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Dataset from: Small-scale patches of detritus as habitat for invertebrates within a Zostera noltei meadow
<p>This dataset is related to "Small-scale patches of detritus as habitat for invertebrates within a <em>Zostera noltei</em> meadow" (Valentina Costa, Renato Chemello, Davide Iaciofano, Sabrina Lo Brutto, Francesca Rossi)</p>
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids (Dataset)
<p><strong>Abstract</strong></p> <p>The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility. </p> <p><strong>Data Navigation</strong></p> <p>Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods.</p> <pre><code>wget https://zenodo.org/record/5130612/files/PSML.zip?download=1 7z x 'PSML.zip?download=1' -o./ </code></pre> <p><strong>Minute-level Load and Renewable</strong></p> <ul> <li>File Name <ul> <li>ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.</li> </ul> </li> <li>- Field Description <ul> <li>Field `<em>time</em>`: Time of minute resolution.</li> <li>Field `<em>load_power</em>`: Normalized load power.</li> <li>Field `<em>wind_power</em>`: Normalized wind turbine power.</li> <li>Field `<em>solar_power</em>`: Normalized solar PV power.</li> <li>Field `<em>DHI</em>`: Direct normal irradiance.</li> <li>Field `<em>DNI</em>`: Diffuse horizontal irradiance.</li> <li>Field `<em>GHI</em>`: Global horizontal irradiance.</li> <li>Field <em>`Dew Point</em>`: Dew point in degree Celsius.</li> <li>Field `<em>Solar Zeinth Angle</em>`: The angle between the sun's rays and the vertical direction in degree.</li> <li>Field `<em>Wind Speed</em>`: Wind speed (m/s).</li> <li>Field `<em>Relative Humidity</em>`: Relative humidity (%).</li> <li>Field `<em>Temperature</em>`: Temperature in degree Celsius.</li> </ul> </li> </ul> <p><strong>Minute-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>case #: The `case 0` folder contains all data of scenario setting #0. <ul> <li>pf_input_#.txt: Selected load, renewable and solar generation for the simulation.</li> <li>pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>Field <em>`time`</em>: Time of minute resolution.</li> <li>Field <em>`Vm_###`</em>: Voltage magnitude (p.u.) at the bus ### in the simulated model.</li> <li>Field <em>`Va_###`</em>: Voltage angle (rad) at the bus ### in the simulated model.</li> <li>Field <em>`P_#_#_#`</em>: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.</li> <li>Field <em>`Q_#_#_#`</em>: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.</li> </ul> </li> </ul> <p><strong>Millisecond-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>Forced Oscillation: The folder contains all forced oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li> info.csv: This file contains the start time, end time, location and type of the disturbance</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Natural Oscillation: The folder contains all natural oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li>info.csv: This file contains the start time, end time, location and type of the disturbance.</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>trans.csv <ul> <li> - Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li> - Field <em>`VOLT ###`</em>: Voltage magnitude (p.u.) at the bus ### in the transmission model.</li> <li> - Field <em>`POWR ### TO ### CKT #`</em>: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.</li> <li> - Field <em>`VARS ### TO ### CKT #`</em>: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.</li> </ul> </li> <li>dist.csv <ul> <li>Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li>Field <em>`####.###.#`</em>: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.</li> </ul> </li> </ul> </li> </ul>
Data for Rasch analysis of the three Field Practice Experiences Scales
<p>Data for Rasch analysis of the three Field Practice Experiences Scales. Data rare from Danish teacher education program students. Contains the following variables:</p> <p>Variables O1 to O12 are the items for the Observed scale</p> <p>Variables P1 to P12 are the items for the Practised scale</p> <p>Variables F1 to F12 are the items to the Received feedback scale</p> <p>Gender: 1 = female, 2 = male</p> <p>Campus: 1 = campus A, 2 = campus B</p> <p>T_Progr (teacher education program): 1 = regular, 2 = other</p> <p>P_level (level of latest field practice placement): 1 = 3rd level, 2 = 2nd level, 3 = 1st level</p> <p>Age: 1 = 24 years and younger, 2 = 25 years and older</p>
DATASETS and OUTCOMES - Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the DRASTIC method: the case of South America
<p>A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.</p> <p>This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.</p> <p>Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).</p>
Raw data from Qin et al. (2018) "Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles"
<p>Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol. 52(23): 13887-13896. [10.1021/acs.est.8b04436]</p> <p>This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript. </p> <p>The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).</p>
DATASET: Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale
<p>This upload contains data for the manuscript "<strong>Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale</strong>." It contains kinetics data, UV-vis data, surface calculations, and activity assays for the systems described in the manuscript.</p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</p>
Ecological barriers mediate spatiotemporal shifts of bird communities at a continental scale
<p>### Ecological barriers mediate spatiotemporal shifts of bird communities ###</p> <p>Marjakangas, Bosco et al. 2022</p> <p>Methods explained in the publication (open access)</p> <p>--> readme file explains how to use the data and code</p>
Data used in the paper: Heatwaves, droughts, and fires: Exploring compound and cascading dry T hazards at the pan-European scale
<p>These datasets were used to analyze European compound and cascading dry hazards. The scripts are publicly available on GitHub: https://github.com/sjsutanto/Dryhazards.git.</p> <p>File Daily_SM_drought_WB_Converted.nc is for soil moisture drought, fwi_1990_2016_binary_95th_lowThreshold.nc is for wildfires, and datacube_2mtpp_19902016_HW.nc is for heatwaves.</p> <p> </p>
Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis
<p>Genetic studies have identified ≥240 loci associated with risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterize the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into <em>cis</em>- and <em>trans</em>-acting genetic components and mapping <em>cis</em>-acting loci associated with miRNA expression (miRNA-eQTLs). We find (i) 84 heritable miRNAs, primarily regulated by <em>trans</em>-acting genetic effects, and (ii) 5 miRNA-eQTLs. We also use several different strategies to identify T2D-associated miRNAs. First, we colocalize miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersect miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and find 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we perform differential expression analysis and identify 14 miRNAs associated with T2D status—including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p—and 4 miRNAs associated with a polygenic score for HbA1c levels—miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.</p>
Earthquake Data for Magnitude and Slip Scaling Relations
<p>Data files useful in earthquake scaling relations. They include compilations of a number of authors.</p> <p>Data files used in papers [Shaw, BSSA, 2013] and [Shaw, BSSA, 2023]. The first set are .txt electronic supplements from [Shaw, 2013]. The second set are a .csv file from [Shaw, 2023] and a .csv file from [Biasi, et al., 2013] used in that paper. Please use the references below if you use the data.</p> <p>-------------------------------------------</p> <p>[Shaw, 2013] files:</p> <p>Bruce E. Shaw,<br> ` Earthquake Surface Slip-Length Data is Fit by Constant Stress Drop and is Useful for Seismic Hazard Analysis',<br> <em>Bulletin of the Seismological Society of America</em>, <em>103</em>, 876, doi:10.1785/0120110258, 2013.</p> <p>The electronic supplement contains four space-delimited plain text tables of data used in the article.</p> <p><strong>File descriptions:</strong></p> <p>Table S1, <strong>BSSA-D-11-00258-esupp_TableS1_dyncm.txt</strong>, contains magnitude-length-width data reproduced from the WGCEP [2003] hazard estimate, taken from a Table in the Appendix D by Ellsworth [2003] USGS Open File Report 03-214.</p> <p>Table S2, <strong>BSSA-D-11-00258-esupp_TableS2.txt</strong>, reproduces a magnitude-area dataset compiled by Hanks and Bakun [2008].</p> <p>Table S3, <strong>BSSA-D-11-00258-esupp_TableS3.txt,</strong> is derived from a surface slip-length dataset compiled by Wesnousky [2008]. Some modifications of this dataset have been made, taking into account new LIDAR results of [Zielke <em>et al.,</em> 2010] of the 1857 M7.8 Fort Tejon event, and the addition of a new large 2008 M7.9 Wenchuan event [Xu <em>et al.,</em> 2009].</p> <p>Table S4, <strong>BSSA-D-11-00258-esupp_TableS4.txt</strong>, combines data from events common to Tables S2 and S3, to enable a comparison of events in common.</p> <p>References</p> <p>Ellsworth, W. L. (2003), Magnitude and area data for strike slip earthquakes, <em>U.S. Geol. Surv. Open File Rep.</em>, 03-214 Appendix D.</p> <p>Hanks, T. C., and W. H. Bakun (2008), M-log A observations of recent large earthquakes, <em>Bull. Seismol. Soc. Am.</em>, <strong>98</strong>, 490.</p> <p>Shaw, Bruce .E (2013)., Earthquake Surface Slip-Length Data is Fit by Constant Stress Drop and is Useful for Seismic Hazard Analysis, <em>Bull. Seismol. Soc. Am.</em>, <strong>103</strong>, 876.</p> <p>Wesnousky, S. G. (2008), Displacement and geometrical characteristics of earthquake surface ruptures: Issues and implications for seismic-hazard analysis and the process of earthquake rupture, <em>Bull. Seismol. Soc. Am.</em>, <strong>98</strong>, 1609.</p> <p>WGCEP (2003), Earthquake probabilities in the San Francisco Bay Region: 2002 to 2031, <em>U.S. Geol. Surv. Open File Rep.</em>, 03-214.</p> <p>Xu, X., X. Wen, G. Yu, G. Chen, Y. Klinger, J. Hubbard, and J. Shaw (2009), Coseismic reverse- and oblique-slip surface faulting generated by the 2008 Mw 7.9 Wenchuan earthquake, China, <em>Geology</em>, <strong>37</strong>, 515, doi:10.1130/G25462A.1.</p> <p>Zielke, O., J. R. Arrowsmith, L. G. Ludwig, and S. O. Akciz (2010), Slip in the 1857 and ear- lier large earthquakes along the Carrizo Plain, San Andreas fault, <em>Science</em>, <strong>327</strong>, 1119, doi: 10.1126/science.1182781.</p> <p>-------------------------------------------</p> <p>[Shaw, 2023] files:</p> <p>Bruce E. Shaw,<br> ` Magnitude and Slip Scaling Relations for Fault Based Seismic Hazard',<br> <em>Bulletin of the Seismological Society of America</em>, 2023.</p> <p><strong>catalogMeanGlobal.csv</strong>: An effort to compile a database from the existing literature of finite source information on recent large and great earthquakes was recently led by a New Zealand group as part of an update to their national seismic hazard model [Stirling, et al., 2022]. They looked globally at M>7.5 shallow earthquakes since 1990, along with an additional subset of the M>7.0 shallow events since 1990 which were less poorly constrained. These events were compiled through an extensive literature review.<br> [Shaw, 2023] took that database and used averages over the width, length, area, magnitudes, and dips from the different sources in the literature for the same events to obtain a finite source data set for each event to analyze further. One issue with this dataset is that the complied data used for the averages did not undergo a thorough review, so there may be potential outliers and problems in individual data points. In aggregate, statistically, it can be useful, which is how it was used. But caution for any individual data point is warranted. It is presented as a useful starting point for further development, It is not a thoroughly vetted or definitive dataset.</p> <p><strong>AppendixR1_TableR2_Biasi_adapted.csv </strong>is adapted from a file compiled and published by [Biasi, et al, 2013]. It contains slip, length, magnitude, and other data for large crustal earthquakes.</p> <p>References</p> <p>Biasi, G. P., R. J. Weldon, and T. E. Dawson (2013). Distribution of slip in ruptures, UCERF3 Appendix F, <em>USGS Open File Report</em>, 2013-1165, Appendix F.</p> <p>Shaw, Bruce E (2023)., Magnitude and Slip Scaling Relations for Fault Based Seismic Hazard, <em>Bull. Seismol. Soc. Am..</em></p> <p>Stirling, M., B. Shaw, M. Fitzgerald, and C. Ross (2022). Selection and evaluation of magnitude - area scaling relations for update of the New Zealand National Seismic Hazard Model, Report to GNS Science NZ, 2022.</p> <p> </p>
New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model: Supplementary Model Files
<p>Supplementary kinematic model input for the JGR: Solid Earth publication "New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model".</p>
Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation - Dataset and code
<p>This repository contains the primary research data and R code used for data analysis for the article "<em>Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation"</em> Published in the journal PLOS ONE (<a href="https://doi.org/10.1371/journal.pone.0268120">https://doi.org/10.1371/journal.pone.0268120</a>)</p> <p>N.B. To run the code unzip the folder 9-RData.zip and save it in the same working directory as the datasets</p> <p>V 2.0 changes:</p> <p>A. Datasets 1-2-3 - small formatting corrections</p> <p>B. Dataset 7 - fixing some errors in the calculations</p> <p>C. Code - minor fixes and seimplicifaction</p> <p> </p>
Dataset for: Bedding scale correlation on Mars in western Arabia Terra
<p>Dataset for: Bedding scale correlation on Mars in western Arabia Terra</p> <p>A.M. Annex et al.</p> <p>Data Product Overview</p> <p>This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.</p> <p><strong>HiRISE Digital Elevation Models (DEMs).</strong></p> <p>HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.</p> <p><strong>HiRISE map-projected imagery (DRGs).</strong></p> <p>Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.</p> <p><strong>3D Topography files (.ply).</strong></p> <p>Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.</p> <p><strong>3D Geological Model outputs (.vtk)</strong></p> <p>VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.</p> <p><strong>Geological Model geologic maps (geologic_map.tif).</strong></p> <p>Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.</p> <p><strong>Beds Geopackage File (.gpkg).</strong></p> <p>Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>uuid</td> <td>String</td> <td>unique identifier</td> </tr> <tr> <td>stratum_order</td> <td>Real</td> <td>0-indexed bed order</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>layer_id</td> <td>Real</td> <td>bed number/index</td> </tr> <tr> <td>layer_id_bk</td> <td>Real</td> <td>unused backup bed number/index</td> </tr> <tr> <td>source_raster</td> <td>String</td> <td>dem file path used</td> </tr> <tr> <td>raster</td> <td>String</td> <td>dem file name</td> </tr> <tr> <td>gsd</td> <td>Real</td> <td>ground sampling distant for dem</td> </tr> <tr> <td>wkn</td> <td>String</td> <td>well known name for dem</td> </tr> <tr> <td>rtype</td> <td>String</td> <td>raster type</td> </tr> <tr> <td>minx</td> <td>Real</td> <td>minimum x position of trace in dem crs</td> </tr> <tr> <td>miny</td> <td>Real</td> <td>minimum y position of trace in dem crs</td> </tr> <tr> <td>maxx</td> <td>Real</td> <td>maximum x position of trace in dem crs</td> </tr> <tr> <td>maxy</td> <td>Real</td> <td>maximum y position of trace in dem crs</td> </tr> <tr> <td>method</td> <td>String</td> <td>internal interpolation method</td> </tr> <tr> <td>sl</td> <td>Real</td> <td>slope in degrees</td> </tr> <tr> <td>az</td> <td>Real</td> <td>azimuth in degrees</td> </tr> <tr> <td>error</td> <td>Real</td> <td>maximum error ellipse angle</td> </tr> <tr> <td>stdr</td> <td>Real</td> <td>standard deviation of the residuals</td> </tr> <tr> <td>semr</td> <td>Real</td> <td>standard error of the residuals</td> </tr> <tr> <td>X</td> <td>Real</td> <td>mean x position in CRS</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>mean y position in CRS</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>mean z position in CRS</td> </tr> <tr> <td>b1</td> <td>Real</td> <td>plane coefficient 1</td> </tr> <tr> <td>b2</td> <td>Real</td> <td>plane coefficient 2</td> </tr> <tr> <td>b3</td> <td>Real</td> <td>plane coefficient 3</td> </tr> <tr> <td>b1_se</td> <td>Real</td> <td>standard error plane coefficient 1</td> </tr> <tr> <td>b2_se</td> <td>Real</td> <td>standard error plane coefficient 2</td> </tr> <tr> <td>b3_se</td> <td>Real</td> <td>standard error plane coefficient 3</td> </tr> <tr> <td>b1_ci_low</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval low</td> </tr> <tr> <td>b1_ci_high</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval high</td> </tr> <tr> <td>b2_ci_low</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval low</td> </tr> <tr> <td>b2_ci_high</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval high</td> </tr> <tr> <td>b3_ci_low</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval low</td> </tr> <tr> <td>b3_ci_high</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval high</td> </tr> <tr> <td>pca_ev_1</td> <td>Real</td> <td>pca explained variance ratio pc 1</td> </tr> <tr> <td>pca_ev_2</td> <td>Real</td> <td>pca explained variance ratio pc 2</td> </tr> <tr> <td>pca_ev_3</td> <td>Real</td> <td>pca explained variance ratio pc 3</td> </tr> <tr> <td>condition_number</td> <td>Real</td> <td>condition number for regression</td> </tr> <tr> <td>n</td> <td>Integer64</td> <td>number of data points used in regression</td> </tr> <tr> <td>rls</td> <td>Integer(Boolean)</td> <td>unused flag</td> </tr> <tr> <td>demeaned_regressions</td> <td>Integer(Boolean)</td> <td>centering indicator</td> </tr> <tr> <td>meansl</td> <td>Real</td> <td>mean section slope</td> </tr> <tr> <td>meanaz</td> <td>Real</td> <td>mean section azimuth</td> </tr> <tr> <td>angular_error</td> <td>Real</td> <td>angular error for section</td> </tr> <tr> <td>mB_1</td> <td>Real</td> <td>mean plane coefficient 1 for section</td> </tr> <tr> <td>mB_2</td> <td>Real</td> <td>mean plane coefficient 2 for section</td> </tr> <tr> <td>mB_3</td> <td>Real</td> <td>mean plane coefficient 3 for section</td> </tr> <tr> <td>R</td> <td>Real</td> <td>mean plane normal orientation vector magnitude</td> </tr> <tr> <td>num_valid</td> <td>Integer64</td> <td>number of valid planes in section</td> </tr> <tr> <td>meanc</td> <td>Real</td> <td>mean stratigraphic position</td> </tr> <tr> <td>medianc</td> <td>Real</td> <td>median stratigraphic position</td> </tr> <tr> <td>stdc</td> <td>Real</td> <td>standard deviation of stratigraphic index</td> </tr> <tr> <td>stec</td> <td>Real</td> <td>standard error of stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_layer_id</td> <td>Integer(Boolean)</td> <td>monotonic layer_id after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_meanc</td> <td>Integer(Boolean)</td> <td>monotonic meanc after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_z</td> <td>Integer(Boolean)</td> <td>monotonic z increasing after projection to stratigraphic index</td> </tr> <tr> <td>meanc_l3sigma_std</td> <td>Real</td> <td>lower 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_u3sigma_std</td> <td>Real</td> <td>upper 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_l2sigma_sem</td> <td>Real</td> <td>lower 3-sigma meanc standard error</td> </tr> <tr> <td>meanc_u2sigma_sem</td> <td>Real</td> <td>upper 3-sigma meanc standard error</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>difference in meanc</td> </tr> <tr> <td>thickness_fromz</td> <td>Real</td> <td>difference in Z value</td> </tr> <tr> <td>dip_cor</td> <td>Real</td> <td>dip correction</td> </tr> <tr> <td>dc_thick</td> <td>Real</td> <td>thickness after dip correction</td> </tr> <tr> <td>dc_thick_fromz</td> <td>Real</td> <td>z thickness after dip correction</td> </tr> <tr> <td>dc_thick_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean thickness_fromz</td> </tr> <tr> <td>dc_thick_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean thickness_fromz</td> </tr> <tr> <td>slr</td> <td>Real</td> <td>slope in radians</td> </tr> <tr> <td>azr</td> <td>Real</td> <td>azimuth in radians</td> </tr> <tr> <td>meanslr</td> <td>Real</td> <td>mean slope in radians</td> </tr> <tr> <td>meanazr</td> <td>Real</td> <td>mean azimuth in radians</td> </tr> <tr> <td>angular_error_r</td> <td>Real</td> <td>angular error of section in radians</td> </tr> <tr> <td>pca_ev_1_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_1 < 99.5%</td> </tr> <tr> <td>pca_ev_2_3_ratio</td> <td>Real</td> <td>pca_ev_2/pca_ev_3</td> </tr> <tr> <td>pca_ev_2_3_ratio_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_2_3_ratio > 15</td> </tr> <tr> <td>xyz_wkb_hex</td> <td>String</td> <td>hex encoded wkb geometry for all points used in regression</td> </tr> </tbody> </table> <p><strong>Geological Model input files (.gpkg).</strong></p> <p>Four geopackage (.gpkg) files represent the input dataset for the geological models, one per study site as specified in the name of the file. The files contain most of the columns described above in the Beds geopackage file, with the following additional columns. The final seven columns (azimuth, dip, polarity, formation, X, Y, Z) constituting the actual parameters used by the geological model (GemPy). </p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth_mean</td> <td>String</td> <td>Mean section dip azimuth </td> </tr> <tr> <td>azimuth_indi</td> <td>Real</td> <td>Individual bed azimuth</td> </tr> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of trace used by the geological model</td> </tr> <tr> <td>dip</td> <td>Real</td> <td>Dip for the trace used by the geological mode</td> </tr> <tr> <td>polarity</td> <td>Real</td> <td>Polarity of the dip vector normal vector </td> </tr> <tr> <td>formation</td> <td>String</td> <td>String representation of layer_id required for GemPy models</td> </tr> <tr> <td>X</td> <td>Real</td> <td>X position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position in the CRS of the sampled point on the trace</td> </tr> </tbody> </table> <p><strong>Stratigraphic Column Files (.gpkg).</strong></p> <p>Stratigraphic columns computed from the Geological Models come in three kinds of Geopackage vector files indicated by the postfixes <code>_sc</code>, <code>rbsc</code>, and <code>rbssc</code>. File names include the wkn site name.</p> <p><strong>sc (_sc.gpkg).</strong></p> <p>Geopackage vector data file containing measured bed thicknesses from Geological Model joined with corresponding Beds Geopackage file, subsetted partially. The columns largely overlap with the the list above for the Beds Geopackage but with the following additions</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>X</td> <td>Real</td> <td>X position of thickness measurement</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position of thickness measurement</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position of thickness measurement</td> </tr> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>bed thickness (m)</td> <td>Real</td> <td>difference of bed elevations</td> </tr> <tr> <td>azimuths</td> <td>Real</td> <td>azimuth as measured from model in degrees</td> </tr> <tr> <td>dip_degrees</td> <td>Real</td> <td>dip as measured from model in degrees</td> </tr> <tr> <td>Dip corrected bed thickness (m)</td> <td>Real</td> <td>dip corrected bed thickness in meters</td> </tr> <tr> <td>lower_point</td> <td>Real</td> <td>lower bed elevation in meters</td> </tr> <tr> <td>upper_point</td> <td>Real</td> <td>upper bed elevation in meters</td> </tr> <tr> <td>_formation</td> <td>Real</td> <td>integer number of formation string</td> </tr> <tr> <td>layer_iid</td> <td>Integer64</td> <td>integer number of layer_id</td> </tr> <tr> <td>bascom_baryte_diff_bt</td> <td>Real</td> <td>diff. in thickness from geomodel measurements</td> </tr> <tr> <td>bascom_baryte_diff_dcbt</td> <td>Real</td> <td>diff. in dip cor. thicknesses ’’</td> </tr> </tbody> </table> <p><strong>rbsc (rbsc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed in a regular grid in the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>rbssc (rbssc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed at the centroids for each section within the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>crescent_shapes.gpkg</strong></p> <p>Geopackage vector file containing the measurements of the crescent features.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of the crescent feature in degrees</td> </tr> </tbody> </table>
Magnetic field data for "Cosmic Rays in Intermittent Magnetic Fields" (magnetic fields produced by the small-scale dynamo)
<p>Magnetic field data for the kinematic dynamo generated magnetic fields (KS) employed in the cosmic ray test particle simulations of Shukurov et al. 2017, <em>ApJL</em>, <strong>839</strong>, L16 [<a href="https://doi.org/10.3847/2041-8213/aa6aa6">https://doi.org/10.3847/2041-8213/aa6aa6</a>]. One dataset was also used in "Relative distribution of cosmic rays and magnetic fields", Seta et al. 2018, <em>MNRAS</em>, <strong>473 </strong>(4), 4544-4557 [<a href="https://doi.org/10.1093/mnras/stx2606">https://doi.org/10.1093/mnras/stx2606</a>]. These studies investigated the effects of magnetic field structure on charged test particle transport and trapping. See the README file for more details.</p> <p> </p>
CrusTome: A transcriptome database resource for large-scale analyses across Crustacea
<p>CrusTome_v0.1.0 Prerelease<br> /ReadMe - this file<br> /crustome_aa_BLAST.tar.gz - CrusTome database of amino acid sequences in BLAST format<br> /crustome_aa_DIAMOND.tar.gz - CrusTome database of amino acid sequences in DIAMOND format<br> /crustome_mrna_BLAST.tar.gz - CrusTome database of mRNA sequences in BLAST format<br> /dict - Dictionary file to translate species IDs. For usage with sed/awk see link to Github site below<br> <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> * <br> * Please note, most of the data files contained in this DOI are <br> * compressed into GZip files (.gz extension). <br> * Mac and Linux OS's can extract this file type natively. <br> * Windows OS requires software to extract the archive. 7-Zip <br> * (http://www.7-zip.org) is free and open source software that will <br> * allow windows PCs to open and decompress the archive. <br> * <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> <br> <strong><em>Pérez-Moreno JL, Kozma MT, DeLeo DM, Bracken-Grissom HD, Durica DS, Mykles DL. 2023. CrusTome: A transcriptome database resource for large-scale analyses across Crustacea. G3: Genes, Genomes, Genetics.</em></strong></p> <p><strong>CrusTome: A transcriptome database resource for large-scale analyses across Crustacea</strong></p> <p>Transcriptomes from non-traditional model organisms often harbor a wealth of unexplored data. Examining these datasets can lead <br> to clarity and novel insights in traditional systems, as well as to discoveries across a multitude of fields. Despite significant <br> advances in DNA sequencing technologies and in their adoption, access to genomic and transcriptomic resources for non-traditional <br> model organisms remains limited. Crustaceans, for example, being amongst the most numerous, diverse, and widely distributed taxa on the planet, often serve as excellent systems to address ecological, evolutionary, and organismal questions. While they are <br> ubiquitously present across environments, and of economic and food security importance, they remain severely underrepresented in <br> publicly available sequence databases. Here, we present CrusTome, a multi-species, multi-tissue, transcriptome database of 201 <br> assembled mRNA transcriptomes (189 crustaceans, 30 of which were previously unpublished, and 12 ecdysozoan outgroups) as an evolving, and publicly available resource. This database is suitable for evolutionary, ecological, and functional studies that employ<br> genomic/transcriptomic techniques and datasets. CrusTome is presented in BLAST and DIAMOND formats, providing robust datasets for sequence similarity searches, orthology assignments, phylogenetic inference, etc., and thus allowing for straight-forward incorporation into existing custom pipelines for high-throughput analyses.</p> <p> <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> <br> For questions regarding released datasets contact:<br> Corresponding Author: Jorge L. Perez-Moreno (Colorado State University)<br> jorgepm@colostate.edu / jpere645@fiu.edu<br> <br> <strong> https://github.com/invertome/crustome</strong></p> <p> </p> <p> <br> <strong>PLEASE CITE:</strong></p> <p>Pérez-Moreno JL, Kozma MT, DeLeo DM, Bracken-Grissom HD, Durica DS, Mykles DL. 2023. CrusTome: A transcriptome database resource for large-scale analyses across Crustacea. G3: Genes, Genomes, Genetics.</p> <p> </p> <p><strong>Funder Information</strong></p> <p>Supported by National Science Foundation grants to DLM (IOS-1922701) and DSD (IOS-1922755). In addition, this work was partially funded by two grants awarded from the National Science Foundation: Doctoral Dissertation Improvement Grant (#1701835) awarded to JPM and HBG and the Division of Environmental Biology Bioluminescence and Vision grant (DEB-1556059) awarded to HBG. Samples in the FICC were collected by grants from The Gulf of Mexico Research Initiative (GOMRI), Florida Institute of Oceanography Shiptime Funding awarded to HBG and DMD; the National Science Foundation Division of Environmental Biology Grant 1556059 awarded to HBG; and the National Oceanic and Atmospheric Administration Ocean Exploration Research (NOAA-OER 2015) grant awarded to HBG.</p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
Dataset for visualizing the atomic-scale origin of metallic behavior in Kondo insulators
<p>This dataset contains the raw data files and analysis steps used to produce the figures in the manuscript "Visualizing the atomic-scale origin of metallic behavior in Kondo insulators" Science 379, 1214–1218 (2023)</p>
Supplementary Datasets and Movies for the Paper "Major California faults are smooth across multiple scales at seismogenic depth"
<p>Supplementary Datasets and Movies for the Paper<br> <strong>M</strong><strong>ajor California faults </strong><strong>are</strong><strong> smooth </strong><strong>across</strong><strong> multiple scales </strong><strong>at </strong><strong>seismogenic </strong><strong>depth</strong><br> by Anthony Lomax and Pierre Henry</p> <p>DOI: <a href="https://doi.org/10.26443/seismica.v2i1.324">https://doi.org/10.26443/seismica.v2i1.324</a></p> <p>Movies S1-2 Seismicity along the central San Andreas fault zone around Parkfield as Figure 1 in the main text. Shows rotating, lateral view around ~S40°E for (Movie S1) NCSS-DD and (Movie S2) NLL-SSST-coherence. See Figure 1 caption in the main text for key to figure elements.</p> <p>Movie S3-7 Animated, rotating later views of NLL-SSST-coherence relocations of seismicity other than Parkfield presented in the main paper and this supplement.<br> Movie_S1_Parkfield_2022_sect_DD_movie_20230401.mp4 Movie_S2_Parkfield_2022_sect_NLL-SSST-coherence_movie_20230401.mp4 Movie_S3_S_Calaveras_2022_NLL-SSST-coherence_movie_20230401.mp4 – Southern Calaveras Fault Zone<br> Movie_S4_Mendocino_2021_NLL-SSST-coherence_movie_20230401.mp4 – Cape Mendocino<br> Movie_S5_MountLewis_1986_NLL-SSST-coherence_movie_20230401.mp4 – Mount Lewis<br> Movie_S6_SW_SanFrancisco_NLL-SSST-coherence_movie_20230401.mp4 - Southwest of San Francisco<br> Movie_S7_Calipatria_2021_NLL-SSST-coherence_movie_20230401.mp4 - Calipatria sequence</p> <p>Datasets S1-5 CSV format catalogs of NLL-SSST-coherence relocation results presented in the main text.<br> CSV fields correspond to data in the <a href="http://alomax.net/nlloc/soft7.00/formats.html#_location_hypphs_">NLLoc Hypocenter-Phase file</a></p> <ul> <li>ds01_Parkfield_2004_NLL_SSST_coherence.csv - Parkfield</li> <li>ds02_S_Calaveras_2022_NLL_SSST_coherence.csv – Southern Calaveras Fault Zone</li> <li>ds03_Mendocino_2021_NLL_SSST_coherence,csv – Cape Mendocino</li> <li>ds04_MountLewis_1986_NLL_SSST_coherence.csv – Mount Lewis</li> <li>ds05_SW_SanFrancisco_2021_NLL_SSST_coherence.csv - Southwest of San Francisco</li> <li>ds06_Calipatria_2021_NLL_SSST_coherence.csv - Calipatria sequence</li> </ul> <p>File S1 (project_run_scripts.zip) Archive of run scripts and related set-up, configuration and other meta-data files for locations cases presented in this paper.</p>
Sea ice core biogeochemical data collected during the 2019 SCALE Winter Cruise
<p><strong>Title: </strong>Biogeochemical profiles of sea ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019.</p> <p> </p> <p><strong>Authors:</strong> Riesna R. Audh, Siobhan Johnson, Mark Hambrock, Hazel Little, Joshua Mirkin, Emmanuel Omatuku, Benjamin Hall, Tokoloho Rampai, Keith MacHutchon, Sebastian Skatulla, Sarah E. Fawcett, Marcello Vichi</p> <p> </p> <p><strong>Data Description:</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>Biogeochemical profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p> <p> </p> <p>A total of four sea ice cores (cores) were sampled during the cruise. Two cores were collected overboard on a consolidated floe that was accessed via a personnel carrier suspended by the ship’s forward crane. Two cores were collected from a pancake that was lifted aboard the ship via a net that was attached to the ship’s aft crane and placed on the helideck for sampling. Profiles were obtained by cutting the cores using a bandsaw in a cold laboratory at -10 °C. The cores were cut into approximately 0.05 m segments, starting from the bottom of the core. These segments were allowed to melt in the dark in an insulated box. The meltwater was filtered for chlorophyll measurements (Welschmeyer, 1994) and the filtrate was analysed for oxygen isotopes (Walker and others, 2015), ammonium (Holmes et al., 1999), phosphate, nitrate, nitrite and silicate (using a SEAL AA500 segmented flow autoanalyser). These values are reported at the depth of the top of the segment in the core in μM. In order to facilitate comparison with the seawater concentrations below the ice, the in-ice nutrients (including NH4+) were salinity normalised using the equation of Fripiat and others (2017):</p> <p> </p> <p><em>C</em><em>norm</em><em> = C</em>SwS<em> </em><em> </em></p> <p> </p> <p>Where C is the measured bulk concentration, Sw is the salinity of the seawater, and S is the corresponding measured bulk salinity of the ice segment.</p> <p> </p> <p>Although sampling of the core occurred from the bottom of the core to the top of the core, the data are reported as the top of the core (snow/ice interface) being 0 m (depth=0 m). </p> <p> </p> <p><strong>This research has been funded by the National Research Foundation of South Africa (NRF)</strong></p> <p><br> </p> <p><strong>Cruise:</strong> VOY-038 (SCALE2019-WINTER) (URL: https://scale.org.za/)</p> <p><strong>Station(s):</strong> VOY-038-MIZ3A</p> <p>VOY-038-MIZ1D</p> <p><strong>Position(s):</strong> -58.13783 S; 0.00442 W</p> <p>-56.8017 S; 0.30262 E</p> <p><strong>Date/Time:</strong> 2019-07-27/10:38:00</p> <p>2019-07-28/09:15:00</p> <p><strong>Method(s):</strong> Overboard coring</p> <p>Pancake lifting via aft crane, on deck coring</p> <p><strong>Parameters:</strong><strong> </strong>Station Number (Station)</p> <p>Date/Time of station (Date/Time)</p> <p>Latitude of station (Latitude)</p> <p>Longitude of station (Longitude)</p> <p>Ice type (Ice Type)</p> <p>Core ID(Core), Pancake identifier A/B/C/D</p> <p>Oxygen isotopes (d18O)</p> <p>Chlorophyll (Chl-a)</p> <p>Ammonium (NH4)</p> <p>Nitrate + Nitrite (NO3+NO2)</p> <p>Nitrite (NO2)</p> <p>Phosphate (PO4)</p> <p>Silicate (Si)</p> <p>Nitrate (NO3)</p> <p>Salinity of the ice segment from physical cores (IceSalinity)</p> <p>Standard deviation of the salinity average from physical cores (IceSalinityStdev)</p> <p>Seawater salinity from CTD (SeawaterSalinity)</p> <p>Salinity normalised nitrate+nitrite (N03+N02_Avg_SalinityNormalised)</p> <p>Salinity normalised ammonium (NH4_SalinityNormalised)</p> <p>Salinity normalised nitrite (NO2_SalinityNormalised)</p> <p>Salinity normalised phosphate (PO4_SalinityNormalised)</p> <p>Salinity normalised silicate (Si_SalinityNormalised)</p> <p>Salinity normalised nitrate (NO3_SalinityNormalised)</p> <p><br> </p> <p> </p> <p><strong>Keywords: </strong>sea ice cores, Antarctica, pancake ice, sea ice, biogeochemistry, winter</p> <p> </p> <p><strong>References</strong><strong>:</strong></p> <p> </p> <p>Fripiat, F., Meiners, K.M., Vancoppenolle, M., Papadimitriou, S., Thomas, D.N., Ackley, S.F., Arrigo, K.R., Carnat, G., Cozzi, S., Delille, B. and Dieckmann, G.S., 2017. Macro-nutrient concentrations in Antarctic pack ice: Overall patterns and overlooked processes. Elementa: Science of the Anthropocene, 5. </p> <p> </p> <p>Holmes, R.M., Aminot, A., Kérouel, R., Hooker, B.A. and Peterson, B.J., 1999. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Canadian Journal of Fisheries and Aquatic Sciences, 56(10), pp.1801-1808. </p> <p> </p> <p>Walker, S.A., Azetsu‐Scott, K., Normandeau, C., Kelley, D.E., Friedrich, R., Newton, R., Schlosser, P., McKay, J.L., Abdi, W., Kerrigan, E. and Craig, S.E., 2016. Oxygen isotope measurements of seawater (18O/16O): A comparison of cavity ring‐down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS). Limnology and Oceanography: Methods, 14(1), pp.31-38. </p> <p> </p> <p>Welschmeyer, N., 1994. A method for the determination of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39, pp.1985-1992. </p> <p> </p>
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