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5,573 results for “optimality”
scMARK an 'MNIST' like benchmark to evaluate and optimize models for unifying scRNA data
<p>Here we present a novel benchmark dataset (scMARK.v2), that consists of 11 published cancer scRNA-seq studies, for which we standardized cell-type author labels and gene identifiers. scMARK.v2 can be used to ask how well models integrate data from different scRNA studies. We also provide a 12th standardized study (Wu et al 2021) that we held-out for evaluation of alignment of data "never seen" before, and a 13th study of newly generated in-vitro scRNA-seq data from cancer and fibroblast cells.</p> <ul> <li>Data is provided as aData *h5ad files that can be read with Python's library <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>.</li> <li>Studies inclided in scMARK.v2 were downsampled to 10,000 cells per study.</li> <li>The difference between <a href="https://zenodo.org/record/5765804">scMARK.v1</a> and scMARK.v2, is that in v2, we provide at least two studies for each cancer type and each cell type; whereas in v1 a handfull of cell types were present only in one study.</li> </ul>
Global dataset for "Global leaf-trait mapping based on optimality theory "
<p>This repository contains Global data used for “<em>Global leaf-trait mapping based on optimality theory</em><strong>” </strong>published in GEB.</p> <ol> <li>Global_Maps_SLA represents climatology of published Global SLA used for comparison (details products see table 1 and figure 4).</li> <li>Global_Maps_Na represents climatology of published Global Narea used for comparison (details see table 1 and figure 4).</li> <li>Global_Maps_Nmass represents climatology of published Global Nmass used for comparison (details see table 1 and figure 4).</li> <li>TS_SLA is simulated time-series of <em>SLA</em> based on optimality theories from 1992 to 2015</li> <li>TS_Na is simulated time-series of <em>Narea </em>based on optimality theories from 1992 to 2015</li> <li>TS_Nmass is simulated time-series of <em>Nmass </em>based on optimality theories<em> </em> from 1992 to 2015</li> <li>TS_LMA_decidudous is simulated time-series of deciduous <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_LMA_evergreen is simulated time-series of evergreen <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_Vcmax25 is simulated time-series of Vcmax25 based on optimality theories from 1982 to 2016</li> </ol>
An optimal envelope ejection efficiency for merging neutron stars
<p>Summary of rapid stellar evolution simulation results computed using binary_c for the paper "An optimal envelope ejection efficiency for merging neutron stars". Version 2.0 of binary_c was used for the simulations. DNSs.tar.gz contains all the table files. More details in the README.txt file and in the paper.</p>
Optimized OPEP Force Field for Simulation of Crowded Protein Solutions
<p>Initial structures (in the PDB format) and LBMD trajectories (in the GROMACS XTC format) of crowded protein solutions simulated using the OPEPv7 force field and presented in the article entitled <em>Optimized OPEP Force Field for Simulation of Crowded Protein Solutions </em>(<a href="https://doi.org/10.1021/acs.jpcb.3c00253">https://doi.org/10.1021/acs.jpcb.3c00253</a>).</p>
Having an optimal race with hypnosis: Efficient mental training with a hypnosis audio-intervention in professional Downhill Mountainbike athletes
<p>We tested 19 professional downhill mountainbike athletes participating in two consecutive races of the IXS Downhill Cup. All athletes listened to a hypnosis audio-intervention at least one time after the first race. We instructed the athletes to use the personal post-hypnotic trigger to elicit an optimal mental state in the second race. At both races, we measured competitive anxiety, self-confidence, state resilience, and flow with questionnaires and assessed heart rate variability (HRV) as a physiological measure of resilience.</p> <p>HRV data was measuared using the Polar H10 chest strap and recorded via the Polar Sensor Logger Application. HRV was analysed with Kubios HRV Standard version 3.5.0 Analysis Software. All data were analysed with R.</p> <p>The original recording of the hypnosis audio-intervention is accessible via this link: <a href="https://www.nina-hoffmann.com/mental">https://www.nina-hoffmann.com/mental</a>.</p>
Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"
<p>"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model. </p>
Datasets describing optimization the cutting regime in the turning of AISI 316L steel based on the NSAG-II and NSAG-III multicriteria algorithms.
<p><em>This work shows the multi-criteria data analysis of the dry and MQL turning process of AISI 316L steel using the evolutionary algorithms of non-dominant class II and III (NSAG-II and NSAG-III). The wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) are used as analysis variables, with the aim of minimizing the wear of the cutting tool based on the optimal selection of parameters. When comparing the results obtained from both methods, we found that NSAG-III was the best alternative for selecting parameters in the turning of specimens, with fewer tool wear and more efficient use of energy consumption.</em> <em>Interpretation of this data</em></p>
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty
<p>We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function.<br> The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster.<br> Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty", presented at IPCO 2023 by the same authors.</p> <p> </p> <p>Paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/s12532-022-00232-2"> https://doi.org/10.1007/s12532-022-00232-2</a><br> Paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/978-3-031-32726-1_5">https://doi.org/10.1007/978-3-031-32726-1_5</a><br> For algorithms solving these problems see: <a href="https://doi.org/10.5281/zenodo.7463371">https://doi.org/10.5281/zenodo.7463371</a></p>
Simplified local infill size optimization for FDM printed PLA parts
<p>The great advantage of additive manufacturing is the fact that hollowed parts with a given infill can be created. However, the standardized commercial slicer software offers a uniform infill pattern creation solution. In engineering practice, the manufactured parts are functional, therefore the appropriate load bearing capacity is mostly mandatory. In this paper a simplified local infill size optimization method has been presented. Based on a Finite Element Analysis the local density of the pattern can be adjusted, according to the emerged local stresses. The results show that independently of the pattern type, if the scaling was applied, the mechanical resistance was improved to the same extent. In case of the worst-performing uniform pattern, 84% improvement in mechanical resistance was achieved with the optimization. In addition, an FDM printing problem has been highlighted, which must be eliminated if the proposed method is used.</p>
TemoaProject: Databases used in Sinha et al. (2024), Diverse Decarbonization Pathways Under Near Cost-Optimal Futures
<p>Contains modeling to generate alternative databases for the U.S. energy system used as part of the Open Energy Outlook. Databases were created using the logic outlined in: https://github.com/adityasinha1992/temoa/tree/mga_parallelized</p>
Freeform topology optimized, dispersive nanostructures for field confinement
<p>Provides the 3D topology optimized nanostructures as STL files, as well as the Lumerical files used to validate their performance presented in the article "Time-Domain Topology Optimization of Arbitrary Dispersive Materials for Broadband 3D Nanophotonics Inverse Design" (https://doi.org/10.1021/acsphotonics.3c00572).</p>
Virtual Flight Trajectory Prediction and Performance Optimization
<p>Data sets collected from running test flights using Prepar-3D flight simulator; extracted using FS-Flight Control software </p>
Strategies for optimal sky subtraction in the low surface brightness regime
<p>Synthetic source master images and associated source property catalogues used in the study "Strategies for optimal sky subtraction in the low surface brightness regime" (Watkins et al.).</p>
Optimal design of virtual screening benchmarks from in vitro screening data
<p>Input Datasets and output data for the creation of a new Benchmark dataset using PubChem Bioassay data. </p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
Optimization procedure of low frequency vibration energy harvester based on magnetic levitation: Datasets and scripts
<p>****** Please view the README.txt file for detailed documentation of data. ******</p> <p> </p> <p>Title: Optimization procedure of low frequency vibration energy harvester based on magnetic levitation: Datasets and scripts<br>Version: 2.0<br>Date of Release: 2023/08/23<br>Identifier: doi:10.5281/zenodo.8317223<br>Permalink: http://dx.doi.org/10.5281/zenodo.8317223</p> <p><br>Associated publication: I. Royo-Silvestre, J. J. Beato-López, C. Gómez-Polo "Optimization procedure of low frequency vibration energy harvester based on magnetic levitation", Applied Energy, Volume 360, 15 April 2024, 122778</p> <p>Link to publication: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.122778" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.apenergy.2024.122778</span></a></p> <p><br>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README file.</p> <p> </p> <p>Contact information: Isaac Royo Silvestre, Universidad Pública de Navarra, Pamplona, Spain., isaac.royo@unavarra.es<br>Co-authors: juanjesus.beato@unavarra.es, gpolo@unavarra.es</p> <p> </p> <p>Dates of data collection: 2023/03<br>Geographic location: Pamplona, Spain</p> <p> </p> <p>This directory contains the following datasets and scripts:</p> <p>SCRIPTS</p> <p>- harvester_op.m: Matlab script to automate the design and optimize a magnetic spring based vibration energy harvester (more information in the associated paper)</p> <p>- harvester_op_par.m: Matlab script, a version of harvester_op.m modified for parallel computing and shorter execution time in multicore computers (file added in v 2.0 of the data upload).</p> <p>DATASETS<br>- data.zip: Experimental data recorded by the datalogger as well as tabular data required to plot curves (compressed zip file) in csv format</p> <p> </p> <p>Specific documentation of each file is described in readme files.</p> <p> </p> <p>Refer to the original manuscript (see above) and the text of the Supplementary Materials published alongside this manuscript for additional information regarding the collection and generation of these data.</p>
Norway energy policy optimization results
<p>These files contain the raw results from master thesis on Norwegian energy policy directions. The each folder has a html file (with basic plots), a netcdf file and a series of csv files. The four scenarios uploaded are the unconstrained (model is free to install capacity as it wants) and base case (current norwegian policy direction), for both a beyond 2030 and beyond 2050 timeline. These results are part of a Master thesis work. Additional results related to transmission and onshore wind policy available upon request to h.guddingsmo@outlook.com.</p>
Metadata related to Optimized Intercropping System (NCBI PRJNA928765)
<p><strong>The Sahel of W Africa is a climate change hotspot, where soil degradation and recurring drought significantly reduce agricultural productivity. Erratic rainfall, exacerbated by climate change, t contributes to ongoing and future food insecurity in this region. The staple crop, pearl millet (Pennisetum glaucum), is grown by subsistence farmers during the rainy season without fertilizer or irrigation (conditions also desired for US domestic biofuel cultivation). Indigenous farming practices demonstrate one solution: where farmers intercrop with an native woody shrub (Guiera senegalensis), millet drought resilience - and crop yields - are dramatically increased. One mechanism of this beneficial interaction: shrubs perform hydraulic redistribution of water to surface soil, assisting crops through growing-season drought periods. Additionally, the moister and more nutrient rich soils under the shrub canopy harbor a distinct and active microbial community; this work examines the role of these microbes in aiding the millet. We have characterized this potential in partnership with the Joint Genome Institute (JGI, project number 505730) to metagenomically sequence the millet rhizo- and endo-sphere communities, with and without shrub intercropping, in long-term experimental plots in Senegal. Using the framework of known plant-microbe beneficial interactions we are analyzing metagenomes for genes and genomes related to plant growth promotion, and metagenomic data is publicly available via NCBI under PRJNA928765. Here, we publish soil chemical, plant biomass, and PLFA data obtained from the OSS during the 2019 sampling season and corresponding 2020 dry season. </strong></p>
Prediction of the potentially suitable areas of Leonurus japonicus with the optimized MaxEnt model
<p><em><span>Leonurus japonicus </span></em><span>Houtt.</span><span> is a traditional Chinese medicinal plant with high medicinal and edible value.</span><span> Wild <em>L. japonicus</em> resources have been reduced dramatically in recent years. This study predicted the response of distribution range of <em>L. japonicus</em> to climate change in China, which provided the scientific basis for the conservation and utilization. In this study, 489 occurrence poin</span><span>ts</span><span> of <em>L. japonicus</em> were selected based on GIS technology and spThin package. The default parameters of the Maxent model were adjusted by using ENMeva1 package of the R environment, and the optimized Maxent model was used to analyze the distribution of <em>L. japonicus</em>. When the feature combination in the model parameters is hing and the regularization multiplier is 1.5, the Maxent model has a higher degree of optimization. With the AUC of 0.830 our model showed a good predictive performance The results showed that <em>L. japonicus</em> was widely distributed in the current period. The maximum temperature of the warmest month, the minimum temperature of the coldest </span><span>month</span><span>, the precipitation of the wettest month, the precipitation of the driest month and altitude were the main environmental factors affecting the distribution of <em>L. japonicus</em>. Under the three climate change scenarios, the suitable distribution area of <em>L. japonicus</em> will range-shift to high latitudes, indicating that the distribution of <em>L. japonicus</em> has a strong response to climate change. The regional change rate is the lowest under the SSP126-2090s scenario and the highest under the SSP585-2090s scenario.</span></p>
Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
<p>DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range.</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.