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.
295
datasets available to search
ShareScore release 0.9.0
Dataset results
295 results for “approximation”
Plots of interaural cues of listening experiment conditions (exact vs. experiment approximation)
<p>The plots show interaural cohehrence (IC) and interaural level difference (ILD) for a subset of experiment conditions presented in the JASA-EL publication 'Surrounding line sources optimally reproduce diffuse envelopment at off-center listening positions'. The experiment compared on-center with off-center listening (0.5 times radius). Off-center listening was simulated by remapped loudspeaker signals in a 24-channel loudspeaker setup. The nearest available loudspeaker was chosen for a direction shift, and the approximated angles (approx.) lead to negligible deviations in interaural cues compared to the interaural cues of an actually shifted listener (exact). Curves result from IC and ILD computations in 320 gammatone frequency bands, using a KU100 HRTF database.</p>
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
<p>Data set consists of 50,000 different configurations for the Metal Organic Framework (MOF) FIGXAU. Was generated by randomly modifying the positions of the atoms and doing an SCF relaxation on each configuration.</p>
Data Set for Self-adapting short-range correlation functional for complete active space-based approximations
<p>Data Set to accompany:</p> <p> Self-adapting short-range correlation functional for complete active space-based approximations</p>
Supplementary Data: MPBoot: Fast phylogenetic maximum parsimony tree inference and bootstrap approximation
<p>Supplementary Data<br> MPBoot: Fast phylogenetic maximum parsimony tree inference and bootstrap approximation<br> Submitted to BMC Evolutionary Biology</p> <p>This record contains PANDIT based dataset and TreeBASE dataset (Nguyen et al. 2015) which are analyzed by different bootstrap methods in the study "MPBoot: Fast phylogenetic maximum parsimony tree inference and bootstrap approximation". The PANDIT based dataset (compressed in file data_pandit.tar.gz) is used to benchmark the accuracy of bootstrap estimates. The TreeBASE dataset (compressed in file data_treebase.tar.gz) is used to benchmark computing times and capability of finding the best-known MP scores. </p> <p>After being uncompressed, the PANDIT based dataset comprises two subdirectories corresponding to the simulated DNA and AA MSAs. They were generated by Seq-Gen (Rambaut and Grass 1997), where the model parameters and true tree were inferred from the original MSAs downloaded from the PANDIT database (Whelan et al. 2006).</p> <ul> <li>Inside "dna" subdirectory, there are 6,207 numbered directories corresponding to 6,207 DNA MSAs. Note that the numbering of these directories is not consecutive because we excluded MSAs where TNT or PAUP* runs did not finish. In each numbered directory N, there are three files: (1) data.N contains the simulated MSA in PHYLIP format; (2) model.N contains the best-fit model detected from the corresponding original MSA; (3) tree.N contains the tree (in Newick format) inferred from the corresponding original MSA. tree.N and model.N are used by Seq-Gen to simulate the MSA in data.N.</li> <li>The "aa" subdirectory is organized similarly for 6,165 AA MSAs.</li> </ul> <p>After being uncompressed, the TreeBASE dataset comprises 115 files corresponding to 115 MSAs. There are:</p> <ul> <li>70 DNA MSAs in PHYLIP format. These files follow the naming scheme dna_[number of sequences]_[number of sites].phy.</li> <li>45 protein MSAs in PHYLIP format. These files follow the naming scheme prot_[number of sequences]_[number of sites].phy.<br> </li> </ul>
Data file needed for modified Lombardi radiative cooling approximation
<p>This is a table containing the equation of state and opacity data required for running Phantom with the radiative cooling approximation introduced in Young et al. (2024). The EoS and opacity calculations are given in Lombardi et al. (2015) and Stamatellos et al. (2007). This method is primarily used to model self-gravitating discs.</p>
Approximation-guided Fairness Testing through Discriminatory Space Analysis
<h3>Description:</h3> <p>This dataset contains the experimental results from the paper titled "Approximation-guided Fairness Testing through Discriminatory Space Analysis".</p> <p>In this paper, we conducted 24 fairness testing tasks using 7 different fairness testing algorithms: AFT (our proposed algorithm), VBT-X, VBT, THEMIS, ExpGA, SG and LIMI. Each execution was repeated 30 times, with a runtime of 1 hour.</p> <h3>Files Included:</h3> <ol> <li><strong>log.txt</strong>: This file contains the logs of each execution. Each log is labeled with an identifier, such as "'aft-LogReg-Adult-sex-0", which represents the 1st execution of the fairness testing task on (LogReg, Adult, sex) using AFT.</li> <li><strong>discriminatory_instances.zip</strong>: This archive includes all the IDIs (individual discriminatory instances) identified during the experiments.</li> <li><strong>res.txt</strong>: This file contains the averaged results of the 30 repetitions. It includes four metrics: #IDIs/sec (the number of indentified IDIs per second), #Tests/sec (the number of generated test cases per second), SuccessRatio (the success ratio of test cases), Diversity (the diversity of IDIs) and Naturalness (the naturalness of IDIs).</li> </ol>
Dataset for the article "Assessing the Partial Hessian Approximation in QM/MM-based Vibrational Analysis"
<p>This dataset contains the raw data and Jupyter notebooks used in the article "Assessing the Partial Hessian Approximation in QM/MM-based Vibrational Analysis."</p>
Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem
<p> It contains all the data used in our experiments, including 5 real graphs (<code>MONDIAL</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, and <code>DBpedia</code>) and 3 synthetic graphs (<code>LUBM-50K</code>, <code>LUBM-500K</code>, and <code>LUBM-5M</code>).</p> <p>Each real KG directory contains 8 files, including:</p> <ul> <li><code>graph.txt</code>: The first value is the number of vertices. Then each line 'u v' means there is an undirected edge between 'u' and 'v'.</li> <li><code>Weightgraph.txt</code>: The first value is the number of vertices. Then each line 'u v w' means there is an undirected edge between 'u' and 'v' weighted by 'w' which is computed by the Informativeness-based Weighting (IW) scheme.</li> <li><code>nodeName.txt</code>: Mapping from vertex ID to vertex name (i.e., entity URI).</li> <li><code>query.txt</code>: Each line is a keyword query containing a set of keyword names.</li> <li><code>kwName.txt</code>: Mapping from keyword ID to keyword name.</li> <li><code>kwMap.txt</code>: Mapping from keyword ID to vertex IDs. The first value of each line is keyword ID, and the rest are vertex IDs.</li> <li><code>UWHBLL.txt</code>: The HBLL index file which was built based on the Unit Weighting.</li> <li><code>IWHBLL.txt</code>: The HBLL index file which was built based on the Informativeness-based Weighting.</li> </ul> <p>Each synthetic directory contains 6 files, including:</p> <ul> <li><code>graph.txt</code>: same as above.</li> <li><code>Weightgraph.txt</code>: same as above.</li> <li><code>nodeName.txt</code>: same as above.</li> <li><code>queryList.txt</code>: Each line contains a set (separated by ',') of sets of vertex IDs.</li> <li><code>UWHBLL.txt</code>: same as above.</li> <li><code>IWHBLL.txt</code>: same as above.</li> </ul> <p>Apart from that, <code>Dbpedia</code> and <code>LUBM-5M</code> also contain a <code>PLLlabel.txt</code> file which was the supplementary file for the HBLL index.</p>
Estimating total species richness: fitting rarefaction by asymptotic approximation
<p class="MsoNormal"><span>Estimating the number of species in a community is important for assessments of biodiversity. Previous species richness estimators are mainly based on non-parametric approaches. Although parametric asymptotic models have been applied, they received limited attention due to specific limitations. Here, we introduce parametric models fitting the probability-based rarefied species richness curve that allow us to estimate the 'Total Expected Species' (TES) in a community based on species' abundance data. We develop two approaches to calculate TES (termed 'TESa' and 'TESb'), based on two slightly different mathematical assumptions regarding Expected Species (ES) models. We provide R functions to calculate both these estimation approaches and their standard deviation. The function also enables users to visualize the estimation. We test the performance of TESa, TESb and their average (TESab) across simulated and empirical data, and compare their bias, precision and accuracy with other, commonly used, non-parametric species richness estimators; the bias-corrected (bc-)Chao1 and the Abundance-based Coverage Estimator (ACE). Simulation reveals that in small samples, TESa shows a tendency to over-estimate and TESb to under-estimate overall species richness. TESab performs well in bias, precision and accuracy when compared to (bc-)Chao1 and ACE estimators. Results from empirical data shows that the variance generated from TES estimates is comparable to that for (bc-)Chao1 and ACE. Our study demonstrates that rarefaction theory in combination with parametric approximation models provides a valuable new approach to estimate the species richness of incompletely sampled communities. <a name="_Hlk114347649"></a>Robust estimates are likely to be obtained where the observed number of species is greater than half of the TES estimation. When the ratio of TESa to the observed richness is >> 2, we suggest the use of TESb or TESab. Although more comprehensive comparisons with other estimators are suggested, we encourage researchers to consider the TES approach in their biodiversity studies as a complement to current existing estimators.</span></p>
Gaussian Approximation Potential for C-doped Boron Nitride
<p>This is an GAP potential for amorphous boron nitride samples. It is trained based on datasets generated with ab-initio molecular dynamics and DFT. It can be used with pair_style quip command. </p>
The Role of Spatial Information in an Approximate Cross-Modal Number Matching Task
<p>Data for article:</p> <p>The Role of Spatial Information in an Approximate Cross-Modal Number Matching Task</p>
Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?
<p>There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, cross-validation represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard MCMC runs under the posterior distribution.</p>
Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"
<p><strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming".</p> <p>Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes.</p> <p>The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong></p>
Computational results and python files for the work "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling"
<p><br> This repository contains data accompanying the paper "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling".</p> <p>The implementation is based on the python-interface of the NGSolve open source Finite Element library (ngsolve.org).</p> <p>The file solve_problem_allione.py represents a minimum working example where the proposed MCS/HDG (set the use_MCS flag) method is used to solve the problem from the numerics section of the paper.</p> <p>The files FlowTemplates.py and krylovspace_extension.py contain a somewhat larger and more modular implementation of the proposed method that also features preconditioned iterative solvers, including support for the NgsAMG NGSolve extension library as well as the NGSolve-PETSc interface.</p> <p>The files errors_hdg.pickle, errors_mcs.pickle and kappas.pickle contain the raw data the tables and pictures in the paper were generated from.</p> <p>This data was generated with the scripts conv3d_hdg.py, conv3d_mcs.py and calc_kappas.py which use the FlowTemplates.py infrastructure.</p>
Properties of binary systems in a one-dimensional approximation
<p>We present numerically obtained tables of properties of stars in a binary system as a function of the effective potential: volume-equivalent radii of the equipotential surfaces, effective accelerations averaged over the same equipotential surfaces and the properties of the L<sub>1</sub> plane cross-sections. The tables are obtained for binaries where the ratios of the primary star mass to the companion star mass are from 10<sup>-6</sup> to 10<sup>5</sup> and include equipotential surfaces up to the star's outer Lagrangian point. We supply a sample code showing how to use our tables to get the average effective accelerations in one-dimensional stellar codes.</p> <p>Updates will be available at <a href="https://github.com/AliPourmand/1D_binary_star_properties">https://github.com/AliPourmand/1D_binary_star_properties</a>. Paper describing the tables can be found on ApJ, <strong>DOI</strong> 10.3847/1538-4357/acd4c1, https://iopscience.iop.org/article/10.3847/1538-4357/acd4c1 If you make use of the provided tables or the code, we ask you to cite the paper (and zenodo deposit if such reference is allowed).</p>
An approximation to gender violences
<p>In this video, Barbara Biglia (URV), researcher and lecturer at the Pedagogy Department, introduces gender related violence and some of its main characteristics. Presentation elaborated by Barbara Biglia (URV) and Alejandra Araiza (UAEH) in the framework of the Project Mainstreaming Sexual and Gender-Related Violence sensibilities into university courses through Photovoice experiences (2020 INDOV00003)</p> <p>Project link: http://www.innovaciondocentegenero.eu/photovoice/</p>
Efficient Approximation of Molecular Kinetics using Random Fourier Features
<p>Dataset to accompany the paper "<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=RqvAsE0AAAAJ&sortby=pubdate&citation_for_view=RqvAsE0AAAAJ:ULOm3_A8WrAC">Efficient Approximation of Molecular Kinetics using Random Fourier Features</a>", Journal of Chemical Physics 159, 074105 (2023), <a href="https://doi.org/10.1063/5.0162619">https://doi.org/10.1063/5.0162619</a>. Contains source code, input data, and result files. The data are compressed to a single zip file to preserve directory structure. See README file for detailed description of individual files.</p> <p>To automatically download and create the computational environment, pull the latest docker image from <a href="https://hub.docker.com/r/fnueske/23_jcp_rff_data/">https://hub.docker.com/r/fnueske/23_jcp_rff_data/</a></p> <p>and run the following command in terminal:</p> <p>docker run --rm -p 8888:8888 mk-rff:latest</p>
Study of a 4-Dose Regimen of a 21-valent Pneumococcal Conjugate Vaccine in Healthy Infants From Approximately 2 Months of Age
ClinicalTrials.gov study NCT06736041. IPD Sharing: YES. Countries: 6. Publications: 1.
Study to Assess the Safety and Immunogenicity of a Single Dose of GlaxoSmithKline's (GSK) Meningococcal MenACWY-CRM Vaccine (Menveo), Administered to Subjects 15 Through 55 Years of Age, Approximately
ClinicalTrials.gov study NCT02986854. IPD Sharing: YES. Countries: 2. Publications: 1.
Data from: AUTOEB: A software for systematically evaluating bipartitions in a phylogenetic tree employing an approximately unbiased test
Open the record for dataset details and reuse information.
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.