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”
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
F I G U R E 4 Assigned ancestry plot using AIC criteria determining optimal K in Riverscape genetics of the orangethroat darter complex
F I G U R E 4 Assigned ancestry plot using AIC criteria determining optimal K of 5. Each color corresponds to a distinct genetic cluster, and each bar represents an individual's assigned ancestry to each cluster. Names above chart correspond to river basin (HUC6). EB—E. burri, EP— E. pulchellum, ES—E. spectabile, and EU—E. uniporum. Glaciated and unglaciated correspond to areas that were/were not covered in ice during the last glacial maximum.
Simulation to optimize the laboratory diagnosis of bacteremia: event times dataset from observational and retrospective studies
<h1>Introduction</h1> <p>Dataset consisting of three .csv files generated from observational and retrospective data analysis for the manuscript titled: Simulation to optimize the laboratory diagnosis of bacteremia, by Gerada et al. Dataset consists of event time-stamps for blood cultures. All identifiers have been stripped. </p> <h1>Dataset summary</h1> <ul> <li>observational_data.csv – time-stamps generated from direct observation of blood culture processing. Note that data is in wide format, so that column names contain the event name. Also, events are not related to a particular specimen, therefore, column lengths are not equal. </li> <li>retrospecitve_audit.csv – time-stamps generated from manualk retrospective audit of laboratory information system. Column `Lab` indicates the incubator. Rows are per specimen. </li> <li>retrospective_data.csv – time-stamps pulled automatically from laboratory information system (without manual curation). Rows are per specimen.</li> </ul>
Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
<p>Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)</p>
Increasing sustainability in palaeoproteomics by optimizing digestion times for large-scale archaeological bone analyses
<p>Palaeoproteomic analysis of skeletal proteomes is used to provide taxonomic identifications for an increasing number of archaeological specimens. The success rate depends on a range of taphonomic factors and differences in the extraction protocols employed. By analyzing 12 archaeological bone specimens from two archaeological sites, we demonstrate that reducing digestion duration from 18 to 3 hours has no measurable impact on the obtained taxonomic identifications. Peptide marker recovery, COL1 sequence coverage, or proteome complexity are also not significantly impacted. Although we observe minor differences in sequence coverage and glutamine deamidation, these are not consistent across our dataset. A 6-fold reduction in digestion time reduces electricity consumption, and therefore CO<sub>2</sub> emission intensities. We furthermore demonstrate that working in 96-well plates further reduces electricity consumption by 60%, in comparison to individual microtubes. Reducing digestion time therefore has no impact on the taxonomic identifications, while reducing the environmental impact of palaeoproteomic projects.</p>
Effective seed sterilization methods require optimization across maize genotypes
<p>Studies of plant-microbe interactions using synthetic microbial communities (SynComs) often require the removal of seed-associated microbes by seed sterilization before inoculation to provide gnotobiotic growth conditions. A diversity of seed sterilization protocols have been developed in the past and have been used on different plant species with various amounts of validation. From these studies, it has become clear that each plant species requires its own optimized sterilization protocol. It has, however, so far not been tested if the same protocol works equally well for different varieties and seed sources of one plant species. We evaluated six seed sterilization protocols on two different varieties (Sugar Bun & B73) of maize. All unsterilized maize seeds showed fungal growth upon germination on filter paper, highlighting the need for a sterilization protocol. A short sterilization protocol with hypochlorite and ethanol was sufficient to prevent fungal growth on Sugar Bun germinants, however, a longer protocol with heat treatment and germination in fungicide was needed to obtain clean B73 germinants. This difference may have arisen from the effect of either genotype or seed source. We then tested the protocol that performed best for B73 on three additional maize genotypes from four sources. Seed germination rates and fungal contamination levels varied widely by genotype and geographic source of seeds. Our study shows that consideration of both variety and seed source is important when optimizing sterilization protocols and highlights the importance of including seed source information in plant-microbe interaction studies that use sterilized seeds.</p>
LC-MS/MS, PSM and BLG properties data from "Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"
<p>This dataset contains the data analyzed in:</p> <p>Rodriguez Palomo I, Nair B, Chang Y, Dartigues B, Dekker K, Mackie M, Evans M, Macleod R, Olsen JV, Collins MJ. (2023) "<em>Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"</em></p> <p>It contains the following data:</p> <ul> <li>raw_files.zip Thermo RAW files for the 0, 4 and 128 days samples</li> <li>benchmark_results.zip PSMs data from the analysis of the RAW files <ul> <li>Data from runs in Mascot, Fragpipe, pFind, Metamorpheus, MaxQuant and DeNovoGUI</li> <li>Parameters and workflow files for MaxQuant and Fragpipe</li> </ul> </li> <li>bovin_blg_prop.zip BLG properties files: amyloid formation, 3D structure and solvent accessibility</li> <li>benchmark_table.csv Table with runs settings for benchmarking</li> <li>parameters_table.xlsx Spreadsheet with software parameters, derived from files used to run each software</li> </ul> <p> </p>
Dataset of structural and energetic descriptors for optimized geometries of the pyrene dimer in the S1 state and Jupyter Notebbok used for the unsupervised clustering, analysis and visualization
<p>Geometrical and energy data extracted from a set of 188 optimized geometries for the pyrene dimer in the first excited singlet state at the TD-CAMB3LYP + D3BJ / 6-31G* / C-CPCM(Cyclohexane) level. </p> <p>Coordinates (in xyz format) of the 188 optimized geometries considered.</p> <p>Jupyter notebook used to perform unsupervised clustering and analysis of the available structures.</p>
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
Off-Grid Ultrasound Imaging by Stochastic Optimization
<p>These are the data files used in the paper "Off-Grid Ultrasound Imaging by Stochastic Optimization".</p> <p>The code for the paper can be found in the corresponding <a title="github repository" href="https://github.com/vincentvdschaft/off-grid-ultrasound" target="_blank" rel="noopener">github repository</a>.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Description</strong></td> <td><strong>Transmit scheme</strong></td> <td><strong>Transducer</strong></td> </tr> <tr> <td>L11-5v_carotid1.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Verasonics L11-5V</td> </tr> <tr> <td>L11-5v_carotid2.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Verasonics L11-5V</td> </tr> <tr> <td>L11-5v_carotid3.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions.</td> <td>Philips S5-1</td> </tr> <tr> <td>S5-1_cirs.hdf5</td> <td>Acquisition of the CIRS-040 phantom in the low attenuation zone.</td> <td>80 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Philips S5-1</td> </tr> <tr> <td>cirs_simulated.hdf5</td> <td>Simulated data similar to CIRS-040 phantom.</td> <td>3 synthetic aperture transmissions.</td> <td>Similar to Philips S5-1</td> </tr> </tbody> </table> <p>A description and unit for the datasets in these files is provided in the dataset attributes.</p>
GloMPO (Globally Managed Parallel Optimization) Benchmark Test Data
<p>Dataset associated with:<br> M. Freitas Gustavo and T. Verstraelen (2021), "GloMPO (Globally Managed Parallel Optimization) - a tool for expensive, black-box optimizations: application to ReaxFF reparameterizations".</p> <p>Contains optimization trajectories using the CMA-ES optimizer applied to various benchmark functions and ReaxFF reparameterizations. Compares optimization results using the GloMPO framework (github.com/mfgustavo/glompo) to unmanaged optimization results.</p>
MPIData: Optimized System Matrix Sampling
<p>This data repository contains magnetic particle imaging data. The files are stored in the <a href="https://arxiv.org/pdf/1602.06072.pdf">MDF file format</a> and the folder structure is organized as an <a href="https://github.com/MagneticParticleImaging/MPIFiles.jl">MDF store</a>. The data is used in this code example: <a href="https://github.com/IBIResearch/IncreasedDynamicRange">https://github.com/IBIResearch/IncreasedDynamicRange</a></p>
Phase optimization of thermally actuated piezoresistive resonant MEMS cantilever sensors (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Phase optimization of thermally actuated piezoresistive resonant MEMS cantilever sensors", published in <em>Journal of Sensors and Sensor Systems </em>on 14 Jan 2019.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Polymeric Property Prediction Using Recurrent Neural Networks with Optimizations
<p>Database of the Dielectric Polymers used for training and testing of the developed Recurrent Neural Network models (DOI:10.5281/zenodo.4280446)</p>
Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"
<p>This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p> <p> </p>
Pareto front of the Bio-SOFC plant (Case 5) optimization
<p>Datasets of the article "Techno-Economic Optimization of an Integrated Biomass Waste Gasifier–Solid Oxide Fuel Cell Plant".</p> <p>With a growing energy demand in a carbon-constrained society, fuels cells powered by renewable fuels, and specifically solid waste, are seen as interesting contributors to the energy portfolio. The alternative energy industry needs to reduce costs, enhance efficiency, and demonstrate durability and reliability to be economically feasible and attractive. This paper addresses biomass waste gasification in distributed energy systems, using a solid oxide fuel cell (SOFC) to produce electricity and heat. The potential and optimal plant efficiency and layout (i.e., anode off-gas (AOG) recirculation point <em>via</em> small-scale turbomachinery and heat exchanger network) are analyzed through a multi-stage approach that includes scenario evaluation and multi-objective optimization <em>via</em> a hybrid optimization strategy with heuristics and mathematical programming. The results in this paper summarize the most convenient operating conditions and provide an optimized heat exchanger network (HEN). The AOG recirculation toward the gasifier combustor is the preferred option; the electrical and thermal efficiencies can separately go up to 49 and 47%, respectively. The combined total efficiency ranges between 76 and 82%, and the area of heat exchange, which corresponds to an amount of heat exchanged between 91 and 117 kW, is within 6–14 m<sup>2</sup>.</p>
Optimized Coefficients for the Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function
<p>This is a supplementary dataset for the publication:</p> <p>I. M. Tanash and T. Riihonen, "Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function with Optimized Coefficients," in<em> IEEE Communications Letters</em>, in press.</p> <p>The dataset contains the sets of the optimized coefficients for the novel GKL minimax approximations and bounds of the Gaussian Q-function, and the optimized coefficients for the GKL approximations in terms of the total error. The corresponding optimized coefficients are found up to 10 terms (N=10) for the two variations of the absolute error and for the relative error in terms of the minimax and the total errors.</p> <p>The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected optimization_criterion, error_type, number of terms, the bound or approximation type, and the variation. See help func_extract_coef for more information.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function in extracting the required coefficients from the dataset, to calculate and plot the corresponding minimax absolute error function which is shown by figure Example.jpg. Another example is given in the same script to extract the coefficients of the total relative error.<br> </p>
Performance of GFN1-xTB for periodic optimization of Metal-Organic Frameworks
<p>GFN-xTB optimised structures of CoRE 2014 and CoRE 2019 structures, with and without lattice optimisation.</p>
Data used to evaluate ORBITS: Optimal Repair-Based Inconsistency-Tolerant Semantics
<p>This dataset provides the input files that were used in the evaluation of the ORBITS system (Optimal Repair-Based Inconsistency-Tolerant Semantics, <a href="https://github.com/bourgaux/orbits">https://github.com/bourgaux/orbits</a>). A detailed description is available in a technical report on arXiv (<a href="https://arxiv.org/abs/2202.07980">https://arxiv.org/abs/2202.07980</a>).</p> <p><strong>Content:</strong></p> <p>Folders <em>cqapri_benchmark</em>, <em>food_inspection_benchmark</em>, and <em>physicians_benchmark</em> contain JSON files of conflict graphs and candidate queries and their causes.<br> These files are named using the following pattern: files of candidate answers and their causes are named <database>_<query>_answers_causes.json, and conflict graphs are named <database>_conflictGraph_<priority relation>.json where <priority relation> says whether the priority relation is score-structured (prio_score) or not (prio_non_score) and the probability (p<proba>) or number of scores (n<number>) used to build the priority relation.</p> <p>Folder <em>original_datasets_and_queries</em> contains the Food Inspection and Physicians datasets used to generate files from <em>food_inspection_benchmark</em> and <em>physicians_benchmark</em>.<br> Files from <em>cqapri_benchmark</em> have been generated from the CQAPri benchmark available at <a href="https://lahdak.lri.fr/CQAPri/CQAPri.php">https://lahdak.lri.fr/CQAPri/CQAPri.php</a>.<br> In all cases, we use ProvSQL (<a href="https://github.com/PierreSenellart/provsql">https://github.com/PierreSenellart/provsql</a>) to build conflict graphs and causes from the datasets.</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.