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138 results for “Time averaging”
Time-averaged borehole temperatures at AM01–AM06 on the Amery Ice Shelf
<p>These are supplementary materials for the paper:</p> <p>Wang, Y., Zhao, C., Gladstone, R., Galton-Fenzi, B., and Warner, R.: Thermal structure of the Amery Ice Shelf from borehole observations and simulations, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-248, in review, 2021.</p> <p>Full description is given in the paper.</p>
The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population: data set
<p>This archive contains pulsar data presented as part of the MNRAS paper: <em>"The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population"</em>.</p> <p>Folded, time-averaged pulse profiles (4 Stokes parameters, 8 frequency channels, 1024 time bins across the period) of the 1271 pulsars listed in Table 1 of the MNRAS paper are included in the ar_files.zip. Ephemerides of these pulsars (as used in the MNRAS paper) are included in the eph_files.zip. The pulsar data are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p> <p>Tables 1, 5, and 6 from the MNRAS paper are included in tables_files.zip as .csv files. The file column_descriptions.txt describes the quantities in columns of these tables.<br> </p>
White Spruce NPP: average NPP per tree by age cohort for 4 time periods between 1993 and 2008
This data set is derived from BNZ LTER inventory plots, where DBH of all trees is measured every 3 to 4 years, and increment changes in AG biomass are derived from allometric equations. Data included in this file were obtained from 1993, 1997, 2000, 2004 and 2008 inventories, generating 4 growth increments between 1997 and 2008. Data are reported by age cohort, and include NPP increments (averaged across all trees within each landscape and successional stage) for all trees of known age (determined from coring).
White Spruce NPP: average NPP per tree by diameter sizeclass for 4 time periods between 1993 and 2008
This data set is derived from BNZ LTER inventory plots, where DBH of all trees is measured every 3 to 4 years, and increment changes in AG biomass are derived from allometric equations. Data included in this file were obtained from 1993, 1997, 2000, 2004 and 2008 inventories, generating 4 growth increments between 1997 and 2008. Data are reported by diameter sizeclass (10 cm increments), and include NPP increments (averaged across all trees within each landscape and successional stage) for which adequate numbers of trees (typically >5) were present to obtain useful measurements (see N in data file).
Aspen NPP: average NPP per tree by diameter sizeclass for 4 time periods between 1993 and 2008
This data set is derived from BNZ LTER inventory plots, where DBH of all trees is measured every 3 to 4 years, and increment changes in AG biomass are derived from allometric equations. Data included in this file were obtained from 1993, 1997, 2000, 2004 and 2008 inventories, generating 4 growth increments between 1997 and 2008. Data are reported by diameter sizeclass (10 cm increments), and include NPP increments (averaged across all trees within each landscape and successional stage) for which adequate numbers of trees (typically >5) were present to obtain useful measurements (see N in data file).
Birch NPP: average NPP per tree by diameter sizeclass for 4 time periods between 1993 and 2008
This data set is derived from BNZ LTER inventory plots, where DBH of all trees is measured every 3 to 4 years, and increment changes in AG biomass are derived from allometric equations. Data included in this file were obtained from 1993, 1997, 2000, 2004 and 2008 inventories, generating 4 growth increments between 1997 and 2008. Data are reported by diameter sizeclass (10 cm increments), and include NPP increments (averaged across all trees within each landscape and successional stage) for which adequate numbers of trees (typically >5) were present to obtain useful measurements (see N in data file).
Balsam Poplar NPP: average NPP per tree by diameter sizeclass for 4 time periods between 1993 and 2008
This data set is derived from BNZ LTER inventory plots, where DBH of all trees is measured every 3 to 4 years, and increment changes in AG biomass are derived from allometric equations. Data included in this file were obtained from 1993, 1997, 2000, 2004 and 2008 inventories, generating 4 growth increments between 1997 and 2008. Data are reported by diameter sizeclass (10 cm increments), and include NPP increments (averaged across all trees within each landscape and successional stage) for which adequate numbers of trees (typically >5) were present to obtain useful measurements (see N in data file).
Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature
<p>The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format: </strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.
<p><strong>DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.</strong></p> <p>Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>)</p> <p><strong>1. Related references:</strong><br> The data set is published in context with the manuscript: <br> [1]<em> The severity of microstrokes depends on local vascular topology and baseline perfusion</em>. <br> F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208</p> <p>The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in:<br> [2]<em> The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow</em>. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426</p> <p>The bi-phasic blood flow model for realistic MVNs has first been published in:<br> [3]<em> Depth-dependent flow and pressure characteristics in cortical microvascular networks</em>. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392</p> <p><em>For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3].</em></p> <p><strong>2. Requirements (software):</strong><br> <em>All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”).</em></p> <p><strong>3. Content:</strong><br> <em>All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "</em>tar –jxf ARCHIVE_NAME"<br> <br> <strong>3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files):</strong><br> <strong>SimulationResults_Baseline.tar.bz2:</strong><br> Folders: MVN1, MVN2<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (<em>generated from prepare_Figure4.py</em>), data_spatial_distribution_AVfactor.pkl (<em>generated from plot_Figure4.py</em>)</p> <p><strong>SimulationResults_SingleCapillaryOcclusions.tar.bz2:</strong><br> Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (<em>only folders:</em> 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out)</p> <p><strong>SimulationResults_MultiCapillaryOcclusions.tar.bz2:</strong><br> Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl</p> <p><strong>3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts):</strong><br> <em>For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses.</em></p> <p><strong>helperFunctions.py</strong>: various functions used by the other analysis scripts</p> <p><strong>plot_Figure1_and_Figure1-supplement_1_a-d.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/*</p> <p><strong>plot_Figure2_and_Figure2_supplement_1_a-d.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/*</p> <p><strong>plot_Figure3.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl <br> <strong>Output</strong>: Figures/Figure_3/*</p> <p><strong>prepare_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl,<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl</p> <p><strong>plot_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/*<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/*</p> <p><strong>plot_Figure5.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/* <br> <strong>Output:</strong> Figures/Figure_5/*</p> <p><strong>plot_Figure6.py:</strong><br> Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/*</p> <p><strong>4. Attributes stored in python dictionaries:</strong><br> <br> <strong>4a. Baseline:</strong><br> <strong>verticesDict: </strong><em>contains all relevant information and data stored at vertices.</em></p> <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> <p><strong>edgesDict:</strong> <em>contains all relevant information and data stored at edges.</em></p> <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm<sup>3</sup>]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch: </strong><em>contains all flow path from DA main brain to AV main branch. For details see [1]</em>.</p> <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> <p><strong>data_spatial_distribution_AVfactor:</strong> <em>contains information on the spatial distribution of venule-sided capillaries (AV-factor > 0.5). For details see [1].</em></p> <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> <p><strong>4b. Occlusion scenarios (both single- and multi-capillary occlusions):</strong></p> <p><strong>verticesDict:</strong></p> <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> <p><strong>edgesDict:</strong></p> <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased:</strong> <em>contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --></em><em> function convert_pathsDict_to_unique_vertexSequence.</em></p> <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>
Data for: Modeling the transition of death assemblages through the mixed layer predicts a downcore increase in time averaging
<p>Understanding how time averaging changes during the burial is essential for using Holocene and Anthropocene cores to analyze ecosystem change, given the many ways in which the time averaging affects biodiversity measures. Here, we use transition-rate matrices to explore how time averaging changes downcore when shells transit through a taphonomically-complex mixed layer into permanently-buried historical layers: this is a null model, without any temporal changes in rates of sedimentation or bioturbation, to contrast with downcore patterns that might be produced by human activity. Assuming stochastic burial and exhumation movements of shells between increments within the mixed layer and stochastic disintegration within increments, almost all combinations of net sedimentation, mixing, and disintegration produce a downcore increase in time averaging (interquartile range, IQR), typically associated with a decrease in kurtosis and skewness and with a shift from right-skewed to symmetrical age distributions. A downcore increase in time averaging is a null expectation wherever bioturbation generates an internally-structured mixed layer (i.e., a surface well-mixed layer is underlain by an incompletely-mixed layer), so that shells are mixed throughout the entire mixed layer at slower rate than they are buried below it by sedimentation. This downcore trend created by mixing is further amplified by the downcore decline in disintegration rate. Using data from the southern California shelf, we find that transition-rate matrices accurately reproduce the downcore changes in IQR, skewness, and kurtosis observed in sediment cores. The right-skewed distributions typical of surface death assemblages – the focus of most actualistic research – might be fossilized under exceptional conditions of episodic anoxia or sudden burial. However, such right-skewed assemblages will not typically transfer into subsurface historical layers and thus will be geologically transient. The deep-time fossil record will be dominated instead by more time-averaged assemblages with weakly skewed age distributions that form in the lower parts of the mixed layer.</p>
Table 6. The average epithelialization time in the control, comparison, and 10% concentration respectively was day 10, day 8 and day 7
<p>Table 6. The average epithelialization time in the control, comparison, and 10% concentration respectively was day 10, day 8 and day 7</p>
Bioturbation increases time averaging despite promoting shell disintegration: a test using anthropogenic gradients in sediment accumulation and burrowing on the southern California shelf
Open the record for dataset details and reuse information.
Data for: Modeling the transition of death assemblages through the mixed layer predicts a downcore increase in time averaging
Open the record for dataset details and reuse information.
Average fold coverage and annotation results of those ORF that were increasing over treatment time
<p>Table listing those ORF that presented an increased frequency across the treatment time</p> <p>This a supplementary material for the doctoral thesis entitle: <em><strong>Understanding microbiome supra-metabolism responses under strong selective pressures as a resource for designing synthetic gene arrangements encoding key co-selected functions for environmental biotechnology applications. </strong></em>Villegas-Plazas M, 2020. Universidad del Valle. Cali, Colombia</p>
1 km Monthly Average Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads
<p>This map contains the data used in the study 'Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads'. In this study a new method is presented to estimate average diffuse aquifer recharge of water table aquifers in temperate climates using time series analysis of water table level fluctuations. Recharge is estimated from time series models fitted to observed heads under the additional constraint that the seasonal harmonic of the observed head is reproduced as the sum of the transformed seasonal harmonics present in precipitation, evaporation, and pumping. The method is applied to measured heads obtained from piezometers situated on and around the ice-pushed sand ridge of Salland in the Netherlands. Results are compared with recharge estimates based on the saturated zone chloride mass balance.</p> <p>The data are ascii text files saved in the following compressed maps:<br> - Groundwater_chloride_concentration.zip<br> - Groundwater_head_time_series.zip<br> - Makkink_reference_evaporation_time_series.zip<br> - Measured_precipitation_time_series.zip<br> - Precipitation_chloride_concentrations.zip<br> - Pumping_time_series.zip</p> <p>For further details, please refer to the readme files contained in each submap.</p>
Supplementary material for the paper: Application of Time-Averaged and Integral-Based Measure for Measurement Results Variability Reduction in GSM/DCS/UMTS Systems
<p>Supplementary material for the paper: Application of Time-Averaged and Integral-Based Measure for Measurement Results Variability Reduction in GSM/DCS/UMTS Systems</p>
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2002.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Data are 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2005.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Theses are the 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2004.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water.of water temperatures at several depths from a moored chain of thermistors. Theses are the 5 minute averages of 30 second measuremsents.
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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.