendar%20pcsm.ttl/content">Farm calendar pcsm.ttl is</a> modelled with the use of Ploutos Common Semantic Model (PCSM) which is an ontology based on the modular reuse of standardised data models. <br>The scope of these two datasets is to allow further experimentation with standardised vs custom modeled datasets.</p>
From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector
<p>This work was supported by the European Union's Horizon 2020 project Nutri2Cycle (Grant agreement No. 773682) </p><p>These datasets are supplementary information to the manuscript titled "<a href="https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12530">From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector</a>"</p>
Videos of farm machinery from above, nadir drone videos.
<p>Used to train AI to detect machinery fleet and other moving objects around robotized tractor. DJI Phantom 4 RTK, about 70 meters altitude. Sample videos.</p>
Empowering Coffee Farming Using Counterfactual Recommendation based RNN-IoT Integrated Soil Fertility Control System
Open the record for dataset details and reuse information.
Figs 2, 3 in Seasonal variations in ixodid tick populations on a commercial game farm in the Limpopo Province, South Africa
Figs 2, 3. Numbers of Rhipicephalus (Boophilus) decoloratus collected in wetter and drier months (2), and in warmer and cooler months (3).
Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
<p><strong>Figures from the publication <em>Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.</em></strong></p> <p> </p> <p>*.fig files can be opened in <code>Matlab</code></p> <p>*.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code></p>
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop.
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980].
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3).
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972).
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977].
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC).
ET0002 - CWRU Farm - CHU 7.850 MHz from April 8, 2024 Solar Eclipse
<p>See <a href="https://doi.org/10.5281/zenodo.13293306">10.5281/zenodo.13293306</a> for system documentation. <br>See <a href="https://doi.org/10.5281/zenodo.14004715">10.5281/zenodo.14004715</a> for python code to parse these files. </p> <p>Location: Chagrin Falls, OH. </p> <p>Lat/lon: </p> <table> <tbody> <tr> <td>41.49308889</td> <td>-81.42378287</td> </tr> </tbody> </table>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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