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5,573 results for “optimality”
Optimized structures of selected stationary points on the potential energy surface of the HC3N + CN reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the HC<sub>3</sub>N + CN potential energy surface published in our article “Semiempirical Potential in Kinetics Calculations on the HC<sub>3</sub>N + CN Reaction” (<em>Molecules</em> <strong>2022</strong>, <em>27(7)</em>, 2297), that can be found in <a href="https://doi.org/10.3390/molecules27072297">https://doi.org/10.3390/molecules27072297</a> .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at M06-2X/6-311+G(d,p) level of theory.</p>
Optimizing targeted conservation for disproportionate benefits in the East Big Creek watershed, Iowa, 2019.
This dataset supports spatial conservation planning in agricultural landscapes by providing field-level information on the placement, cost, and ecological performance of perennial conservation practices in the East Big Creek watershed, central Iowa. The dataset includes modeled configurations of prairie strips, riparian buffers, and nutrient-removal wetlands, with associated estimates of nitrate-nitrogen reduction and biodiversity enhancement. These estimates were derived using open-source geospatial tools and publicly available data, including the Agricultural Conservation Planning Framework (ACPF) and a modified Healthy Farm Index (HFI). The dataset enables users to explore how small-scale changes in land use can yield substantial environmental benefits, including improved water quality and increased biodiversity, while considering cost constraints. It is intended to support conservation practitioners, land use planners, and researchers in evaluating trade-offs and synergies among multiple ecosystem services in working landscapes.
Optimizing sampling across methods improves the power of ecological monitoring data
Transect-based monitoring has long been a valuable tool in ecosystem monitoring. These transects are often used to measure multiple ecosystem attributes. The line-point intercept (LPI), vegetation height, and canopy gap intercept methods comprise a set of core methods, which provide indicators of ecosystem condition. However, users struggle to design a sampling strategy that optimizes the ability to detect ecological change using transect-based methods. We assessed the sensitivity of these core methods on a one-hectare plot to transect length, number, and sampling interval to determine: 1) minimum sampling required to describe ecosystem characteristics and detect change for each method and 2) optimal transect length and number for all three methods to make recommendations for future analyses and monitoring efforts. We used data from 13 National Wind Erosion Research Network locations spanning the western US, which included 151 measurements over time across five biomes. We found that longer and increased numbers of transects were more important for reducing sampling error than increased sample intensity along transects. For all methods and indicators across plots, three 100-m transects reduced sampling error so that indicator estimates fall within an 95% confidence interval of +/- 5% for canopy gap intercept and LPI-total foliar cover, +/- 5 cm for height and +/- two species for LPI-species counts. For the same criteria at 80% confidence intervals, two 100-m transects are needed. Site-scale inference was strongly affected by sample design, consequently our understanding of ecological dynamics may be influenced by sampling decisions.
Temporal SNR optimization through RF coil combination in fMRI: The more, the better? - DATASET
Open the record for dataset details and reuse information.
Supplementary Material for "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations"
<p>This is the supplementary material for our publication "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Conflict of Interest, Sample analysis by HPLC, Table S1 and S1, Figure S1 and a suggested workflow for NPase-catalyzed nucleoside synthesis.</p> <p>The .xlsx file contains an implementation of the simplified formula for yield prediction (equation (4) ), using previously reported thermodynamic data (10.5281/zenodo.3459298).</p> <p>The Python code and all data calculated from numerical solutions of the system of equilibrium constraints is available elsewhere (10.5281/zenodo.3522588).</p>
Fig. 8 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 8. Redundancy analysis (RDA): individual species-gear relationships explained by morphological shape variability, using incidence data. In blue, fishing gear acronyms: beach seine (BS), cast net (CN), fish trap (FT), hook and line (HL), inner encircling gillnet (EG1), large gillnet (LG), marginal encircling gillnet (EG2), otter trawl (OT), small gillnet (SG).
Fig. 4 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 4. Morphospace represented by shape variation of all species in the nine fishing gears grouped, using the relative warps: a. 1 and 2; b. 1 and 3. Acronyms: Anc tri, Anchoa tricolor; Bat sop, Bathygobius soporator; Bot oce, Bothus ocellatus; Cen par, Centropomus parallelus; Chi spi, Chilomycterus spinosus; Dia rho, Diapterus rhombeus; Elo sau, Elops saurus; Epi mar, Epinephelus marginatus; Hip rei, Hippocampus reidi; Lut ana, Lutjanus analis; Lut cya, Lutjanus cyanopterus; Mal del, Malacoctenus delalandii; Myr pun, Myrophis punctatus; Oph gom, Ophichthus gomesii; Pol vir, Polydactylus virginicus; Sca cri, Scartella cristata; Sco bra, Scomberomorus brasiliensis; Sel set, Selene setapinnis; Sel vom, Selene vomer; Sph tes, Sphoeroides testudineus; Str mar, Strongylura marina; Sym tes, Symphurus tesselatus; Tra myo, Trachinocephalus myops. Threatened species (Hip rei, Lut ana, Lut cya, Epi mar) are highlighted in red.
Experimental Results for the study "A Modular Hybridization of Particle Swarm Optimization and Differential Evolution"
<p>This repository contains the experiment results and R scripts to analyze the data for the study "A Modular Hybridization of Particle Swarm Optimization andDifferential Evolution", which is accepted in <em>The Genetic and Evolutionary Computation Conference</em> (GECCO) '20 conference: </p> <p>Rick Boks, Hao Wang, and Thomas Bäck. 2020. A Modular Hybridization of Particle Swarm Optimization and Differential Evolution. In <em>Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. </em>ACM, New York, NY, USA, 8 pages. <a href="http://https: //doi.org/10.1145/3377929.3398123">https: //doi.org/10.1145/3377929.3398123</a></p> <p>Bibtex:</p> <pre><code class="language-markdown">@inproceedings{BoksWB20, author = {Rick Boks and Hao Wang and Thomas B\"ack}, title = {{A Modular Hybridization of Particle Swarm Optimization and Differential Evolution}}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2020, Canc\'un, Mexico, July 8-12, 2020}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3321707.3321816}, doi = {doi.org/10.1145/3377929.3398123, }</code></pre> <p><strong>Data description:</strong> we benchmarked <strong>800 </strong>different<strong> </strong>hybridizations of the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on a well-known continuous black-box problem set called <a href="https://coco.gforge.inria.fr/">COCO/BBOB</a>, which consists of 24 test functions. 30 independent runs are conducted for each algorithm on each problem.</p> <ul> <li>'ERT.csv': a data frame with columns DIM (5D or 20D), funcId (F1-24), algId (algorithm names), target (<span class="math-tex">\(10^{\{-8,-7, \ldots, 1\}}\)</span>), ERT (expected running time), and sd (standard deviation).</li> <li>'raw-data.csv': the running time recorded in each independent run. </li> <li>'analysis.R': the R script that generates ERT tables in the paper.</li> <li>'ecdf.R': the R script that renders the ECDF (empirical cumulative distribution function) plots in the paper.</li> </ul>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"
<p>The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"</p> <p><strong>1. Introduction</strong></p> <p>Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem.</p> <p><strong>2. Paper</strong></p> <p>This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation.</p> <p><strong>3. Data</strong></p> <p>This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results.</p> <p><strong>4. Version History</strong></p> <p>This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>.</p> <p><strong>5. Contact</strong></p> <p>If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</p>
Edge mode engineering for optimal ultracoherent SiN membrane designs
<p>Raw dataset for all the figures of the article entitled</p> <p>'Edge mode engineering for optimal ultracoherent SiN membrane designs',</p> <p>and corresponding scripts for data analysis.</p>
Data set of the article: Using Machine Learning for Web Page Classification in Search Engine Optimization
<p>Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"</p> <p>Abstract of the article:</p> <p>This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research. </p>
Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology
<p>Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/ </p>
Data from: Optimizing Coastal Restoration with the Stress Gradient Hypothesis
Restoration efforts have been escalating worldwide in response to widespread habitat degradation. However, coastal restoration attempts notoriously vary in their ability to establish resilient, high-functioning ecosystems. Conventional restoration attempts disperse transplants in competition-minimizing arrays, yet recent studies suggest that clumping transplants to maximize facilitative, intraspecific interactions improves restoration success. Here, we modify the Stress Gradient Hypothesis to generate predictions about where each restoration design will perform best across environmental stress gradients. We then test the model by combining measurements of physical stress with a field experiment manipulating transplant density and configuration across coastal dune elevational zones and latitudes. In hurricane-damaged Georgia (USA) dunes, grass transplanted in competition-minimizing (low-density, dispersed) arrays exhibited the highest growth, resilience to disturbance, and dune formation in low stress conditions. In contrast, facilitation-maximizing (high-density, clumped) arrays exhibited the highest survivorship in high stress conditions. Transplant survival was significantly lower on Massachusetts compared to Georgia dunes, suggesting there are thresholds above which intraspecific facilitation cannot overcome local stressors. Thus, switching from competition-minimizing to facilitation-maximizing transplant designs with increasing environmental stress can improve multiple metrics of restoration success, suggesting our modified Stress Gradient Hypothesis offers a roadmap for how to rebuild resilient, high-functioning coastal ecosystems.
Data for "An optimized genome-wide virus-free CRISPR screen for mammalian cells"
<p>This is the raw data from the article "An optimized genome-wide virus-free CRISPR screen for mammalian cells " and scripts for analyzing it.</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure
<p>Data and scripts used to generate figures presented in the paper "Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure" submitted to the Future Generation Computer Systems Journal.</p>
Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers (Data)
<p>Auxiliary materials for ICPE 2016 paper titled:<br /> "Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers".</p> <p> </p>
Dataset for "Economic irrationality is optimal during noisy decision making"
<p>1. Excel Files</p> <ul> <li>“experiment*.xlsx” files contain raw data. In ‘Notes’ an explanation of the relevant variables is offered</li> <li>“Parameters.xlsx” file contains the best-fitting parameters of the selective integration model (the version that omits early noise). Each tab corresponds to a different experiment.</li> </ul> <p> </p> <p>2. Matlab files</p> <p> </p> <p>Matlab files in the ‘code’ file allow one to reproduce simulation results in fig1b, fig3b and fig3d. The ‘selective_sim.m’ file offers a basic simulation of the selective integration model. </p> <p> </p> <p> </p> <p>For questions and further requests please contact Konstantinos Tsetsos: k.tsetsos62@gmail.com</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.