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519 results for “optimisation”
Experimental data to the publication "Genetic-optimised aperiodic code for distributed optical fibre sensors"
<p>The source data underlying Figs. 3-5 and Supplementary Figs. 6, 8-14 are provided as a Source Data file.</p>
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Datasets: Enhancing Anger Management via Reinforcement Learning: A Comparative Analysis of the PPO Algorithm with Optimised Hyperparameters
Open the record for dataset details and reuse information.
F I G U R E 4 in Toward optimising reproductive output of Eristalis tenax (Diptera: Syrphidae) for commercial mass rearing systems
F I G U R E 4 Time to first hatch (hours) and number of hatched larvae per egg cluster (mean ± SE) for Eristalis tenax egg clusters reared at 12 (n = 6), 16.5 (n = 5), 21.5 (n = 6), 25.5 (n = 5), 30 C (n = 6). Letters indicate significant differences in time to first hatch (bold) and hatched larval output at each temperature (p <0.05).
T A B L E 1 in Toward optimising reproductive output of Eristalis tenax (Diptera: Syrphidae) for commercial mass rearing systems
T A B L E 1 Summary of percentage of females mated, observed total egg cluster count and expected total egg cluster count proportionate to the number of females per cage, and differences between observed and expected egg cluster counts (% of expected) for Eristalis tenax at three different sex ratio treatments: 20:40, 30:30 and 40:20 female to male.
F I G U R E 3 in Toward optimising reproductive output of Eristalis tenax (Diptera: Syrphidae) for commercial mass rearing systems
F I G U R E 3 Percentage of total Eristalis tenax egg cluster output per week from eclosion, from four cages in the 30:30 sex ratio treatment (n = 196) and three cages in the 60 fly per cage stocking density treatment with a 1:1 sex ratio (n = 62).
F I G U R E 2 Survival curves for female Eristalis tenax flies observed for 11 in Toward optimising reproductive output of Eristalis tenax (Diptera: Syrphidae) for commercial mass rearing systems
F I G U R E 2 Survival curves for female Eristalis tenax flies observed for 11 weeks in four adult density treatments; 15:15 (n = 45), 30:30 (n = 90), 60:60 (n = 180), 120:120 (n = 360). Letters indicate significant differences (p <0.05) between survival curves of treatments.
F I G U R E 1 in Toward optimising reproductive output of Eristalis tenax (Diptera: Syrphidae) for commercial mass rearing systems
F I G U R E 1 Percentage of mated Eristalis tenax females in a captive population over time following eclosion (mean ± SE); 2 weeks (n = 15), 4 weeks (n = 29), 5 weeks (n = 15), 7 weeks (n = 4), 9 weeks (n = 16).
Supplement for "Tactic Script Optimisation for Aesop"
<p>This is the supplement for the paper "Tactic Script Optimisation for Aesop", to be published at CPP 2025. Please refer to the enclosed README for further information.</p>
One year HARMONIE-AROME SCM with cy40REF and cy40NEW for optimisation Turbulence scheme
<p>One year of HARMONIE-AROME SCM output (cy40REF and cy40NEW) used for the optimisation of the turbulence scheme (see Fig 14 in Model development in practice: A comprehensive update to the boundary layer schemes in HARMONIE-AROME cycle 40, de Rooy et al., 2022, Geosc. Model Dev.).</p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
Optimising bioelectrochemical systems using Hartree HPC Datasets
<p>A dataset containing simulation results for the optimising bioelectrochemical systems project.</p> <p>This contains steady-state data packed within TAR files. This data has been extracted and summarised within the CSV files.</p> <p>Data was gathered from adapting the mathematical model for simulation of bioelectrochemical systems published in Day et al 2022, this code will be released upon the submission of JDays thesis in late 2022. Steady-state data is saved as compressed NumPy files and can be extracted using python.</p> <p> </p>
Codes and data for: Clustering optimisation method for highly connected biological data
<p>Currently, data-driven discovery in biological sciences resides in finding segmentation strategies in multivariate data that produce sensible descriptions of the data. Clustering is but one of several approaches and sometimes falls short because of difficulties in assessing reasonable cutoffs, the number of clusters that need to be formed or that an approach fails to preserve topological properties of the original system in its clustered form. In this work, we show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data.</p> <p>The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data. The resulting clustering approach only relies on metrics derived from the inherent properties of the clustering. The new method facilitates knowledge for optimised clustering, which is easy to implement.<br>We discuss how the clustering optimisation strategy corresponds to the viable information content yielded by the final segmentation. We further elaborate on how the clustering results, in the optimal solution, corresponds to prior knowledge of three different data sets.</p> <p>This is the dataset and the codes required to conduct the above-mentioned analysis.</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)
Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation
Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)
Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)
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.