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486 results for “hierarchic”
Hierarchical herbivore exclosure vegetation canopy cover at 3 sites across grassland-shrubland ecotones, 2022
The goal of this dataset is to examine long-term effects of multiple herbivore groups on canopy cover of plants across a shrub encroachment gradient (i.e., Ecotone Study) using herbivore exclusion treatments. Plots (2x2-m) were controls (open to all herbivores), large herbivore exclusion (lagomorph, rodent access), or full exclusion (no herbivore access). Plots were established in 2001 across grassland-shrubland ecotones in patches of black grama (Bouteloua eriopoda with >75% cover). Biomass of B. eriopoda was physically removed from the center 40x40-cm2 patch of each treatment to simulate disturbance. We sampled the controls and herbivore exclosure plots in summer 2022 to evaluate the long-term influence of herbivore exclusion on B. eriopoda recovery and overall canopy cover.
Sharpening of Hierarchical Visual Feature Representations of Blurred Images
Open the record for dataset details and reuse information.
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"
<p>DFEI dataset</p> <p><em>The full description can also be found in README.md.</em></p> <p>The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project describes a full event interpretation at the LHCb experiment, situated at the Large Hadron Collider in CERN, Geneva. An “event” consists of detector responses that were converted to tracks - each track represents a particle.</p> <p>The aim of the algorithm is to make sense of the tracks and bundle together tracks coming from the same origin, as well as interpreting their decay hierarchy.</p> <p>Generated events</p> <p>The events in this dataset are based on simulation generated with <a href="https://www.pythia.org/">PYTHIA8</a> and <a href="https://evtgen.hepforge.org/">EvtGen</a>, in which the particle-collision conditions expected for the LHC Run 3 are replicated as shown in the table.</p> <table> <thead> <tr> <th>LHCb period</th> <th>Num. vis. pp collisions</th> <th>Num. tracks</th> <th>Num. b hadrons</th> <th>Num. c hadrons</th> </tr> </thead> <tbody> <tr> <td>Runs 3-4 (Upgrade I)</td> <td> ∼ 5</td> <td> ∼ 150</td> <td> ≪ 1</td> <td> ∼ 1</td> </tr> </tbody> </table> <p>Additionally, an approximate emulation of the LHCb detection and reconstruction effects is applied, as described in the paper in the appendix “Simulation”. In the generated dataset, each event is required to contain at least one b-hadron, which is subsequently allowed to decay freely through any of the standard decay modes present in PYTHIA8. On average, 40% of those events contain more than one b-hadron decay, with a maximum b-hadron decay multiplicity of five. Only charged stable particles that have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region (as defined in the paper) are included in the datasets.</p> <p>Datasets</p> <p>The datasets are divided in three categories</p> <p>Training and testing</p> <p>The file <code>Dataset_InclusiveHb_Training.root</code> contains the training dataset (40,000 events) test dataset (10,000 events) of inclusive decays.</p> <p>Evaluation</p> <p>The inclusive dataset <code>Dataset_InclusiveHb_Evaluation.root</code> contains the evaluation events (50,000).</p> <p>Exclusive decays</p> <p>In addition to this inclusive dataset, several other smaller samples (of few thousand events each) have also been generated, requiring that all the events in each sample contained a specific (exclusive) type of b-hadron decay. The specific modes have been chosen to be representative of the most common classes of decay topologies of physics interest for LHCb. These samples contain only events in which all the particles originating from each of the considered exclusive decays have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region.</p> <p>The datasets contained are:</p> <ul> <li><code>Dataset_Bd_DD.root</code></li> <li><code>Dataset_Bd_Kpi.root</code></li> <li><code>Dataset_Bd_Kstmumu.root</code></li> <li><code>Dataset_Bs_Dspi.root</code></li> <li><code>Dataset_Bs_Jpsiphi.root</code></li> <li><code>Dataset_Bu_KKpi.root</code></li> <li><code>Dataset_Lb_Lcpi.root</code></li> </ul> <p>More information on them can be found in the paper.</p> <p>Loading the data</p> <p>The dataset is saved in the binary ROOT format with a key-array mapping. It can be loaded using the <a href="https://github.com/scikit-hep/uproot5#readme">uproot</a> Python library to convert it to a pandas DataFrame or similar.</p> <p>An example snippet is given here:</p> <pre><code>import uproot # treename = "Particles" treename = "Relations" with uproot.open('/path/to/file.root') as file: df = file[treename].arrays( # we can specify only a set of branches # ['EventNumber', "FromSamePV_true"], library='pd') # 'pd' for pandas </code></pre> <p>The returned <code>file</code> behaves like a mapping that contains two different data holders. They are accessible with <code>Relations</code> or <code>Particles</code> that contain either the relations between the particles or the particles themselves.</p> <p>Regarding the <code>Relations</code>, only edges connecting two different particles are contained in the dataset. The edges are treated as not directional, so a single edge is considered for each pair of particles.</p> <p>Variables</p> <p>The relevant features used in the GNN are described in the following. A cartesian right-handed coordinate system is used, with the <em>z</em> axis pointing along the beamline, the <em>x</em> axis beinng parallel to the horizontal and the <em>y</em> axis being vertically oriented. When specified in the name of the variables, the suffix “_true” refers to ground-truth information, and the suffix “_reco” refers to the output of the emulated LHCb reconstruction.</p> <ul> <li> <p>General:</p> <ul> <li>EventNumber: unique number to identify the event that the entry belongs to.</li> </ul> </li> <li> <p>Node variables:</p> <ul> <li> <p>ParticleKey: unique number to identify each particle in a given event.</p> </li> <li> <p>Identity (ID): numerical code identifying the type of particle, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> <li> <p>FromPrimaryBeautyHadron: boolean variable indicating whether the particles has been produced in a beauty hadron decay or not.</p> </li> <li> <p>Transverse momentum (<em>p</em><sub><em>T</em></sub>): component of the three-momentum transverse to the beamline, i.e. the <em>x</em> and <em>y</em> component combined.</p> </li> <li> <p>Impact parameter with respect to the associated primary vertex (IP): distance of closest approach between the particle trajectory and its associated primary vertex (proton-proton collision point), defined as the one with the smallest IP for the given particle amongst all the primary vertices in the event.</p> </li> <li> <p>Pseudorapidity (<em>η</em>): spatial coordinate describing the angle of a particle relative to the beam axis, computed as <em>η</em> = arctanh(<em>p</em><sub><em>z</em></sub>/∥<em>p⃗</em>∥).</p> </li> <li> <p>Charge (<em>q</em>): for the stable particles under consideration, the charge can take the value 1 or -1.</p> </li> <li> <p><em>O</em><sub><em>x</em></sub>, <em>O</em><sub><em>y</em></sub>, <em>O</em><sub><em>z</em></sub>: cartesian coordinates of the origin point of the particle.</p> </li> <li> <p><em>p</em><sub><em>x</em></sub>, <em>p</em><sub><em>y</em></sub>, <em>p</em><sub><em>z</em></sub>: cartesian coordinates of the three-momentum.</p> </li> <li> <p><em>P</em><em>V</em><sub><em>x</em></sub>, <em>P</em><em>V</em><sub><em>y</em></sub>, <em>P</em><em>V</em><sub><em>z</em></sub>: cartesian coordinates of the position of the associated primary vertex.</p> </li> </ul> </li> <li> <p>Edge variables:</p> <ul> <li> <p>FirstParticleKey: ParticleKey of one of the two particles connected by the edge.</p> </li> <li> <p>SecondParticleKey: ParticleKey of the other particle, verifying FirstParticleKey > SecondParticleKey.</p> </li> <li> <p>FromSamePrimaryBeautyHadron: boolean variable indicating whether the two particles originate from the same beauty hadron decay.</p> </li> <li> <p>Opening angle (<em>θ</em>): angle between the three-momentum directions of the two particles.</p> </li> <li> <p>Momentum-transverse distance (<em>d</em><sub> ⊥ <em>P⃗</em></sub>): distance between the origin point of the two particles defined on a plane which is transverse to the combined three momentum of the two particles.</p> </li> <li> <p>Distance along the beam axis (<em>Δ</em><sub><em>z</em></sub>): difference between the <em>z</em>-coordinate of the origin points of the two particles.</p> </li> <li> <p><em>F</em><em>r</em><em>o</em><em>m</em><em>S</em><em>a</em><em>m</em><em>e</em><em>P</em><em>V</em>: boolean variable indicating whether the two particles share the same associated primary vertex.</p> </li> <li> <p>Order of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>O</em><em>r</em><em>d</em><em>e</em><em>r</em>): variable that can take the values 0, 1, 2 or 3, as explained in the paper.</p> </li> <li> <p>Identity of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>I</em><em>D</em>): numerical code identifying the particle type of the ancestor, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> </ul> </li> </ul>
Brain Hierarchical Atlas 2 (BHA2)
<p>Elucidating the intricate relationship between the structure and function of the brain, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Magnetic Resonance Imaging (MRI) has helped in the understanding of this matter, with diffusion images providing information about structural connectivity (SC) and resting-state functional MRI revealing the functional connectivity (FC).</p> <p>Furthermore, the brain operates by discrete multiscale computations in both the time and spatial domains, in a way that is far from known (Churchland and Sejnowski, The MIT Press, 1994). To advance in the understanding of this puzzle, a dual structure-function hierarchical clustering strategy was proposed in (Diez et. al, SciRep, 2015), providing a common skeleton shared by structure and function. Here, we further extend this approach by:<br> 1. Fine-tuning the amount of matching between SC and FC via a free-parameter gamma. Specifically, when gamma is set to 0, SC is fully recovered, while when gamma is set to 1, FC is recovered. In between these extremes, a fusion scenario occurs, where both SC and FC contribute to the connectivity patterns. The raw data to generate the SC and FC matrices came from (Babayan et. al, Scientific Data, 2019), and can be downloaded from https://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html.<br> 2. Making use of brain-transcriptomic data to shed light on biological interpretability of brain-related diseases in the gamma-modulated multiscale structure-function correspondence.<br> 3. Providing to the scientific community open data of different scenarios of structure-function sharing and at different spatial scales, and open code to generate them in a MRI dataset.</p> <p>The dataset is organized in the following way:</p> <p>data<br> │ ├───iPA_nROIS [Different spatial scales 183, 391, 568, 729, 964, 1242, 1584, 1795 and 2165]<br> │ │ ├───iPA_nROIS.nii.gz [Brain parcellation image]<br> │ │ ├───iPA_nROIS.csv [MNI Coordinates and location of the brain parcellation ROIs]<br> │ │ ├───SC [Structural connectivity matrices]<br> │ │ ├───FC [Functional connectivity matrices]<br> │ │ ├───ts [Resting-state functional connectivity timeseries]<br> │ │ | ├───confounds [Confounds used to filter the timeseries]<br> │ │ ├───gamma-trees [Gamma-trees of nROIs levels]<br> │ │ ├───transcriptomics.csv [Transcriptomics' of each ROI]</p> <p>If you want to use this dataset, please cite:</p> <p><em>Antonio Jimenez-Marin, Ibai Diez, Asier Erramuzpe, Sebastiano Stramaglia, Paolo Bonifazi, Jesus M Cortes</em>. <strong>Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain</strong>. biorxiv. 2023. <a href="https://doi.org/10.1101/2023.08.04.551953">https://doi.org/10.1101/2023.08.04.551953</a></p>
Neritina snails hierarchical distribution
This data set includes N. virginea densities and sizes relative to streambed substrate type, water depth, and habitat type in a low land reach in RÃÂo Mameyes near PR Route 3 bridge, and presence and inland extent of distribution of in 32 coastal rivers around the island. Hydrologic and water chemistry variables are reported as predictors of snail distribution at regional and stream-network scales. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Simulated genetic data in a hierarchical metapopulation structure
<p>The data are linked to a research article entitled: “<em>Interactions between microenvironment, selection and genetic architecture drive multiscale adaptation in a simulation experiment” </em>in<em> Journal of Evolutionary Biology</em> (see References).</p> <p>In this research on multiscale adaptation, we simulated a hierarchical metapopulation structure with four populations, two environments per population and three patches per environment, in a two-step procedure:</p> <ul> <li>an initialization step without selection, with eight combinations of mutation type, selfing rate and QTL number parameters (2 modes each); out of 200,000 simulated generations in each case, we chose one with appropriate characteristics as a starting point for the next step;</li> <li>a selection step with all possible combinations of the following parameters: environmental pattern (4 modes), environmental range (5 modes), selection intensity (4 modes), fecundity (3 modes).</li> </ul> <p>This resulted in 240 scenarios for each initialized metapopulation, i.e. 1,920 scenarios in total. Each scenario was replicated 10 times, i.e. 19,200 simulation runs.</p> <p>The archive includes all data needed to reproduce the simulations and analyses, or to re-use the simulated metapopulations for other analyses. It has the following structure (further detailed below):</p> <ol> <li><strong>NemoScripts directory </strong>contains the <em>Nemo </em>input files used to perform simulations for the initialization step and the selection step;</li> <li><strong>RScripts directory </strong>contains the <em>R</em> scripts to read the <em>Nemo </em>output files, compute synthetic variables(*), and produce the figures as they appear in the publication and supplementary material (*: long computations, therefore we also directly provide those synthetic variables in the Data directory);</li> <li><strong>Data directory </strong>contains the <em>Nemo </em>output files, the synthetic variables, and other data needed to reproduce the figures; this directory can be used as a working directory for the <em>R</em> scripts (recommended).</li> </ol> <p>Running the following command in a terminal <strong><em>tar –xzvf Archive_PC_SOM_IS_FL.tar</em></strong> will create a directory named <strong><em>Archive_PC_SOM_IS_FL</em></strong>, which detailed content is described in the <strong><em>README.pdf</em></strong> file.<br> Warning: the extracted archive is large (460Go, >40,000 files) and extraction may take some time.</p>
Dataset for "An alternative to market-oriented energy models: nexus patterns across hierarchical levels"
<p>Dataset used for the publication "Di Felice, Louisa Jane, Maddalena Ripa, and Mario Giampietro. "An alternative to market-oriented energy models: Nexus patterns across hierarchical levels." <em>Energy Policy</em> 126 (2019): 431-443.". The dataset follows the distinction across hierarchical levels as specified in the publication.</p> <p>The same dataset was also used for a case study developed for the MAGIC project, available <a href="http://magic-nexus.eu/case_study/electric-grid-catalonia-illustrations-musiasem">here</a>. </p>
GRTSmh_diffres: the raster data source GRTSmaster_habitats converted to 9 hierarchical cell address levels at the corresponding lower resolution
<p>The <code>GRTSmh_diffres</code> data source file is a file collection, composed of nine monolayered GeoTIFF files of the <code>INT4S</code> datatype plus a GeoPackage with six polygon layers:</p> <ul> <li> <p>The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the <code>GRTSmh_brick</code> data source (<a href="https://doi.org/10.5281/zenodo.3354403">link</a>). This means that they provide the decimal (i.e. base 10) integer values of these <em>higher hierarchical levels</em> of the GRTS cell addresses of the raw data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-<code>NA</code> cells of the original <code>GRTSmaster_habitats</code> raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf5">source code</a> to do such things.</p> </li> <li> <p>The GeoTIFF files provide the respective levels 1 to 9 of the <code>GRTSmh_brick</code> data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of <code>NA</code> cells around Flanders at level 0 implies that, with decreasing resolution, the raster's extent increases and larger areas outside Flanders are covered by non-<code>NA</code> cells along the border.</p> </li> </ul> <p>The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes.</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_30_GRTSmh_diffres">'n2khab-preprocessing' at commit ecadaf5</a> for the creation from the <code>GRTSmh_brick</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution
<p>The data source file is a 10-layered GeoTIFF file, derived from the raster data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Both GeoTIFFs (<code>GRTSmaster_habitats</code>, <code>GRTSmh_brick</code>) use the <code>INT4S</code> datatype. The <code>GRTSmh_brick</code> data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from <code>GRTSmaster_habitats</code> (with GRTS cell addresses at the resolution level).</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_20_GRTSmh_brick">'n2khab-preprocessing' at commit ecadaf5</a> for its creation from the <code>GRTSmaster_habitats</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector <code>32 * 2^(0:9)</code> (minimum: 32 meters, maximum: 16384 meters).</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html</p> </blockquote>
Data used in the "H-FISTA: A hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra" publication
<p>Dynamic spectra used in the "A new approach to phase retrieval for pulsar dynamic spectra" publication. These data can be used to reproduce the results in the paper</p>
Higher-order H1-conforming hierarchical basis functions for triangles and tetrahedra
<p>This data set contains H1-conforming basis functions for triangular and tetrahedral finite elements. They are hierarchical in terms of the polynomial order and feature symmetries in terms of barycentric coordinates. Moreover, they are pairwise orthogonal with respect to the interpolation operator. The resulting element matrices exhibit high levels of sparsity and moderate condition numbers in the higher-order case.</p> <p>The functions are given in terms two different formats:</p> <p>*.mat MATLAB data file with a MATLAB structure.</p> <p>*.xml file with a structure.</p>
Data used in the manuscript - A Hierarchical Approach for Evaluating Athlete Performance with an Application in Elite Basketball
<p>The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data. </p> <p>The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.</p> <p>Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.</p> <p>However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset. </p>
H.I.D.R.A.: A Hierarchical, Interactive and Dynamic Recognition Architecture for Product Categorization
<p>The Hierarchical, Interactive and Dynamic Recognition Architecture (H.I.D.R.A.) for Product Categorization is a new intelligent system architecture developed by Elo7 to easily evolve its category tree and automatically classify millions of products, thus improving the page ranking of our marketplace.</p>
Data from: Hierarchical social networks shape gut microbial composition in wild Verreaux's sifaka
<p>In wild primates, social behaviour influences exposure to environmentally acquired and directly transmitted microorganisms. Prior studies indicate that gut microbiota reflect pairwise social interactions among chimpanzee and baboon hosts. Here, we demonstrate that higher-order social network structure—beyond just pairwise interactions—drives gut bacterial composition in wild lemurs, which live in smaller and more cohesive groups than previously studied anthropoid species. Using 16S rRNA gene sequencing and social network analysis of grooming contacts, we estimate the relative impacts of hierarchical (i.e. multilevel) social structure, individual demographic traits, diet, scent-marking, and habitat overlap on bacteria acquisition in a wild population of Verreaux's sifaka (<em>Propithecus</em> <em>verreauxi</em>) consisting of seven social groups. We show that social group membership is clearly reflected in the microbiomes of individual sifaka, and that social groups with denser grooming networks have more homogeneous gut microbial compositions. Within social groups, adults, more gregarious individuals, and individuals that scent-mark frequently harbour the greatest microbial diversity. Thus, the community structure of wild lemurs governs symbiotic relationships by constraining transmission between hosts and partitioning environmental exposure to microorganisms. This social cultivation of mutualistic gut flora may be an evolutionary benefit of tight-knit group living.</p>
A Hierarchical Network-Oriented Analysis of UserParticipation in Misinformation Spread on WhatsApp
<p>#Authors: Gabriel Peres Nobre, Carlos Henrique Gomes Ferreira, Jussara Marques de Almeida<br> #2021</p> <p>Script to read a Database file of messages and, in the end, extract user communities based on content co-sharing.</p> <p>We provide a database file with the anonymized messages shared in WhatsApp. </p>
Supporting data for "Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach"
<p>Combined camera trap and live trap dataset underlying the analyses in a Biological Conservation paper (https://doi.org/10.1016/j.biocon.2017.05.007), provided in .csv format. This spatially- and temporally-replicated dataset is suitable for occupancy modelling.</p> <p>The first 3 columns in the dataset are:</p> <p>1) Trap location name – old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively</p> <p>2) Sampling occasion number – camera trap and live trap occasions have the prefixes “Lvtrap” and “Ctrap”, respectively, and are defined in the paper</p> <p>3) Calendar year in which sampling took place (most locations were sampled in > 1 calendar years)</p> <p>Following these 3 columns, there are 66 columns for each of the mammal species detected during the study (species common names are used). The values for each species represent the number of independent captures, as defined in the paper. This can be reduced to detection/non-detection data (zeroes and ones), if needed, for occupancy modelling.</p>
Dataset for manuscript 'CeyeHao: AI-driven microfluidic flow programming with hierarchically assembled obstacles in microchannel and receptive-field-augmented neural network'
<p>This dataset contains:<br>1. The dataset used to train the models related to the manuscript 'CeyeHao: AI-driven microfluidic flow programming using hierarchically assembled obstacles in microchannel with receptive-field-augmented neural network'.<br>2. A checkpoint of trained 'CEyeNet' proposed in the manuscript.<br>3. Example microchannels designed in the manuscript to produce semantic flow profiles</p> <p>This dataset is intended for research and academic purpose.</p> <p>Detailed description please refer to the enclosed ReadMe.txt.</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.