Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,573
datasets available to search
ShareScore release 0.9.0
Dataset results
5,573 results for “optimality”
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems
<p>The datasets in this release support the results presented in the paper</p> <blockquote> <p>P. Jamshidi, G. Casale, "An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems", accepted for presentation at MASCOTS 2016.</p> </blockquote> <p>An open access to the paper is available at https://arxiv.org/abs/1606.06543</p> <blockquote> <p>Also open source code is available at https://github.com/dice-project/DICE-Configuration-BO4CO</p> </blockquote> <p>The archive contains 10 comma separated datasets representing performance measurements (throughput and latency) for 3 different stream benchmark applications. These have been experimentally collected on 5 different cloud cluster over the course of 3 months (24/7). Each row in the datasets represents a different configuration setting for the application and the last two columns represent the average performance of the application measured over the course of 10 minutes under that specific configuration setting. The datasets contains a full factorial and exhaustive measurements for all possible settings limited to a predetermined interval for each variable. Each dataset is named in the following format: "<em>benchmark_application-dimensions-cluster_name</em>". For example, "wc-6d-c1" refers to WordCount benchmark application with 6 dimensions (i.e., we varied 6 configuration parameters) and the application was deployed on c1 cluster (OpenNebula, see Appendix). This resulted in a dataset of size 2880, i.e., it has taken 2880*10m=480h=20days for collecting the data! </p> <p>For more information about the data refer to the appendix of the paper: https://arxiv.org/abs/1606.06543. </p> <p>When referring to the dataset or code please cite the paper above.</p>
Optimized geometries for selected ions of ionic liquids and small molecules
<p>The geometries of these selected chemical entities were optimized at the ab initio or semiempirical levels of theory. They can be conveniently used to create more complicated systems through combining species like the free PACKMOL software offers. </p>
Optimization of SPECT/CT based lung dose calculation for Holmium-166 hepatic radioembolization
<p><strong>Background</strong>: Quantitative SPECT of the lungs after intra-arterial hepatic radioembolization using Holmium-166 (<sup>166</sup>Ho)microspheres is essential to assess therapy safety. A SPECT estimated lung absorbed dose resulting from radioembolization of more than 30 Gy is a contra-indication for therapy. Earlier we showed the superiority of Monte Carlo-based iterative reconstructions over conventional reconstructions due to its quantitative nature, required for dosimetry, at the cost of substantial computation times. In clinical routine, however, the limited available time between scout imaging and therapy constrains its application. To reduce computation times, we investigated the minimum number of iterations required to guarantee a clinical acceptable accuracy in lung dose estimation using patient and phantom data.</p> <p><strong>Methods</strong>: <sup>166</sup>Ho scout SPECT data (range: 222-283 MBq) were used from 9 patients. SPECT images were Monte Carlo-based OSEM reconstructed (iterations: 30, subsets: 8). Additionally, the 4D XCAT anthropomorphic phantom was used to mimic SPECT studies with an injected scout activity of 250 MBq and with varying lung doses ranging from 0 to 15.6 mGy/MBq. These studies were reconstructed in the same way as the patient data.</p> <p><strong>Results: </strong>In all patients the lung absorbed dose upon OSEM convergence ranged from 0 to 0.025 mGy/MBq, and ranged from 0.002 to 0.078 mGy/MBq after five iterations, still well below the allowed 30 Gy in case treatment was proceeded. In the phantom data, the estimated lung dose ranged from 0.004 to 15 mGy/MBq upon convergence and from 0.03 to 13.9 mGy/MBq after five iterations, simulating situations well below and above an estimated treatment lung dose of 30 Gy. Importantly, the lung absorbed dose upon OSEM convergence was underestimated by 15% as compared to the actual simulated lung dose, and the dose after five OSEM iterations was underestimated by 9% as compared to the dose upon convergence. Both underestimations were irrespective of the magnitude of the lung dose and thus can be easily corrected for.</p> <p><strong>Conclusions</strong>: The number of OSEM iterations necessary for a quantitative estimate of the lung dose can be reduced from 30 to 5. The resulting six fold reduction in calculation time enables data processing of the scout images before therapy administration.</p>
Data for: Improving Kieker's Scalability by Employing Linked Read-Optimized and Write-Optimized NoSQL Storage
<p>We show, how polyglot persistence increase Kieker's scalability by employing separate read-optimized and write-optimized noSQL storage. For this purpose we extended Kieker to store its monitoring output in Apache Cassandra , which is a write-optimized wide-column noSQL database. For the analysis of the generated monitoring output we are using ElasticSearch, a read-optimized document store noSQL storage. We are interlinking read-optimized and write-optimized noSQL storage within our Regression Benchmarking Execution Environment (RBEE). To ensure scalability we are employing a container infrastructure. The mentioned noSQL storages and the linker are operating within one single Docker container which scales horizontally.</p> <p>For generating reference values, we instrumented a Java SE application with Kieker's file system writer and measured throughput and method's execution times. Consecutively, we instrumented the same Java SE application with our Apache Cassandra writer and measured throughput and method's execution time. Finally, we compared measurement results of Kieker's file system writer with the measurement results of our Apache Cassandra writer.</p>
Dataset for "A QM-CAMD approach to solvent design for optimal reaction rates"
<p>Dataset accompanying "A QM-CAMD approach to solvent design for optimal reaction rates". </p> <p>All Gaussian09 files for Step 2 of the proposed QM-CAMD algorithm</p> <p>Sample GAMS file for Step 4</p> <p>Excel spreadsheet containing a summary of results for all 3 case studies</p>
Optimal copula models for the observed discharge and nitrate concentration in the Lower Illinois River
<p>Investigating the complex relationships between water quality parameters and river discharge has been an area of active research for decades. The objective of this study is to apply bivariate distributions to river nitrate concentration and discharge and to suggest the procedure of choosing the best bivariate distribution in the selected models. Nitrate concentration (NO<sub>3</sub>) data were measured from 1972 to 2012 at five stations in the Lower Illinois River basin, USA. The bivariate distribution was represented by applying three copula models to explore the dependence between discharge and nitrate concentration. The marginal distributions for the copula models include the generalized Pareto (GPA), generalized extreme value (GEV), two-parameter lognormal (LN2), and three-parameter lognormal (LN3) distributions as well as the three copula models of Clayton, Frank, and Gumbel, respectively. Each procedure was tested by the robust diagnostic, the probability plot correlation coefficient (PPCC) test, and the goodness-of-fit test ( statistic). Consequently, the Frank copula model with GPA for stream flow–GPA for nitrate concentration performed more accurately for data from the mainstream stations, Havana (D-31) and Valley City (D-32) and for those from a station on one tributary, Oakford station along the Sangamon River (E-25). However, along other tributaries such as the La Moine River at Ripley (DG-01) and Spoon River at Seville (DJ-08), the Clayton copula model with NL3 for stream flow–GPA for nitrate concentration showed better fit. This study enabled us to develop a procedure to determine an optimal copula for application to the environmental sciences and suggest a case study for the application of copula models to discharge, as a hydrologic variable, and nutrient concentration as a water quality variable.</p>
Data for analysis in "Towards optimal cosmological parameter recovery from compressed bispectrum statistics"
<p>Measures of the three point function extracted from a suite of simulations using 4 different estimators: namely, the bispectrum, modal estimator, integrated bispectrum, line correlation function. Also supplied are power spectrum measures across the same simulations. <br> <br> The measures are done across 200 fiducial and 60 non-fiducial cosmology simulations, at 3 redshifts. Further details on what was done can be attained by reading the document, Overview.md/Overview.pdf, attached to the bundle. Even more details can be acquired by reading the paper this data was prepared for at https://arxiv.org/abs/1705.04392! </p>
Matrix multiplication software and results bundle for paper "Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library" for P^3MA submission
<p>This is the archive containing the matrix multiplication software and the results of the publication "<em>Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library</em>" submitted to the P^3MA workshop 2017.</p> <p><strong>The archive has the following content:</strong></p> <ul> <li>Source code for the (tiled) matrix multiplication in "src": <ul> <li>regular version in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-compatible-alpaka-0-1-0</li> <li>Commit: a63ba4810d6bfcca62c68dd57408af15028e78a3</li> </ul> </li> <li>forked version for XL in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-xl-workaround</li> <li>Commit: 1fee028eccb8cf7b677e8071233e08aa9f81846a</li> </ul> </li> </ul> </li> <li>The compiled binaries and the results of the tuning and scaling runs are in "runs" in sub folders for each type of run and architectures.</li> </ul>
Dataset supplementing B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)
<p>Dataset presented in B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)</p>
Dataset for article: Integer programming for optimal yaw control of wind farms
<div> <p>This is the dataset for the article "Integer programming for optimal yaw control of wind farms". We provide the integer programs (lp-files) and corresponding solver log files for each case of our series of experiments. Submission of manuscript: September 2024 (v1). Major revision of manuscript: February 2025 (v2). Minor revision of manuscript: April 2025 (v3).</p> </div>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Machine Learning-Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets
<p>Effective maintenance planning for bridges is crucial for maintaining their performance, safety, and minimizing maintenance costs. Timely implementation of interventions can improve the performance of bridges and avoid the need for costly interventions. However, bridge maintenance is often delayed due to inadequate planning and budget allocation, as well as resource constraints such as funding. With availability of historical condition data of bridges in databases such as the National Bridge Inventory (NBI) and National Bridge Elements (NBE), there is an opportunity to use data-driven methods to predict deterioration of bridge elements and optimize their maintenance interventions to maximize performance of bridges. This paper presents the development of a novel system that uses Machine Learning (ML) techniques to predict condition of concrete bridge elements and binary linear programming optimization method to identify the optimal selection of maintenance interventions and their timing to maximize the performance of bridges while complying with available annual budgets. Four ML methods are explored: decision tree, random forest, gradient boosting, and support vector machines. The results of the ML evaluation show that, while the values of the predictive performance metrics varied for different elements, random forest method had the best performance for all elements. A case study of a concrete bridge is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The case study results show that the developed model identifies optimal maintenance interventions for various annual budgets over a 50-year study period. The primary contributions of this research to the body of knowledge are: (1) development of a novel system that integrates machine learning techniques and linear programming for predicting bridge element conditions and optimizing maintenance interventions; (2) modeling and predicting the deterioration of bridge elements based on health index metric; and (3) generating long-term maintenance plans for each of bridge elements to maximize the performance of bridges within available annual budgets. The present system is expected to support decision makers, such as highway agencies, in allocating limited financial resources for bridge maintenance more efficiently and cost-effectively.</p>
Figure 1. A in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 1. A timeline representing the release of predatory species P. persimilis and S. punctillum to control the twospotted mite T. urticae in a croton greenhouse.
Figure 3 in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 3. The comparison between the population of T. urticae in predators' greenhouse and control greenhouse.
Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies
<p>Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies.</p>
Data for Cost Optimization of Shedding Mechanisms as a Product Family
<p>This the data from the numerical examples presented in "Accelerating Cost Optimization of Existing Product Family Designs Using Sensitivity Analysis on Component Commonalities" article. It contains the data of the results for the cost optimization of a shedding mechanism as a product family from two types of optimization: (i) brute-force and (ii) guided optimization: proposed method which uses sensitivity analysis on component commonalities. Each datum (.mat file) contains design variables (length of the four-bar mechanisms and motor locations) of a specific commonality matrix.</p>
Optimally interpolated dissolved oxygen based on the World Ocean Database 2018 and CMIP6 models
<p>Optimal interpolation of observed and modeled dissolved oxygen data from WOD18 and CMIP6. Technical details are provided in the publication (Ito et al., 2023). </p><p>Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of global O2 loss in optimally interpolated historical ocean observations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-72, in review, 2023.</p>
[DAEMON DEMO] ITAREA: Optimal Deployment
<p><strong>ITAREA: Optimal Deployment </strong>presented live by UMA.</p> <p>Also available at YouTube: <a title="https://youtu.be/PYh-v5CXNNM" href="https://youtu.be/PYh-v5CXNNM">https://youtu.be/PYh-v5CXNNM</a></p> <p>Tracking, predicting and reducing the energy consumption of B5G networks.</p>
Software for optimizing treatment to slow the spatial propagation of invasive species: Code and results
<p>Slowing the spread of invasive species is a major challenge. How can we achieve this goal in the most cost-effective manner? This package includes the complete code and simulation results that help finding the optimal, most cost-effective treatment to slow the spread of a propagating species. This package accompanies the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). The file general_model_code.zip contains the code for the general model; the file spongy_moth_model_code.zip contains the code for the spongy moth model; and the file general_model_simulation_results.zip contains the results for the general model; and the file spongy_moth_model_simulation_results.zip contains the results for the spongy moth model.</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.