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135
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Dataset results
135 results for “High dimensionality”
Raw data of high-dimensional aptamer selection generated by ProSELEX
<p>This is the raw selection dataset generated by ProSELEX pipeline (https://www.nature.com/articles/s41557-023-01207-z). The target of selection is human myeloperoxidase (MPO). See https://github.com/dwangnu/AptaZ for instructions.</p>
Data from: Female mate preferences on high dimensional shape variation for male species recognition traits
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Navigating the 16-dimensional Hilbert space of a high-spin donor qudit using magnetic and electric fields
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Data from: High-dimensional imaging of vestibular schwannoma reveals distinctive immunological networks across histomorphic niches in NF2-related schwannomatosis
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Data from: A method for analysis of phenotypic change for phenotypes described by high-dimensional data
The analysis of phenotypic change is important for several evolutionary biology disciplines, including phenotypic plasticity, evolutionary developmental biology, morphological evolution, physiological evolution, evolutionary ecology and behavioral evolution. It is common for researchers in these disciplines to work with multivariate phenotypic data. When phenotypic variables exceed the number of research subjects—data called 'high-dimensional data'—researchers are confronted with analytical challenges. Parametric tests that require high observation to variable ratios present a paradox for researchers, as eliminating variables potentially reduces effect sizes for comparative analyses, yet test statistics require more observations than variables. This problem is exacerbated with data that describe 'multidimensional' phenotypes, whereby a description of phenotype requires high-dimensional data. For example, landmark-based geometric morphometric data use the Cartesian coordinates of (potentially) many anatomical landmarks to describe organismal shape. Collectively such shape variables describe organism shape, although the analysis of each variable, independently, offers little benefit for addressing biological questions. Here we present a nonparametric method of evaluating effect size that is not constrained by the number of phenotypic variables, and motivate its use with example analyses of phenotypic change using geometric morphometric data. Our examples contrast different characterizations of body shape for a desert fish species, associated with measuring and comparing sexual dimorphism between two populations. We demonstrate that using more phenotypic variables can increase effect sizes, and allow for stronger inferences.
Mass cytometry data for "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma"
<p>Raw suspension mass cytometry data supporting the publication "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma".</p>
Dataset for Learning in Continuous Action Space for Developing High Dimensional Potential Energy Models
<p>The NN potentials developed in this study and the other available MLIP methods such as GAP, SNAP, qSNAP, and MEGNET used for benchmarking.</p>
Reproducible, high-dimensional imaging in archival human tissue by Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF)
<p>1. SingleChannelMIBI.zip: Single-channel MIBI-TOF images</p> <p>All folders are labeled as Slide[Number]Stain[Number]_Point[Number]_[TMACoreIndex], where the slide number and stain number correspond to the slide and day of staining, the point number corresponds to the order in which the images were collected for each slide, and the TMA core index corresponds to the ID of the tissue microarray core. Each folder contains single-channel TIFFs for each marker. See paper for details.</p> <p>2. SegmentationOutput.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021, https://www.deepcell.org/predict). Output of Mesmer that delineates the single cells in each of the images is included here. Naming convention is the same as above.</p> <p>3. DataTables.zip: Data tables that are needed to run mpi_ppp_ihc_regression.ipynb</p> <p>Contains MIBI-TOF data (ionpath_processed_data.csv), MIBI-TOF calibration data (calibration_data.csv), IHC data (ihc_data.csv), and a map of each sample to its tissue type (tissue_data.csv). Also includes cell table output from Mesmer with the cell clusters appended to the table (cell_table_size_normalized_clusters.csv).</p>
Evaluating Feature Attribution Methods in the Image Domain: High-Dimensional Datasets
<p>Here you can find the versions of the Places-365 and Caltech-256 datasets used in the paper Evaluating Feature Attribution Methods in the Image Domain.</p>
Source data for "High-κ perovskite membranes as insulators for two-dimensional transistors"
<p>IMPORTANT: The data must not be reused in other studies or publications without the approval of the corresponding authors. Additional data related to this paper may be requested from the corresponding authors upon reasonable request.</p>
High dimensionality of stoichiometric niches in soil fauna
The ecological niche is a fundamental concept to understand species coexistence in natural communities. The recently developed framework of the multidimensional stoichiometric niche (MSN) characterizes species niches using chemical elements in living organisms. Despite the fact that living organisms are composed by multiple elements, stoichiometric studies have so far mostly focused on carbon (C), nitrogen (N), and phosphorus (P), and therefore a quantitative analysis of the dimensionality of the MSN in living organisms is still lacking, particularly for animals. Here we quantified ten elements composing the biomass of nine soil animal taxa (958 individuals) from three trophic groups. We found that all ten elements exhibited large variation among taxa, which was partially explained by their phylogeny. Overlaps of MSNs among the nine soil animal taxa were relatively smaller based on ten elements, compared with those based on only C, N, and P. Discriminant analysis using all ten elements successfully differentiated among the nine taxa (accuracy: 90%), whereas that using only C, N, and P resulted in a lower accuracy (60%). Our findings provide new evidence for MSN differentiation in soil fauna and demonstrate the high dimensionality of organismal stoichiometric niches beyond C, N, and P.
Raw data: Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides
<p>Raw data for the manuscript titled:</p> <p><strong>Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides</strong></p> <p> </p>
(simulated Data:) Detecting interactions in high dimensional data using Cross Leverage Scores
<p>We upload the data according to the paper "Detecting interactions in high dimensional data using Cross Leverage Scores" (Teschke et al., 2024) which we submitted at the Biometrical Journal. The data is also being uploaded on behalf of the paper's co-authors, Katja Ickstadt and Alexander Munteanu.</p> <p>Furthermore we upload some Intermediate results, because calculations takes very long time and need lots of memory. You can therefore use them for further insights.</p> <p>For details of the simulation, we refer to Teschke et al. (2024).</p>
Research data supporting "Surface Enhanced Raman Scattering Artificial Nose for High Dimensionality Fingerprinting"
<p>Experimental raw research data supporting the publication: Kim N., Thomas M.R. et al., 2019, Nature Communications.</p>
Subsystem Discovery in High-Dimensional Time-Series Using Masked Autoencoders
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Data from: Mapping beta diversity from space: Sparse Generalized Dissimilarity Modelling (SGDM) for analysing high-dimensional data
1. Spatial patterns of community composition turnover (beta diversity) may be mapped through Generalised Dissimilarity Modelling (GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional datasets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing datasets, including a time series of Landsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformed GDM models when fit on high-dimensional datasets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single date multispectral imagery. 4. This approach improved the direct use of high-dimensional remote sensing data, such as time series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional datasets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity.
First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach
<p>First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems is a significant task to be solved in many science and engineering fields, but remains still an open challenge. The present paper develops a novel approach, termed ‘fractional moments-based mixture distribution’, to address such challenge. This approach is implemented by capturing the extreme value distribution (EVD) of the system response with the concepts of fractional<br> moment and mixture distribution. In our context, the fractional moment itself is by definition a high-dimensional integral with a complicated integrand. To efficiently compute the fractional moments, a parallel adaptive sampling scheme that allows for sample size extension is developed using the refined Latinized stratified sampling (RLSS). In this manner, both variance reduction and parallel computing are possible for evaluating the fractional moments. From the knowledge<br> of low-order fractional moments, the EVD of interest is then expected to be reconstructed. Based on introducing an extended inverse Gaussian distribution and a log extended skew-normal distribution, one flexible mixture distribution model is proposed, where its fractional moments are derived in analytic form. By fitting a set of fractional moments, the EVD can be recovered via the proposed mixture model. Accordingly, the first-passage probabilities under different<br> thresholds can be obtained from the recovered EVD straightforwardly. The performance of the proposed method is verified by three examples consisting of two test examples and one engineering problem.</p>
High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors
<p>Data to reproduce figures in submitted manuscript "High-dimensional multivariate autoregressive model estimation of<br> human electrophysiological data using fMRI priors"</p> <p>https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1</p>
Raw results for medium- and high-dimensional settings
<p>Raw results of the Best Subset Selection, Forward Stepwise, Lasso and Elastic-Net methods for medium- and high-dimensional settings.</p>
High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival
<p>Annotated single cell count data for "High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival"</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.