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5,371 results for “Flow”
How flexible, slit and rigid barriers mitigate two-phase geophysical mass flows: a numerical appraisal
<p>The supplementary videos SV1 and SV2 (presented in Figures 4 and 5) show geophysical flows impacting flexible, slit, and rigid barriers via pile-up mode and runup mode, respectively.</p> <p>The measurement data used for comparison in Figure 7 from 14 centrifuge tests with bouldery and the boulder-debris mixture flows (Song et al., 2018b, 2019), and dry granular flow against open-type dams (Choi et al., 2020) were published open access in their articles.</p> <p>The unique dataset, comprising normalized data obtained from analytical models (Li et al., 2021; Song et al., 2021a), empirical relations (Cui et al., 2015), experiments (Armanini et al., 2020; Choi et al., 2020; Cui et al., 2015; Hu et al., 2020; Song et al., 2021a, 2021b; Tiberghien et al., 2007; Vicari et al., 2021), field events (Hübl et al., 2009), and practical design values (Armanini, 1997; Hungr et al., 1984; Kwan & Cheung, 2012; Wendeler, 2016), is plotted in Figure 10 for comparison.</p> <p>All these articles can be found in the PDF file entitled “References in Figures 7 and 10”.</p>
Dark matter flow dataset from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Source data belonged to "Geometric flow control in lateral flow assays: Macroscopic single-phase modeling"
<p>This record contains all the necessary data to obtain the results of the study "Geometric flow control in lateral flow assays: Macroscopic single-phase modeling" (<a href="https://doi.org/10.1063/5.0093316">https://doi.org/10.1063/5.0093316</a>).</p>
Codes for "High-throughput parallel optofluidic 3D-imaging flow cytometry"
<p>Codes used in Ugawa & Ota. "High-throughput parallel optofluidic 3D-imaging flow cytometry". Small size data is also included.</p>
Nearly three centuries of Lava Flow Subsidence at1 Timanfaya, Lanzarote
<p>The following datasets are available for each InSAR dataset - Envisat and Sentinel-1, covering the island of Lanzarote between 2004/01/17 -2010/01/30 and 2016/09/14-2020/06/25 respectively:</p> <p><br> Line of Sight (LOS) velocities<br> LOS velocity standard deviations<br> LOS displacement time series<br> Vertical velocities<br> Vertical velocity standard deviations <br> East-West velocities<br> East-West velocity standard deviations</p> <p>The velocities and standard deviations are all geocoded (latitude/longitude in decimal degrees) geotiff files in mm/yr at 90 m resolution for Envisat and 30 m resolution for Sentinel-1. Negative values in the LOS data represent movement away from the satellite; in the vertical data represent subsidence; and in the East-West data represent westward movement. The velocities were calculated from the displacement time series as a least squares inversion and the standard deviations were calculated within LiCSBAS using a percentile bootstrap method. </p> <p>The Sentinel-1 time series is given as a 3D matrix (lon x lat x dates) within a .h5 file where each slice is the cumulative displacement (mm) in a lon lat grid format for each epoch date. The latitude and longitude are then given as separate vectors within the .h5 file.</p> <p>The Envisat time series is given as a .dat file where the first two columns are longitude and latitude. The remaining columns are the cumulative displacement (mm) for each epoch date.</p> <p>The epoch dates for both are given as separate text files and are given in the format YYYYMMDD.</p>
Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations
<p><span>Inference of recent population divergence requires fast evolving markers and necessitates to differentiate shared genetic variation caused by ancestral polymorphism and gene flow. Theoretical research shows that the use of compound marker systems integrating linked polymorphisms with different mutational dynamics, such as a microsatellite and its flanking sequences, can improve estimation of population structure and inference of demographic history, especially in the case of complex population dynamics. However, empirical application in natural populations has so far been limited by lack of suitable methods for data collection. A solution comes from the development of sequence-based microsatellite genotyping which we used to study molecular variation at 36 sequenced nuclear microsatellites in seven <em>Quercus canariensis</em> and four <em>Q. faginea</em> rear-edge populations across Algeria. We aim to decipher their taxonomic relationship, past evolutionary history and recent demographic trajectory. First, we compare the estimation of population genetics parameters and simulation-based inference of demographic history from microsatellite sequence alone, flanking sequence alone or the combination of linked microsatellite and flanking sequence variation. Second, we apply random forest approximate Bayesian computation to identify which of these sequence types is most informative. Whereas analysing microsatellite variation alone indicates recent interspecific gene flow, additional information gained by integrating nucleotide variation in flanking sequences, by reducing homoplasy, suggests ancient interspecific gene flow followed by drift in isolation instead. The weight of each polymorphism in the inference also demonstrates the value of linked variations with contrasted mutation dynamic to improve estimation of both demographic and mutational parameters.</span></p>
Supplementary Movies and Dataset for Debris Flows, Debris Avalanches and Rock Avalanches Impacting A Flexible Ring Net Barrier.
<p>The supplementary movies S1, S2, and S3 (presented in Figures 1, S4, and S6) show typical debris flow, debris avalanche, and rock avalanche impacting a flexible ring net barrier with = 6 m/s, respectively.</p> <p>The experimental data used for comparison in Figure 2b from the large-scale flume test V6-B1 with a flexible ring net barrier was published open access in the below article (Vicari <em>et al.,</em> 2021).</p> <p>Vicari, H., Ng, C. W., Nordal, S., Thakur, V., De Silva, W. R. K., Liu, H., & Choi, C. E. (2021). The Effects of Upstream Flexible Barrier on the Debris Flow Entrainment and Impact Dynamics on a Terminal Barrier. <em>Canadian Geotechnical Journal</em>, <em>59</em>(6), 1007-1019. <a href="https://doi.org/10.1139/cgj-2021-0119">https://doi.org/10.1139/cgj-2021-0119</a></p>
Data collection for article "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar's Forest Frontier Landscape"
<p>This dataset contains the nine ecosystem service models (in .neta format) underlying the publication "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar’s Forest Frontier Landscape". The ecosystem models were implemented using the commercial software Netica (version 6.05) for constructing and analysing Bayesian Networks.</p>
Intermediate data files from the compilation of Economy-wide Material Flow Accounts for the Domestic Extraction of abiotic materials
<p>These files represent a selection of intermediary files from the compilation of material flow accounts on Domestic Extraction (DE) of abiotic materials. The main output of this compilation has been integrated in the UNEP IRP Global Material Flow Database (GMFD).</p> <p>These files include input files (e.g. IDs for data harmonization, or factors for data conversion), as well as output files (e.g. supplementary information, or detailed data accounts before aggregation and integration into the GMFD).</p> <p>Please note: These files are published exclusively for the purpose of making this information available to interested parties in a transparent and orderly fashion, in particular to other research projects who may have use for it. Therefore, these files are not associated with any publication and have not been adjusted or formatted with regard to any publications standards, i.e. they are uploaded exactly as they have been processed in the respective R Github repository of the underlying data compilation.</p> <p>The following description attempts to give a short overview of the respective types of files and their contents. For more detailed information on the data compilation, please refer to chapters 6, 8, and 10 in the <a href="https://resourcepanel.org/sites/default/files/irp_technical_annex_global_material_flows_database.pdf">technical report</a> of the GMFD.</p> <p> </p> <p><strong>Main data output (i.e. detailed material flow accounts)</strong></p> <p><em>DE_met_min_fos_CCC_2021-11-04.csv</em>: Data aggregated to the official categories (CCC/TCCC) used for integration into the GMFD. With IDs, without names (e.g. for materials and countries).</p> <p><em>DE_met_min_fos_CCC_with_names_2021-11-04.csv</em>: Same as above, but with names.</p> <p><em>DE_met_min_fos_Detailed_2021-11-04.csv</em>: Detailed accounts, as compiled, before final aggregation. With IDs, without names.</p> <p><em>DE_met_min_fos_Detailed_with_names_2021-11-04.csv</em>: Same as above, but with names.</p> <p> </p> <p><em>DE_met_min_fos_Detailed_2022-05-24.csv: </em>Slightly revised version from May 2022. But not included in current GMFD version.</p> <p> </p> <p><strong>ID and concordance tables</strong></p> <p><em>ccc_vs_mat_ids.csv:</em> Concordance table for allocation of detailed material accounts to aggregated CCC accounts.</p> <p><em>country_ids.csv:</em> General ID table for country IDs and names</p> <p><em>estimated_ids.csv:</em> Material IDs which are assigned during application of ore estimation factors.</p> <p><em>geo_exist.csv:</em> Table for consistent geographic adjustment of data for specific countries which have disintegrated over time (not including regions like Germany, Yemen, Ethiopia, Sudan, which all have been dealt with individually if necessary).</p> <p><em>material_ids.csv:</em> General ID table for material IDs and names.</p> <p><em>source_country_ids.csv:</em> Concordance table for country IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>source_material_ids.csv:</em> Concordance table for material IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>source_unit_ids.csv:</em> Concordance table for unit IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>unit_ids.csv:</em> General ID table for unit IDs and names.</p> <p> </p> <p><strong>Conversion factors</strong></p> <p><em>conversion_elements.csv:</em> Factors applied for conversion from metal compounds to elemental metals.</p> <p><em>conversion_factors_units.csv:</em> Factors applied for unit conversions.</p> <p> </p> <p><strong>Ore estimation factors</strong></p> <ul> <li>These factors represent content-to-ore ratios (i.e. "tonnes of extracted ore/mineral per ton of produced content”).</li> </ul> <p><em>all_integrated_est_fac_2021-11-04.csv:</em> Factors for estimation of metal and mineral ores (all which have been compiled/integrated)</p> <p><em>applied_est_fac_2021-11-04.csv:</em> Factors for estimation of metal and mineral ores (which have actually been applied)</p> <p><em>average_metal_prices_1990-2020.csv:</em> Metal prices applied in the compilation of estimation ratios.</p> <p><em>raw_metal_to_ore_ratios_fineprint.csv:</em> Raw metal-to-ore ratios derived from FINEPRINT mining data.</p> <p><em>raw_metal_to_ore_ratios_snl.csv:</em> Raw metal-to-ore ratios derived from SNL mining data.</p> <p> </p> <p><strong>Intermediate files from data processing</strong></p> <p><em>all_interm_conv_integr_2021-12-13.csv:</em> Harmonized, converted (units & elemental metals), integrated (i.e. without double counting). Before any estimations (ores & construction minerals) and before final cleaning/formatting.</p> <p><em>wmd_bgs_usgs_interm_harmonized_2021-12-13.csv:</em> All harmonized raw data from WMD/BGS/USGS, before any further processing (i.e. with double counting).</p> <p> </p> <p><strong>Comparison data (for mining accounts from FINEPRINT project)</strong></p> <p><em>data_for_comparison_2022-05-24.csv:</em> Data set specifically compiled for comparison with accounts on production of mines from the FINEPRINT project. Has been applied for verification of data published in <em>"Jasansky et al. (2022) An open database on global coal and metal mining"</em>.</p> <ul> <li>Includes all available types of materials which have been reported (e.g. ores and metals and metal compounds) <ul> <li>meaning: also materials which are associated with each other, for example iron ore and iron (and would therefore have been selectively integrated/excluded in the GMFD compilation).</li> </ul> </li> <li>Excludes any double counting for the exact same material from different data sources</li> <li>Harmonized IDs, converted units</li> <li>Includes metal compounds approximated/converted from reported elemental metals (where possible)</li> </ul> <p> </p> <p><strong>materialflows.net</strong></p> <p><em>data_sunburst_material_profiles_20220607.csv:</em> Full detail of data underlying the Sunburst visualization "Global Domestic Extraction in 2019, by material group" in section "Raw Material Profiles" on materialsflow.net. However, for all available years. Includes data on Domestic Extraction of biomass. Includes detail for CCC, MFA13+, MFA4+</p> <p> </p> <p><strong>Outliers</strong></p> <p><em>overview_adjusted_outliers_2021-12-22.csv:</em> Overview of outliers which were adjusted during final formatting.</p> <p> </p> <p><strong>Other</strong></p> <p><em>approximation_tailings_detailed_2021-09-26.csv:</em> Estimation of tailings based on reported amounts of ores/minerals and their respective contents.</p> <p> </p>
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Understanding Sampling Bias in the Global Heat Flow Compilation
<p>Geothermal heat flow measurements, including calculated weights and geological, tectonic and topographic settings.</p> <p>Paper in review (July 14th, 2022)</p>
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
Global Positioning System Based on Optical Flow and Convolutional Neural Network
<p>Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.</p>
Synthetic river flow videos dataset
<p>###### ######### ########## ###### ######### ##########<br> <strong> Synthetic river flow videos for evaluating image-based velocimetry methods</strong><br> ###### ######### ########## ###### ######### ##########<br> # Year : 2022<br> # Authors : G.Bodart (guillaume.bodart@inrae.fr), J.Le Coz (jerome.lecoz@inrae.fr), M.Jodeau (magali.jodeau@edf.fr), A.Hauet (alexandre.hauet@edf.fr)</p> <p>###<br> <em>This file describes the data attached to the article</em></p> <p>### -><strong> 00_article_cases</strong><br> ########################<br> <em>This folder contains the data used in the case studies: synthetic videos + reference files.</em></p> <p> - 00_reference_velocities<br> -> Reference velocities interpolated on a regular grid. Data are given in conventionnal units, i.e. m/s and m.</p> <p> - 01_XX<br> -> Data of the first case study</p> <p> - 02_XX<br> -> Data of the second case study</p> <p>### -> <strong>01_dev</strong><br> #############<br> <em>This folder contains the Python libraries and Mantaflow modified source code used in the paper. The libraries are provided as is. Feel free to contact us for support or guidelines.</em></p> <p> - lspiv<br> -> Python library used to extract, process and display results of LSPIV analysis carried out with Fudaa-LSPIV</p> <p> - mantaflow-modified<br> -> Modified version of Mantaflow described in the article. Installation instructions can be found at http://mantaflow.com</p> <p> - syri<br> -> Python library used to extract, process and display fluid simulations carried out on Mantaflow and Blender. (Require the lspiv library)</p> <p>### -> <strong>02_dataset</strong><br> #################<br> <em>This folder contains synthetic videos generated with the method described in the article. The fluid simulation parameters, and thus the reference velocities, are the same as those presented in the article. </em></p> <p> - The videos can be used freely. Please consider citing the corresponding paper.</p>
Environmental Flows Turbine Optimization
<p>This is a dataset of the results from determining the capacity (MW) of potential minimum flow turbines based on available environmental flows. This includes the turbine capacity from two methods of data processing, as well as the iterative screening envelopes to determine locations of likely opportunities for implementing these environmental flow turbines. </p> <p>Cite this dataset as well as the manuscript by the authors that is available through Renewable and Sustainable Energy Reviews.</p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
Data collection for article "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar"
<p>This dataset contains supply, demand, and flow maps from nine ecosystem service models (as layer files in .tif format) underlying the publication "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar". </p>
Intersection Vehicular Traffic Flow Scheduling Datasets
<p><strong>Background</strong></p> <p>Vehicular traffic congestion remains a major problem in most modern cities of the world. Therefore, efforts to minimize these congestions and their accompanying effects continuously receive much attention from researchers, traffic engineers, policymakers, etc.</p> <p>One important element required in the efforts of traffic engineering for the minimization of traffic congestion is data. Data influence the modeling and deployment of traffic scheduling systems. Most importantly, the objectives of modeling and deploying the traffic scheduling system influence the nature of the data to be used.</p> <p><strong>Aim</strong></p> <p>The aim of this study was to generate and provide a dataset that may be used in the training of computationally intelligent systems having basic objects of minimizing waiting time, travel time, etc. at various intersections (isolated intersections or roundabouts).</p> <p><strong>Methodology</strong></p> <p>The dataset (<strong><em>.csv</em></strong>) consisted of waiting time (W), queue length (Q), and phase duration (P). The waiting time is the time duration vehicles have waited at an intersection/roundabout before being scheduled to utilize the intersection/roundabout. The queue length refers to the number of vehicles (vehicular count) waiting at an intersection/roundabout. Phase duration is the time period a given vehicular flow (lane) is assigned the green wave to utilize the intersection/roundabout. The dataset was obtained through repeated training, testing, and modification of phase duration and the Adaptive Neuro-Fuzzy Inference System (ANFIS) model.</p> <p>The dataset considered bounded conditions on the three parameters (W, Q, P). The W and Q were bounded between zero and ninety – [0, 90] and the P is [13, 50]. That is, when W and Q are greater than or equal to the upper bound, the upper bound is used. Every flow may be assigned a minimum P of 13s and a maximum of 50s. The P-bounds assumed that the lower bound is large enough for vehicles on the assigned traffic flow to move to the safe region of the intersection before the scheduling system switches assignment to another traffic flow. </p> <p><strong>Conclusion</strong></p> <p>The dataset may be used as a benchmark dataset for the improvement of traffic flow controllers as well as other datasets.</p>
DATASET: In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet
<p>This repository contains all the data and code used to analyse these data related to the paper "In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet" by Clerx et al. (2022), published in "The Cryosphere".</p>
Discrete Flow Cytometry of Underway Samples from TN398 Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of picophytoplankton populations from discrete flow cytometry data collected underway during the University of Washington School of Oceanography undergraduate senior thesis cruise (TN398) during December 2021 from Honolulu to San Diego, crossing through the Great Pacific Garbage Patch. The data includes cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus, Synechococcus, and and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</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.