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652 results for “allocation”
Data from: Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks
<p>Many genes and signaling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signaling networks that must faithfully complete a developmental program while also defending against parasites, and trait signaling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.</p>
Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages
<p>The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help.</p> <p>We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society).</p> <ul> <li><strong>Dataset S1. </strong>XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li><strong>Dataset S2.</strong> XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>
Dataset: FlexShares Real Assets Allocation Index Fund (ASET) 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: FlexShares Real Assets Allocation Index Fund (ASET) 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: Relative Sentiment Tactical Allocation ETF (MOOD) 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 Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 4. Variation in feeding activity (standard residuals, regression between LS x WS), visceral fat storage, body condition (standard residuals, regression between LS x TW) and reproductive effort (GSR) of Hemiodus unimaculatus, before (Pre-1 and 2) and after (Post-1 and 2) the construction of Lajeado Dam.
Fig. 3 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 3. Variation in feeding activity (standard residuals, regression between LS x WS), visceral fat storage, body condition (standard residuals, regression between LS x TW) and reproductive effort (GSR) of Hemiodus microlepis, before (Pre-1 and 2) and after (Post-1 and 2) the construction of Lajeado Dam.
Fig. 2 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 2. Variation in feeding activity (standard residuals, regression between LS x WS), visceral fat storage, body condition (standard residuals, regression between LS x TW) and reproductive effort (GSR) of Argonectes robertsi, before (Pre-1 and 2) and after (Post-1 and 2) the construction of Lajeado Dam.
Fig. 1 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 1. Relative abundance of A. robertsi, H. microlepis, and H. unimaculatus in Pre- and Post-impoundment periods, in sites distributed along the reservoir (combined within zones: Fluvial, Transition, and Lacustrine).
Fig. 5 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 5. Relative mean GSI vs. relative mean HSI of color pattern variants of Cichla temensis. Points for GSI represent the mean value for each CPV grade as compared to the range encountered. Points for HSI represent the mean value for each CPV grade compared to the range encountered.
Fig. 3 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 3. Biplot of the uniform components in each direction (UniX and UniY) of morphometrical differences in 80 specimens of Cichla temensis in 4 color variation patterns (CPV) as measured by 9 Thin Plate Spline (TPS) distortion variables (V1-V9). Colored numbers indicate the CPV grade of individuals. The total spread of scores among individuals of each CPV are indicated by an envelope (solid line polygon) calculated as the minimum convex hull for that group. Position in the plot relative to other individuals indicates the degree of similarity in morph. Vectors point in the direction of gradient change for that TPS variable and the magnitude indicates the strength of the gradient. Angles between vectors indicate the TPS interset correlations.
Figure 3 in Size-dependent sex allocation in Solanum lycocarpum St. Hil. (Solanaceae)
Figure 3. Proportion of male and hermaphrodite flowers as a function of plant size (small group versus large group).
Figure 2 in Size-dependent sex allocation in Solanum lycocarpum St. Hil. (Solanaceae)
Figure 2. Number of flowers per plant as a function of the plant size. The number of flowers in smaller group (minimum = 0.000; maximum = 36.00; mean = 5; median = 10.6; standard error = 2.54) versus in larger group (minimum = 3.00; maximum = 54.00; mean = 29.00; median = 30.00; standard error = 3.51).
Figure 4 in Size-dependent sex allocation in Solanum lycocarpum St. Hil. (Solanaceae)
Figure 4. Interaction between the floral attributes according to the flowers gender. The circles represent hermaphrodite flowers and the squares represent male flowers. (A) Corolla diameter (cm) versus anther size (cm); (B) Flower Biomass versus anther size (cm).
Figure 1 in Size-dependent sex allocation in Solanum lycocarpum St. Hil. (Solanaceae)
Figure 1. Flowers of Solanum lycocarpum. (A) Hermaphrodite flower; (B) Male flowers. The scale bars units were 3 cm for each picture.
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 3. Sharing and merging effect on successful allocation
<p>Fig (3) shows the effect of merging and sharing resources by auctioneer in term of success<br> rate of allocation. As shown in it, these factors improve successful allocation rate especially in<br> higher system load.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor
<p>Each consumer is looking for utilizing its requested service with minimum price before its<br> deadline. To utilize a service all required resources should be allocated before deadline and<br> otherwise service failed to utilize and consumer must pay penalty to providers for all other<br> resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable<br> price of the market. Since consumers are generally sensitive to deadline in acquiring requested<br> service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent<br> time dependent bid price formula is determined in.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 5. Comparison on successful allocation
<p>Figure 5 illustrates comparison of successful allocation rate between ICDA and other<br> methods. In the proposed method, intelligent allocation and also time consideration enable<br> consumers to acquire more resources before the deadline and as a result, the number of successful<br> allocation is higher than other methods.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
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.