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472 results for “Continuous data”
Replication Data for: Continuously moving table MRI with golden angle radial sampling
<p>Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.</p>
Figure 4. Results from the phylogenetic analysis using discrete data only. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 4. Results from the phylogenetic analysis using discrete data only. A, strict consensus of 275 most parsimonious trees with equal character weights; length 276 steps, consistency index (CI) = 0.35, retention index (RI) = 0.59, and rescaled consistency index (RC) = 0.22. B, strict consensus of four most parsimonious trees with implied character weighting (k = 3) (tree length 23.11). Psammosteidae taxa in bold.
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
<p>The representation of a land cover type (i.e., habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g., wetlands) while for common habitats (e.g., forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e., the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g., using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology, and ecological conservation.</p>
Continuous Average Straightness Data
<p><strong>Description. </strong>Real-world data used to test our implementation of the average continuous Straightness, and associated results.</p> <p><strong>Source code. </strong>The source code is available on GitHub: <a href="https://github.com/CompNet/SpatialMeasures">https://github.com/CompNet/SpatialMeasures</a></p> <p><strong>Citation. </strong>If you use these data, please cite the following article:</p> <ul> <li>V. Labatut, “Continuous Average Straightness in Spatial Graphs,” <em>Journal of Complex Networks</em>, 6(2):269–296, 2018. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01571212">hal-01571212</a>⟩ DOI: <a href="http://doi.org/10.1093/comnet/cnx033">10.1093/comnet/cnx033</a></li> </ul> <p><br><code>@Article{Labatut2018,</code><br><code> author = {Labatut, Vincent},</code><br><code> title = {Continuous Average Straightness in Spatial Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2018},</code><br><code> volume = {6},</code><br><code> number = {2},</code><br><code> pages = {269-296},</code><br><code> doi = {10.1093/comnet/cnx033},</code><br><code>}</code></p>
NAPS Continuous Data: across 52 Census Divisions from 1980-2019
<p>The National Air Pollution Surveillance (NAPS) Program is a national system of air pollution sampling sites managed by Environment and Climate Change Canada, with cooperation from Provincial, Territorial, and regional partners who manage the sites and report data.</p> <p>This dataset contains aggregated and imputed continuous monitoring pollutant concentrations from 52 Canadian census divisions spanning 1980-2019.</p>
"Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data". Raw data and JANA refinement files.
<p><strong>Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data</strong><br> 3D ED data sets of 5 compounds and JANA refinement files of 12 compounds</p> <p><strong>Relevant tools</strong><strong>:</strong></p> <ul> <li>PETS2: data reduction and analysis of electron diffraction patterns <ul> <li>Download program and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Palatinus, L. <em>et al.</em> Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst. B</em><strong>75</strong>, 512–522 (2019). <a href="https://doi.org/10.1107/S2052520619007534">DOI: 10.1107/S2052520619007534</a></li> </ul> </li> <li>JANA2006: crystal structure model refinement program <ul> <li>Download program from <a href="http://jana.fzu.cz/">http://jana.fzu.cz/</a> and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Results here were obtained with JANA2006. We recommend using JANA2020.</li> <li>Petricek, V., Dusek, M. & Palatinus, L. Crystallographic Computing System JANA2006: General features. <em>Z. Kristallogr.</em> <strong>229</strong>, 345–352 (2014). <a href="https://doi.org/10.1515/zkri-2014-1737">DOI: 10.1515/zkri-2014-1737</a></li> </ul> </li> <li>DYNGO: Bloch wave program, calculates dynamical diffraction intensities and derivatives <ul> <li>Program automatically included in JANA2006/JANA2020</li> <li>Palatinus, L., Petříček, V. & Corrêa, C. A. Structure refinement using precession electron diffraction tomography and dynamical diffraction: theory and implementation. <em>Acta Cryst. A</em><strong>71</strong>, 235–244 (2015). <a href="https://doi.org/10.1107/S2053273315001266">DOI: 10.1107/S2053273315001266</a></li> </ul> </li> </ul> <p><strong>3D ED data sets:</strong></p> <p>STW_HPM-1 (RT) was measured on a JEOL JEM-2100-LaB6 and diffraction patterns were recorded with an ASI Timepix detector. Another sample of STW_HPM-1 was measured at a temperature of 100 K after cryotransfer with a Titan Krios (CETA-D detector). The other data sets were measured on an FEI Tecnai G2 20 (Olympus SIS Veleta, CCD). Each data set contains the raw diffraction patterns (*.tif) and the basic input files needed to reproduce the data reduction with PETS2 as used in the associated publication (*.pts2, *.celllist, *.cenloc). Step-by-step tutorials are provided for quartz and glycine (and selected steps for abiraterone acetate) at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a>.</p> <ul> <li>α-quartz, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>natrolite, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>cobalt aluminophosphate (CAP), static ED patterns recorded in 0.1° steps (3 data sets from 2 crystals)</li> <li>abiraterone acetate, stepwise continous-rotation (5 data sets from 5 crystals)</li> <li>STW_HPM-1, continuous-rotation (1 data set, room temperature)</li> <li>STW_HPM-1, continuous-rotation (1 data set, <em>T</em> = 100 K, cryotransfer)</li> </ul> <p><strong>JANA refinement and CIF files:</strong></p> <p>CIF (Crystallographic Information Framework) files include two data items. The first is related to the dynamical and the second to the kinematical refinement. Relevant parameters and statistics specific for dynamical refinement are found in the field _refine_special_details.</p> <p>JANA files are provided for the dynamical and kinematical refinement at the stage after the final refinement cycle together with the original input files generated by PETS2. For quartz and natrolite, relevant files for the refinements against precession-assisted 3D ED data are included. For abiraterone acetate and limaspermidine, relevant files for the absolute structure determination are included.</p> <ul> <li>α-quartz</li> <li>albite</li> <li>mordenite</li> <li>natrolite</li> <li>STW_HPM-1</li> <li>cobalt aluminophosphate (CAP)</li> <li>CAU-36</li> <li>α-glycine</li> <li>carbamazepine</li> <li>(+)-limaspermidine</li> <li>abiraterone acetate</li> <li>MBBF4</li> </ul> <p>For the kinematical refinements based on more than one data set, the self-written tool "CompInt" (unpublished) was used. The tool can be found in the file "tool_scalehkl_compint.zip". Input (*.hkl, *.compint) and output files (*.scalehkl) are provided in the respective folder with the JANA files.</p> <p>Raw data sources of other data sets relevant for the associated publication are given in the SI of the associated publication.</p>
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
<p>The amount of semi-natural habitat surrounding farm fields is a common but inconsistent predictor of natural enemy populations and predation services. Standard land cover metrics may not accurately capture the actual availability of limiting resources for natural enemies and can miss important dynamics across space and time. Theory from animal movement and landscape ecology predicts that regions with more, spatio-temporally continuous resources (i.e. food, shelter) should have larger predator populations and enhanced biological control. To test these predictions empirically, we designed a study measuring aphids, lady beetles, and predation services in agricultural landscapes in Wisconsin, USA. In two study years, we sampled lady beetles and aphids in 336 crop fields (corn, soybean, alfalfa, and small grains) and adjacent semi-natural habitat patches (grasslands and woodlands) across 24 1.5 km buffer landscapes at 4–7 time points each, and in one year we assessed predation rates with sentinel egg cards. We used aphid counts to model habitat-specific aphid phenologies, from which we calculated landscape indices of prey amount and continuity. These indices, along with semi-natural habitat area, were used to predict lady beetle abundance. While there were strong differences in the abundance and timing of aphids by habitat, semi-natural habitat amount was still a better predictor of lady beetle counts and sentinel egg predation than either aphid amount or continuity indices in these landscapes.</p> <p>Synthesis and application: Our findings confirm the robust relationship between lady beetles and semi-natural habitat in agricultural landscapes, and highlight the complexities of measuring fine-scale resource heterogeneity in real landscapes. Retaining or adding woodland and grassland patches in agricultural landscapes is likely to support larger lady beetle populations and enhance predation in crop fields. Our results suggest that these habitats may be more important for shelter than prey continuity, though this mechanism warrants further investigation. Future work should continue to refine experimental methods for the successful integration of landscape ecology and animal behavior to support conservation goals.</p>
X-ray image reconstruction for continuous acquisitions with a generalized motion model: Data
<p>This dataset contains two experimentally measured X-ray scans, one reference scan and one scan in which the object translates while rotating. The reference scan consists of 3600 projection images, with one flat field and dark field image. The roto-translational scan consists of 360 projections, also with a flat field and dark field image. The acquisition files containing all relevant specifications of the scanner and the acquisition settings are also supplied.</p> <p>The code for reconstruction is available at <a href="https://github.com/BenHuyge/RACE">GitHub.</a></p>
Data for: Why do phylogenomic analyses of early animal evolution continue to disagree? Sites in different structural environments yield different answers
<p>Supporting data for "Why do phylogenomic analyses of early animal evolution continue to disagree? Sites in different structural environments yield different answers" submitted by A Pandey and EL Braun. File is a gzipped tarball including protein multiple sequence alignments, phylogenetic trees, and other supporting data; see included README for details.</p>
Continuous rotation electron diffraction data for zeolite SSZ-27
<p><strong>Raw data for SSZ-27 (as-synthesized)</strong></p> <p>The directories labeled <strong>S**</strong> contain the raw data for the SSZ-27 phase, those labeled <strong>C**</strong> for the SSZ-26 impurity.</p> <p>Each directory contains the following:</p> <ul> <li>cred_log.txt, data collection log file</li> <li>SMV, Directory with data in SMV format and XDS processing output</li> <li>tiff, Directory with raw data in TIFF format</li> <li>tiff_image, Directory with defocused images showing the position of the crystal</li> <li>pets.pts, input file for PETS</li> <li>beam_centers.txt, a table with the position of the primary beam</li> </ul> <p>Then there are three other directories:</p> <ul> <li>XSCALE, contains the scaling results from the 14 SSZ-27 crystals that were used for the cluster analysis</li> <li>SSZ-26_cluster_1, contains the clustering results and refinement data for SSZ-26</li> <li>SSZ-27_cluster_4, contains the clustering results and refinement data for SSZ-27</li> </ul> <p>The data were collected using the software <a href="https://github.com/stefsmeets/instamatic">instamatic</a> and processed using <a href="http://xds.mpimf-heidelberg.mpg.de/">XDS</a>/<a href="https://github.com/stefsmeets/edtools">edtools</a>.</p>
Serial Rotation Electron Diffraction (automated continuous RED) raw data sets
<p><strong>Serial Rotation Electron Diffraction (automated continuous RED) raw data sets </strong></p> <p>Containing:</p> <p>TIFF images for particle recognition</p> <p>SMV files for XDS processing</p> <p>XDS input files (automatically generated)</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Data supporting publication: Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces
<p>Dataset for "Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces" Figure 1 to Figure 5.</p>
Timing data for algorithms for calculating steady state distributions of continuous time Markov chains
<p>Data showing the timings for a number of algorithms for the computation of the steady state of a continuous time Markov chain:</p> <ul> <li>Numeric integration;</li> <li>Matrix exponential;</li> <li>Eigenvector;</li> <li>Solve a linear algebraic system;</li> <li>Approximately solving a linear algebraic system.</li> </ul> <p>All calculations were done using implementations in Python for a blog post at https://vknight.org/blog/</p>
Figure data to "Continuous similarity transformation for critical phenomena: easy-axis antiferromagnetic XXZ model"
<p>This collection of data is complementary to the publication "Continuous similarity transformation for critical phenomena: easy-axis antiferromagnetic XXZ model", Matthias R. Walther, Dag-Björn Hering, Götz S. Uhrig, Kai P. Schmidt, arXiv:2211.05689 (https://arxiv.org/abs/2211.05689).</p> <p>It contains the data points calculated by the method of Continuous Similarity Transformation(CST) used in Figures 3,5,6,7 and 8 in the CSV-Format.</p> <p>For details on the CST, the used error estimates and physical quantities we refer the the publication.</p> <p>For details on the format we recommend the README.md file.</p>
Raw data to "Continuously varying critical exponents in long-range quantum spin ladders"
<p>This directory contains the data used to generate the numerical results in the work "Continuously varying critical exponents in long-range quantum spin ladders [1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.md file.</p> <p>[1]: P. Adelhardt and K. P. Schmidt, Continuously varying critical exponents in long-range quantum spin ladders, <a href="https://www.scipost.org/SciPostPhys.15.3.087">SciPost Phys. 15, 087 (2023)</a></p>
Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters Calibration and Measurement Data Set
<p>This data set contains calibration data for all sensors incorporated in the sensor node. It provides comparison measurements of TPL fluorescence taken by the node and reference spectrofluorimeter. Initial test measurements, as well as site measurements, are also provided. Finally, data from a heuristic method of TPL detection in the presence of algae and mud are also given.</p>
Data from a tidal cycle continuous survey using ADCP and CTD in Sand Shoal Inlet, Virginia
<p><strong>Title: </strong>Data from a tidal cycle continuous survey using ADCP and CTD in Sand Shoal Inlet, Virginia.</p> <p><strong>Author/Data Collector: </strong>Chunyan Li</p> <p><strong>Point of Contact, PI, Originator: </strong>Chunyan Li (cli@lsu.edu)</p> <p>These are data files from a 12.5-hour continuous survey done in Sand Shoal Inlet in Eastern Shore of Virginia on the Delmarva Peninsula on May 12, 1999.</p> <p>The ADCP data files are: SS01002T.000, SS01003T.000, and SS01004T.000.</p> <p>The CTD data files are named as SNDSHL**.CNV (a total of 33 files).</p> <p> </p> <p>ADCP data are measured from the RDI’s old program Transect.</p> <p><em>Ship Track:</em> Repeated samplings of ADCP were made along four transects connected by four points:</p> <p> [1] 37 17.918 N, 75 48.596 W</p> <p> [2] 37 18.344 N, 75 48.248 W</p> <p> [3] 37 17.884 N, 75 47.981 W</p> <p> [4] 37 18.054 N, 75 47.664 W</p> <p><em>CTD stations:</em></p> <p><em> {1} 37 18.174 N, 75 48.378 W</em></p> <p><em> {2} 37 17.978 N, 75 47.787 W</em></p> <p><em> Cast # Station #<br> 00 1<br> 01 2<br> 02 1<br> 03 2<br> 04 1<br> 05 2<br> 06 1<br> 07 n/a extra cast at ADCP [2]<br> 08 2<br> 09 1<br> 10 2<br> 11 n/a extra cast north of a front<br> 12 1<br> 13 2<br> 14 1<br> 15 2<br> 16 1<br> 17 2<br> 18 1<br> 19 2<br> 20 1<br> 21 2<br> 22 1<br> 23 2<br> 24 1<br> 25 2<br> 26 1<br> 27 2<br> 28 1<br> 29 2<br> 30 1<br> 31 2<br> 32 1<br> 33 2</em><br> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>Thanks to Arnoldo Valle-Levinson and Larry Atkinson for the research support. The field trip participants included Cristobal Creyes, Bob Bray, RC Kidd, Rosario Sanay, Kris Holderied, Andres Sepulveda as well as a person by the first name Wildo. The data were used in Li (2002).</p> <p><strong>References</strong></p> <p>Li, C. (2002). Axial convergence fronts in a barotropic tidal inlet - Sand Shoal Inlet, VA, <em>Continental Shelf Research</em>, 22: 2633-2653. <a href="https://doi.org/10.1016/S0278-4343(02)00118-8">https://doi.org/10.1016/S0278-4343(02)00118-8</a>.</p>
Current velocity data from a quasi-continuous survey using ADCP in Satilla River Estuary, Georgia.
<p><strong>Title: </strong>Current velocity data from a quasi-continuous survey using ADCP in Satilla River Estuary, Georgia.</p> <p><strong>Author/Data Collector: </strong>Chunyan Li</p> <p><strong>Point of Contact, PI, Originator: </strong>Chunyan Li (cli@lsu.edu)</p> <p><strong>Description:</strong></p> <p>These are velocity profile data from vessel-towed ADCP obtained in the Satilla River Estuary during a survey conducted on 17–18 November 2004. The instrument was an RDI 600 KHz Workhorse ADCP. The survey was done mostly during daylight time along a 90-degree bend in the middle of the Satilla River Estuary. The observations covered nearly two semidiurnal tidal cycles with 15 repetitions and 33 hours in measurement time. The route is about 11 km in length and covers both the West and East sides of the bend of the channel. Note that tide in this area is essentially semidiurnal which makes 12-h observations sufficient to resolve the main tidal constituents.</p> <p>The ADCP was mounted on one side of the boat approximately 0.4 m below the surface. The vertical bins were 0.5 m. The surveys were conducted at an average cruise speed of about 2.5–3 m/s except at the turns when the vessel had to slow down and during CTD casts when the vessel had to stop for a few minutes. A Seabird Electronic SBE 19 plus CTD was used to measure the vertical profiles of water temperature, salinity, fluorescence, light attenuation, and dissolved oxygen at two to three locations during each survey. Note that only ADCP data are included in this dataset.</p> <p>The data are averaged at about 8-second intervals, excluding bad data. The data presented here are in ASCII with the generic format provided by the RDI’s software WinRiver II output. There are a total of seven data files. There are:</p> <p> </p> <p>ADCP_Nov17_2004_Satilla_River_000t.000</p> <p>ADCP_Nov17_2004a_Satilla_River_000t.000</p> <p>ADCP_Nov17_2004a_Satilla_River_001t.000</p> <p>ADCP_Nov17_2004a_Satilla_River_002t.000</p> <p>ADCP_Nov18_2004a_Satilla_River_000t.000</p> <p>ADCP_Nov18_2004a_Satilla_River_001t.000</p> <p>ADCP_Nov18_2004a_Satilla_River_002t.000</p> <p>Here is an example of the data –</p> <p> 50 25 42 124 10 20 1</p> <p>4 11 17 15 3 34 86 19 10 3.220 -0.410 331.714 17.460</p> <p>-50.84 11.65 -0.88 0.42 0.00 4.00 0.00 -21.08 7.38 7.38 7.52 7.31</p> <p>3.57 6.85 0.78 -3.48 3.57</p> <p>30.9708933 -81.5066250 -47.77 0.00 3.6</p> <p>-4.8 -1.0 -0.5 -12.5 10.0 -12.5 10.0 1.28 6.28</p> <p>30 cm BT dB 0.44 0.082</p> <p> 1.28 50.99 309.66 -39.3 32.5 -3.2 -3.9 99.7 101.2 102.8 101.7 100 -0.03</p> <p> 1.78 53.58 304.67 -44.1 30.5 -0.2 4.0 102.6 103.8 105.5 104.4 100 -0.04</p> <p> 2.28 43.23 318.41 -28.7 32.3 -1.7 -5.8 102.3 103.7 105.2 104.6 100 -0.02</p> <p> 2.78 53.59 320.14 -34.3 41.1 -2.3 8.6 101.3 103.0 105.1 104.3 90 -0.08</p> <p> 3.28 46.16 304.23 -38.2 26.0 -0.6 2.2 100.1 103.6 104.0 102.8 100 -0.06</p> <p> 3.78 55.55 309.19 -43.1 35.1 -1.2 -2.4 100.1 101.3 102.7 102.0 100 -0.09</p> <p> 4.28 47.84 308.45 -37.5 29.7 1.8 2.5 99.6 100.8 101.7 100.9 100 -0.02</p> <p> 4.78 47.27 311.81 -35.2 31.5 -2.4 5.7 98.5 101.7 102.0 100.8 100 -0.02</p> <p> 5.28 40.47 308.48 -31.7 25.2 0.5 0.1 98.1 101.0 101.4 100.8 100 -0.05</p> <p> 5.78 43.89 314.42 -31.3 30.7 -1.8 5.7 97.8 100.9 101.2 101.0 100 -0.11</p> <p> 6.28 44.51 317.27 -30.2 32.7 0.8 2.2 97.3 101.4 100.9 100.6 100 -0.00</p> <p> 6.78 38.19 315.72 -26.7 27.3 -3.2 35.8 107.0 104.9 101.9 112.0 50 2147483647</p> <p> 7.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 121.6 255 0 2147483647</p> <p> 7.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 8.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 8.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 9.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 9.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 10.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 10.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 11.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 11.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 12.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 12.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 13.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 13.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 14.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 14.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 15.28 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p> 15.78 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p>The data were used in Li et al. (2008).</p> <p><strong>References</strong></p> <p>Li, C., C. Chen, D. Guadagnoli, and I. Y. Georgiou (2008). Geometry-induced residual eddies in estuaries with curved channels: Observations and modeling studies, <em>Journal of Geophysical Research</em>, Vol. 113, C01005, doi:10.1029/2006JC004031.</p>
Current velocity data from a continuous survey using a towed ADCP in Wilmington River Estuary, Georgia, USA
<p><strong>Title: </strong>Current velocity data from a continuous survey using a towed ADCP in Wilmington River Estuary, Georgia, USA</p> <p><strong>Author/Data Collector: </strong>Chunyan Li</p> <p><strong>Point of Contact, PI, Originator: </strong>Chunyan Li (cli@lsu.edu)</p> <p><strong>Description:</strong></p> <p>These are velocity profile data from vessel towed ADCP obtained in the Wilmington River Estuary during a survey conducted on Sep. 29, 2004, for ~ 11.5 hours. The instrument was an RDI 600 KHz Workhorse ADCP.</p> <p>The ADCP was mounted on a sled towed by the boat. The vertical bins were 0.5 m. The surveys were conducted at an average cruise speed of about 2.5–3 m/s except at the turns when the vessel had to slow down and during CTD casts when the vessel had to stop for a few minutes. A Seabird Electronic SBE 19 plus CTD was used to measure the vertical profiles of water temperature, salinity, fluorescence, light attenuation, and dissolved oxygen during the survey. Note that only ADCP data are included in this dataset.</p> <p>The data are averaged at about 30-second intervals, excluding bad data. The data presented here are in ASCII with the generic format provided by the RDI’s software WinRiver II output. There are a total of two data files. There are:</p> <p>ADCP_Sep29_2004_WM_000_ASC.TXT</p> <p>ADCP_Sep29_2004_WM_001_ASC.TXT</p> <p>Here is an example of the data –</p> <p> 50 50 42 50 1 20 1</p> <p>4 9 29 11 32 26 57 468 60 2.361 -0.824 118.753 24.276</p> <p>49.04 93.15 -0.17 -0.46 0.00 6.00 0.00 3.08 6.63 6.64 6.68 6.59</p> <p>30.53 28.91 26.93 14.22 30.45</p> <p>32.00327167 -81.01664167 31.50 92.41 30.4</p> <p>-22.0 -7.2 -3.2 -11.2 10.0 -10.8 10.0 1.53 5.53</p> <p>50 cm BT dB 0.43 0.073</p> <p> 1.53 52.35 193.13 -11.9 -51.0 -0.3 3.5 92.1 94.8 94.6 95.6 100 -2.02</p> <p> 2.03 49.89 189.47 -8.2 -49.2 -0.1 -2.1 97.7 99.9 100.4 100.4 100 -2.28</p> <p> 2.53 52.68 187.22 -6.6 -52.3 0.7 4.0 99.0 101.3 101.7 101.2 98 -2.84</p> <p> 3.03 48.92 190.92 -9.3 -48.0 -0.2 5.1 99.2 101.3 101.9 101.6 100 -2.21</p> <p> 3.53 51.77 185.37 -4.8 -51.5 0.3 5.7 98.6 101.4 102.0 101.2 100 -2.99</p> <p> 4.03 48.03 186.14 -5.1 -47.8 1.0 1.2 98.5 101.1 101.9 101.0 100 -2.64</p> <p> 4.53 50.69 190.02 -8.8 -49.9 0.5 3.6 98.5 101.2 101.9 101.2 100 -2.32</p> <p> 5.03 43.70 186.57 -5.0 -43.4 0.7 4.4 98.3 101.2 101.9 101.2 100 -2.34</p> <p> 5.53 41.22 186.13 -4.4 -41.0 1.5 -0.3 98.5 101.6 102.0 101.4 83 -2.35</p> <p> 6.03 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647</p> <p>The ADCP data were used in Li et al. (2008).</p> <p><strong>Acknowledgements</strong></p> <p>I would like to thank Captain Harry Carter who assisted me by driving the boat for the whole day. He also assisted me with CTD casts, deployment, and retrieval of other CTDs. It was a great day working out with him.</p> <p><strong>References</strong></p> <p>Li, C., C. Chen, D. Guadagnoli, and I. Y. Georgiou (2008). Geometry-induced residual eddies in estuaries with curved channels: Observations and modeling studies, <em>Journal of Geophysical Research</em>, Vol. 113, C01005, doi:10.1029/2006JC004031.</p> <p> </p>
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