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5,573 results for “optimality”
Data from: Optimizing the genetic composition of a translocation population: incorporating constraints and conflicting objectives
Translocations of threatened species can reduce the risk of extinction from a catastrophic event. For plants, translocation consists of moving individuals, seeds, or cuttings from a native (source) population to a new site. Ideally a translocation population would be genetically diverse and consist of fit founding individuals. In practice, there are challenges to designing such a population, including constraints on the availability of material, and tradeoffs between different goals. We present an approach for designing a translocation population that identifies sets of founders that are optimized according to multiple criteria (e.g., genetic diversity), while also conforming to constraints on the representation of different founders (e.g., propagation success). It uses flexible inputs, including SNP genotypes, matrices of similarity between individuals, and vectors of phenotype data. We apply the approach to a critically endangered plant, Hibbertia puberula subsp. glabrescens (Dilleniaceae), which was genotyped at thousands of SNP loci. The goals of minimizing genetic similarity among the founding individuals and maximizing genetic diversity were largely complementary – populations optimized for one of these criteria were near-optimal for the other. We also performed analyses in which we minimized genetic similarity among founding individuals while imposing selection (against hypothetical deleterious alleles, and against undesirable phenotypes, respectively), and here characterized sharp tradeoffs. This is useful in allowing the benefits of selection to be weighed against 'costs' in terms of genetic similarity. In sum, we present an approach for designing a translocation population that allows flexible inputs, the imposition of realistic constraints, and examination of conflicting goals.
Data from: Optimized application of the secreted Nano-luciferase reporter system using an affinity purification strategy
Secreted Nano-luciferase (secNluc) is a newly engineered secreted luciferase that possesses advantages of high structural stability, long half-life, and glow-type kinetics together with high light emission intensity, and thus would become one of the most valuable tools for bioluminescence assays. However, like other secreted luciferases, secNluc has to mix with the components in the conditioned medium surrounding test cells, or in the biological samples such as blood or urine after being secreted. These components may interfere with secNluc-catalyzed bioluminescence reactions and thus limit the application of the secNluc reporter system. In this study, we first examined the effects of three factors, pH, serum and residual reagents, on secNluc-catalyzed bioluminescence reactions, finding that these factors could interfere with bioluminescence reactions and result in background signal. To resolve these problems, we applied a simple affinity purification strategy in which secNluc was fused with a FLAG-tag, and anti-FLAG magnetic beads were used to catch and transfer the fusion protein to PBST, an optimal buffer for secNluc-catalyzed bioluminescence reactions that was identified in this study. The results indicated that this strategy could not only negate the interferences from serum or residual reagents and enhance the stability of light emission but also greatly increase signal intensity through enzyme enrichment. This strategy may contribute to biomedical studies that utilize secNluc and other secreted luciferases, especially those requiring superior sensitivity, low background noise and high reproducibility.
Dataset from 'Billino, J. & Drewing, K. (2017). Age effects on visuo-haptic length discrimination: Evidence for optimal integration of senses in senior adults. Mulitsensory Research
<p>Dataset associated with the following publication:</p> <p>Billino, J. & Drewing, K. (2017). Age effects on visuo-haptic length discrimination: Evidence for optimal integration of senses in senior adults. Multisensory Research.</p> <p>The folder contains a data file and a description file providing column labels.</p> <p>For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
Results Dataset for Addressing the Sustainable Aviation Fuel Grand Challenge: Economic and Environmental Insights from Feedstock Optimization
<p>This dataset contains the results from the reserach article titled "Addressing the Sustainable Aviation Fuel Grand Challenge: Economic and Environmental Insights from Feedstock Optimization"</p>
Optimizing Bank Loan Approval with Cutting-Edge Deep Learning model
<p>Abstract</p><p>For any bank or financial institution, managing loans and controlling leverage is one of the most<br>important tasks they have to undertake. A bank cannot function efficiently without a well-<br>designed loan-to-deposit business model. As technology continues to evolve, the mechanism of<br>handling and granting loans underwent a significant change with the introduction of use cases<br>concerning machine learning and data science.<br>Hence, this data-driven research utilized advanced machine learning techniques to analyze and<br>manipulate the data, aiming to predict the best possible way to recommend a loan to a client.<br>These predictions are based on modified yet unique features created from the data obtained from<br>the client. The dataset was tested using two different methodologies: a logistic regression model<br>and a Neural Network algorithm. Both of these methodologies produced high-level accuracy<br>rates. However, the latter outperformed the currently used methodologies by over 20%, resulting<br>in an accuracy of 90%.<br>The successful research results were obtained due to the use of a perfectly balanced, unbiased,<br>and cleaned dataset, as well as the well-executed combination of activation functions for the<br>Neural Network model. A performance assessment was conducted based on a confusion matrix<br>evaluation to demonstrate its feasibility and performance </p>
Supplementary material to `Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.5'
<p>This repository contains the supplementary material to the following paper: Hoffmann, L., Haghighi Mood, K., Herten, A., Hrywniak, M., Kraus, J., Clemens, J., and Liu, M., Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.5, Geoscientific Model Development, submitted, 2023.</p><p> </p>
Pulsed 193 nm Excimer laser processing of 4H-SiC(0001) wafers with radiant exposure dependent "in situ" reflectivity studies for process optimization.
<p>Conference paper</p>
Dataset related to article "Optimal Predictors of Postoperative Complications After Gastrectomy: Results from the Procalcitonin and C-reactive Protein for the Early Diagnosis of Anastomotic Leakage in Esophagogastric Surgery (PEDALES) Study"
<p>This record contains raw data related to article "Optimal Predictors of Postoperative Complications After Gastrectomy: Results from the Procalcitonin and C-reactive Protein for the Early Diagnosis of Anastomotic Leakage in Esophagogastric Surgery (PEDALES) Study"</p><p><strong>Abstract</strong></p><p><strong>Background: </strong>The aim of this study was to define whether procalcitonin (PCT) is an earlier and more accurate predictor than C-reactive protein (CRP) for anastomotic leakage (AL) and major infective complications (MICs).</p><p><strong>Methods: </strong>This was a prospective multicentric observational study conducted in three Italian centers, including all patients undergoing gastrectomy from May 2016 to April 2021. The endpoint was the assessment of the discrimination and accuracy achieved by the PCT and CRP values measured from POD1 to POD7 for predicting the occurrence of AL and MICs. Accuracy was assessed by calculating the area under the receiver operating curve (AUROC) values and Youden's statistics. Two charts were created for risk stratification during the postoperative course.</p><p><strong>Results: </strong>The rate of AL was 4.6%, with a median day of occurrence on POD5 (range 3-26). The overall rate of major infective complications was 19.9%, with a median day of occurrence on POD6 (range 2-30). PCT showed a significant association with AL on POD6 and POD7 and a significant association with MICs on POD2, while CRP values showed a significant association with AL on POD4 and a significant association with MICs on POD1. No difference in the prediction of AL was observed between PCT and CRP, while CRP was found to be a superior predictor of major infective complications on POD5 (p = 0.024) and POD7 (p = 0.035).</p><p><strong>Conclusions: </strong>PCT was not superior to CRP as an early predictor of AL and major infective complications after gastrectomy. CRP should be used as the reference screening postoperative marker.</p>
Optimization and Enhancement of the Peroxidase-like Activity of Hemin in Aqueous Solutions of Sodium Dodecylsulfate
Open the record for dataset details and reuse information.
Automatic Jorek Calibration via Batch Bayesian Optimization Data
<p>The dataset contains the essential information for replicating the Plots in the paper 'Automatic Jorek Calibration via Batch Bayesian Optimization'. The reader can also find the complete list of JOREK simulations performed.</p>
Silver Nanoparticles Encapped by Dihydromyricetin: Optimization of Green Synthesis, Characterization, Toxicity and Anti-MRSA-infection for Zebrafish (Danio Rerio).
Open the record for dataset details and reuse information.
Optimation of image encryption using fractal Tromino and polynomial Chebyshev based on chaotic matrix
Open the record for dataset details and reuse information.
Meta-Memetic Optimization: A Supporting Analysis of the GOD Framework
<p>This paper presents a supplementary proof to the GOD (Gravitational Omnipotent Dimensionality) framework, introducing the concept of Meta-Memetic Optimization—a recursive, self-correcting process through which cognitive and knowledge systems refine themselves. By extending the principles of memetic coherence, dissemination entropy, and recursive self-similarity, this analysis offers a new layer of interaction that facilitates the continuous improvement of knowledge propagation and cognitive evolution.</p>
PILOT-GM-VAE: Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders
<p><strong>Datasets for PILOT-GM-VAE.<br>This part of the data includes Breast Cancer, Kidney_KPMP(only kidney tissue and all samples), Myocardial Infarction(2), and Kidney Cancer.<br>Please for more details read the data sets section of the paper.</strong></p>
Optimization of Human Neural Progenitor Cells for an Imaging-Based High-Throughput Phenotypic Profiling Assay for Developmental Neurotoxicity Screening
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A new ventilated window with PCM heat exchanger—Performanceanalysis and design optimization
<p>The dataset includes the published data in the article A new ventilated window with PCM heat exchanger—Performance analysis and design optimization. For the details of the data description please refer to the paper.</p>
Learning the Characteristics of Engineering Optimization Problems with Applications in Automotive Crash
<p>Oftentimes the characteristics of real-world engineering optimization problems are not well understood. In this paper, we introduce an approach for characterizing highly nonlinear and Finite Element (FE) simulation-based engineering optimization problems, focusing on ten representative problem instances from automotive crashworthiness optimization. By computing characteristic Exploratory Landscape Analysis (ELA) features, we show that these ten crashworthiness problem instances exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used Black-Box Optimization Benchmarking (BBOB) problem set. Using clustering approaches, we demonstrate that these ten problem instances are clearly distinct from the BBOB test functions. Further analysis of the crashworthiness problem instances reveal that, as far as ELA concerns, they are most similar to a class of artificially generated functions. We identify such artificially generated functions and propose to use them as scalable and fast-to-evaluate representatives of the real-world problems. Such artificially generated functions could be used for the automated design of an optimization algorithm for specific real-world problem classes.</p>
Code, benchmarks and experiment data for the AAAI 2019 paper "Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. Except for the code base, it contains the same files as the Zenodo entry for our IPC 2018 planner Delfi: https://zenodo.org/record/6683892.</p> <p>sievers-et-al-aaai2019-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018. While this code base contains a few more scripts used for this AAAI paper compared to the IPC 2018 code, it hasn't been updated to contain, e.g., a requirements.txt file for setting up a Python virtual environment like we did for our IPC 2018 planners. Please see katz-et-al-ipc2018-delfi1.zip and katz-et-al-ipc2018-delfi2.zip in the above mentioned Zenodo entry.</p> <p>sievers-et-al-aaai2019-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models.</p> <p>sievers-et-al-aaai2019-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-aaai2019-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-aaai2019-raw-data.zip and sievers-et-al-aaai2019-parsed-data.zip contain the experimental data. Directories in sievers-et-al-aaai2019-raw-data.zip (without the "-eval" ending) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories in sievers-et-al-aaai2019-parsed-data.zip (with the "-eval" ending) contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>The image data set used for training can be found online: https://github.com/IBM/IPC-image-data</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
Code, benchmarks and experiment data for the IPC 2018 entry "Delfi: Online Planner Selection for Cost-Optimal Planning"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>katz-et-al-icp2018-delfi.zip and katz-et-al-icp2018-delf2.zip contain the implementation based on Fast Downward (the two folders only differ in how the planner selection works). It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018.</p> <p>katz-et-al-ipc2018-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models.</p> <p>katz-et-al-icp2018-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks).</p> <p>katz-et-al-icp2018-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab).</p> <p>katz-et-al-icp2018-raw-data.zip and katz-et-al-icp2018-parsed-data.zip contain the experimental data. Directories in katz-et-al-icp2018-raw-data.zip (without the "-eval" ending) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories in katz-et-al-icp2018-parsed-data.zip (with the "-eval" ending) contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>The image data set used for training can be found online: https://github.com/IBM/IPC-image-data</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p> <p> </p>
Lion Statue - optimized
Based on [Löwe](https://sketchfab.com/3d-models/lowe-4522a4cdc1c14190bf1a8811fa27da32) by [noe-3d.at](https://sketchfab.com/www.noe-3d.at) made available under the CC0 Public Domain dedication. I used [rapidcompact.com](http://rapidcompact.com) optimize this excellent 3D scan, choosing default settings and a 3mb target filesize. Object info: > Upright sitting lion in front of the National Library Vienna. The lion made of brown sandstone from Horitz in the Czech Republic holds a heraldic cartouche with one paw. The four lions in front of the entrance were created by Fritz Zerritsch. Source: Objaverse 1.0 / Sketchfab
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.