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209 results for “probabilistic”
Data Set for Probabilistic Indoor Temperature Forecasting
<h1>1. Dataset Manifest</h1> <p>This text provides a description of the dataset used for model training and evaluation in our study "A Tutorial on Deep Learning for Probabilistic Indoor Temperature Forecasting". The dataset consists of various simulated thermal and environmental parameters for different room configurations. Below, you will find a table detailing each column in the dataset along with its description and unit of measurement. </p> <h2>1.1. Columns Description</h2> <table> <tbody> <tr> <th>Column Name</th> <th>Description</th> <th>Unit</th> </tr> <tr> <td><code>time</code></td> <td>Time stamp of the measurement</td> <td>-</td> </tr> <tr> <td><code>ZweiPersonenBuero.TAir</code></td> <td>Air temperature inside a two-person office</td> <td>°C</td> </tr> <tr> <td><code>heatStat.Heat.Q_flow</code></td> <td>Heating rate in the room</td> <td>W</td> </tr> <tr> <td><code>weaDat.AirPressure</code></td> <td>Atmospheric pressure</td> <td>Pa</td> </tr> <tr> <td><code>weaDat.AirTemp</code></td> <td>Outside air temperature</td> <td>°C</td> </tr> <tr> <td><code>weaDat.SkyRadiation</code></td> <td>Longwave sky radiation</td> <td>W/m²</td> </tr> <tr> <td><code>weaDat.TerrestrialRadiation</code></td> <td>Terrestrial radiation</td> <td>W/m²</td> </tr> <tr> <td><code>weaDat.WaterInAir</code></td> <td>Absolute humidity</td> <td>g/kg</td> </tr> <tr> <td><code>VAir</code></td> <td>Air volume in the room</td> <td>m³</td> </tr> <tr> <td><code>AExt0</code></td> <td>Exterior wall area facing the south</td> <td>m²</td> </tr> <tr> <td><code>AExt1</code></td> <td>Exterior wall area facing the north</td> <td>m²</td> </tr> <tr> <td><code>AInt</code></td> <td>Total interior wall area</td> <td>m²</td> </tr> <tr> <td><code>AFloor</code></td> <td>Floor area of the room</td> <td>m²</td> </tr> <tr> <td><code>AWin0</code></td> <td>Window area facing the south</td> <td>m²</td> </tr> <tr> <td><code>AWin1</code></td> <td>Window area facing the north</td> <td>m²</td> </tr> <tr> <td><code>azi0</code></td> <td>Azimuth (direction) of the first exterior wall</td> <td>rad</td> </tr> <tr> <td><code>azi1</code></td> <td>Azimuth (direction) of the second exterior wall</td> <td>rad</td> </tr> <tr> <td><code>id</code></td> <td>Unique identifier for the room configuration</td> <td>-</td> </tr> <tr> <td><code>is_holiday</code></td> <td>Indicator whether the day is a holiday (1 for yes, 0 for no)</td> <td>-</td> </tr> </tbody> </table> <h2>1.2. Note on Multi-Value Columns</h2> <div> <p>For rooms with multiple exterior walls (rooms 15-30):</p> <ul> <li><strong>AExt</strong>: {Exterior wall 1 area, Exterior wall 2 area}</li> <li><strong>AWin</strong>: {Window area on exterior wall 1, Window area on exterior wall 2}</li> <li><strong>azi</strong>: {Azimuth of exterior wall 1, Azimuth of exterior wall 2}</li> </ul> <p>Example:</p> <ul> <li><strong>AExt</strong> = {10, 15}</li> <li><strong>AWin</strong> = {2, 0}</li> <li><strong>azi</strong> = {0, 3.1415}</li> </ul> <p>This indicates two exterior walls with areas of 10 m² and 15 m² facing south (0 rad) and north (3.1415 rad), respectively. The south-facing wall has a window of 2 m², while the north-facing wall has no window.</p> <h2>1.3. Data Sources</h2> <div> <ul> <li><strong>Room Model</strong>: Simulated using the reduced-order package of the Modelica Buildings Library.</li> <li><strong>Weather Data</strong>: Provided by the German Meteorological Service (DWD) in Test Reference Year (TRY) format.</li> </ul> <p>This comprehensive dataset provides crucial parameters required to train and evaluate thermal models for different room configurations. The simulation data ensures a diverse range of environmental and occupancy conditions, enhancing the robustness of the models.</p> <h2>1.4. Data scaling</h2> <p>The data set contains the raw data as well as the scaled data used for training and testing the model. The scaling was carried out using the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html" target="_blank" rel="noopener">StandardScaler</a> package.</p> <h2>1.5. Weather data license</h2> <p>This data set contains weather data recorded by the DWD under license „Datenlizenz Deutschland – Namensnennung – Version 2.0" (<a href="https://www.govdata.de/dl-de/by-2-0" target="_blank" rel="noopener">URL</a>). The data is provided by "<a href="https://www.bbsr.bund.de" target="_blank" rel="noopener"><em>Bundesinstitut für Bau-, Stadt- und Raumforschung</em></a>". The data can be downloaded from <a href="https://www.bbsr.bund.de/BBSR/DE/forschung/programme/zb/Auftragsforschung/5EnergieKlimaBauen/2013/testreferenzjahre/01-start.html?pos=2" target="_blank" rel="noopener">here</a>. We use data from the year 2015 from Heilbronn. We have added the weather data to the data set unchanged.</p> </div> </div>
Convallaria dataset for microscopy image denoising benchmark as used in Probabilistic Noise2Void paper
<p>Convallaria dataset for microscopy image denoising benchmark as used in Probabilistic Noise2Void paper (https://ieeexplore.ieee.org/document/9098336)</p>
Simulated probabilistic speed profiles for selected routes in Prague
<p><strong>Simulated probabilistic speed profiles for 1608 road segments in Prague, Czech Republic</strong></p> <p>This dataset contains simulated speed profiles for selected road segments in Prague, Czech Republic. The profiles describe changes in average driving speed on a particular segment over a given time interval discretised to a time steps of a fixed size. Number of the time steps is stored in the "<strong>IntervalsPerSegment</strong>" attribute of the root group. Size of a single time step in <strong>seconds</strong> is stored in the "<strong>TimeStep</strong>" attribute. Total length of time interval covered by the profiles is computed as IntervalsPerSegment x TimeStep.</p> <p>The file contains two types of profiles.</p> <p>The first type are plain speed profiles which provide just average driving speed assigned to each time step - dataset "<strong>speed_profile</strong>".</p> <p>The second type are probabilistic speed profiles which contain discrete probability distribution of the driving speed for particular segment and given time step. The probability distribution in this case has 4 support points, corresponding to 4 Levels of Service (LoS). The first LoS corresponds to an optimal (freeflow) speed and the last one describes the worst case (accident, heavy traffic, etc.). The discrete distribution is stored in the "<strong>prob_profile</strong>" dataset with the following format:</p> <p>Column 1 - Speed corresponding to the first LoS in <strong>km/h</strong><br> Column 2 - Probability assigned to this speed (float between 0.0 - 1.0)<br> ...</p> <p>Sampling from this distribution is implemented by the <a href="http://www.keithschwarz.com/darts-dice-coins/">Vose's alias method</a>. We provide precomputed tables for implementing this method for each segment in datasets "<strong>alias</strong>" and "<strong>probability</strong>".</p> <p>Further applications of the probabilistic speed profiles are available in:</p> <p>Golasowski, M., Tomis, R., Martinovič, J., Slaninová, K., & Rapant, L. (2016, September). Performance evaluation of probabilistic time-dependent travel time computation. In <em>IFIP International Conference on Computer Information Systems and Industrial Management</em> (pp. 377-388). Springer, Cham.</p> <p><strong>File structure</strong></p> <p>Profiles for the individual segments are stored as subgroups in the root group. Names of the subgroups correspond to unique road segments identifiers corresponding to a subset of road network of Prague.</p> <p>The identifiers correspond to a routing index published here: <a href="https://doi.org/10.5281/zenodo.2275556">10.5281/zenodo.2275556</a></p> <pre><code class="language-javascript">HDF5 "profiles_8runs.h5" { GROUP "/" { ATTRIBUTE "IntervalsPerSegment" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 59 } } ATTRIBUTE "NumberOfProfiles" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 4 } } ATTRIBUTE "TimeStep" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 60 } } ATTRIBUTE "Version" { DATATYPE H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLTERM; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "1.0" } } GROUP "66678115" { DATASET "alias" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 59, 4 ) / ( 59, 4 ) } } DATASET "prob_profile" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 59, 8 ) / ( 59, 8 ) } } DATASET "probability" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 59, 4 ) / ( 59, 4 ) } } DATASET "speed_profile" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 1, 59 ) / ( 1, 59 ) } } }</code></pre> <p> </p>
Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data
<p>Superficial white matter bundle atlas constructed using fiber clustering algorithms, based on 100 subjects of HCP database (probabilistic tractography). The ROIs of Desikan-Killiany atlas where used to label the final clusters.</p> <p>The atlas is composed of 525 fascicles, 267 bundles in the left hemisphere and 258 bundles in the right hemisphere. It has 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI.</p>
Probabilistic classification of the Fermi-LAT 4FGL-DR3 catalog sources
<p>Multi-class classification of Fermi LAT 4FGL-DR3 sources into six or nine classes using random forest (RF) and neural network (NN) algorithms.</p> <p>Probabilistic catalogs are in files:</p> <p>4FGL-DR3_6class_GMM_nmin100_prob_cat.csv<br> 4FGL-DR3_6class_RF_nmin100_prob_cat.csv<br> 4FGL-DR3_9class_GMM_nmin15_prob_cat.csv</p> <p>Files containing definition of the six or nine groups of physical classes of the 4FGL-DR3 catalog and summary of the expected numbers of associated and unassociated sources in each of the six or nine groups:</p> <p>4FGL-DR3_6class_GMM_nmin100_summary.csv<br> 4FGL-DR3_6class_RF_nmin100_summary.csv<br> 4FGL-DR3_9class_GMM_nmin15_summary.csv</p> <p>The identifies in file names, GMM_nmin100, RF_nmin100, GMM_nmin15, refer to the method how the groups of physical classes were determined: (1) using the Gaussian mixture model (GMM) or random forest (RF), (2) requiring at least 100 associated sources in a group (nmin100) or at least 15 sources in a group (nmin15).</p> <p>A detailed description can be found at: http://arxiv.org/abs/2301.07412</p>
Supplementary materials for "Adaptive Probabilistic Forecasting of Electricity (Net-)Load"
<p>Supplementary materials for “Adaptive Probabilistic Forecasting of Electricity (Net-)Load”, presently under review. Only underlying data is included for now. Code will be included in a future update of this deposit.</p>
Data repository for Jarvis et al. (2023): The probabilistic nature of dune collisions in 2D"
<p>This is a data repository associated with Jarvis et al. (2023) "The probabilistic nature of dune collisions in 2D" published in Earth Surface Dynamics.<br> </p>
Awake Intraoperative Language Mapping and Chinese Probabilistic Map Construction
ClinicalTrials.gov study NCT03641391. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
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Data from: PROTAX-fungi: a web-based tool for probabilistic taxonomic placement of fungal ITS sequences
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Supporting data for: Planted conifer seedling survival in a post-wildfire landscape in New Mexico: From experimental planting to predictive probabilistic surfaces
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Data from: Stimulus discriminability may bias value-based probabilistic learning
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Supplementary figures for: Of traits and trees: probabilistic distances under continuous trait models for dissecting the interplay among phylogeny, model, and data
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Data from: Probabilistic interfractional motion carbon ion radiation therapy dose distribution for prostate cancer shows rectum sparing with moderate target coverage degradation
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Data from: Extending null scenarios with Faddy distributions in a probabilistic randomization protocol for presence-absence data
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Data from: Probabilistic species tree distances: implementing the multispecies coalescent to compare species trees within the same model-based framework used to estimate them
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Tempo and mode in karyotype evolution revealed by a probabilistic model incorporating both chromosome number and morphology
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A method for probabilistic assessment of tunneling-induced damage to surface structures considering soil-structure interaction effects
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Raw and normalized count data for "Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling"
<p>SingleCellExperiment objects containing raw and normalized counts, as well as reduced dimension representations and cell type annotations for both the follicular lymphoma samples (sce_follicular_annotated_final.rds) and high grade serous ovarian cancer samples (sce_hgsc_annotated_final.rds) as detailed in the paper, as well as reactive lymph node data (sce_RLN_normalized.rds).</p>
Data from: Integrating sequence evolution into probabilistic orthology analysis
Orthology analysis, that is, finding out whether a pair of homologous genes are orthologs — stemming from a speciation — or paralogs — stemming from a gene duplication - is of central importance in computational biology, genome annotation, and phylogenetic inference. In particular, an orthologous relationship makes functional equivalence of the two genes highly likely. A major approach to orthology analysis is to reconcile a gene tree to the corresponding species tree, (most commonly performed using the most parsimonious reconciliation, MPR). However, most such phylogenetic orthology methods infer the gene tree without considering the constraints implied by the species tree and, perhaps even more importantly, only allow the gene sequences to influence the orthology analysis through the a priori reconstructed gene tree. We propose a sound, comprehensive Bayesian Markov chain Monte Carlo-based method, DLRSOrthology, to compute orthology probabilities. It efficiently sums over the possible gene trees and jointly takes into account the current gene tree, all possible reconciliations to the species tree, and the, typically strong, signal conveyed by the sequences. We compare our method with PrIME-GEM, a probabilistic orthology approach built on a probabilistic duplication-loss model, and MRBAYESMPR, a probabilistic orthology approach that is based on conventional Bayesian inference coupled with MPR. We find that DLRSOrthology outperforms these competing approaches on synthetic data as well as on biological data sets and is robust to incomplete taxon sampling artifacts.
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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)
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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.