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1,880 results for “Dimensionality”
Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization
<p><span>Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named </span><span>Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models.</span> <span>Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.<br><br>This project provide the source dataset used to train and evaluate the overall model.<br></span></p>
Datasets for "Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning"
<p>This upload contains the datasets used for the experiments in:</p> <p>Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning</p> <p>Frank Hu, Michael S. Chen, Grant M. Rotskoff, Matthew W. Kanan, and Thomas E. Markland</p> <p>https://arxiv.org/abs/2408.08284</p> <p> </p> <p>For file descriptions and usage, please refer to the supplied README.md file.</p> <p> </p> <p> </p>
The reconstructed three-dimensional nitrate field dataset for the pan-European ocean using a continual learning-based multilayer perceptron
<p>Based on a newly developed continual learning-based multilayer perceptron model and environmental features, we reconstructed the pan-European 3D ocean nitrate field from 2010 to 2023. The reconstructed field features a monthly temporal resolution, a horizontal spatial resolution of 0.25 degrees, and 63 depth levels, with vertical intervals ranging from 5 to 50 m.</p>
Phases in the 2-Dimensional Falicov-Kimball Model
<h2>Introduction</h2> <p>The dataset consists of a subset of 1,467,059 configurations obtained using Monte Carlo (MC) simulations of the Falicov-Kimball Model (FKM) on a 2-dimensional 12x12 square lattice with periodic boundary conditions.</p> <p>The FKM [1,2] is one of the simplest models of interacting fermions that displays a variety of distinct phases and phenomena. It considers two types of particles: itinerant (light) f-electrons and localized (heavy) d-electrons on a lattice. In its spineless version it can be understood as a special limit of the Hubbard model where electrons with one spin species, e.g. spin up, are localized. Its advantage over the Hubbard model is that it is accessible with a sign-problem-free Monte Carlo [3].</p> <p>The main parameters of the FKM are the hopping parameter (t), which defines the mobility of itinerant electrons across neighboring lattice sites, and the interaction parameter (U), which represents the on-site Coulomb repulsion between the itinerant and localized electrons. In our MC simulations, the parameter t has been set to 1, making U the only relevant parameter in the FKM Hamiltonian. Another important parameter in the MC simulations is the temperature (T).</p> <p>Further details of the MC simulations can be found in Ref. [3]. For each pair of U and T values, 20 independent MC simulations have been run, each producing 500 configurations. To keep the configurations in the dataset uncorrelated, just a subset as large as 5% of all the produced configurations has been randomly selected for further analysis.</p> <p>The dataset can be used to train unsupervised machine learning models for phase classification or supervised regression models for predicting simulation parameters.</p> <h2>Dataset Structure</h2> <p>The dataset consists of two CSV files:</p> <ul> <li> <strong>fkm_data.csv:</strong> Contains 1,467,059 configurations taken at various values of U (ranging from 0 to 12) and T (ranging from 0.005 up to 0.3). The values of U and T are chosen from a grid where U changes in steps of dU=0.25, and T changes in steps of dT=0.005. Multiple independent configurations are provided for each pair of U and T values.</li> <li> <strong>fkm_data_ht.csv:</strong> Contains an additional 342,320 configurations taken at higher temperatures, T, ranging from 0.35 up to 1.0 with a step of dT=0.05. The range and step of U values remain the same as in the previous file. These additional configurations can help regularize training of unsupervised machine learning models.</li> </ul> <p>Each configuration is stored as a row in the attached CSV files. A configuration vector consists of:</p> <ul> <li><strong>144 binary components</strong> (0s and 1s) indicating site occupancy by f-electrons.<br> In the CSV files, these components are named "f1", "f2", ..., "f144", where the number denotes the site.</li> <li><strong>144 float components</strong> (between 0 and 1) indicating the electron density of d-electrons.<br> In the CSV files, these components are named "d1", "d2", ..., "d144," where the number denotes the site.</li> <li><strong>2 float components</strong> specifying the corresponding values of U and T. <br>These values are in columns labeled "U" and "T," respectively.</li> </ul> <p>The values of U and T are given in units of the hopping parameter t.</p> <p>An accompanying Jupyter notebook, <strong>load_fkm_data.ipynb</strong>, demonstrates how to load the data and view its structure.</p> <h2>References</h2> <p>[1] J. Hubbard, Proc. Royal Soc. Lond. A 276, 238 (1963).<br>[2] L. M. Falicov and J. C. Kimball, Phys. Rev. Lett. 22, 997 (1969).<br>[3] M. M. Maśka and K. Czajka, Phys. Rev. B 74, 035109 (2006).</p>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</p>
Numerical simulation data of a two-dimensional flow around a fixed circular cylinder
<p>Numerical results of a two-dimensional flow past a fixed circular cylinder in the vortex shedding regime. The solver is Cadyf, an in-house fluid-structure interaction code using finite element method (<a href="https://doi.org/10.1016/j.jcp.2008.11.032">doi.org/10.1016/j.jcp.2008.11.032</a>). The Reynolds number is Re = 100. The boundary conditions and numerical integration are described in <a href="https://doi.org/10.1017/jfm.2021.252">doi.org/10.1017/jfm.2021.252</a>.</p> <p>The fixed_cylinder_atRe100 raw file contains the values of the flow velocity, pressure, and node coordinates at each time step. Tables of the results can be extracted using the file processing code in text_flow.py.</p> <p>The Re100_fixe00.reactions raw file contains the values of the total force and torque applied on the cylinder at each time step. Tables of the results can be extracted using the file processing code in reactions_process.py.</p>
Data from: Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost
Chimney swifts (Chaetura pelagica) are highly manoeuvrable birds notable for roosting overnight in chimneys, in groups of hundreds or thousands of birds, before and during their autumn migration. At dusk, birds gather in large numbers from surrounding areas near a roost site. The whole flock then employs an orderly, but dynamic, circling approach pattern before rapidly entering a small aperture en masse. We recorded the three-dimensional trajectories of ≈1 800 individual birds during a 30 min period encompassing flock formation, circling, and landing, and used these trajectories to test several hypotheses relating to flock or group behaviour. Specifically, we investigated whether the swifts use local interaction rules based on topological distance (e.g. the n nearest neighbours, regardless of their distance) rather than physical distance (e.g. neighbours within x m, regardless of number) to guide interactions, whether the chimney entry zone is more or less cooperative than the surrounding flock, and whether the characteristic subgroup size is constant or varies with flock density. We found that the swift flock is structured around local rules based on physical distance, that subgroup size increases with density, and that there exist regions of the flock that are less cooperative than others, in particular the chimney entry zone.
The first Silurian trilobite with three-dimensionally preserved soft parts reveals novel appendage morphology
<p>The first Silurian trilobite known with soft parts preserved, a <i>Dalmanites</i> species, is described from the Herefordshire Lagerstätte. Biramous appendages and much of the alimentary system are evident. High-fidelity three-dimensional preservation reveals a novel, double arrangement of the exopod filaments, interconnected by a presumed membranous sheet. This morphology explains a misinterpretation of the exopod as supporting spiral structures, originally reported nearly 150 years ago. The new exopod morphology is considered primarily respiratory in function and comparison with other trilobite limbs suggests that it may be unique to Phacopida.</p>
Appendix 3 of "Analyses of three-dimensional species associations reveal departures from neutrality in a tropical forest"
<p>Temporal changes in three-dimensional structure of crowns in Luquillo, Puerto Rico. Each axis represents the overlap of one species over the other, measured as standardized effect sizes (SES). That is, deviations from a null model that randomizes crown three-dimensional positions. A high value of "SES of <em>Guarea guidonia</em> over <em>Psychotria brachiata</em>" means that <em>Guarea guidonia</em> shades <em>Psychotria brachiata</em> more than expected by chance, and so on.<br> <br> The coloured dots in the background of the figure represent all the observed relationships between species. Gray dots represent random relationships between pairs of species (i.e. similar to the expected by the null model). Black dots represent horizontal segregation between species. Blue dots represent horizontal aggregation but vertical segregation between species. Red dots represent three-dimensional aggregation between species.<br> <br> The larger green or red dots connected by lines represent the temporal changes observed for the relationship between the two target species in each figure. There are four censuses represented. Larger dots represent the more recent censuses. The green dots reflect the relationship between the two target species in the low-disturbance area within the Luquillo Forest Dynamics Plot. The red dots reflect the relationship between the two target species in the high-disturbance area within the Luquillo Forest Dynamics Plot.</p>
Theoretical analysis and simulations of two-dimensional Fourier transform spectroscopy performed on exciton-polaritons of a quantum-well microcavity system
<p>Dataset of the publication “Theoretical analysis and simulations of two-dimensional Fourier transform spectroscopy performed on exciton-polaritons of a quantum-well microcavity system“, H. Rose, J. Paul, J. K. Wahlstrand, A. Bristow, and T. Meier, Proceedings of the SPIE 11684, 1168414 (2021) ( <a href="https://doi.org/10.1117/12.2576696">https://doi.org/10.1117/12.2576696</a> ). The zip file includes the data on which the plots shown in figure 2 are based.</p>
Video from: Two-dimensional hybrid model of gradient drift instability and enhanced electron transport in a Hall thruster
<p>The video data for Fig. 3 in "Two-dimensional hybrid model of gradient drift instability and enhanced electron transport in a Hall thruster."</p>
Determination of the spinel content in cycled Li1.2Ni0.13Mn0.54Co0.13O2 using three-dimensional electron diffraction and precession electron diffraction
<p>(hkl,I)-lists for the 150 times cycled NMC material</p>
High-dimensional Anomaly Detection with Radiative Return in e+e- Collisions
<p>Numpy files of Pythia + Delphes simulated e+e- collisions used as DNN/PFN training inputs.</p>
Data and Results for: Cost-effective, high-throughput fruit phenotyping system for three-dimensional reconstruction of fruit form
<p>The dataset contains the raw data used for generating the results in the paper, and the reconstructed results at 1mm and 2mm voxel resolution.</p> <p><strong>Raw data</strong>: Samples are gathered into groups of five, DataObjects1-5.zip, ... , DataObjects56-59.zip.</p> <p>Once uncompressed, each directory holds the images and configuration information used for each sample. For instance, '1_obj' is for the first sample, and the 'data' folder has the image files by camera. In this case one camera was used, 'cam2', and there are 62 images. 'pattern_square_mm_external.txt', 'pattern_square_mm_internal.txt', and 'rotate_specification_file.txt' are all configuration files needed for the calibration step.</p> <p> </p> <p><strong>Results</strong>: Reconstructions results are in compressed folders 'reconstruction-results-1mm.zip' and 'reconstruction-results-2mm.zip' for 1 mm and 2 mm voxel resolutions, respectively. These results are three-dimensional model files than can be viewed with a variety of software, we have used the free MeshLab: https://www.meshlab.net/ .</p> <p> </p> <p> </p> <p> </p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
<p><span>The datasets store both motion and observation information of a single fluorescent sub-diffraction limit-sized particle moving in a three-dimensional confined environment. The confined motion is following a nonlinear model driven by non-Gaussian noise, the observation is formed by engineered Double-helix (DH) point spread function (PSF) and captured by scientific complementary metal-oxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo - Expectation Maximization (SMC-EM), we extended it to handle the DH-PSF for encoding the three-dimensional position of the particle in two-dimensional image plane of the camera. We focus on studying the datasets at low signal and low signal-to-background ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches. </span></p>
Imaging Mass Cytometry for high-dimensional tissue profiling in the eye
<p>Imaging mass cytometry data (folders containing single tiffs + cell masks) generated for the analysis of healthy conjunctiva and conjunctival melanoma.</p>
Research data supporting: "A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields"
<p>This repository contains the data used in the paper of Capelli <em>et al. </em>"A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields", published on Journal of Physical Chemistry B (DOI: 0.1021/acs.jpcb.1c02503).<br> <br> The archive traj_processed.tar.gz contains the trajectories converted in xyz format with the dimensions of the box.</p> <p>The archive trajectories_xtc.tar.gz contains the raw trajectories (of the membranes without solvent) in gromacs xtc format with a .tpr binary file. <br> </p>
Three-dimensional simulations of rapidly rotating core-collapse supernovae: finding a neutrino-powered explosion aided by non-axisymmetric flows
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Suwa 2016 Monthly Notices of the Royal Astronomical Society: Letters, Volume 461, Issue 1, p.L112-L116.</p> <p> </p> <p> </p>
Reproduction package for the paper "Multi-dimensional population modelling using frbpoppy: Magnetars can produce the observed fast radio burst sky"
<p>This is a basic reproduction package for the paper "Multi-dimensional population modelling using frbpoppy: Magnetars can produce the observed fast radio burst sky" by David Gardenier & Joeri van Leeuwen (2021).</p> <p>* arXiv: <a href="https://arxiv.org/abs/2012.06396">arXiv:2012.06396</a><br> * DOI: <a href="https://doi.org/10.1051/0004-6361/202040119">10.1051/0004-6361/202040119</a></p> <p> </p>
Tracking wildlife energy dynamics with unoccupied aircraft systems and 3-dimensional photogrammetry
<p>We present a novel application using unoccupied aircraft systems (UAS; drones) for structure-from-motion three-dimensional (3-D) photogrammetry of multiple, free-ranging animals simultaneously. Pinnipeds reliably haul-out on shore for pupping and breeding each year, accompanied by dramatic female-to-pup mass transfer over a short lactation period and males lose mass while defending mating territories. This provides a tractable study system for validating the use of UAS as a non-invasive tool for tracking energy dynamics in wild populations.</p> <p>UAS imagery of grey seals (<i>Halichoerus grypus</i>) was collected at Saddle Island, Nova Scotia. A multirotor UAS was piloted in 360-degree orbits around relatively dense animal aggregations and georeferenced images were used for construction of a 3-D point cloud, orthomosaic, and Digital Surface Model for animal volumetric measurements. Directly following UAS survey, a subset of adult females were hand-measured (morphometrics, blubber depth, n=21 handlings [15 were unique animals]) and female-pup pairs were weighed (adult females: n=32 [24]; pups: n=33 [23]) to validate that UAS 3-D photogrammetric models provided accurate animal volume and mass estimates.</p> <p>UAS two-dimensional body length measurements were sensitive to animal recumbency and posture. The new UAS 3-D photogrammetric method overcame these constraints, and aerial-derived body volume measurements were equivalent to those collected from the ground. UAS body volume measurements precisely predicted 'true' body mass (mean-absolute-error, adult female: 8 kg, 2.1% body mass; pup: 4.1 kg, 9.8%), and exhibited a stronger relationship with total body mass than with blubber volume.</p> <p>The method was applied to 673 free-ranging animals to characterize volume and mass dynamics across lactation and breeding for a much larger sample size than would be possible using traditional ground methods. Indeed, 1-46 animals (mean±SE: 9.2±1.2) were modeled concurrently within the focal area of a UAS flight. Application of the method also captured significant inter-annual variation in body volume/mass dynamics, and female-to-pup energy transfer efficiencies were lower when there was low sea-ice extent. The UAS 3-D photogrammetric method presented in this study is likely to be broadly applicable to other species, and the ability to measure whole groups of free-ranging animals at once makes strides towards 'weighing populations'.</p>
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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.