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1,063 results for “search”
Implementing YewPar: a Framework for Parallel Tree Search [Dataset]
<p>Dataset and scripts for "Implementing YewPar: a Framework for Parallel Tree Search"</p>
Searching for phylogenetic patterns of Symbiodiniaceae community structure among Indo-Pacific Merulinidae corals
<p>Symbiodiniaceae DNA sequences and community datasets</p>
Fig. 1. Search effort for Bangasternus between 2020–2022 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Fig. 1. Search effort for Bangasternus between 2020–2022. Symbol color represents presence or absence of B. fausti: white depicts presence, green depicts iNaturalist observations, and unfilled depicts absence. White triangles depict sites that had B. fausti and at least two species of Centaurea whereas unfilled triangles depict sites that had no B. fausti but had both C. diffusa and C. stoebe ssp. micranthos. Photo of B. fausti on C. diffusa by N. G. Earley. Figure prepared in QGIS version 3.16.6 with basemaps from https://github.com/stamen/terrain-classic and https://github.com/CartoDB/ basemap-styles.
Instances and results of the paper "Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance"
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Implementation and Results of a "Randomized Local Search for Two-Dimensional Bin Packing and a Negative Result for Frequency Fitness Assignment"
<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) applied to the two-dimensional bin packing problem with and without orientation. As benchmark dataset, we use the <code>beng</code>, <code>A</code>, and <code>class</code> instances from <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> as well as the four non-trivial Almost Squares in Almost Squares (<a href="https://math.vu.nl/~sbhulai/publications/data_analytics2016b.pdf"><code>Asqas</code></a>) instances</p> <p>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>Rui Zhao, Zhize Wu, Daan van den Berg, Matthias Thürer, Tianyu Liang, Ming Tan, and Thomas Weise. Randomized Local Search for Two-Dimensional Bin Packing and a Negative Result for Frequency Fitness Assignment. 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 files of <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> and other 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. Rui ZHAO (赵睿) 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:zr1329142665@163.com">zr1329142665@163.com</a>.</p>
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>
Implementation and Results of a "Randomized Local Search vs. NSGA-II vs. Frequency Fitness Assignment on The Traveling Tournament 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 as well as NSGA-II applied to the Traveling Tournament 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>Cao Xiang, Zhize Wu, Daan van den Berg, and Thomas Weise. Randomized Local Search vs. NSGA-II vs. Frequency Fitness Assignment on The Traveling Tournament 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. Xiang CAO (曹翔) 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:452015026@qq.com">452015026@qq.com</a>.</p>
Scrutinizing the Isotropic Gamma-Ray Background in Search of Dark Matter
<p>We include the files produced for "Scrutinizing the Isotropic Gamma-Ray Background in Search of Dark Matter" by Ilias Cholis and Iason Krommydas. These files do not include fitting to the IGRB data.</p>
Datasets and models for constrained retrosynthesis with multi-objective search and disconnection-aware Chemformer
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Replication package for the paper: "Code Clone Configuration as a Multi-Objective Search Problem"
<p><strong>Replication Package Context</strong></p> <p>This is the replication package for the paper "Code Clone Configuration as a Multi-Objective Search Problem". The paper was originally published in the <em>International Symposium on Empirical Software Engineering and Measurement</em> (ESEM).</p> <div> <div><strong>Search Algorithms and Datasets for MC3 Problem</strong></div> <div>This repository contains implementations of search algorithms for solving the MC3 (Multi-objective Code Clone Configuration) problem. </div> <div>Additionally, it includes various files related to Elasticsearch configurations, I/O operations, and other supporting resources. For more information about the project files and folders, you can read the README.md for general information.</div> </div>
FIGURE 3 in In search of an identity for Salvia bertolonii (Lamiaceae)
FIGURE 3. Boxplots (quartiles with outliers) for each of the 10 analysed floral characters of populations of Salvia pratensis (Mediterranean pops.–light red, continental pops.–red), S. saccardiana (blue) and S. verbenaca (green) in eastern Adriatic area. Population numbers on the axis 1 follow Fig. 2a, character measurements (in mm) are given on axis 2.
FIGURE 1 in In search of an identity for Salvia bertolonii (Lamiaceae)
FIGURE 1. Population samples included in the analyses, reviewed herbarium sheets, field observations and literature data for (a) Salvia pratensis and S. verbenaca and (b) S. saccardiana.
Automatically deglitched dataset for "Searching the InSight seismic data for Mars's background free oscillations"
<p>The file Deglitched_sols.zip includes the dataset employed in the article "Searching the InSight seismic data for Mars's background free oscillations", published in<em> Seismological Research Letters.</em></p> <p>It includes 966 ".mseed" files, where each file corresponds to each automatically deglitched (SEISglitch + Twistpy) sol employed in the analysis. </p> <p>This dataset corresponds to the data depicted in Figure 6, and it was employed to produce Figure 8 (final results) of the manuscript. </p>
Drone-based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions
<p>Supplementary_Video_S1.mp4</p> <p>Supplementary_Video_S2.mp4</p> <p>Supplementary_Video_S3.mp4</p> <p>Supplementary_Video_S4.mp4</p> <p>Supplementary_Video_S5.mp4</p> <p>Supplementary_Video_S6.mp4</p>
Core competencies of a scholarly journal editor. Database and citation search report and PRISMA_flow_diagram
<p><strong>Report on Database and Citation search and PRISMA_flow_diagram</strong></p>
Search strategies for a systematic review of tools used for costing outbreaks in acute hospital settings
<p>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question: </p> <p><span>What costing tools have been developed for use by healthcare workers to measure resource utilisation (and or cost data) attributable to outbreaks in acute hospitals from the healthcare perspective? </span></p>
Data from: Male black widows parasitize mate-searching effort of rivals to find females faster
Mate-searching success is a critical precursor to mating, but there is a dearth of research on traits and tactics that confer a competitive advantage in finding potential mates. Theory and available empirical evidence suggest that males locate mates using mate-attraction signals produced by receptive females (personal information) and avoid inadvertently-produced cues from rival males (social information) that indicate a female has likely already mated. Here we show that western black widow males use both kinds of information to find females efficiently, parasitizing the searching effort of rivals in a way that guarantees competition over mating after reaching a female's web. This tactic may be adaptive because female receptivity is transient, and we show that (a) mate searching is risky (88% mortality) and (b) a strongly male-biased operational sex ratio (from 1.2:1 to >10:1) makes competition inevitable. Males with access to rivals' silk trails moved at higher speeds than those with only personal information, and located females even when personal information was unreliable or absent. We show that following rivals can increase the potential for sexual selection on females as well as males and argue it may be more widespread in nature than is currently realized.
Modeling System Behaviour from Log Analysis Using Meta-Heuristic Search
<p>Dataset used to perform the experiments in our paper on <em>Modeling System Behaviour from Log Analysis Using Meta-Heuristic Search </em>(BSc. thesis, 2021).</p>
Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping
<p>Dataset -- Brief summary of the automated approaches for searching and selecting studies for secondary studies in software engineering </p>
Automated Support for Searching and SelectingEvidence in Software Engineering: A Cross-domainSystematic Mapping
<p>Data from the studies that adrress search and selection approaches. </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.