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5,573 results for “optimality”
Data from: Reproductive skipping as an optimal life history strategy in the southern elephant seal, Mirounga leonina
Intermittent breeding by which organisms skip some current reproductive opportunities in order to enhance future reproductive success is a common life history tradeoff among long-lived, iteroparous species. The southern elephant seal Mirounga leonina engages in intermediate breeding when body condition is low. While it is anticipated that this strategy may increase the lifetime reproductive output of this species, the conditions under which reproductive skipping are predicted to occur are not clear. Here I develop a dynamic state variable model based on published data that examines when southern elephant seals are predicted to optimally skip reproduction in order to maximize lifetime reproductive output as a function of current body mass, maternal age, and survivorship. I demonstrate that the optimal reproductive strategy for this species can include reproductive skipping, and that the conditions where this is optimal depend on patterns of mass-dependent adult female survival. I further show that intermittent breeding can increase lifetime reproductive output, and that the magnitude of this benefit increases with the ability of individual animals to replenish depleted body mass through foraging. Finally, I show that when the environment is variable and foraging is reduced in bad years, the benefit of adopting an optimal strategy that includes reproductive skipping increases asymptotically with the frequency of bad years. These results highlight the importance of characterizing the pattern of adult survival in this species, as well as the need to identify other factors that may influence the prevalence and benefits of reproductive skipping.
Data from: Optimal group size in a highly social mammal
What are the costs and benefits for animals living in groups of different sizes? Balancing the trade-offs between within-group competition (which favors smaller groups) and between-group competition (which favors larger groups) suggests that intermediate-sized groups may be best, yet empirical support for this prediction has largely been lacking. Using long-term data on wild baboons, we provide novel evidence that individuals living in intermediate-sized groups have energetically optimal space-use strategies and lower glucocorticoid (stress hormone) concentrations than individuals in either large or small groups. Our results offer new insight into the costs and benefits of group living.
Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm
<p>Data repository for the paper "Beating classical heuristics for the binary paint shop problem with the quantum" by Michael Streif, Sheir Yarkoni, Andrea Skolik, Florian Neukart, and Martin Leib.</p> <p> </p>
FIGURE 8. Character optimizations onto a 50 in Evolution of trichome morphology in Mimosa (Leguminosae-Mimosoideae)
FIGURE 8. Character optimizations onto a 50% majority-rule Bayesian tree based on a plastid phylogeny (modified from Simon et al. 2011). The evolution of glandular trichomes (a) and branched trichomes (b) are mapped onto the phylogeny using unordered parsimony optimization. Letters close to nodes correspond to main clades as in Simon et al. (2011), and vertical bars correspond to outgroups. Terminals with two or more states are represented by several squares. Please refer to Simon et al. (2011) for details on species names and clades.
FIGURE 2 in A reassessment of apheloriine millipede phylogeny: additional taxa, Bayesian inference, and direct optimization (Polydesmida: Xystodesmidae)
FIGURE 2. Preferred phylogram reconstructed using Bayesian inference from the new data set comprising an additional 29 species. Names in bold are new taxa sequenced for this study—15 of which are new species, indicated by "n. sp." Harmonic mean likelihood ≥ -18315.52. Thickened, black branches denote posterior clade probability values ≥ 0.95. Boxes above branches indicate clades supported by different analyses: "Circle B", Bayesian inference, posterior clade probability ≥ 0.95; "Circle P", Direct optimization, node recovered in ≥ 95% of trees generated under the preferred '121' gap-opening and extension parameters summarized in a 95% majority rule consensus; and "Circle M", Maximum likelihood, nonparametric bootstrap clade support values ≥ 95%. Numbers in boxes indicate support values <95%. Light gray box = Southern clade; medium gray box = Appalachioria species; dark gray box = Brachoria species. Exemplar species names followed by specimen numbers in parentheses. Gyalostethus monticolens branch abbreviated with a break for ease of illustration (branch length = 1.047, about 1.6X greater than shown).
FIGURE 4 in Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria
FIGURE 4. Tree-alignment + parsimony: strict consensus of 536 most parsimonious trees of 94413 steps showing relationships among 430 terminals of Brachycephaloidea and 25 outgroup taxa. Dashed branches indicate clades that collapse due to the wildcard Eleutherodactylus dilatus. Numbers above branches are Goodman-Bremer values excluding E. dilatus and those below branches are jackknife percentages excluding (upper) and including (lower) E. dilatus. Linnaean taxa follow previous studies except when otherwise indicated. Non-monophyletic taxa are highlighted in red (monophyletic taxa within paraphyletic groups remain in black).
FIGURE 12 in Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria
FIGURE 12. Similarity-alignment + parsimony: strict consensus of 205 most parsimonious trees of 105810 steps for a dataset of 17233 aligned sites of mitochondrial and nuclear DNA showing relationships among 430 terminals of Brachycephaloidea and 25 outgroup taxa. Dashed branches indicate clades that collapse due to the wildcard Eleutherodactylus dilatus. Numbers above branches are Goodman-Bremer values excluding E. dilatus and those below branches are jackknife percentages excluding (upper) and including (lower) E. dilatus; jackknife values reported as a dash (-) were recovered in <5 pseudoreplicates. Non-monophyletic taxa of other authors or differing from the results of our similarity-alignment + parsimony are highlighted in red (monophyletic taxa within paraphyletic groups remain in black).
FIGURE 8 in Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria
FIGURE 8. Relationships among families and subfamilies of Brachycephaloidea recognized by Hedges et al. (2008a): (A) similarity-alignment + maximum likelihood of analysis 3 of Hedges et al. (2008a); (B) similarity-alignment + maximum likelihood of the 17-gene analysis of Heinicke et al. (2009); (C) similarity-alignment + maximum likelihood of total evidence analysis of Pyron & Wiens (2011); (D) tree-alignment + parsimony, this study; (E) similarity-alignment + parsimony, this study; (F) similarity-alignment + maximum likelihood, this study. Non-monophyletic taxa are highlighted in red.
Bayesian Analysis of Quasar Lightcurves with a Running Optimal Average: PyROA Fits to COSMOGRAIL Data
<p>Available as .zip files are the data/results of using PyROA to fit to the COSMOGRAIL gravitationally lensed quasar data. In each .zip are the individual lightcurves for each image as a .dat file, a plot of the results, a corner plot of the sampled parameters and three pickle objects. These are samples.obj, samples_flat.obj and X_t.obj.</p> <ul> <li>The first of these, samples.obj, contains all the MCMC samples in an array with shape (Nsamples, Nwalkers, Ndim).</li> <li>The second, samples_flat.obj, contains the flattened samples, where the burn-in has been removed and is an array with shape (Nsamples_final, Ndim), where Nsamples_final is the number of samples with burn-in removed. This was used to generate the corner plot and obtain the best fit parameters.</li> <li>The last, X_t.obj, contains the driving lightcurve, X(t), as described in Donnan et al. 2021. This is an array of the form [t, X, X_errs], where X is the value of the driving lightcurve at time, t, with errors, X_errs.</li> </ul> <p>For objects with more than two images, folders where other images were the reference are included. The sigma parameter in the corner plot is the extra error added to the flux data which is labelled as <em>s<sub>i</sub></em> in the paper. The original data file downloaded from the COSMOGRAIL website is also included as a csv file.</p>
Supporting data for Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions
<p>Supporting data for "Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions" by Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri</p> <p>See README for data description.</p> <p>The code is available at: https://github.com/FPAL-Stanford-University/PadeOps</p>
Optimal Spectral Sampling Forward Model Test Data
<p>Binary and ASCII files for tests of the OSS forward model application.</p>
Derived Optimal Linear Combination Evapotranspiration (DOLCE)
<p>The Derived Optimal Linear Combination Evapotranspiration (DOLCE) dataset consists of monthly Evapotranspiration (ET) and their associated uncertainty on a global scale. DOLCE is derived at 0.5 spatial resolution and monthly temporal resolution over the period 2000-2009.</p> <p>DOLCE is derived by weighting six existent global ET products based on their ability to match site-level data from 159 globally distributed flux tower sites.</p> <p>The six evapotranspiration products are: GLEAM V2A, GLEAM V2B, GLEAM V3A, MOD16, MPIBGC and PML .</p> <p>DOLCE is a mosaic of three tiers, we derive each tier from a different weighting ensemble and subset of FLUXNET data. The dominant part (i.e. tier1) involves six ET products and data from 138 flux towers, tier2 involves 5 products and 151 flux towers, while tier3 involved 2 products and 159 sites.</p> <p>This dataset was produced by Dr Sanaa Hobeichi as part of her PhD thesis and of the Centre of Excellence for Climate System Science "The role of land surface forcing and feedbacks for regional climate" research program.</p>
Optimal Stopover Model: a state dependent habitat selection model for staging passerines during migration
<p>1. During their seasonal migration, birds stage in areas comprising stopover sites of varying quality. Given that migrating birds have a limited information about their environments, they may land at a low-quality stopover site in which their fuel deposition rate (FDR) is low. Birds landing at such sites, should decide either to extend their stopover duration or to quickly depart in search for a better site. These decisions, however, strongly depend on their body condition upon landing.</p> <p>2. To understand the decision-making process of passerines within a stopover area, comprising stopover sites of varying quality, prior to the crossing of a large ecological barrier, we constructed a state-dependent habitat selection model. The model assumes that even if migrating birds have an expectation of encountered area quality, they cannot control for their initial landing site. Once landing, movement between a low to high quality stopover site will occur only if the body condition of these birds is high to the extent that they can entail the energetic cost of movement. Birds in the model aim to maximize their fuel load at the end of the stopover period, to suffice for successfully crossing a large ecological barrier.</p> <p>3. The model is based on empirical data on autumn migrating Blackcaps (Sylvia atricapilla), collected at two important stopover sites in the Negev desert of Israel. Migrating passerines staging at these two sites differ in their fuel deposition rate and body condition. The model shows that the optimal behavior when arriving at a low-quality stopover site is to abandon it quickly. However, as lean individuals cannot entail the costs of searching for an alternative site, they have no other choice but to stay there even if their chances to successfully cross the Sahara Desert ahead are low.</p> <p>4. Our model can be applied to other ecological systems. Proper use of this model may allow good assessment of stopover site quality, as indicated by the bird's fuel deposition rate, regardless of specific site characteristics. Hence it can help applying targeted management decisions regarding the maintenance of stopover sites or establishment of new ones.</p>
Fig. 2 in A Report On Optimal pricing and replenishment strategies for non-instantaneous decaying items with selling price and current stock level dependent demand
Fig. 2. Experimental results of Callosobruchus analis parental crosses. A) Hatch rate, B) Apterous offspring ratio, C) Offspring sex ratio. Replicates were eight for ap/ap females mated with ap/ap, +/ap, and +/+ males, eight for +/+ females with +/+ males, seven for +/ap females mated with ap/ap, +/ap males, and 11 for +/+ females mated with ap/ap males.
Figure 2 in Estimating optimal sample size for tardigrade morphometry
Figure 2. The relationship between estimation error and sample size has an asymptotic character both for means (A) and for ranges (B), meaning that the larger the sample size, the smaller the estimation error, but the reduction in error diminishes with the increasing sampling effort. Graphs show means ƚSD calculated from six species means (see Table 3). Dashed horizontal lines represent acceptable error rates: A, for means, errors under the dashed line are acceptable (i.e. sample sizes n ≥ 20; B, for ranges, errors above the dashed line are acceptable (i.e. none of the shown sample sizes returns an acceptable range estimation).
Figure 1 in Estimating optimal sample size for tardigrade morphometry
Figure 1. Exemplary graphs illustrating the results of simulations for mean (A) and range estimations (B) of the buccal tube length in the experimental population of Diphascon higginsi (N = 404; global mean = 22.2 µm; global range = 15.1– 25.6 µm = 10.5 µm). A, mean estimation graph: the solid horizontal line represents the global mean whereas the two dashed lines delimit the acceptable error zone, i.e. ƚ10% of the global range (21.2–23.3 µm); dots below and above the global mean delimit the zone within which 95% of computed means fell; the arrow points to the sample size at which 95% of computed means fell within the acceptable error around the global mean (n = 15). B, range estimation graph: the solid horizontal line represents the global range whereas the dashed line delimits the acceptable error zone, i.e. –20% of the global range (8.4 µm); dots at each sample size delimit the zone within which 95% of computed ranges fell; the arrow points to the sample size at which 95% of computed ranges fell within the acceptable error below the global range (n = 130). Graphs for all traits and species are available in the Supplementary Materials.
Figures 5–6. Optimal trees obtained under Bayesian analyses. Fig. 5. Mkv model. Fig. 6 in Phylogenetic analysis of Micrathena and Chaetacis spiders (Araneae: Araneidae) reveals multiple origins of extreme sexual size dimorphism and long abdominal spines
Figures 5–6. Optimal trees obtained under Bayesian analyses. Fig. 5. Mkv model. Fig. 6. MkvG model. Posterior probabilities values are indicated below branches.
Figures 3–4. Optimal trees obtained under parsimony analyses. Fig. 3 in Phylogenetic analysis of Micrathena and Chaetacis spiders (Araneae: Araneidae) reveals multiple origins of extreme sexual size dimorphism and long abdominal spines
Figures 3–4. Optimal trees obtained under parsimony analyses. Fig. 3. Unweighted analysis [length = 575.3; consistency index (CI) = 0.305; retention index (RI) = 0.693]. Fig. 4. Implied weighted analysis (k = 5; length = 579.718; fit = 105; CI = 0.303; RI = 0.689). Bremer supports and symmetric resampling values are indicated below and above branches, respectively. Symmetric resampling values are given in frequency differences (GC; Goloboff et al., 2003).
Supporting Data for "Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties" Manuscript
<p>Training data and xTB calculated properties of sampled structures from the generative active learning experiments reported in the manuscript.</p>
Deploying Near Optimal Delay Constrained Paths with Segment-Routing in Massive Scale Networks
<p>Dataset consisting of 5 large topologies used for the evaluation of Best2cop - multi-area version. <br> <br> The topologies themselves are written as a list of edges following the format : <br> <strong>source target delay cost area</strong> <br> "cost" being the IGP cost of the link, and "area", the IGP area to which the edge belongs.</p> <p>Each topology has a descriptive file showcasing some of the network's properties (density, diameter...) as well as an HTML file allowing to see the topology on an actual world map. Note that while we consider the road infrastructure, the links are represented in an abstract fashion for readability purposes.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.