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5,371 results for “Flow”
Fig. 2 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 2 — Mountly mean wind, and current during high-flow (a, b) and low-flow (c, d) seasons
Fig. 1 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 1 — Map of the study area and microplastic release point
Magneto-Stokes Flow in a Shallow Free-Surface Annulus
<p>In this study, we analyse "magneto-Stokes" flow, a fundamental magnetohydrodynamic (MHD) flow that shares the cylindrical-annular geometry of the Taylor-Couette cell, but uses applied electromagnetic forces to circulate a free-surface layer of electrolyte at low Reynolds numbers. The first complete, analytical solution for time-dependent magneto-Stokes flow is presented and validated with coupled laboratory and numerical experiments. Three regimes are distinguished (shallow-layer, transitional, and deep-layer flow regimes), and their influence on the efficiency of microscale mixing is clarified. The solution in the shallow-layer limit belongs to a newly-identified class of MHD potential flows, and thus induces mixing without the aid of axial vorticity. We show that these shallow-layer magneto-Stokes flows can still augment mixing in distinct Taylor dispersion and advection-dominated mixing regimes. The existence of enhanced mixing across all three distinguished flow regimes is predicted by asymptotic scaling laws and supported by three-dimensional numerical simulations. Mixing enhancement is initiated with the least electromagnetic forcing in channels with order-unity depth-to-gap-width ratios. If the strength of the electromagnetic forcing is not a constraint, then shallow-layer flows can still yield the shortest mixing times in the advection-dominated limit. Our robust description of momentum evolution and mixing of passive tracers makes the annular magneto-Stokes system fit for use as an MHD reference flow.</p>
Cardiovascular MRI (CMR) 3D Cine, 4D Flow and Exercise Stress 4D flow datasets
<p>MRI Datasets for "<strong>Motion-robust free-running volumetric cardiovascular MRI" </strong>(<a href="https://doi.org/10.1002/mrm.30123">https://doi.org/10.1002/mrm.30123</a>)</p> <p><strong>Reconstruction Code Repository</strong>: <a href="https://github.com/OSU-MR/motion-robust-CMR">https://github.com/OSU-MR/motion-robust-CMR</a><br><br><strong>Cite as</strong>: Arshad SM, Potter LC, Chen C, et al. Motion-robust free-running volumetric cardiovascular MRI. Magn Reson Med. 2024; 92(3): 1248-1262. doi: 10.1002/mrm.30123<br> </p> <p>Contains: </p> <ol> <li>3D cine Cartesian bSSFP Acquisition (Undersampled data sorted into 20 cardiac bins)</li> <li>4D flow Cartesian Acquisition (Undersampled data sorted into 20 cardiac bins)</li> <li>Stress 4D flow Cartesian Acquisition (Undersampled data sorted into 20 cardiac bins)</li> </ol> <p><br><strong>Prepared by</strong>:</p> <p><strong>Syed Murtaza Arshad</strong> (<a href="arshad.32@osu.edu">arshad.32@osu.edu</a>)</p> <p>(<a href="https://github.com/syedmurtazaarshad">https://github.com/syedmurtazaarshad</a>)</p> <p> </p>
RNA flow additional data sets
<p>Additional raw data files and mathematical derivations to accompany the publication in Mol. Cell. (2024) by Ietswaart, Smalec, Xu, et al: Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle.</p>
Videos of lava flows on 16th November 2021 at the South Delta of the 2021 Cumbre Vieja eruption (La Palma, Canary Islands).
<p>Videos of field observations made on 16th November 2021 at 15:00 UTC from a point (UTM coordinates 28-R, 213992, 3167808 ) on the coastline of the South lava delta formed during the 2021 eruption of Cumbre Vieja volcanic rift at La Palma (Canary Islands).</p> <p>Video 1 shows a high-mobility lava flowed down the marine cliff over a well-developed ramp.</p> <p>Video 2 shows the ocean entrance of the lava flow, accompanied by the formation of a large gas plume in which vertical steam jets containing dark particulate matter. These jets, up to 10 m high, included lava fragments of diameter >10 cm that were projected to a maximum of 10–15 m away, following ballistic trajectories until falling into the sea and often leaving traces of white steam behind them. </p>
Benchmark DEBORA: experimental and MCFD databank for high-pressure boiling pipe flow
<div> <div> <div> <div> <div> <table> <tbody> <tr> <td> <div> <div> <div> <div> <p><em><strong>DEBORA experimental and numerical databank associated to the benchmark : Assessment of MCFD compared to high-pressure boiling pipe flow measurements.</strong></em></p> <p>This repository contains experimental data used during the benchmark DEBORA, along with predictions from the different participants. The data comes with a script to load and manipulate the results in Python dictionnaries and a small tool to plot the figures from the electronic Supplement.</p> <p>All the details describing this databank are given in the publication. </p> </div> </div> </div> </div> </td> </tr> </tbody> </table> </div> </div> </div> </div> </div>
Persistent gene flow suggests an absence of reproductive isolation in an African antelope speciation model
<p>African antelope diversity is a globally unique vestige of a much richer world-wide Pleistocene megafauna. Despite this, the evolutionary processes leading to the prolific radiation of African antelopes are not well understood. Here, we sequenced 145 whole genomes from both subspecies of the waterbuck, an African antelope believed to be in the process of speciation. We investigated genetic structure and population divergence and found evidence of a mid-Pleistocene separation on either side of the eastern Great Rift Valley, consistent with vicariance caused by a rain shadow along the so-called 'Kingdon's Line'. However, we also found pervasive evidence of not only isolated and recent, but also widespread historical gene flow across the Rift Valley barrier. By inferring the genome-wide landscape of variation among subspecies, we found 14 genomic regions of elevated differentiation, including a locus that may be related to each subspecies' distinctive coat pigmentation pattern. We investigated these regions as candidate speciation islands. However, we observed no significant reduction in gene flow in these regions, nor any indications of selection against hybrids. Altogether, these results suggest a pattern whereby climatically driven vicariance is the most important process driving the African antelope radiation, and suggest that reproductive isolation may not set in until very late in the divergence process.</p>
FlyWire Information Flow Ranks
<p>This repository contains the results of the information flow ranl caluclations used in the analysis around Figure 6 in the manuscript "Neuronal wiring diagram of an adult brain".</p> <p>We applied the information flow model from Schlegel et al. [1] starting from different sets of neurons as indicated by the filename. For each seed set, we ran the model 10,000 times and took the average for each neuron (layer_mean in the feather files). </p> <p>The feather files can be read using pandas (Python): df = pd.read_feather(<path>)</p> <p><strong><a href="https://elifesciences.org/articles/66018" target="_blank" rel="noopener">[1] Schlegel et al., Information flow, cell types and stereotypy in a full olfactory connectome. Elife (2021).</a></strong></p>
Collective flow of circadian clock information in honeybee colonies (Data)
<p>This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".</p> <p><strong> </strong></p> <p>Paper: Collective flow of circadian clock information in honeybee colonies</p> <p><strong> </strong></p> <p>Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.</p> <p><strong> </strong></p> <h2>Description of included files</h2> <p>All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.</p> <p><strong> </strong></p> <h3><strong>mean_velocity_2016.csv and mean_velocity_2019.csv</strong></h3> <p>The mean velocity for each bee and age is averaged over 10-minute time windows.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Mean euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> </ul> <p> </p> <h3><strong>velocity_2088_2019.csv and velocity_5101_2019.csv</strong></h3> <p>The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> </ul> <p> </p> <h3><strong>cosinor_2016.csv and cosinor_2019.csv</strong></h3> <p>A cosinor fit of the velocity per bee for a time window of 3 consecutive days according to the method proposed by <a href="https://doi.org/10.1186/1742-4682-11-16">Cornelissen</a>.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>mesor: Rhythm-adjusted mean of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>amplitude: Amplitude of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>phase: Acrophase of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_value: P-value of an F-test for overall significance of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_mesor: P-value of the mesor coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_amplitude: P-value of the amplitude coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_acrophase: P-value of the phase coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_reject: P-value of a F-test for model validity.</p> </li> <li> <p>r_squared: R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>r_squared_adj: Adjusted R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality.</p> </li> <li> <p>p_hom: P-Value of F-test of variance homogeneity of cosinor fit.</p> </li> <li> <p>dw: Durbin-Watson statistic for the independence of the residuals.</p> </li> <li> <p>p_runs: P-value of runs test of independence of residuals.</p> </li> <li> <p>RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model</p> </li> <li> <p>SSPE: Pure error sum of squares of cosinor fit.</p> </li> <li> <p>bee_id: Numeric unique identifier per individual bee.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> <li> <p>date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12.</p> </li> <li> <p>n_data_points: Number of data points per cosinor fit.</p> </li> <li> <p>data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock.</p> </li> <li> <p>night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock.</p> </li> <li> <p>ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary.</p> </li> <li> <p>fit_type: Median time bin in seconds used for fit, e.g. 3600 means that the median over a time window of 3600s is used for the fit.</p> </li> <li> <p>ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value.</p> </li> <li> <p>ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value.</p> </li> <li> <p>ci_mesor_lower: Confidence interval lower bound for the mesor fit value.</p> </li> <li> <p>ci_mesor_upper: Confidence interval upper bound for the mesor fit value.</p> </li> <li> <p>ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value.</p> </li> <li> <p>ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value.</p> </li> </ul> <p><strong> </strong></p> <h3><strong>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</strong></h3> <p>The bee interactions and their post-interaction velocity change. An interaction between two bees (bee0 and bee1) is defined when two bees are detected simultaneously in the hive with a confidence threshold of 0.25, the distance between the markings on their thorax bodies is no more than 14 mm. These interactions are combined into one interaction if the same detections occur within a time interval of 1 second or less between them. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0: Numeric unique identifier per individual bee.</p> </li> <li> <p>bee_id1: Numeric unique identifier per individual bee.</p> </li> <li> <p>interaction_start: Timestamp indicating interaction start time point.</p> </li> <li> <p>interaction_end: Timestamp indicating interaction end time point.</p> </li> <li> <p>x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee0: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee1: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee0: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee1: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>vel_change_bee0: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee0: Numeric post-interaction relative change of velocity: rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>vel_change_bee1: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee1: Numeric post-interaction relative change of velocity:rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> </ul> <p>Modeling bees as rectangular mask to determine if bees body overlap when interacting - see more details in <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer repository</a>:</p> <ul> <li> <p>x_trans_focal_bee0: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee0: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee0: Translated and rotated theta relative to hive.</p> </li> <li> <p>x_trans_focal_bee1: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee1: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee1: Translated and rotated theta relative to hive.</p> </li> <li> <p>overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p>amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee0: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee1: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h3><strong>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</strong></h3> <p>A null model for bee interactions and their post-interaction velocity change. The interaction null model is created by taking the distribution of the start and end times of a given interaction dataframe and selecting two random bees at those times that the bees were detected in the hive at that time. These pairs of bees are considered as "interacting" and their post-interaction speed change is calculated. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. The position data is relative to pixels not to the hive coordinates.</p> <p>As this is a null model the following keys are the same as described in the <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Interaction</a> data frame.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0</p> </li> <li> <p>bee_id1</p> </li> <li> <p>interaction_start</p> </li> <li> <p>interaction_end</p> </li> <li> <p>x_pos_start_bee0</p> </li> <li> <p>y_pos_start_bee0</p> </li> <li> <p>theta_start_bee0</p> </li> <li> <p>x_pos_start_bee1</p> </li> <li> <p>y_pos_start_bee1</p> </li> <li> <p>theta_start_bee1</p> </li> <li> <p>x_pos_end_bee0</p> </li> <li> <p>y_pos_end_bee0</p> </li> <li> <p>theta_end_bee0</p> </li> <li> <p>x_pos_end_bee1</p> </li> <li> <p>y_pos_end_bee1</p> </li> <li> <p>theta_end_bee1</p> </li> <li> <p>vel_change_bee0</p> </li> <li> <p>rel_change_bee0</p> </li> <li> <p>vel_change_bee1</p> </li> <li> <p>rel_change_bee1</p> </li> <li> <p>age_bee0</p> </li> <li> <p>phase_bee0</p> </li> <li> <p>amplitude_bee0</p> </li> <li> <p>r_squared_bee0</p> </li> <li> <p>p_value_bee0</p> </li> <li> <p>age_bee1</p> </li> <li> <p>amplitude_bee1</p> </li> <li> <p>r_squared_bee1</p> </li> <li> <p>p_value_bee1</p> </li> <li> <p>phase_bee1</p> </li> </ul> <p> </p> <h3><strong>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</strong></h3> <p>Graph-theoretic interaction tree paths. By tracing back interactions that positively influenced the speed of a focal bee, we constructed a graph-theoretic tree structure starting from a young rhythmic bee and recursively adding activating (velocity change parent > 0) individuals. We examined the impact of sequential interactions among bees occurring between 10 am and 3 pm, focusing on a subgroup of n = 1000 bees that are significantly rhythmic, younger than 5 days old, and peak in activity after 12 pm. We limited the time window between interactions to 30 minutes and capped the cascade duration at 2 hours to ensure causal relevance. The resulting interaction trees are collected and each node of all paths in the interaction trees are concatenated to this dataframe.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>bee_id: Numeric unique identifier per individual bee which is a node in the tree.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place.</p> </li> <li> <p>x_pos: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>vel_change_parent: <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Absolute velocity change</a> of bee of parent node.</p> </li> <li> <p>age: Age in days of node bee. Can be NaN if the bee has no associated death date.</p> </li> <li> <p>is_root: Bool indicating if node is root of tree.</p> </li> <li> <p>depth: Depth of node in tree. E.g. depth of root is 0.</p> </li> <li> <p>is_leaf: Bool indicating if node is leaf of tree.</p> </li> <li> <p>n_children: Number of children of the subtree of the node.</p> </li> <li> <p>parent: Numeric bee_id of parent node.</p> </li> <li> <p>tree_id: Numeric unique identifier of tree.</p> </li> <li> <p>time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction.</p> </li> <li> <p>path_id: Numeric unique identifier of path.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p> r_squared: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p> phase: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h2>Software used to acquire and analyze the data:</h2> <p><a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a></p> <p><a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a></p> <p> </p>
Implications of Lateral Groundwater Flow Across Varying Spatial Resolutions in Global Land Surface Modeling
<p>This folder contains the data used for plotting and analysis in the manuscript.</p>
The application of a mode of double preconcentration and double stripping steps in combination with a flow system to decrease the detection limit and increase the selectivity of determinations in stripping analysis of As(III)
<p><span>Due to the fact that arsenic </span><span>has very harmful and toxic effects on living organisms </span><span>monitoring of its content in various environmental samples </span><span>at trace level concentrations</span><span> </span><span>is desirable and highly recommended. Such advantages as low cost and portable instrumentation, achieving low detection limits and most of all the possibility of the direct determination of As(III) with no separation step make stripping voltammetry suitable for the determination of arsenic.</span><span> </span><span> <br></span></p> <p><span>These data concer the application of double deposition and double stripping steps mode in combination with a flow system for anodic stripping voltammetric determination of As(III) ions. An advantage of the proposed way of the voltammetric measurements is a decrease of the detection limit as compared to the values obtained using a traditional three-electrode system thanks to an initial preconcentration of the analyte on the first working electrode as well as the significant increase in a selectivity of As(III) determination thanks to the possibility of solution exchange after the first deposition step.</span></p> <p><span><span>The proposed way of the voltammetric measurements leads to the following analytical advantages: (i) a decrease of the detection limit as compared to the values obtained using traditional three-electrode system by an initial preconcentration of the analyte using the large-surface area electrode; (ii) an increase of the selectivity of voltammetric determinations thanks to the application of a flow system and exchange the solution after the first deposition step; (iii) simplification of performing the measurements in comparison to the original version of double accumulation and stripping steps mode due to the lack of a need to change the position of the first working electrode; (iv) the possibility of analysis of samples with a complex matrix thanks to the use of a flow system leading to minimization of matrix effects in the second deposition step. The developed procedure of As(III) determination offers a wide linear dynamic range in the range from 1 × 10<sup>-9</sup> to 5 × 10<sup>-8</sup> mol L<sup>-1</sup> at a deposition time of 120 s at both working electrodes. The detection limit for As(III) determination was 4.8 × 10<sup>-10</sup> mol L<sup>-1</sup>. The undoubted advantage of the described procedure is its high selectivity. The tolerable limit of excess of Cu(II), which is the main interferent in the course of anodic stripping voltammetric As(III) determination, was significantly increased thanks to the solution exchange after the first deposition step. The correctness of the proposed procedure was confirmed by analysis of a real water sample and lake water certified reference material by obtaining satisfactory recovery values. The additional advantage of the proposed procedure is a miniaturization of the measurement system and the use of environmentally friendly electrodes.</span></span></p>
Figure 2. Flow chart of the CoDOA (Kose & Arslan, 2015).0Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>The related algorithm steps can be visualized with a flow chart as shown in Figure 2 [26].</p>
A Power-Aware, Self-Adaptive Macro Data Flow Framework
<p><em><strong>Abstract: </strong>The dataflow programming model has been extensively used as an effective solution to implement efficient parallel programming frameworks. However, the amount of resources allocated to the runtime support is usually fixed once by the programmer or the runtime, and kept static during the entire execution. While there are cases where such a static choice may be appropriate, other scenarios may require to dynamically change the parallelism degree during the application execution. In this paper we propose an algorithm for multicore shared memory platforms, that dynamically selects the optimal number of cores to be used as well as their clock frequency according to either the workload pressure or to explicit user requirements. We implement the algorithm for both structured and unstructured parallel applications and we validate our proposal over three real applications, showing that it is able to save a significant amount of power, while not impairing the performance and not requiring additional effort from the application programmer.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p> <p> </p>
Flow at the Earth's core mantle boundary between 1900.0 and 2014.0
<p>The files contain the spherical harmonics coefficients of the velocity field at the Earth's core mantle boundary between 1900.0 and 2014.0.</p> <p>Uncertainties are provided through the standard deviation associated with each coefficient.</p>
Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation (Supporting data)
<p>The dataset contains sample numerical results associated with the manuscript under the same title, "Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation", published in Journal of Computational Physics (2018), https://doi.org/10.1016/j.jcp.2018.04.028.</p>
On the structure of the drifton phase space and its relation to the Rayleigh–Kuo criterion of the zonal-flow stability
<p>These are the supplementary data that can be used to generate figure 3, 4, and 5 in the manuscript "On the structure of the drifton phase space and its relation to the Rayleigh--Kuo criterion of the zonal-flow stability". The abstract of this manuscript reads as follows:</p> <p>The phase space of driftons (drift-wave quanta) is studied within the generalized Hasegawa--Mima collisionless-plasma model in the presence of zonal flows. This phase space is made intricate by the corrections to the drifton ray equations that were recently proposed by Parker [J. Plasma Phys. 82, 595820602 (2016)] and Ruiz et al. [Phys. Plasmas 23, 122304 (2016)]. Contrary to the traditional geometrical-optics (GO) model of the drifton dynamics, it is found that driftons can be not only trapped or passing, but they can also accumulate spatially while experiencing indefinite growth of their momenta. In particular, it is found that the Rayleigh--Kuo threshold known from geophysics corresponds to the regime when such "runaway" trajectories are the only ones possible. On one hand, this analysis helps visualize the development of the zonostrophic instability, particularly its nonlinear stage, which is studied here both analytically and through wave-kinetic simulations. On the other hand, the GO theory predicts that zonal flows above the Rayleigh--Kuo threshold can only grow; hence, the deterioration of intense zonal flows cannot be captured within a GO model. In particular, this means that the so-called tertiary instability of intense zonal flows cannot be adequately described within the quasilinear wave kinetic equation, contrary to some previous studies.</p>
Comparison of brittle- and viscous creep in quartzites: Implications for semi-brittle flow of rocks
<p>A data compilation of deformation experiments performed on quartzites in .pdf and .xlsx formats.</p>
Dataset - Permeability and viscoelastic fracture of a model tumor under interstitial flow
<p>This includes all the experimental data which are presented in the paper titled "Permeability and viscoelastic fracture of a model tumor under interstitial flow", Soft Matter, 2018 by Quang D. Tran, Marcos and David Gonzalez-Rodriguez.</p> <p>In each data folder, we have attached a Readme file to instruct readers how to analyze or compute our data.</p>
AllScale: Reference data of a flow over a plane. DPW-4 performed with Fine/Open
<p>The present computation has been performed on a 66 millions cells mesh. The turbulence model used is the Spalart Allmaras using CPU-Booster.</p> <p> </p> <p>Flow configuration : </p> <ul> <li>M = 0.85</li> <li>Re = 5 000 000</li> <li>AoA = 0.040472087 radians</li> </ul> <p>Integral results: </p> <ul> <li>CL = 0.49328</li> <li>CD = 2.701862e-2</li> <li>CM = -4.89841e-2</li> </ul> <p>Geometry and refernce data can be found on the DPW-4 website : https://aiaa-dpw.larc.nasa.gov/Workshop4/DPW4-geom.html</p> <p>The data set provide one cgns file of the 3D mesh with the primitives variables in order to compare the reference application and the AllScale prototype. </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.