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1,773 results for “Supplementary data,”
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Supplementary Information and Data for "Unveiling the 3D Morphology of Epitaxial GaAs/AlGaAs Quantum Dots"
<p>Raw and processed TEM and AFM data for the article <strong><em>Unveiling the 3D Morphology of Epitaxial GaAs/AlGaAs Quantum Dots</em></strong>.</p> <p>Paper: <a href="https://doi.org/10.1021/acs.nanolett.4c02182" target="_blank" rel="noopener">https://doi.org/10.1021/acs.nanolett.4c02182</a></p> <p>Preprint: <a href="https://arxiv.org/abs/2405.16073" target="_blank" rel="noopener">https://arxiv.org/abs/2405.16073</a></p> <p>The TEM data has a PDF information file included with description of the file types and how to open them.</p> <p>The AFM Nanosurf .nid files can be opened, e.g., with <a href="http://gwyddion.net/" target="_blank" rel="noopener">Gwyddion</a>.</p>
Supplementary data for "Resonant or asymmetric: The status of sub-GeV dark matter"
<p>The files in this record contain supplementary data for the study, "Resonant or asymmetric:<br>The status of sub-GeV dark matter". Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="https://github.com/tegonzalo/pippi" target="_blank" rel="noopener">pippi</a>.</p>
Supplementary material: Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium
<p>The ridge plots attached here accompany the supplement to the manuscript <em>Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium</em> (Dzinalija et al., 2024)<em>. </em>These are full results of Figures 1, 2C, and 3C of the manuscript and Figures S3, S5, S7 and S9 of the supplement depicting whole-brain Bayesian multilevel models run using the Regional Bayesian Analysis toolbox (RBA; Chen et al., 2019). Whole-brain analyses were parcelated into the Schaefer-Yeo 7-network 200-parcel cortical atlas (Schaefer et al., 2018) and the Melbourne 32-region subcortical atlas (Tian et al., 2020). Results are presented according to contrast of interests: [Negative > Neutral], [OCD > Neutral], [Threat > Neutral], and [OCD > Threat], and effects of interest: [Diagnosis = OCD or HC], [MED = medication], [AO = age of onset], [YBOCS = OCD severity], and [Intercept = task effect]. </p> <p>P+ values denote the probability that there is increased brain activation in a given region of the Schaefer 200-parcel 7-network cortical atlas and Melbourne 32-region subcortical atlas. We used the guidelines proposed by Chen et al. (2019) to infer credibility of evidence, namely taking a positive posterior probability (P+) of <0.10 or >0.90 as indication of moderate evidence and, <0.05 or >0.95 or <0.025 or >0.975 as strong or very strong evidence, respectively. To interpret the pairwise comparisons presented as Group1-vs-Group2, posterior distributions to the right of the green no-effect line represent regions in which individuals in Group 1 show credible evidence for higher activation than individuals in Group 2. Regions with posterior distributions to the left of this line show credible evidence for higher activity in Group 2 than in Group 1. (Darker) red color represents regions in which individuals in Group 1 show moderate-to-very-strong evidence for higher activation than Group 2. (Darker) blue color represents regions in which Group 2 show moderate-to-very-strong evidence for higher activation than Group 1. Grey color represents regions in which there is no strong evidence of a difference between Group 1 and Group 2. Schaefer-Yeo 200-parcel atlas name abbreviations can be retrieved <a href="https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations">via this link.</a></p>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
Supplementary data for the article "Spin reorientation in premartensite and austenite Ni-Mn-Ga".
<p>Supplementary data for the article “Spin reorientation in premartensite and austenite Ni-Mn-Ga” by Alexej Perevertov, Ross H. Colman and Oleg Heczko.</p> <p>Dataset - worksheet, M(H) loop at T = 252K. Columns - time (seconds); Field (A/m), Magnetic polarization, J(T); dJ/dt (T/s); dH/dt (A/(m*s))</p> <p>Supplementary video 1. The DS curves and J(H) hysteresis loops were displayed on the graphs in the post-processing LabView graphs for every temperature for 0.1sec and the screen recording was done using the VLC player/recorder (the capture device – desktop).</p> <p>Supplementary Video 2 - we have cut a 6x0.15mm disk, perpendicular to the long side of our sample with the face of the disk in the [100] – [110] plane. The disk was placed in a constant field of 50mT. On the heating cycle, immediately after martensitic transformation the disk [100] axis is parallel to the field. Close to the premartensitic transformation the disk spontaneously rotates by 45º, with the field then parallel to the [110] direction. With further heating, close to the room temperature, it rotates back to [100].</p>
Supplementary data: Rain-on-snow events in mountainous catchments under climate change
<p>The file in this record represents supplementary data for the journal paper Hotovy, O., Nedelcev, O., Seibert, J., Jenicek, M. (2024): Rain-on-snow events in mountainous catchments under climate change submitted to Hydrology and Earth System Sciences.<br>The presented files contain daily simulations of the HBV rainfall-runoff model for 93 mountain catchments in Czechia, Germany and Switzerland. The model simulated different water balance components, such as runoff, base flow, snow water equivalent, evapotranspiration, and soil and groundwater storages for the study period 1980-2010 as well as hydrological projections assuming different increases in air temperature and precipitation.</p>
Supplementary data for "Heterometallic perovskite-type metal-organic framework with an ammonium cation: structure, phonons, and optical response"
<p>Optimised structures of [NH<sub>4</sub>][Na<sub>0.5</sub>M<sub>0.5</sub>(COOH)<sub>3</sub>] (M = Al, Cr)</p> <p>Phonon output for [NH<sub>4</sub>][Na<sub>0.5</sub>Cr<sub>0.5</sub>(COOH)<sub>3</sub>]</p> <p>Gif of the T’(NH<sub>4</sub><sup>+</sup>) mode (no. 23). The c-axis is the vertical direction.</p> <p>For further information please see the associated publication.</p>
Supplementary Data: Impact of vacuum stability, perturbativity and XENON1T on global fits of Z2 and Z3 scalar singlet dark matter (arXiv:1806.11281)
<p> </p> <p><strong>Supplementary Data</strong></p> <p> </p> <p><em>Impact of vacuum stability, perturbativity and XENON1T on global fits of Z<sub>2</sub> and Z<sub>3</sub> scalar singlet dark matter</em> <a href="https://arxiv.org/abs/1806.xxxxx"><em>arXiv:</em></a><em><a href="https://arxiv.org/abs/1806.11281">1806.11281</a></em></p> <p>The files in this record contain data for the scalar singlet dark matter models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> "Scalar singlet Mark II" paper.</p> <p>The files consist of</p> <ul> <li>30 regular YAML files</li> <li><code>StandardModel_SLHA2_scan.yaml</code>, a universal YAML fragment included from the other YAML files</li> <li>14 hdf5 files. 8 of these correspond to the complete set of combined samples for each fit. These 8 fits are generated from all binary permutations of three run properties: Z2 or Z3 model, with or without absolute vacuum stability demanded, and with constraints from the 2017 or 2018 XENON1T data. These 8 hdf5 files are used to generate the profile likelihood plots in the paper. The other 6 hdf5 files are the results of T-Walk runs, and are used to generate the posterior pdfs in the paper.</li> <li>Some example pip files for producing plots from the hdf5 files using <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball <code>best_fits_yaml.tar.gz</code> containing YAML files of the best-fit point in each of the 8 fits.</li> </ul> <p>The files follow the naming scheme <code>SingletDM_[model]_[slice]_[vacuum]_[xenon]_[prior]_[scanner].yaml</code>.</p> <ul> <li>model: <code>Z2</code> or <code>Z3</code></li> <li>slice: <code>full</code>, <code>lowmass</code>, <code>neck</code> or absent (for hdf5 files)</li> <li>vacuum: <code>ms</code> (metastable) or <code>vs</code> (absolute vacuum stability)</li> <li>prior: <code>logmu3</code>, <code>flatmu3</code> or absent (for Z<sub>2</sub> scans)</li> <li>scanner: <code>TWalk</code> or absent (implies Diver scans in the case of YAML files, and indicates merged samples potentially from both Diver and T-Walk in the case of hdf5 files)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with GAMBIT 1.2.0, commit e4d3f739, and the pip files are tested with pippi 2.1, commit c094b8c8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip files are examples only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo. </p> </li> </ol> <p> </p>
Supplementary data to accompany "phyloFlash: A pipeline for rapid SSU rRNA-targeted profiling of metagenomes"
<p>Usage examples of the phyloFlash pipeline applied to shotgun metagenomic data sets.</p> <p>The phyloFlash software is available from https://github.com/HRGV/phyloFlash. Examples were generated with phyloFlash v3.3b.</p>
Supplementary data and scripts for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes"
<p>Supplementary data for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes". The data set consists of two folders: “Bali-Phy” and “RandomPermutationTests”. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
Supplementary phylogenetic data for Manzano-Marín et. al. 2020 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems"
<p>Supplementary data for Manzano-Marín et. al. 2019 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems".</p> <p>The data set consists of four folders:</p> <p>1) "Buchnera_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Buchnera</em> and resulting tree in NEWICK format.</p> <p>2) "Erwinia_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Erwinia</em> and resulting tree in NEWICK format.</p> <p>3) "Hamiltonella_phylo”: FASTA-formatted nucleotide alignment files of each gene and NEXUS-formatted files used for Bayesian phylogenetic reconstruction of <em>Hamiltonella</em> symbionts.</p> <p>4) "HGT_genes": FASTA-formatted nucleotide alignment files of each horizontally transferred gene and non-horizontally transferred genes nupC, and <em>gpmA</em>. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p> <p>5) "Tn3_pylo": FASTA-formatted amino acid alignment files of mobile elements related to the Tn3 family resolvase/invertase found in <em>Hamiltonella</em>-associated <em>Erwinia haradaeae</em> symbionts. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p>
Olfactory receptors from tuatara - supplementary data from the genome project
<p>Olfactory receptor genes identified from the genome assembly of tuatara (<em>Sphenodon punctatus</em>).</p> <p>File 1: List of intact OR genes</p> <p>File 2: alignment file used to infer phylogenetic relationship between intact tuatara ORs and other species of terrestrial squamates and two species of bird.</p> <p>File 3: Excel spreadsheet listing OR identification from scaffolds (includes data on pseudogenes and partials)</p>
Supplementary data files for Manzano-Marín 2020 "No evidence for Wolbachia as a nutritional co-obligate endosymbiont in the aphid Pentalonia nigronervosa"
<p>Supplementary data for Manzano-Marín 2019 "No evidence for Wolbachia as a nutritional co-obligate endosymbiont in the aphid Pentalonia nigronervosa".</p> <p>The data in "supplementary_data.tar.gz" consists of six folders:</p> <p>1) "Buchnera_GenBank_annotation”: GenBank-formatted file of the annotated genes of <em>Buchnera</em> from <em>Pentalonia nigronervosa</em> (BPn).</p> <p>2) "Buchnera_gene_BLAST_search”: Tabular BLAST output files and FASTA-formatted files of the identified <em>Buchnera</em> Bpn genes.</p> <p>3) "P_nigronervosa_blastx_binning”: Tabular BLAST output files and FASTA-formatted files of the <em>Buchnera</em>, <em>Wolbachia</em>, and mitochondrion bins.</p> <p>4) "P_nigronervosa_BOWTIE_map_vs_bins": BAM-formatted alignment files for read libraries vs. <em>Buchnera</em> and <em>Wolbachia</em> bins from <em>Pentalonia nigronervosa</em>.</p> <p>5) "P_nigronervosa_BOWTIE_map_vs_genes": BAM-formatted alignment files for read libraries vs. genes from <em>Buchnera</em> and <em>Wolbachia</em> bin from <em>Pentalonia nigronervosa</em>.</p> <p>6) "P_nigronervosa_SPAdes_assembly": Output files for pooled SPAdes assembly.</p> <p>Also, the filtered and trimmed FASTQ files used for genome assembly can be found in the comrpessed file "read_files_clean.tar.gz".</p>
Supplementary data and scripts for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and de novo emergence of E5 oncogenes"
<p>Supplementary data for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and <em>de novo</em> emergence of E5 oncogenes". The data set consists of three folders: “Alignments”, “Bali-Phy” and “RandomPermutationTests”. The “Alignments” folder contains the different alignments used for phylogenetic tree construction and comparison. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations - supplementary information
<p>This repository contains the supplementary data to the following publication:</p> <p>"A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations", by the same authors.</p>
Supplementary data to "Three scenarios for coal power in Vietnam"
<p>This dataset includes the history of coal power generation in Vietnam, listing generation units capacity, creation date, and current status up to March 2019.</p> <p>It defines three scenarios for the future. “Blazing up” corresponds to the power development plan 7 revised and updated as of March 2019. “Closed window” tells what we think would happen under pure market forces. “Coal peak” tells what could happen if the State continues to steer the electricity system into the energy transition, decisively and without imposing high costs to stakeholders.</p> <p>Corresponds to Table 2 and 3 in the manuscript.</p> <p>#VIETSE</p>
USGS Table AHG Parameters And Supplementary Data
<p>Password Key: 69262qRead ; For more information please email: <strong>sha17hab.afshari@gmail.com</strong> / <strong>safshar00@citymail.cuny.edu</strong> </p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying river channel flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry and geophysical properties (e.g., bed-roughness, channel slope, channel planform, sediment load, etc.) along their reaches. Variations in the shape of the channel bed geometry are affected by several interacting features including the effect of different flow regimes, channel slope, sediment load, etc. Simplifying the river bed geometries will reduce the burden of assembling the required data and computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions that relate key hydraulic variables (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologists since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure that was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> In doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
USGS Table AHG Parameters And Supplementary Data MannN
<p>For more information please send me an email to either: <strong>sha17hab.afshari@gmail.com</strong> or <strong>safshar00@citymail.cuny.edu</strong></p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying or carrying out numerical analyses and of the flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry, slope, and geophysical properties (bed-roughness, channel slope, etc.) along with their reaches. Variations in the shape and size of the channel bed geometry result from several interacting features of the river system including the effect of different flow regimes, slope, sediment load, etc. Simplifying the river bed geometries could reduce the burden of assembling the required data, so implementing less detailed routing procedures could lower the computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions which relate river key the hydraulics (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologist since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure which was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Given “refined” dataset, estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> Doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S. 2019. USGS Table AHG Parameters And Supplementary Data (Version v1.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2563440</li> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
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.