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111 results for “flow dynamics”
Brain Dynamics During Flow Experiences
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The MAT_STOCKS database: economy-wide material flows and material stock dynamics around the world
<p>Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.</p> <p>This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (<em>MISO2) </em>(Wiedenhofer et al. 2024)<em>. </em>MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository. </p> <p><strong>The MAT_STOCKS database version 1.0 </strong>provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:</p> <ul> <li>Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)</li> <li>Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)</li> <li>Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)</li> <li>177 countries</li> <li>1900 to 2016 </li> </ul> <p>All units in kilotons. Paramter names are in accordance with the system definition given in the publication.</p> <p>Additionally, this repository includes all data presented in the figures of the related journal article.</p> <p><strong>Further information</strong></p> <p>This dataset complements the following scientific article:</p> <p>Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, <a href="https://doi.org/10.1111/jiec.13575">https://doi.org/10.1111/jiec.13575</a></p> <p>The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <em>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</em></p> <p><strong>Funding</strong></p> <p>This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.<br><br></p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
Flow Magnetic Tweezers: Gyrase dynamics in absence of drug Ciprofloxacin
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils in absence of Ciprofloxacin, reaction of Gyrase to external torque and eventual wash with SDS.</p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
Data to generate the figures of: "Symmetry breaking of azimuthal waves: Slow-flow dynamics on the Bloch sphere"
<p>The folder contains the data and scripts to generate all the figures of the paper, with detailed instructions.</p> <p>No experimental data was used for this article.</p>
Prospective dynamic and probabilistic material flow analysis of graphene-based materials in Europe from 2004 to 2030
<p>This dataset is related to the following publication:</p> <p>Title: Prospective dynamic and probabilistic material flow analysis of graphene-based materials in Europe from 2004 to 2030</p> <p>Authors: Hyunjoo Hong, Florian Part, Bernd Nowack </p> <p>Submitted to the journal Environmental Science & Technology in June 2022.</p> <p>The files contain an input file and final codes and raw results of the DMFA model for graphene-based materials.</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Flow dynamics and pump kinematics in polychaete burrows constructed in a transparent mud analog
We used Particle Tracking Velocimetry (PTV) to measure fluid flow within burrows constructed by the polychaete Alitta succinea in a transparent mud analog. We also measured the kinematics of the undulatory pumping by the polychaete that drives flow through the burrow. The flow velocity data is presented in the spreadsheet worm_burrow_particle_tracking_data.csv and consists of the x and y coordinates (in mm) of each tracked particle, the time at which it was tracked (in seconds) and the velocity of the particle at that time (in mm per second). The ClipID is the reference of the video clip the data is from, and is a unique identifier. The SequenceID is retained between the pump dynamics data and the particle tracking data, because worm kinematics and flow dynamics were recorded simultaneously. Each tracked particle in a given sequence has a unique TrackID. The worm kinematics data consists of the track of the peak of the undulatory wave created as an individual polychaete ventilates its burrow and is presented in the spreadsheet worm_burrow_pump_dynamics_data.csv. The variables included are the x and y coordinates of the wave peak (in mm), the time at which the point was taken (in seconds) and the instantaneous velocity of the wave peak at that time (in mm per second). The ClipID is the reference of the video clip the data is from, and is a unique identifier. The SequenceID is retained between the pump dynamics data and the particle tracking data, because worm kinematics and flow dynamics were recorded simultaneously. Each tracked wave in a given sequence has a unique TrackID. The metadata, in the spreadsheet worm_burrow_metadata.csv, gives the polychaete Individual ID (a unique identifier for each specimen used) for each Clip ID and Sequence ID from the data spreadsheets, the location in the burrow at which the video was taken (between the head of the worm and the burrow entrance is "ahead", between the tail of the worm and the burrow exit is "behind", and a video of
ss-PDMFA: size-specific, probabilistic, dynamic material flow analysis
<p><strong>Description of the dataset:</strong></p> <p>This dataset is related to the following publication:</p> <p>Title: Size-specific, dynamic, probabilistic material flow analysis of titanium dioxide releases into the environment</p> <p>Authors: Yuanfang Zheng, Bernd Nowack </p> <p>Submitted to the journal Environmental Science & Technology in November 2020.</p> <p>The files contain input files, final codes and raw results of the ss-PDMFA model.</p> <p> </p>
Data and results for manuscript "Flow dynamics in hyper-saline aquifers: hydro-geophysical monitoring and modeling"
<p>The paper presents a general methodology that will help understand how freshwater and saltwater may interact in natural porous media, with a particular view at practical applications such as the storage of freshwater underground in critical areas such as semi-arid zones around the Mediterranean sea. The methodology is applied to a case study in Sardinia and shows how a mix of advanced monitoring and mathematical modeling tremendously advance our understanding of these systems.</p> <p>This package contains the raw cross-hole time-lapse ERT data, additional field data, the ERT inversion results of the field data as well as the modeling data in terms of the concentration distribution of the density-dependent flow and transport model and the inverted synthetic ERT monitoring results.</p>
Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"
<p>Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks". </p> <p>There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance. </p>
Investigating the Unsteady Dynamics of a Multi-Jet Impingement Cooling Flow Using Large Eddy Simulation - Promotional Video and Image
<p>Video: Volume rendering of temperature field obtained from a large eddy simulation of 9 inline impinging jets in a narrow channel.</p> <p>Image: Turbulent structures as isosurface of Q-criterion with an illustration of laser sheet used for PIV measurement.</p> <p>The results were presented at the ASME Turbo Expo 2024 (paper number GT2024-122465) and published in the ASME Journal of Turbomachinery (<a href="https://doi.org/10.1115/1.4066508">https://doi.org/10.1115/1.4066508</a>). The accepted manuscript of the paper is available under <a href="https://elib.dlr.de/207257/">https://elib.dlr.de/207257/</a>.</p> <p>The simulations were performed on DLR's HPC system <a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/hpc-cluster/cara">CARA</a> within the DLR project InnoCool.</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>
Research data supporting "Platinum Nanocatalyst Amplification: Redefining the Gold Standard for Lateral Flow Immunoassays with Ultra-Broad Dynamic Range"
<p>Research data supporting the publication: Loynachan C. N., et al., 2017, ACS Nano, DOI: http://dx.doi.org/10.1021/acsnano.7b06229.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Data basis of "Investigation of Railway Network Capacity by Means of Dynamic Flows"
<p>Input data for the article <strong>Investigation of Railway Network Capacity by Means of Dynamic Flows (Nikolayzik, Maus and Nießen).</strong></p> <p>The dataset contains two files for each analysed scenario (complete network, upper subnetwork, lower subnetwork).</p> <p>The first file ("input_data_infrastructure_{scenario}.csv") contains information on the investigated infrastructure:<br>For each station the number of available tracks is listed and for the lines information on whether it is a single- or double-track line, the average minimum headway time, hourly capacity limits and travel times for the different train types are included. The information is thereby split into two parts, depending on whether the core network or the linking lines are described.</p> <p>The second file ("input_data_trains_{scenario}.csv") contains the trains that can generally be scheduled in the considered network, including information on the corresponding train type, departure frequencies, their routes and a minimally allowed dwell time.</p> <p> </p> <p>Further, the file "input_data_route_conflicts_nodes.py" contains the information on which routes inside a station exclude each other as is described in the article.</p> <p> </p> <p> </p>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Data and scripts belonging to "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape"
<p>Data and scripts belonging to Kaandorp et al., 2021 "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape". Hydrology and Earth System Sciences. </p>
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
<p>Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.</p>
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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)
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.