Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning

Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning."

opencc-by-4.0Jun 2022View details →

Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow

Data for the six test cases of 2D turbulence explored in the paper, Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow.

opencc-by-4.0Jun 2022View details →

Let's save the kingdom! The impact of gamification on flow and academic achievement in secondary school students

Datos del Flow

opencc-zeroJun 2022View details →

Clines on the seashore: The genomic architecture underlying rapid divergence in the face of gene flow

Adaptive divergence and speciation may happen despite opposition by gene flow. Identifying the genomic basis underlying divergence with gene flow is a major task in evolutionary genomics. Most approaches (e.g., outlier scans) focus on genomic regions of high differentiation. However, not all genomic architectures potentially underlying divergence are expected to show extreme differentiation. Here, we develop an approach that combines hybrid zone analysis (i.e., focuses on spatial patterns of allele frequency change) with system-specific simulations to identify loci inconsistent with neutral evolution. We apply this to a genome-wide SNP set from an ideally suited study organism, the intertidal snail Littorina saxatilis, which shows primary divergence between ecotypes associated with different shore habitats. We detect many SNPs with clinal patterns, most of which are consistent with neutrality. Among non-neutral SNPs, most are located within three large putative inversions differentiating ecotypes. Many non-neutral SNPs show relatively low levels of differentiation. We discuss potential reasons for this pattern, including loose linkage to selected variants, polygenic adaptation and a component of balancing selection within populations (which may be expected for inversions). Our work is in line with theory predicting a role for inversions in divergence, and emphasizes that genomic regions contributing to divergence may not always be accessible with methods purely based on allele frequency differences. These conclusions call for approaches that take spatial patterns of allele frequency change into account in other systems.

opencc-by-4.0Jun 2022View details →

Data supporting paper "Efficient numerical simulation of density-driven flows: Application to the 2- and 3-D Elder problem"

Data supporting paper "Efficient numerical simulation of density-driven flows: Application to the 2- and 3-D Elder problem", Water Resources Research.

opencc-zeroFeb 2020View details →

Data from: Origin and role of the cerebrospinal fluid bidirectional flow in the central canal

The circulation of cerebrospinal fluid (CSF) plays pivotal roles for body axis formation and brain development. During embryogenesis, CSF is rich in particles and proteins and flows bidirectionally in the central canal. The origins of bidirectional flow and its impact on development are unknown. Experiments combined with modeling and simulations demonstrate that the bidirectionality of CSF flow is generated locally by caudally-polarized motile cilia confined to the ventral wall of the central canal. Such active bidirectional flow of the CSF accelerates the long-range transport of particles propagating rostrally and caudally. In addition, spontaneous muscle contractions increase local CSF flow and consequently enhance long-range transport of extracellular lipidic particles. Focal ablation of the channel connecting brain ventricles to the central canal reduces embryo length, indicating that long-range transport contributes to embryonic growth. Our study also demonstrates that at this early stage, motile cilia ensure the proper formation of the central canal.

opencc-zeroDec 2011View details →

Data from: Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa)

While motorways are often assumed to influence the movement behaviour of large mammals, there are surprisingly few studies that show an influence of these linear structures on the genetic make-up of wild ungulate populations. Here, we analyse the spatial genetic structure of red deer (Cervus elaphus) and wild boars (Sus scrofa) along a stretch of motorway in the Walloon part of Belgium. Altogether 876 red deer were genotyped at 13 microsatellite loci, and 325 wild boars at 14 loci. In the case of the red deer, different genetic clustering tools identified two genetic subpopulations whose borders matched the motorway well. Conversely, no genetic structure was identified in the case of the wild boar. Analysis of isolation-by-distance patterns of pairs of individuals on the same side and on different sides of the motorway also suggested that the road was a barrier to red deer, but not to wild boar movement. While telemetry studies seem to confirm that red deer are more affected by motorways then wild boar, the red deer sample size was also much larger than that of the wild boars. We therefore repeated the analysis of genetic structure in the red deer with randomly sub-sampled datasets of decreasing size. The power to detect the genetic structure using clustering methods decreased with decreasing sample size.

opencc-zeroJul 2019View details →

Data from: Flow imaging microscopy as a novel tool for high-throughput evaluation of elastin-like polymer coacervates

Biological and bioinspired polymer microparticles have broad biomedical and industrial applications, including drug delivery, tissue engineering, surface modification, environmental remediation, imaging, and sensing. Full realization of the potential of biopolymer microparticles will require methods for rigorous characterization of particle sizes, morphologies, and dynamics, so that researchers may correlate particle characteristics with synthesis methods and desired functions. Toward this end, we evaluated biopolymer microparticles using flow imaging microscopy. This technology is widely used in the biopharmaceutical industry but is not yet well-known among the materials community. Our polymer, a genetically engineered elastin-like polypeptide (ELP), self-assembles into micron-scale coacervates. We performed flow imaging of ELP coacervates using two different instruments, one with a lower size limit of approximately 2 microns, the other with a lower size limit of approximately 300 nanometers. We validated flow imaging results by comparison with dynamic light scattering and atomic force microscopy analyses. We explored the effects of various solvent conditions on ELP coacervate size, morphology, and behavior, such as the dispersion of single particles versus aggregates. We found that flow imaging is a superior tool for rapid and thorough particle analysis of ELP coacervates in solution. We anticipate that researchers studying many types of microscale protein or polymer assemblies will be interested in flow imaging as a tool for quantitative, solution-based characterization.

TIGAR tropical heat source experiments with realistic background flows

A barotropic version of the TIGAR (Transient Inertia-Gravity And Rossby wave dynamics) model has been run at T170 resolution to simulate the effect of tropical heat sources mimicking convection in different realistic background flows. The heat source was treated as a forcing of the continuity equation. The background flows include seasonal mean zonally-averaged zonal wind from 1993, 1999, 2009, 2012 and 2016 derived from ERA5. TIGAR solves the rotating shallow water equations by applying Hough harmonics as spectral basis functions thereby enabling the analysis of Rossby and Inertia-gravity wave dynamics. More details of the model are available at:  <a href="https://doi.org/10.1002/qj.4006">https://doi.org/10.1002/qj.4006</a> .

Less-well-developed crustal flow in the central Tibetan Plateau revealed by receiver function and surface wave joint inversion

A crustal flow model has been previously used to explain the crustal extension of the Tibetan Plateau. However, the existence of massive crustal flow in the central plateau is still controversial. We conducted a joint inversion of receiver functions and surface wave dispersions from the 2-D broadband seismic array SANDWICH deployed in the central plateau. The crustal S-wave velocity structure with high vertical resolution shows a low-velocity layer (LVL) in the middle-lower crust beneath most stations. The S-wave velocity of this LVL is mostly within 3.0-3.4 km/s, reflecting a melt volume percentage (MVP) ≤ 7%, except at two stations. Our study suggests that there is not a high enough melt volume in central Tibet to develop crustal flow, which requires an MVP ≥ 7% to decrease rock strength. The formation of extensional structures in the central plateau may contribute to ductile deformation in the middle-lower crust but not crustal flow.

Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers

<ol> <li>The first file is the flow profile data calculated by analytical solution in the steady-state flow.  The calculation region is 30cm×30cm.</li> <li>The secend file is the temperature response data calculated by finite difference method. The simulated temperature response data corresponding to all heat pulse experiments  are calculated and listed here.</li> </ol>

Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers

<ul> <li>In the laboratory, 42 heat pulse experiments with 7 different Darcy’s velocities and 6 different heating powers had been done. The results are summarized in a relationship of temperature increase with time.</li> <li>The temperature responses were continuously monitored over a 30min period. Data were collected every 2s throughout the recording period. In order to strictly monitor the actual power to the heater, the voltage and current delivered to the heater were also recorded simultaneously with the temperature.</li> <li>The heater was switched on and lasted for 1 min to generate a heat pulse. The time when the heater was switched on was selected as the initial time of data analysis.</li> </ul>

Stress effects on wave velocities of rocks: Contribution of crack closure, squirt flow and acoustoelasticity

These data are the P- and S-wave waveform data obtained by Ba et al. (2019-2022) through ultrasonic experimental measurements on rock samples at different pressures. In the package of data, each folder of Sample A1, Sample A2, Sample A3, Sample A4, Sample A5, Sample A6, Sample B1, Sample B2, Sample B3, Sample B4, Sample B5, and Sample B6 indicates sample number. For each sample with different pore fluids, waveforms measured at different pressures are saved as different TXT files. For the title of each TXT file, it includes the sample number, saturation condition, temperature, differential pressure value and wave type. For instance, “Sample A1_fullwater_20℃_15MPa_Pwave” denotes that P-wave waveform of sample A1 with the full water saturation is measured at 20℃ and the differential pressure of 15MPa, while “Sample B2_dry_130℃_20MPa_Swave” denotes that S-wave waveform of dry sample B2 is measured at 130℃ and the differential pressure of 20 MPa. The package also includes the TXT file of sample lengths. In the TXT files of Samples A1-A6, the columns give wave amplitudes. The time interval is 0.05 μs. The travel times of P-wave and S-wave in the measured system are 8.4 μs and 14.45 μs, respectively. In the TXT files of Samples B1-B6, the first column gives time and the second column gives wave amplitude.

Data of 1962 and 1983 lava flows and scoriae in Miyakejima

Excel sheets of Table S1 and S2

Data for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"

Data for the main figures for "Uncovering basal friction in northwest Greenland using an ice flow model and observations of the past decade"

Supplementary material for the manuscript "Transition to chaos and modal structure of magnetized Taylor–Couette flow"

These data and codes are the supplementary materials for the manuscript "Transition to chaos and modal structure of magnetized Taylor–Couette flow". The description of the data is in the file "DATA_OVERVIEW.txt".

openAug 2022View details →

Dataset (flow rates, heads, holdups, droplet size distributions, Sauter mean diameters) for a DN300 Pump-Mixer

This dataset contains all measured data and the supplementary material of the publication "Fluid dynamics in a continuous pump-mixer" submitted at applied sciences, MPDI

Video Data: Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation

Video dataset accompanying "Spatial Biases in Optic-Flow Sampling for Self-Motion Estimation in Natural Environments." See accompanying <a href="https://github.com/eacooper/AlexanderOpticFlow">Github repository</a> for more documentation and analysis code.

Data of Publication "Theoretical rheo-physics of silk: Intermolecular associations reduce the critical specific work for flow-induced crystallization"

Data of Publication: Theoretical rheo-physics of silk: Intermolecular associations reduce the critical specific work for flow-induced crystallization Charley Schaefer and Tom C. B. McLeish Citation: Journal of Rheology 66, 515 (2022); doi: 10.1122/8.0000411 View online: https://doi.org/10.1122/8.0000411

Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation

Documentation to reproduce in silico analyses related to the manuscript Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure by Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih Published in Nature Communications (doi: 10.1038/s41467-022-33464-w) All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426).  For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366). Simulation results: All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: verticesDict.pkl and edgesDict.pklcontaining all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder Baseline contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder Dilated contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders dc_x.x contain the simulation results for the different diameter changes. Note that, folder dc_0.0 contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: toDilate and base_capillary).    Reproducing figure 8: Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0). Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure. Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below).  Output: All created Figures are saved in the folder Figures. The associated source data is available in Excel format in the folder SourceData.   Edge attributes: diameter: vessel diameter [µm] mainAV: 1 if ascending venule main branch, 0 otherwise connectivity: vertex tuple to define location of edge flow: flow rate [µm3/ms] mainDA: 1 if descending arteriole main branch, 0 otherwise nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary htt: tube hematocrit [-] toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise   Vertex attributes: index: vertex index pressure: pressure [mmHg] nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary coords: vertex coordinates x,y,z [µm] pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes   Obtaining simulation results: General: <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> Specific for current application: <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> Demo: <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (G_averaged.pkl)</li> </ul>