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.
26
datasets available to search
ShareScore release 0.9.0
Dataset results
26 results for “Global optimization”
Mapping the global distribution of C4 vegetation using observations and optimality theory
<p>This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details:</p> <p>Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. <em>Nature Communications.</em> https://doi.org/10.1038/s41467-024-45606-3.</p> <p><strong>Update (Nov 2023): </strong>we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc).</p> <p><strong>Update (August 2023): </strong>we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc).</p> <p><strong>Update (June 2023): </strong>we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).</p>
DIRECTLib - a library of global optimization problems for DIRECT-type methods
<p><strong>DIRECTLib - a library of a box and generally-constrained global optimization problems for DIRECT-type methods</strong></p> <p>In this library, we present an extended collection of a box and generally constrained global optimization test problems (in MATLAB format) typically used in benchmarking various DIRECT-type [1] methods in the relevant literature (see, e.g., [2-6] and references given therein).</p> <p>File: <strong>WCGO_Test_results.xlsx </strong>contains<strong> </strong>experimental results presented in: <a href="https://arxiv.org/abs/2109.14912">https://arxiv.org/abs/2109.14912</a></p> <p><strong>References</strong></p> <ol> <li>Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993) ‘Lipschitzian optimization without the Lipschitz constant’, <em>Journal of Optimization Theory and Applications</em>, 79(1), pp. 157–181. <strong>doi</strong><strong>: 10.1007/BF00941892</strong>.</li> <li> <p>R. Paulavičius, J. Žilinskas. (2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. <strong>doi:10.1007/978-1-4614-9093-7</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT, Optimization Letters 12 (7) 1699–1712. <strong>doi:10.1007/s11590-017-1228-4</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization, Structural and Multidisciplinary Optimization 59 (6) 2155–2175. <strong>doi:10.1007/s00158-018-2181-2</strong>.</p> </li> <li> <p>L. Stripinis, J. Žilinskas, L. G. Casado, R. Paulavičius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. <em>Applied Mathematics and Computation</em>, <a href="https://doi.org/10.1016/j.amc.2020.125596">DOI: 10.1016/j.amc.2020.125596</a></p> </li> <li> <p>L. Stripinis, R. Paulavičius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. <em>Optimization Letters</em>, 15, p. 1865-1884, <a href="https://doi.org/10.1007/s11590-021-01726-z">DOI: 10.1007/s11590-021-01726-z</a></p> </li> </ol>
Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services"
<p>Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services" (Qiu et al. 2025), including source data, R scripts, and output results.</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>
Global dataset for "Global leaf-trait mapping based on optimality theory "
<p>This repository contains Global data used for “<em>Global leaf-trait mapping based on optimality theory</em><strong>” </strong>published in GEB.</p> <ol> <li>Global_Maps_SLA represents climatology of published Global SLA used for comparison (details products see table 1 and figure 4).</li> <li>Global_Maps_Na represents climatology of published Global Narea used for comparison (details see table 1 and figure 4).</li> <li>Global_Maps_Nmass represents climatology of published Global Nmass used for comparison (details see table 1 and figure 4).</li> <li>TS_SLA is simulated time-series of <em>SLA</em> based on optimality theories from 1992 to 2015</li> <li>TS_Na is simulated time-series of <em>Narea </em>based on optimality theories from 1992 to 2015</li> <li>TS_Nmass is simulated time-series of <em>Nmass </em>based on optimality theories<em> </em> from 1992 to 2015</li> <li>TS_LMA_decidudous is simulated time-series of deciduous <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_LMA_evergreen is simulated time-series of evergreen <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_Vcmax25 is simulated time-series of Vcmax25 based on optimality theories from 1982 to 2016</li> </ol>
Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
<p>DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range.</p>
LOGiC - Lapatinib Optimization Study in ErbB2 (HER2) Positive Gastric Cancer: A Phase III Global, Blinded Study Designed to Evaluate Clinical Endpoints and Safety of Chemotherapy Plus Lapatinib
ClinicalTrials.gov study NCT00680901. IPD Sharing: YES. Countries: 23. Publications: 2.
Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
Open the record for dataset details and reuse information.
Optimized geometries for Global Aromatic Ring Currents in Neutral Porphyrin Nanobelts
<p>Optimized geometries for Global Aromaticity in Neutral Porphyrin Nanobelts</p>
Comparison Results for Global Optimization Solvers Using a MIMO System With Two, Three and Four Active Layers as a Benchmark
<p>Benchmark data for GPU-based interval optimization of a simulated MIMO system with two, three and four active layers. We compare popular optimization tools from C-XSC and GNU Octave to purely brute-force based techniques for the GPU. Execution times were measured in seconds.</p>
Global Study Comparing a rivAroxaban-based Antithrombotic Strategy to an antipLatelet-based Strategy After Transcatheter aortIc vaLve rEplacement to Optimize Clinical Outcomes
ClinicalTrials.gov study NCT02556203. IPD Sharing: Not stated. Countries: 17. Publications: 5.
Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model
Open the record for dataset details and reuse information.
Leveraging a global, rederated, real-world data network to optimize investigator-initiated pediatric clinical trials: The TriNetX Pediatric Collaboratory Network
Open the record for dataset details and reuse information.
Dataset of some transition state problems solved with a deterministic global optimization method.
<p>Data of all solutions found for each problem and data for each (iterations vs open nodes) graph.<br /> </p>
Datafiles for: "Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets"
<p>Datafiles for results of the experiments reported in the paper "Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets"</p>
Improved global soil salinity and sodicity mapping through Box-Cox-based sample transformation and feature optimization
<p>We introduce a novel framework that leverages the Box-Cox transformation to address the skewed distribution of samples and incorporates additional critical predictors to enhance the accuracy of soil salinity (measured as electrical conductivity of the saturated soil extract, ECe) and sodicity (measured as exchangeable sodium percentage, ESP) estimates.</p> <p>We provide high-resolution (1 km × 1 km) global maps of soil salinity and sodicity from 1980 to 2022 in GeoTIFF file format for each year.</p> <p>Note: the scale factor for ECe and ESP maps is 0.001</p>
Optimization of global crop distribution could reduce greenhouse gas emissions by one third but requires expanding international trade
Open the record for dataset details and reuse information.
Data from: A global optimization paradigm based on change of measures
Open the record for dataset details and reuse information.
IPRC/SOEST Aquarius V5.0 Optimally Interpolated Sea Surface Salinity 7-Day global Dataset
The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing in situ from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.
Multi-Mission Optimally Interpolated Sea Surface Salinity Global Monthly Dataset V1
This is a level 4 product on a 0.25-degree spatial and monthly temporal grid. The product is the monthly mean of the level 4 OISSS dataset using three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide and Addendum I to the product Technical Notes.
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.