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407 results for “greenhouse”
Fig. 1 in Species Composition And Structure Of The Communities Of Plant-Parasitic And Free-Living Soil Nematodes In The Greenhouses Of Botanical Gardens Of Ukraine
Fig. 1. Dendrogram of similarity of the nematode communities in the greenhouses of botanical gardens of Ukraine (amalgamation by the method of complete linkage). Explanation of the abbreviations is given in table 2. Рис. 1. Дендрограмма сходства нематодных сообществ в оранжереях ботанических садов Украины (объединение по методу полной связи). Расшифровка сокращений дана в таблице 2.
Fig. 2 in Species Composition And Structure Of The Communities Of Plant-Parasitic And Free-Living Soil Nematodes In The Greenhouses Of Botanical Gardens Of Ukraine
Fig. 2. Dendrogram of similarity of plant-parasitic nematodes' communities in the greenhouses of botanical gardens of Ukraine (amalgamation by the method of complete linkage). Explanation of the abbreviations is given in table 2.
Canadian fossil fuel production and greenhouse gas emissions compared to predictions following the 2.0°C scenario
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. McGlade and Ekins (2015) proposed quotas for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0°C by 2100. The proportion of each quota that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets already emitted within the 2010-2050 period. Emissions from five database are used in the calculations.</p>
Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy
<p>The data presented here are from the research article entitled "Greenhouse gas mitigation potential of replacing diesel fuel with wood-based bioenergy in an arctic Indigenous community: A pilot study in Fort McPherson, Canada". Based on a pilot study realized in Northern Canada and life cycle assessment, we provide a set of key parameters and operational data gathered along the biomass supply chain to build a GHG mitigation scenario and compute the quantity and timing of GHG savings in the off-grid community of Fort McPherson, NWT. Given that GHG mitigation scenarios are often assessed against a relative fossil-fuel reference scenario, we are providing two categories of data; 1) data for the reference fossil fuel scenario and; 2) data along the upstream operations of biomass supply chains. Both categories contain data related to the operational processes as well as forest growth or decomposition of unused feedstock. Although the data presented are mostly derived from the boreal forest, they could help guide other communities beyond the boreal to develop a renewable bioenergy system and assess their GHG mitigation options.</p>
Climate and Crop variables of tomato greenhouse simulation
<p>Climate and growth variables of a simulated tomato greenhouse are shown.</p> <p>Description of columns:</p> <p>----------------------------------</p> <p>'DateTime' : Time Stamp [dd-MM-yyyy hh:mm:ss]</p> <p>'Cppm' : outdoor Concentration of CO2 [ppm]</p> <p>'Wind' : wind velocity [m/s]</p> <p>'HR' : exterior relative humidity [%]</p> <p>'Rad' : exterior radiation [W/m^2]</p> <p>'Temp' : outdoor temperature [K]</p> <p>'Temp__Tcover' : temperature of cover [K]</p> <p>'Temp__Tair' : temperature of air [K]</p> <p>'Temp__Tfloor' : temperature of floor [K]</p> <p>'Temp__Tsoil' : temperature of soil [K]</p> <p>'QT__QT' : heat loss of crop by evapotranspiration [W]</p> <p>'QS__R_int' : Indoor Radiation [W/m^2]</p> <p>'Gas__C_w' : Absolute Humidity [kg/m^3]</p> <p>'Gas__C_c' : CO2 Concentration [kg/m^3]</p> <p>'Gas__rho_i' : Air density [kg/m^3]</p> <p>'Gas__C_c_ppm' : CO2 Concentration [ppm]</p> <p>'Gas__HRInt' : Indoor relative humidity [%]</p> <p>'R' : Ratio of ventilation [1/s]</p> <p>'Windows__value' : percent open window [%]</p> <p>'Screen__value' : percent open screen [%]</p> <p>'Carbon__Cbuff' : dry carbon in buffer by square meter of cultivation [kg/m^2]</p> <p>'Carbon__Cfruit' : dry carbon in fruit by square meter of cultivation [kg/m^2]</p> <p>'Carbon__Cleaf' : dry carbon in leaf by square meter of cultivation [kg/m^2]</p> <p>'Carbon__Cstem' : dry carbon in stem by square meter of cultivation [kg/m^2]</p> <p>'Tsum' : Acumulative temperature [ºC day]</p> <p>'C_Total' : total dry carbon by square meter of cultivation [kg/m^2]</p> <p>'WC' : water capacity of crop [kg/m^2]</p> <p>'LAI' : leaf area index [-]</p> <p>'CC' : CO2 flux from crop to air</p> <p>'VPD' : Vapor pressure deficit [Pa]</p> <p>'Water__WaterFlows__WaterUptake' : Water uptake of crop by square meter of cultivation [kg/(sm^2)]</p> <p><br> </p>
Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type
<p>Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"</p>
Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>In the past generation greenhouses it was enough to have one cabled measurement point in<br> the middle to provide the information to the greenhouse automation system. The system itself was<br> usually simple without opportunities to control locally heating, lights, ventilation or some other<br> activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and<br> soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In<br> this paper we proposed a multi-agent methodology for integrated management systems in<br> greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and<br> environment parameters. Each controlled process of the greenhouse environment is modeled as an<br> autonomous agent with its own inputs, its own outputs and its own interactions with the other<br> agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In<br> this way, any possible conflicting decisions of conventional environmental control methodologies<br> are resolved through negotiations between the agents so that the possible optimal integrated solution<br> is achieved. The developed system is simple, cost effective, and easily installable.</p>
Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The complete agents “environment” is shown in figure 6. Each agent receives the necessary<br> environmental or plant condition measurements. While each agent keeps its autonomy and has its<br> own “personal” goals, it interacts with the other agents through some specific communication<br> language, in the “Agent discussion area”, where the overall goal is taken into account. This<br> interaction takes place under the presence of information from the existing models and the target<br> goals. At that point, each agent has decided on its best strategy.</p>
Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>Figure 2 illustrates how the sensor<br> nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a<br> better understanding of the microclimate layers which typically exist in the greenhouse, and to<br> figure out what kind of differences occur in the climate between lower and upper flora.</p>
Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The negotiations between agents are subject to optimization based on “knowledge” that is<br> derived from complete production models, yield models or even sparse models as expressed in<br> fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models<br> provide additional information useful to the design of a successful strategy for optimal management<br> [15] (illustrated in Figure 5).</p>
Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The multi agent method creates a non-structured environment using agents, these agents, in<br> order to reach the global optimum, must be capable of performing transactions between each other,<br> achieving several “deals”, which is performed though a common vocabulary, a finite set of<br> information exchange, a finite set of possible actions, penalties, etc. The power of each agent<br> depends on the degree of contribution of its represented process to the final output of the entire<br> system. Each agent communicates with the environment and adapts to its own internal state as well<br> as to the state of the entire system. In this systems, for the realization of the multi agent architecture<br> the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].</p>
Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p> The value of the final product incorporates not only quantity issues but also<br> quality issues, which are difficult to be measured or even estimated. The environment of each agent<br> is defined by the same parameters that define the physical environment in addition to internal states<br> reported by each agent.</p>
Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The greenhouse protects the plants from the extreme weather conditions. However, if the<br> period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting<br> allows the grower to extend the growing season. It enables a year-round producing of plants or<br> makes it possible for the grower to start sowing in early spring and continue season till the first<br> frost. Plants need about 10-12 hours light to improve growth. When the plants are producing<br> flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the<br> photosynthetic activity in different wavelengths of light radiation [16].</p>
Data and code for 'Worldwide greenhouse gas emissions of green hydrogen production and transport'
<p>This data and code accompanies a Nature Energy article with the title 'Worldwide greenhouse gas emissions of green hydrogen production and transport'. In the article ‘Worldwide greenhouse gas emissions of green hydrogen’, we quantify project-specific greenhouse gas emissions for 1,025 green hydrogen projects in 2030, as well as green hydrogen transport emissions for three transport modes: pipeline, liquid hydrogen shipping and ammonia shipping. This repository entry contains the data and code used to produce the outputs presented in the article.</p>
Data from a greenhouse with and without 'Stringless Blue Lake' bean crop
<p>Data are presented from an environment managed by a microcontroller, which contains a set of sensors and actuators for its operation, data are collected between the months of June and September 2021 belonging to: a prototype greenhouse where Stringless Blue Lake beans are grown and another prototype greenhouse with the same physical characteristics where the variables are sensed without any type of crop and therefore without any control action.</p>
Figure 1 in Soybean Cyst Nematode Population Development and Its Effect on Pennycress in a Greenhouse Study
Figure 1: Influence of inoculation level and crop treatment on final SCN egg population density in the greenhouse evaluation experiment. Soybean-S was soybean genotype 'Sturdy'; PC-MN103 was pennycress genotype 'MN103'; PC-MN106 was pennycress genotype 'MN106'; and PC-MN108 was pennycress genotype 'MN108'. The genotypes were sourced from the University of Minnesota pennycress and soybean breeding programs. Error bars denote standard error. Within an inoculation level, bars with the same lowercase letter did not differ in SCN population density using Tukey–Kramer least-square means (P <0.05). SCN, soybean cyst nematode.
Dataset for "The Role of Microbial Communities in Biogeochemical Cycles and Greenhouse Gas Emissions within Tropical Soda Lakes"
<p>Here, we make available 27 raw metagenomic files in fastq.gz associated to the article: "The Role of Microbial Communities in Biogeochemical Cycles and Greenhouse Gas Emissions within Tropical Soda Lakes". This files is not paired, with forward as _1.fastq.gz and reverse as _2.fastq.gz. The abstract of manuscript is described below:<br><br></p> <p>Abstract</p> <p>Although anthropogenic activities are the primary drivers of increased greenhouse gas (GHG) emissions, it is crucial to acknowledge that wetlands are a significant source of these gases. Brazil's Pantanal, the largest tropical inland wetland, includes numerous lacustrine systems with freshwater and soda lakes. This study focuses on soda lakes to explore potential biogeochemical cycling and the contribution of biogenic GHG emissions from the water column, particularly methane. Both seasonal variations and the eutrophic status of each examined lake significantly influenced GHG emissions. Eutrophic turbid lakes (ET) showed remarkable methane emissions, likely due to cyanobacterial blooms. The decomposition of cyanobacterial cells, along with the influx of organic carbon through photosynthesis, accelerated the degradation of high organic matter content in the water column by the heterotrophic community. This process released byproducts that were subsequently metabolized in the sediment leading to methane production, more pronounced during periods of increased drought. In contrast, oligotrophic turbid lakes (OT) avoided methane emissions due to high sulfate levels in the water, though they did emit CO2 and N2O. Clear vegetated oligotrophic turbid lakes (CVO) also emitted methane, possibly from organic matter input during plant detritus decomposition, albeit at lower levels than ET. Over the years, a concerning trend has emerged in the Nhecolândia subregion of Brazil's Pantanal, where the prevalence of lakes with cyanobacterial blooms is increasing. This indicates the potential for these areas to become significant GHG emitters in the future. The study highlights the critical role of microbial communities in regulating GHG emissions in soda lakes, emphasizing their broader implications for global GHG inventories. Thus, it advocates for sustained research efforts and conservation initiatives in this environmentally critical habitat.</p> <p><strong> </strong></p>
Figure 3 in Parasitism rate of Plutella xylostella (Lepidoptera: Plutellidae) larvae in greenhouse by Tetrastichus howardi (Hymenoptera: Eulophidae) females at different densities
Figure 3. Progeny per Tetrastichus howardi (Hymenoptera: Eulophidae) female with a density of one, three, six, nine, 12, 15 or 18 females of this parasitoid per Plutella xylostella (Lepidoptera: Plutellidae) pupae.
Figure 2 in Parasitism rate of Plutella xylostella (Lepidoptera: Plutellidae) larvae in greenhouse by Tetrastichus howardi (Hymenoptera: Eulophidae) females at different densities
Figure 2. Total progeny of Tetrastichus howardi (Hymenoptera: Eulophidae) per pupa of Plutella xylostella (Lepidoptera: Plutellidae) with different densities of females of this parasitoid in semi-field conditions.
Fig. 4 in Greenhouse evaluation of neonate and adult applications of Coleomegilla maculata (Coleoptera: Coccinellidae) to control twospotted spider mite infestations
Fig. 4. Spider mite infestation levels in each treatment 20 d afer treatment application. Data represent the non-transformed mean number of mites, at specified developmental stage, per square cm of leaf surface (abaxial) ± SEM (n = 4). Different letters above bars indicate significant difference (P <0.05, Holm-Sidak multiple comparisons).
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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.