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486 results for “Hierarchical”
Benchmark dataset for CATH hierarchical clustering tools (GeMMA/FunFHMMEr, MARC, FRAN and eMMA)
<p>Benchmark dataset for CATH SuperFamily 3.40.50.620 (HUPS).</p> <p>Contains Functional Families alignments and Hidden Markov Models generated by GeMMA/FunFHMMER, MARC, FRAN and CATH-eMMA and Python code used to assess their quality (EC purity, DOPS, Neff) and intermediate steps by the MARC and FRAN pipelines (pooling, randomisation, renaming).</p> <p>3.4.50.620_full_superfamily_sequences.fasta contains all HUPs superfamily sequences, the FunFams are a subset of these.</p> <p>all_starting_clusters_sequences.fasta contain the sequences included in the starting clusters used in the analyses.</p> <p>3.40.50.620_embedded.pt includes embeddings for the HUPs superfamily generated using the ESM2 Protein Language Model.</p> <p> </p>
The Category-Modifier system: a hierarchical classification scheme for vertebrate tooth marks - supplementary tables
<p>Preserved records of tooth-bone interactions, known as tooth marks, can yield a wealth of information regarding organismal behavior and ecology. For this reason, workers in a wide range of disciplines, but particularly paleontology, have inspected and interpreted these features for decades. Although previous studies have gleaned invaluable insights, they have also described tooth marks using terminological frameworks that have been incompletely defined, have incorporated behavioral hypotheses in definitions, and/or have been inconsistently applied. To address these problems, we introduce the Category-Modifier (CM) system, the first system to both sort tooth marks into clearly defined main categories and use descriptive modifiers to characterize their appearance more precisely. The CM system is designed to apply to a wide range of vertebrates, to enable comparisons across disciplines and studies, and to help researchers keep their investigations into behavioral hypotheses free of circular reasoning.</p>
PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning
<h2>Description:</h2> <p>a. <strong>Feature Engineering</strong>:</p> <p> The file <strong>data_of_145_features.csv</strong> consists of the datasets of the features. The datasets correspond to Fig. 1(a) of the paper.</p> <p>b. <strong>HierCrystalNet of PiezoTensorNet</strong>:</p> <p> The saved models after training HierCrystalNet are placed inside the zip folders, namely, (i)<strong> classification_saved_models.zip</strong>, (ii) PG1_cubic_saved_models.zip, (iii) PG2_tetragonal42m_saved_models.zip, (iv) PG3_orthorhombic222_saved_models.zip, (v) PG4_hex6tetra4mm_saved_models.zip, and (vi) PG5_orthorhombicmm2_saved_models.zip.</p> <p>c. <strong>ModularEnsembleNet of PiezoTensorNet </strong>:</p> <p> The prediction_results.zip folder consists the prediction results of the ModularEnsembleNet. </p> <p>d. <strong>Input data of piezoelectric tensors and stiffness tensors for the</strong> <strong>finite element analysis</strong>: </p> <p>(i) <em><strong>piezoelectric_tensors_AlN_B0.3Er0.5Al0.2N_alloy.zip</strong></em>: The coefficients of piezoelectric tensors (array shape: 3x6) for AlN alloy and B0.3Er0.5Al0.2N alloy are respectively presented in <strong>undoped_AlN_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_unrotated_piezoelectric_tensor.csv</strong> files. Both of these files contain the data for samples whose laboratory coordinate system is same with that of the orientation of crystal lattice (all of the three Euler angles theta, psi and phi = 0 degree ), and so the compositional effect on the piezoelectric behavior can be assessed solely without considering the orientation effect. Rotation or other transformation can cause the vector value of (theta, phi, phis) other than (0,0,0) which indicates that the laboratory coordinate system of a sample being different than its lattice orientation. The effect of two different magnitudes of rotation on the coefficients of piezoelectric tensors for B0.3Er0.5Al0.2N alloy are revealed through the files <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_1_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_2_piezoelectric_tensor.csv</strong>. Orientation 1 refers to<strong> theta = 0 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360 degree</strong> whereas <strong> theta = 180 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360</strong> for orientation 2. The units of the coefficients are C/m^2. The description of the methodology on how to generate this data in this format is provided at https://piezoelectrictensorsdatabase.streamlit.app/ .</p> <p>(ii) <em><strong>stiffness_tensors_AlN_B0.3Er0.5Al0.2N_alloy.csv</strong></em>: The stiffness tensors (array shape: 6x6) for the two materials AlN alloy and B0.3Er0.5Al0.2N alloy at room temperature are presented through the four csv files. The elements of stiffness tensors in the datasets are presented in GPa. The Cijkl tensors are provided first for isotropy and then for orthotropy assumptions. The description of these tensors for AlN and B0.3Er0.5Al0.2N materials are provided in the web apps: https://isotropic-elasticity.streamlit.app/ and https://orthotropic-elasticity.streamlit.app/</p>
Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study
<p><em>Objective:</em> Mean arterial pressure is widely used as the variable to monitor during anesthesia. But there are many other variables proposed to define intraoperative arterial hypotension. The goal of the present study was to search arterial pressure variables linked with prolonged postoperative length of stay (pLOS).</p> <p><em>Design: </em>Retrospective cohort study of adult patients having received general for a scheduled non cardiac surgical procedure between 15<sup>th</sup> July 2017 and 31st December 2019.</p> <p><em>Methods:</em> pLOS was defined as a stay longer than the median (main outcome), adjusted for surgery type and duration. 330 arterial pressure variables were analyzed and organized through a clustering approach. An unsupervised hierarchical aggregation method for optimal cluster determination, employing Kendall's tau coefficients and a penalized Bayes information criterion was used. Variables were ranked using the absolute standardized mean distance (aSMD) to measure their effect on pLOS. Finally, after multivariate independence analysis, the number of variables was reduced to three.</p> <p><em>Results:</em> Our study examined 9,516 patients. When LOS is defined as strictly greater than the median, 34% of patients experienced pLOS. Key arterial pressure variables linked with this definition of pLOS included the difference between the highest and lowest pulse pressure values computed throughout the surgery (aSMD[95%CI] =0.39[0.31-0.40], p<0.001), the accumulated time pulse pressure above 61mmHg (aSMD = 0.21[0.17-0.25], p<0.001), and the lowest MAP during surgery (aSMD= 0.20[0.16-0.24], p<0.001).</p> <p><em>Conclusions: </em>By applying a clustering approach, three arterial pressure variables were associated with pLOS. This scalable method can be applied to various dichotomized outcomes.</p>
Copy number variation heterogeneity & hierarchical cancer classifications
<p>This is the CNV data from progenetix on the NCIt morphology tree used in the study Copy number variation heterogeneity reveals biological inconsistency in hierarchical cancer classifications. The columns are separated by tabs, and the values indicate the max CNV value of the biosample on the corresponding bin (1MB) of the genome.</p>
Dataset for Interspeech 2018 submission: Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
<p>This dataset contains the materials for training, testing the joint and HSMM models mentioned in the paper "<em>Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions"</em>.</p> <p>The filename list of this dataset can be found in the function <em>get_train_test_recordings_joint()</em> of <em>./general/trainTestSeparation.py</em> file. The dataset contains the Praat TextGrids and .wavs of the variables: <em>train_primary_school, val_primary_school</em> and <em>test_primary_school</em>. For accessing other datasets such as <em>train_nacta_2017, train_nacta</em> and <em>train_sepa</em>, please download them from the links:</p> <p>jingju dataset part1: <a href="https://zenodo.org/record/1185154">https://zenodo.org/record/1185154</a></p> <p>jingju dataset part2: <a href="https://doi.org/10.5281/zenodo.842229">https://doi.org/10.5281/zenodo.842229</a></p> <p>Once you have downloaded these three datasets, you need to set the paths in <em>./general/filePathShared.py</em>.</p> <p>Set <em>path_jingju_dataset</em> to the parent path of these three datasets.</p> <p>Set <em>primarySchool_dataset_root_path</em> to the path of the interspeech2018 dataset (the current dataset).</p> <p>Set <em>nacta_dataset_root_path</em> to the path of the jingju dataset part1.</p> <p>Set <em>nacta2017_dataset_root_path</em> to the path the jingju dataset part2.</p> <p>For more information on this paper, please refer to the Github page: <a href="https://github.com/ronggong/interspeech2018_submission01">https://github.com/ronggong/interspeech2018_submission01</a></p> <p> </p>
Vehicle-to-cell hierarchical overview of a typical electrified powertrain architecture
<p>Schematic depicting the vehicle-to-cell hierarchical overview of a typical electrified<br> powertrain architecture. This represents the system-level context within which the proposed<br> layer optimisation framework has been developed. Two xEV powertrains — a) a Battery Electric<br> Vehicle (BEV), and b) a series Plug-in Hybrid Electric Vehicle (PHEV) are chosen as examples to<br> demonstrate how the methodology facilitates common module designs for such battery packs.</p>
Research data supporting "Rolling Circle Transcription-Amplified Hierarchically Structured Organic-Inorganic Hybrid RNA Flowers for Enzyme Immobilization""
<p>Raw research data supporting the publication:</p> <p>Wang Y. et al., 2019, ACS Applied Materials and Interfaces, DOI: 10.1021/acsami.9b04663</p>
Data and Code For : "The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."
<p>The repository contains the principal 4D-STEM datasets and code used in the paper:</p> <p>"The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."</p> <p> </p> <p>* The measured and analyzed material is p(g3T2).</p> <p>* The code can be adjusted and used for the analysis of other conjugated polymers.</p> <p>* It should be noted that newer py4DSTEM versions with additional capabilities were released since the paper was </p> <p>submitted. </p> <p> </p> <p><strong>Contents:</strong></p> <p><strong>1. 4D-STEM_DATA_OMIECs.zip : </strong></p> <p><strong>4D-STEM data : </strong></p> <ul> <li>Dry_CL_2p1.dm4. </li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li> Calibrant_Dry_CL_2p1.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 15 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 25, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 46, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 15, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 80, E(extraction voltage): 300 kV, temprature: LN.</p> <p> </p> <p><strong>2. Notebooks.zip : </strong></p> <p><strong>Jupyter Lab Notebooks : </strong></p> <ul> <li>1A_pg3T2_dry_LN_CL2p1.ipynb. </li> </ul> <p>Analysis of dry film using CL 2.1.</p> <p>Goes with datasets: Dry_CL_2p1.dm4 and Calibrant_Dry_CL_2p1.dm4. </p> <ul> <li>1A_pg3T2_dry_LN_CL2p7.ipynb</li> </ul> <p>Analysis of dry film using CL 2.7.</p> <p>Goes with datasets: Dry_CL_2p7.dm4 and Calibrant_Dry_CL_2p7.dm4. </p> <ul> <li>1B_pg3T2_water_LN_CL2p7.ipynb</li> </ul> <p>Analysis of hydrated in water film using CL 2.7.</p> <p>Goes with datasets: Hydrated_Water_CL_2p7.dm4 and Calibrant_Hydrated_Water_CL_2p7.dm4. </p> <ul> <li>1C_pg3T2_NaCl_LN_CL2p7. </li> </ul> <p>Analysis of hydrated in 0.1 M NaCl(aq) film using CL 2.7.</p> <p>Goes with datasets: Hydrated_NaCl_CL_2p7.dm4 and Calibrant_Hydrated_NaCl_CL_2p7.dm4.</p> <ul> <li>aux_func.py: </li> </ul> <p>Contains auxilary functions and required for running the other notebooks.</p>
Bayesian hierarchical model gridded solar-induced fluorescence (BHM gridded SIF) data product
<p>This archive provides the solar-induced fluorescence (SIF) data product documented in "Estimation of solar-induced chlorophyll fluorescence using Bayesian hierarchical regression". The archive includes daily NetCDF files with the global gridded SIF estimates and associated uncertainties.</p>
Data from: A test of the hierarchical model of litter decomposition
Our basic understanding of plant litter decomposition informs the assumptions underlying widely applied soil biogeochemical models, including those embedded in Earth system models. Confidence in projected carbon cycle-climate feedbacks therefore depends on accurate knowledge about the controls regulating the rate at which plant biomass is decomposed into products such as CO2. Here, we test underlying assumptions of the dominant conceptual model of litter decomposition. The model posits that a primary control on the rate of decomposition at regional to global scales is climate (temperature and moisture), with the controlling effects of decomposers negligible at such broad spatial scales. Using a regional-scale litter decomposition experiment at six sites spanning from northern Sweden to southern France – and capturing both within and among site variation in putative controls – we find that contrary to predictions from the hierarchical model, decomposer (microbial) biomass strongly regulates decomposition at regional scales. Further, the size of the microbial biomass dictates the absolute change in decomposition rates with changing climate variables. Our findings suggest the need for revision of the hierarchical model, with decomposers acting as both local- and broad-scale controls on litter decomposition rates, necessitating their explicit consideration in global biogeochemical models.
Dataset for "Bayesian hierarchical models for combining misaligned two-resolution metrology data"
<p>This file contains the datasets used in the paper, Xia, Ding, and Mallick, 2011, “Bayesian hierarchical models for combining misaligned two-resolution metrology data,” <em>IIE Transactions</em>, Vol. 43, pp. 242 – 258.</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
The hidden hierarchical nature of soft particulate gels
<p>This dataset contains data for the manuscript "The hidden hierarchical nature of soft particulate gels".</p>
The Hierarchical Task data in Digital Twin Railway Systems
<p>Data sets for simulating hierarchical<strong>-</strong>task scheduling in cloudSim for digital twin railway system, with the number of tasks ranging from 100 to 1500</p>
Hierarchical drivers of cryptic biodiversity on coral reefs
<p>Declines in habitat structural complexity have marked ecological outcomes, as currently observed in many of the world's ecosystems. Coral reefs have provided a model for such changes in marine ecosystems, but our understanding has been centred on corals and fishes at broad spatial scales when metazoan diversity on coral reefs is dominated by small cryptic taxa (herein: 'cryptofauna'). Given the paucity of studies and high taxonomic complexity of the cryptofauna, both of which limit a priori hypotheses, we asked whether hierarchical structuring theory provides a compelling framework to impose order and quantify pattern. In general terms, we explored whether cryptic communities are sufficiently described by broad seascape parameters or limited by a set of processes operating at their distinctly nested microhabitat scale. To address this theory and gaps in knowledge for the cryptofauna, we characterised community structure in coral rubble, an eroded coral condition where biodiversity proliferates. Rubble was sampled along a depth and exposure gradient at Heron Island on the Great Barrier Reef, Australia, to parameterise environmental and morphological indicators of sessile taxa and motile cryptofauna communities. We employed a hierarchical study framework from microhabitat to seascape scales, which were evaluated using non-structured multivariate analyses and Bayesian structural equation modelling. While the non-structured analyses showed the effects of seascape on the cryptobenthos and its community, this approach overlooked the finer hierarchical patterns in rubble ecology revealed only in the structured model. Seascape parameters (exposure and depth) influenced microhabitat complexity (i.e., rubble branchiness), which determined the cover of sessile organisms on rubble pieces, which shaped the motile cryptofauna community. Rubble is likely to be increasingly prevalent on coral reefs in the Anthropocene and is typically associated with low seascape-level complexity and reduced macrofaunal richness. Parallel with hierarchical structuring theory, we show a similar response operating at the microhabitat scale whereby low rubble complexity (i.e., branchiness) reduces cryptobenthic structure, diversity and size spectra. We expect there may be an initial increase in biodiversity and trophodynamic processes derived from branching rubble, but a delay in ecosystem-scale outcomes if coral, and thus rubble, generation and complexity cannot be sustained in a future ocean.</p>
STL files: Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing
<p>STL files for paper titled modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing</p>
Predicting Mathematics Anxiety and Achievement: Unveiling the Significance of Student and Teacher Attributes through Hierarchical Linear Modeling
<p>This study aimed to determine the predictive power of student and teacher characteristics on students' math anxiety and achievement.</p>
Calibration Dataset - HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models
<p>Calibration dataset for the study case presented in the paper "HPOSS: A hierarchical portfolio optimization stacking strategy<br> to reduce the generalization error of ensembles of models".</p> <p>It encompasses a .h5 file with a dataset called "Calibrations_LHS", which consists of a 80x5 numpy array of float numbers corresponding to (d1(m),d2(m),d3(m),d4(m),zeta_max(Pa)), where d_i, i = 1,...,4 are dimensions (in meters) of the I-beam and zeta_max is the maximum bending stress (in Pascals) developed in a simply such supported beam with 1 m of length after a point load of 1000N is applied at its center.</p>
BSSE_QGF_61757 L010-L016 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61757 L010 to L016</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
ScienceDex guides
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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.