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5,573 results for “optimality”
ROS-Specific Huntingtin Interactions: Optimization of Poly ADP Ribose Detection by Immunofluorescence
<p>Optimization of conditions for the detection of poly ADP ribose by immunofluorescence.</p>
Optimization of Gang-operated On-load Tap Changers in Multi-conductor Radial Networks: Formulation and Convex Relaxation
<p>DSS files for case studies in CIGRE conference paper</p> <p>Only the secondary of the regulator can tap between 0.9 and 1.1, this is not specified in the DSS files themselves</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: Animated Fig. 1
<p>This repository includes the animated version of Fig. 1 in https://arxiv.org/abs/1905.04900.</p>
Experimental data from the JAIR 2020 paper "Saturated Cost Partitioning for Optimal Classical Planning"
<p>This data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and to save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Supporting dataset for : Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)
<p>This dataset supports the conference paper, 'Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)', <a href="https://doi.org/10.17868/67963">deposited and available in Strathprints</a> and presented at the 14th International Conference Open Repositories (OR2019). It also supports the journal article, 'Enhancing content discovery of open repositories: an analytics-based evaluation of repository optimizations', published in Publications. Full details of these publications are as follows: </p> <ul> <li>Macgregor, G. (2019). <em>Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)</em> . (pp. 1-11). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/67963">https://doi.org/10.17868/67963</a> </li> <li>Macgregor, G. (2020). Enhancing content discovery of open repositories: an analytics-based evaluation of repository optimizations. <em>Publications</em>, <em>8</em>(1), [8]. <a href="https://doi.org/10.3390/publications8010008">https://doi.org/10.3390/publications8010008</a></li> </ul> <p>The dataset comprises a series of comma-separated values plain text documents (.csv). The .csv files contain data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data from Google Analytics and figures on annual full-text repository deposits made to the Strathprints repository. A README.txt is included, describing in more detail the nature and the structure of the data.</p> <p>All data relate to the EPrints repository, <a href="https://strathprints.strath.ac.uk/">Strathprints</a>, based at the University of Strathclyde.</p>
Input data for article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations"
<p>Input dataset used when performing LES simulations for journal article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations" (Karttunen et al., in preparation). The dataset was used with the PALM model system revision 3698 and most likely it won't work on older or newer versions.</p> <p>Instructions for use:<br> A precursor run must be run first. Output data (BINOUT) of it should be linked into a BININ directory of the actual scenario runs. You'll most likely have to tweak the CPU grid settings in ENVPAR and PARIN files in order to fit them to your computational resources. For more information on usage please refer to the PALM model documentation available online in <a href="https://palm.muk.uni-hannover.de/trac/wiki/doc">https://palm.muk.uni-hannover.de/trac/wiki/doc</a>.</p>
Fig. 12 in Optimality of phylogenetic nomenclatural procedures
Fig. 12 Nonsensical statement under branch-based definitions, examples with cases involving 4 species. The taxon under scrutiny is defined as>B∼A (i.e. as 'the clade consisting of B and all organisms or species that share a more recent ancestor with B than with A', or as 'the most inclusive clade containing B but not A'); for further explanations, see text
Fig. 8 in Optimality of phylogenetic nomenclatural procedures
Fig. 8 Variations of the inferred ancestral character states under cladotypic procedure, in relation to alternative phylogenetic hypotheses (A–G are species); the two tress are assumed to be produced from a single matrix (though the latter could not be designed). Definition of taxon T under scrutiny involves character state 1 as exhibited by species B and C; lineages designated by the definition are indicated by bold lines; for further explanations, see text
Fig. 1 in Optimality of phylogenetic nomenclatural procedures
Fig. 1 Retrieval of hierarchy information based on a ranked taxonomy using standardized endings, case of balanced topology representing relationships among eight genera (A–H; I–N are more
Fig. 6 in Optimality of phylogenetic nomenclatural procedures
Fig. 6 Variations of the inferred ancestral character states under nodebased procedure, in relation to alternative phylogenetic hypotheses (A–G are species); case of a given character state matrix providing two equally most parsimonious trees. Taxon T under scrutiny is the one
Fig. 4 in Optimality of phylogenetic nomenclatural procedures
Fig. 4 Necessary and sufficient number of words for naming species and taxa under the traditional procedure, and under a non-ranked procedure using uninominal species names; case of an unbalanced topology involving 8 species; see text for explanations
Fig. 3 in Optimality of phylogenetic nomenclatural procedures
Fig. 3 Necessary and sufficient number of words for naming species and taxa under the traditional procedure, and under a non-ranked nomenclatural procedure using uninominal species names; case of a balanced topology involving 8 species; see text for explanations
Dataset for Machine Learning-Based Prediction and Optimization of As-Extruded Viability in Extrusion-Based 3D Bioprinting
<p>The dataset supports the findings presented in the paper "Machine learning-based prediction and optimization framework for as-extruded cell viability in extrusion-based 3D bioprinting."</p> <ul> <li>Sodium alginate viscosity data: "alg_i1g_viscosity_data.zip"</li> <li>Cross Power Law parameter fitting results: "alg_i1g_viscosity_fittings.zip"</li> <li>Rheological stability measurement: "alg_i1g_contact_angle_data.zip"</li> <li>OpenFOAM simulation results: "alg_i1g_simulation_data.zip"</li> <li>Post-extrusion cell viability results: "cell_viability_data.zip"</li> </ul> <p><strong>Keywords:</strong> 3D bioprinting; cell viability; shear stress; numerical analysis; machine learning; alginate<br><br></p> <p><strong>Code availability statement</strong><br>The scripts used for data analysis, machine learning models, and numerical simulations in this study are available on GitHub at: <a href="https://github.com/KORINZ/in-silico-bioink-viability-prediction">https://github.com/KORINZ/in-silico-bioink-viability-prediction</a></p>
Fig. 2. A–G, Aphaena discolor. A–B in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 2. A–G, Aphaena discolor. A–B, specimen on his host-tree, Kirirom, 9.V.2015 (J. Constant). C, habitat in Kirirom, 9.V.2015 (J. Constant). D, Pursat, Cardamom, 2.I.2009 (J. Holden). E, Koh Kong, Tatai, 5.III.2012 (G. Chartier). F–G, Dichoptera sp. Chambok, 5.V.2015 (J. Constant). H, Kalidasa nigromaculata, Siem Reap, Angkor, 10.VIII.2014 (S. De Greef). I, Penthicodes atomaria tended by a cockroach, Cardamom Mts, 4.VIII.2013 (A. Anker). J, P. pulchella, Siem Reap, 18.IX.2013 (S. De Greef). K, P. variegata, Mondulkiri, O Reang District, 19.V.2015 (B. Barca). L–M, Polydictya tricolor, Siem Reap, Angkor, 1.VIII.2013 (S. De Greef). N–O, Polydictya sp., 8 km NNW Angkor, 6.XI.2013 (E. Smith).
Fig. 1 in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 1. Call to collaboration to the study of Fulgoridae of Cambodia posted on Facebook on May 18th, 2015.
Fig. 3. A–B, Pyrops candelaria. A in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 3. A–B, Pyrops candelaria. A, Chambok, 1.IX.2014 (S. Phauk). B, Kampong Tralach, 21.IX.2013 (O. Rodriguez). C–D, P. coelestinus, Chambok, 1.IX.2014 (S. Phauk). E, P. condorinus, Koh Kong, Tatai, 24.V.2015 (G. Chartier). F, P. ducalis, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). G–I, P. peguensis. G, Tumpor, Cardamom, 12.VIII.2009 (J. Holden). H, Chambok, 5.V.2015 (J. Constant). I, idem, biotope. J, P. spinolae, Ratanakiri, Veun Sai Siem Pang, 23.II.2015 (Marduk). K, P. viridirostris, Chambok, 7.V.2015 (J. Constant). L–N, Saiva gemmata. L, nymph, Chambok, 7 May 2015 (J. Constant). M, adult tended by a cockroach, Chambok, 7.V.2015 (J. Constant). N, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). O–Q, Zanna sp. O–P, Koh Kong, Tatai, 1.XI.2012 (G. Chartier). Q, Kampot, 21.XII.2013 (K.W. Meier-Doernberg).
Implementation and Results of a "Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem"
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA applied to the Quadratic Assignment Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Jiayang Chen, Zhize Wu, Sarah L. Thomson, and Thomas Weise. Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. In <em>16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24)</em>. November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Jiayang CHEN (陈嘉阳) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:cjy65820607@163.com">cjy65820607@163.com</a>.</p>
PanDDA analysis of ligand screen against the NSP3 macrodomain of SARS-CoV-2: ligands from FrankenROCS fragment-linking pipeline and subsequent optimization of AVI-313
<p>This deposition contains the X-ray diffraction data used to run PanDDA in the ligand screen against the NSP3 macrodomain of SARS-CoV-2 described in Correy et al. 2024 (doi: https://doi.org/10.1101/2024.08.25.609621). Compounds were from fragment linking using FrankenROCS and subsequent optimization of AVI-313. </p> <p>frankenROCS_mac1.tar.gz contains structure factor intensities, PanDDA input/output and refined models/maps.</p> <p>frankenROCS_mac1_ligand-bound-states.tar.gz contains the ligand-bound states extracted from the multi-state PDB files.</p>
Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures
<p>This is the training data for the paper "Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures"</p>
Data for the publication "Wind farm layout optimization with alignment constraints", submitted to Wind Energy Science, 2024
<p>The data in pickle format contains the optimal layouts corresponding to the numerical applications of the paper "Wind farm layout optimization with alignment constraints" submitted in Wind Energy Science, 2024.</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.