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5,573 results for “Optimality”
CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part-I
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. Due to size limit of zenodo.org, we split the whole dataset into 2 parts, and this is the Part 1.</p> <p>We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p>
Atomic coordinates for "Optimizing Surface Active Sites via Burying Single Atom in Subsurface Lattice for Boosted Alkaline Methanol Oxidation"
<p>Atomic coordinates of the optimized computational models in the manuscript of "Optimizing Surface Active Sites via Burying Single Atom in Subsurface Lattice for Boosted Alkaline Methanol Oxidation"</p>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Machine learning prediction of enzyme optimal pH
<p>- Enzyme catalytic optimum pH dataset (pHopt, 9855 proteins)</p> <p>- Secreted bacterial optimum environment pH dataset (pHenv, 1.9 million proteins)</p> <p>- Model for predicting pHopt of enzymes (EpHod)</p> <p>- Code for using the EpHod model are in <a href="https://github.com/beckham-lab/EpHod">GitHub</a>. Paper in <a href="https://doi.org/10.1101/2023.06.22.544776">BioRxiv</a></p> <p> </p>
Optimized geometries for Global Aromatic Ring Currents in Neutral Porphyrin Nanobelts
<p>Optimized geometries for Global Aromaticity in Neutral Porphyrin Nanobelts</p>
Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"
<p>this dataset contain: </p> <ol> <li>2m temperature de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA</li> <li>2m temperature observaton data, obs_t2m</li> <li>24H QPF model forecast data on station location, ECMWF, NCEP, CMA-GFS, in raw_data_r24.zip</li> <li>24H precipitation data, in raw_data_r24.zip</li> <li>source code (in python)</li> </ol> <p> </p> <p>how to use it: </p> <p>1. prepare data and python environment<br> 1.1 if you want to run [Station_FCST_MMWB.py] or [Station_FCST_MMWB_r24.py] , please download the station forecast and observation data<br> 1.2 neet meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva<br> 1.3 need cartopy to draw picture FigS01. </p> <p>2. try the 2m temperature blending methods <optional><br> 2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/<br> 2.2 run the Station_FCST_MMWB.py in python environment </p> <p>3. try the 24h QPF multi blending methods <optional><br> 3.1 unzip the [raw_data_r24.zip] file into ./raw_data_r24/<br> 3.2 run the Station_FCST_MMWB_r24.py in python environment</p> <p>4. draw figures<br> 4.1 run Fig01.py in python environment <br> 4.2 run Fig02.py in python environment <br> 4.3 run Fig03.py in python environment <br> 4.4 run FigA01.py in python environment <br> 4.5 run FigS01.py in python environment </p>
Optimized Analytical Workflow for Single-Nucleus Transcriptomics in Main Metabolic Tissues
<p>Single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful approach for studying cellular heterogeneity in metabolic tissues. However, snRNA-seq analysis remains challenging due to low gene expression and data complexity. Here, we introduce an optimized analytical workflow for snRNA-seq data from 67 samples across four main metabolic tissues white adipose tissue, hypothalamus, muscle and liver. We emphasized the importance of key steps including ambient RNA removal, doublet identification, normalization and data integration to ensure accurate downstream analysis. This workflow offers a valuable resource for researchers in metabolism, facilitating deeper insights into cellular diversity and metabolic function through rigorous snRNA-seq analysis.</p>
Raw PKPD data for "Identifying an optimal dihydroartemisinin-piperaquine dosing regimen for malaria prevention in young Ugandan children"
<p>Raw pharmacokinetic and malaria outcomes data for manuscript "Identifying an optimal dihydroartemisinin-piperaquine dosing regimen for malaria prevention in young Ugandan children"</p>
Experimental Data for the Paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient"
<p><strong><em>The data for the paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient"</em></strong></p> <p>This is the data set with the experimental results for our paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient." We conduct more than 56 million runs, consuming more than 6.5*10<sup>14</sup> FEs as well as 150 processor years, ensuring that our results are statistically sound and rigorous. Here, you can find all the results, all the program codes used for obtaining the results, and all the tables and figures produced from the results, and the program codes used to produce them.</p> <p><strong><em>Included Files</em></strong></p> <ul> <li><code>ffa-empirical-complexity_results.tar.xz</code> (size packed 11.7 GiB, unpacked 335.2 GiB): The complete set of log files. For each run of each experiment, one distinct text-based log file is created. The log file contains every improving step of the algorithm, the final result, and the system configuration. This archive is very large and unpacked it will occupy more than 335 GiB of hard disk space.</li> <li><code>ffa-empirical-complexity_end_of_run_results_and_stats.tar.xz</code> (size packed 861.4 MiB, unpacked 6,295.9 MiB): The end-of-run result qualities and consumed runtime as well as statistics thereof. These information have been extracted from the log files and are provided in form of semicolon-separated values text files. These files are much easier to consume. They do not reflect the progress of the single runs, but only their end results.</li> <li><code>ffa-empirical-complexity_sources.tar.xz</code> (size packed 65.5 MiB, unpacked 173.6 MiB): The complete set of <code>Java</code> sources that was used to perform the experiments. Since the random seeds of the random number generators are created in a deterministic way, you could execute this code and obtain the exactly same log files in terms of consumed FEs, improving steps, and end results as we provide in <code>ffa-empirical-complexity_results.tar.xz</code>. (Of course, your systems configuration and measured runtime in milliseconds would probably be different.)</li> <li><code>ffa-empirical-complexity_evaluator.tar.xz</code> (size packed 4,015.8 KiB, unpacked 4,510 KiB): The <code>Java</code> and <code>R</code> source codes that are used to extract the end results from the log files, compute all relevant statistics, and produce the graphics in our article.</li> <li><code>ffa-empirical-complexity_evaluation.tar.xz</code> (size packed 417.3 KiB, unpacked 590.0 KiB): The high-level conclusions produced by the evaluator from the raw data, including tables and figures.</li> <li><code>ffa-empirical-complexity_saga_ffa_on_plateau.tar.xz</code> (size packed 7,238.9 KiB, unpacked 26.8 MiB): We also conducted an additional experiment to better understand the behavior of the SAGA algorithm variants using FFA on the Plateau problem. Here we provide source codes and result log files of this experiment. The result log files are much more comprehensive, as we tried to figure out why these algortihms were able to solve the Plateau problems (and they ultimately helped us to successfully do so).</li> </ul> <p><strong><em>License</em></strong></p> <p>The copyright holder of this dataset is Prof. Dr. Thomas Weise (see <a href="#contact">Contact</a>). The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/en/legalcode">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong><em>Contact</em></strong></p> <p>If you have any questions or suggestions, please contact the corresponding author of this dataset, Prof. Dr. <a href="http://iao.hfuu.edu.cn/team/director">Thomas Weise</a> of the Institute of Applied Optimization (<a href="http://iao.hfuu.edu.cn/">IAO</a>) at <a href="http://www.hfuu.edu.cn/english/main.htm">Hefei University</a> [<a href="http://www.hfuu.edu.cn">合肥学院</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>
Intralocus sexual conflict over optimal nutrient intake and the evolution of sex differences in lifespan and reproduction
<p>Despite widespread variation in lifespan across species, three clear patterns exist: sex differences in lifespan are ubiquitous, lifespan is commonly traded against reproduction, and nutrition has a major influence on these traits and how they trade-off. One process that potentially unites these patterns is Intralocus Sexual Conflict (IASC) over the optimal intake of nutrients for lifespan and reproduction. If nutrient intake has sex-specific effects on lifespan and reproduction and nutrient choice is genetically linked across the sexes, IASC will occur and may prevent one or both sexes from feeding to their nutritional optima. Here we determine whether this process is operating in the cricket <i>Gryllodes sigillatus</i>. We show that protein and carbohydrate intake have contrasting effects on lifespan and reproduction in the sexes and that there are strong positive intersexual genetic correlations for the intake of these nutrients under dietary choice. This divergence in nutrient effects, combined with the genetic architecture for nutrient choice is predicted to accelerate the evolutionary response of nutrient intake in males but constrain it in females, suggesting they are losing the conflict. Supporting this view, males and females were shown to regulate nutrient intake to a common ratio that was not perfectly optimal for lifespan or reproduction in either sex, especially in females. Our findings show that IASC over the optimal intake of nutrients is likely to be an important process generating sex differences in lifespan and reproduction and may help explain why females age faster and live shorter than males in <i>G. sigillatus</i>.</p>
Evaluating alternative study designs for optimal sampling of species' climatic niches
Ecologists have traditionally studied intraspecific variation by sampling species across their geographic ranges. However, whether this classic approach produces samples that accurately represent species' climatic niches is largely unknown. Alternative, niche-based study designs using species' climatic niches to inform sampling locations should more efficiently and completely capture the breadth of the niche, but the magnitude of this difference and how it may vary is unclear. Here we use conifers as a model system to explore these issues and reach specific recommendations for future sampling designs. Using an independent dataset of high-quality species' occurrences, we first show that recent publications examining variation across geographic space do a poor job of capturing the full breadth of species' niches, such that on average, only 22% of species' niche space was sampled. This was also true of a large compiled database, the International Tree-Ring Data Bank (ITRDB), which yielded average niche coverage of only 45%. Finally, we simulated common sampling designs (i.e., random points, grids, and transects) in both geographic and niche-based sampling frameworks. Using two sampling metrics, niche coverage and niche undersampling, we measured how completely and evenly these simulated studies characterized the niches of 64 North American conifers. Niche-based sampling better represented species' niches than geographic sampling, with the magnitude of this difference depending on study design and sample size. Niche-based gridded study designs achieved the most complete sampling at all but the smallest sample sizes, covering ~15-25% more of a species' niche than similar designs implemented geographically. With fewer than 10 samples, however, all study designs performed poorly, and niche-based transects achieved slightly higher niche coverage. Consequently, when more than a handful of samples are collected, we recommend that studies seeking to characterize variation across a species' niche consider using a gridded study design implemented in a niche-based sampling framework.
Self-Directed Online Machine Learning for Topology Optimization
<p>Code and results of the paper "Self-Directed Online Machine Learning for Topology Optimization".</p> <p>See latest updates at https://github.com/deng-cy/deep_learning_topology_opt</p>
Dynamic combinatorial optimization of in vitro and in vivo heparin antidotes
<p>This folder contains selected molecular dynamics simulations movies for the paper " Dynamic combinatorial optimization of <em>in vitro</em> and <em>in vivo</em> heparin antidotes"</p>
Performance comparison of optimization methods on variational quantum algorithms
<p>This repository contains the code, data and notebooks to reproduce the plots from the manuscript "Performance comparison of optimization methods on variational quantum algorithms"</p>
data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease
<p>data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease</p>
Dataset of Paper "Optimization and parallelization of the Discrete Ordinate Method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches"
<p>Dataset of Paper "Optimization and parallelization of the Discrete Ordinate Method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches":</p> <ul> <li>Time profiling of the DOM model stages.</li> <li>Speed-up and parallel fraction of the “global results generation” stage in the DOM model.</li> <li>Comparison of computational time between original and modified DOM model stages.</li> <li>Scalability of the peer-to-peer communication (OpenFOAM) and master-slave communication (ANSYS Fluent) architectures.</li> <li>Results of the benchmarking of the model with ANSYS Fluent in three reactors.</li> <li>Mesh of the jerrycan.</li> <li>Mesh of the anular reactor</li> <li>Mesh of the tubular reactor couple to a compound parabolic collector.</li> </ul>
Data for: Optimizing broad ion beam polishing of zircaloy-4 for electron backscatter diffraction analysis
<p>This is the data to support a manuscript that explores how to optimize sample preparation of zircaloy-4 using broad ion beam polishing.</p> <p>If you wish to follow-up on this data, please contact Dr Ben Britton (ben.britton@ubc.ca).</p> <p>The data was collected and curated by Ning Fang and Ruth Birch.</p>
data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease – a comparison of three screening tools used in the GLIM diagnostic algorithm
<p>data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease – a comparison of three screening tools used in the GLIM diagnostic algorithm </p>
SESMG model scenarios of the study "Indicators for the optimization of sustainable urban energy systems based on energy system modeling"
<p>This folder contains the model scenarios belonging to the publication "<strong>Indicators for the optimization of sustainable urban energy systems based on energy system modeling</strong>" (<a href="https://doi.org/10.1186/s13705-021-00323-3">https://doi.org/10.1186/s13705-021-00323-3</a>).</p> <p>The individual scenarios can be executed and evaluated with the <strong>Spreadsheet Energy System Model Generator (<a href="https://github.com/chrklemm/SESMG">SESMG</a>)</strong> <a href="https://doi.org/10.5281/zenodo.5412027">v0.0.4</a>, respectively <a href="https://doi.org/10.5281/zenodo.5520513">v0.2.0</a>.</p> <p>The file names are to be understood as follows:</p> <p><em>"scenario name"_"(dispatch) optimization criterion"_"scenario concretization"_"further scenario concretization"_"associated program version"</em>.xlsx.</p> <p>For example, the title name "<em>Scenario3_C_4MW_Biogas_SESMGv0.0.4.xlsx</em>" contains the following information:<br> - This file belongs to scenario 3 (see main publication for details).<br> - Dispatch optimized according to energy costs C (see main publication for details).<br> - The scenario contains 4 MW biogas CHP capacity (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.0.4.</p> <p>Another example. The title name "<em>optimization_C_80PercentDemand_70PercentEmissions_SESMGv0.1.1.xlsx</em>" contains the following information:<br> - This file belongs to the optimization scenario (see main publication for details).<br> - The primary optimization criterion is energy costs C (see main publication for details).<br> - Energy demand was capped at 80 percent and emissions at 70 percent of baseline (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.1.1.<br> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors would like to thank Prof. Dr. Peter Vennemann (Münster University of Applied Sciences) for the constructive discussion regarding this article. This research has been conducted within the R2Q project, funded by the German Federal Ministry of Education and Research (BMBF) - grant number 033W102A and the junior research group energy sufficiency funded by the German Federal Ministry of Education and Research (BMBF) as part of its Social-Ecological Research funding priority, funding number 01UU2004A. </p>
(D2.1_dataset01) Parameter values used in the optimal loads split algorithm to find the baseload
<p>In Task 2.1 of hybridGEOTABS project, an <em>optimal load splitting algorithm (OLSA) </em>was developed and validated. The OLSA splits the building thermal loads (calculated in T2.2 of the project) to the baseload and residual loads. The baseload is defined as the maximum share of GEOTABS that allows to minimise the energy use of the entire (primary + secondary) heating and cooling system, while maintaining thermal comfort in the building and by using and taking into account the thermal storage in the TABS. The algorithm is documented in detail in D2.1 of the hybridGEOTABS project. This document reports all the parameter values used in the OLSA.</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.