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5,573 results for “Optimality”
Figure 2 in Crop physiological considerations for combining variable-density planting to optimize seed costs and weed suppression
Figure 2. Schematic diagram representing the workflow process of the area planting optimization model. The graph on the bottom left corresponds to low-density planting yields of maize (red circles, solid line, y = 288.5 − 2.07x), cotton (gray triangles, dashed line, y = 176 − 1.58x), and soybean (blue squares, dotted line, y = 86.5 − 0.70x) in g seed−1.
From optimality to prestige: Investigating human-animal interactions at Late Mesolithic Hoge Vaart-A27 (Almere, the Netherlands) using a Prey Choice Model
<h2><strong>This dataset is from my Research Master's Thesis from Groningen University. </strong></h2> <p><strong>The dataset includes;</strong></p> <ul> <li>The CSV data necessary to recreate all models and graphs from the thesis</li> <li>The R.Script with all codes</li> </ul> <p><strong>Used packages and programming language: </strong></p> <ul> <li>Kassambara, A. (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots (R package version 0.6.0) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://doi.org/%3Chttps://CRAN.R-project.org/package=ggpubr%3E."> <https://CRAN.R-project.org/package=ggpubr>.</a></li> <li>Mei, W., Yu, G., & Greenwell, B. M. (2022). ggtrendline: Add Trendline and Confidence Interval to ‘ggplot’ (R package version 1.0.3) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=ggtrendline"> https://CRAN.R-project.org/package=ggtrendline</a></li> <li>Pedersen, T. L. (2024). patchwork: The Composer of Plots (R package version 1.2.0) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=patchwork"> https://CRAN.R-project.org/package=patchwork</a></li> <li>R Core Team. (2022). R: A Language and Environment for Statistical Computing (R version 4.2.1) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://www.r-project.org/"> https://www.R-project.org/</a></li> <li>Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.</li> <li>Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.<a href="https://ggplot2.tidyverse.org"> https://ggplot2.tidyverse.org</a></li> <li>Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.<a href="https://doi.org/10.21105/joss.01686"> https://doi.org/10.21105/joss.01686</a></li> </ul> <p><strong><span>Abstract</span></strong></p> <p><em><span>The research of the faunal assemblage from Hoge Vaart-A27 (Almere, the Netherlands) provides a new perspective on investigating the connections between foraging strategies and socio-cultural dynamics of the Late Mesolithic and Early Swifterbant communities in Northwest Europe. The significance of this topic lies in its potential to help elucidate the broader socio-cultural aspects of foraging and human-animal interactions during this period. Despite extensive research, there remains a gap in comprehending how prestige influenced prey selection alongside optimal foraging strategies. The study aims to address this gap by employing zooarchaeological methods combined with prey choice modelling to investigate prey selection complexities in Flevoland’s transitioning cultural and physical landscape. The methods include analyses of species abundance and detailed examination of red deer, horse, aurochs and wild boar. The key findings reveal that while (optimal) foraging strategies were practised at Hoge Vaart-A27, they were significantly influenced by motivations such as prestige. The results contribute significantly to research by offering a glimpse into how Northwest European foragers could have perceived and interacted with big game beyond subsistence. </span></em></p> <p><strong><span>Keywords</span></strong><span> </span><span>human-animal relationships, prestige, profitability, prey choice, Swifterbant Culture, optimal foraging theory, costly signalling theory</span></p>
Optimized atomic structures of thin Ag films on Pt(111) and Pd(111) surfaces
<p>VASP coordinate files of thin Ag films on Pt(111) and Pd(111) surfaces and the summary spreadsheet of corresponding energies. </p>
Supporting materials for the methodology for optimal combinations of agroecological practices (AEPs)
<p><span>The assessment framework is developed by first reviewing existing agroecological sustainability assessment tools. Indicators are collected based on literature review. Then they are synthesized into the holistic agroecology assessment framework. New indicators are developed during the project and are also used to address any context-specific data needs or cover gaps of the existing tools. </span></p>
Methodology for optimal combinations of agroecological practices (AEPs)
<table> <tbody> <tr> <td>The assessment framework is developed by first reviewing existing agroecological sustainability assessment tools. Indicators are collected based on literature review. Then they are synthesized into the holistic agroecology assessment framework. New indicators are developed during the project and are also used to address any context-specific data needs or cover gaps of the existing tools.<span> </span></td> </tr> </tbody> </table>
Fig. 2 in Protocol Optimization For Genomic Dna Extraction And Rapd-Pcr Of Alien Ponto-Caspian Amphipod Pontogammarus Robustoides
Fig. 2. RAPD fingerprints results from different samples of Pontogammarus robustoides with primers OPA-02 (1-12 runners- different samples of Pontogammarus robustoides; K- control) using RAPD-PCR 10 × Taq buffer with KCl.
Fig. 3. Agarose gel image Fig. 4 in Optimization Of Dna Extraction Protocol For Dna Isolation From Air-Dried Collection Material For Further Phylogenetic Analysis (Coleoptera: Carabidae)
Fig. 3. Agarose gel image Fig. 4. Agarose gel image (successful PCR amplification) (failed PCR amplification) M: marker (bp) M: marker (bp) A1: Agonum fuliginosum Panzer, 1809 A: Agonum fuliginosum Panzer, 1809 A2: Agonum thoreyi Dejean, 1828 O: Omophron aequale aequale Morawitz, 1863 O: Omophron aequale aequale Morawitz, 1863 N: Notiophilus semistriatus Say, 1823 N: Notiophilus semistriatus Say, 1823 Nk: negative control. Nk: negative control.
Fig. 1 in Optimization Of Dna Extraction Protocol For Dna Isolation From Air-Dried Collection Material For Further Phylogenetic Analysis (Coleoptera: Carabidae)
Fig. 1. Photo of Omophron aequale jacobsoni Fig. 2. Photo of Omophron aequale jacobsoni Semenov, 1922 before incubation. Semenov, 1922 after 16 h (56°C) incubation time in tissue lysis buffer with proteinase K.
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Figure 5 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 5. Shoots proliferation from node explant of 'Flordaguard' peach rootstock cultured in semi-solid MS-½N (without PGR). A. Mean number of shoots per explant; B. number of elongated shoots; C. and mean length of shoots obtained in half strength MS (½N) – double-phase under different concentrations of BAP (0.5–3.0 mg L-1) for 'Flordaguard' peach rootstock.
Figure 4 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 4. Shoots proliferation from node explant of 'Flordaguard' peach rootstock cultured on full strength MS medium. A. Growth and development of isolated shoot with symptoms of hyperhydricity; B. Growth and development of multiple shoot with shoot-tip necrosis (STN).
Figure 3 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 3. Shoot proliferation in 'Flordaguard' peach rootstock. A. Induction of multiple shoot in explants cultured in half strength MS (½N) containing 1.0 mg L-1 of GA and 1.0 g L-1 of activated charcoal (double-phase medium); B. Growth and development of isolated shoots after 20 3 days in double-phase medium.
Figure 2 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 2. Yellow clams (Amarilladesma mactroides) numbered with a black permanent ink and a red nail polish covered with a bicomponent adhesive based on epoxy resin (Araldite) on the day that it was first marked (a and b) and after 10 days buried in the experimental maintenance system (c).
Figure 1. Experimental unit model built with a 150 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 1. Experimental unit model built with a 150 mm PVC pipe and coupling, used in the maintenance of yellow clam (Amarilladesma mactroides) broodstock in the laboratory.
Figure 4 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 4. Histological sections of the tissues of yellow clam (Amarilladesma mactroides) sampled at the beginning and at the end of trial II (a, b) and trial III (c, d): (a) gonad of female clam with residual and immature oocytes in stage 6 (totally spawned) at the beginning of trial II; and (b) gonad of female clam in stage 5 (partially spawned) at the end of trial II, where it is possible to distinguish immature peduncular oocytes attached to the wall (IO), some free mature polygonal oocytes (MPO) and residual oocytes (RO); (c) gonad of female clam in stage 3 with immature (IO) and mature polygonal oocytes (MPO); (d) gonad of female clam stage 4, where several mature polygonal oocytes (MPO) may be observed.
Figure 3 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 3. Mean daily consumption of microalgae (mixture of Isochrysis galbana and Chaetoceros muelleri on trial I or only C. muelleri on trial II and III) estimated for each individual (cells.mL−1. clam−1) of yellow clam (Amarilladesma mactroides) in trials I, II and III.
Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes
<p>Yield and Conversion measurements for iodoalkylation reaction of four different terminal alkynes. The reaction conditions as well as the equivalent of the reactants and reagents for each of the three optimizers are listed in the corresponding JSON file. </p>
Data for "Grand canonically optimized grain boundary phases in hexagonal close-packed titanium"
<p>Supporting data of GB structures and scripts for the paper titled "Grand canonically optimized grain boundary phases in hexagonal close-packed titanium."</p>
Mutation-guided Metamorphic Testing of Optimality in AI Planning
<p>Experimental results presented in the article <em>Mutation-guided Metamorphic Testing of Optimality in AI Planning</em>, both the figures and the raw data.</p> <p> </p> <p>The framework itself is hosted on GitHub (see the link below).</p> <p>In order to use the data with the framework, unzip the files of the archive <em>results.zip</em> inside the folder <em>results/</em>.</p>
Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"
<div> <div> <div> <p>This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation.</p> </div> </div> </div>
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.