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1,196 results for “Minerals”
Figure 3 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
Figure 3. Average of micronutrients Fe, Mn, B, Cu, and Zn uptake (mg/plant) by shoots of corn seedlings grown in Guam cobbly clay soil, either inoculated (■) or not inoculated (♦) with Glomus aggregatum and provided one of four volumes of water: W1=7200 mL, W2=3600 mL, W3=1800 mL, and W4=900 mL during the 3-week experiment. Crossbars represent standard deviations of means of four replications.
Dataset: Sprott Copper Miners ETF (COPP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sprott Junior Copper Miners ETF (COPJ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes Gold Miners ETF (AUMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes Silver Miners ETF (AGMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes Silver Miners ETF (AGMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Valkyrie Bitcoin Miners ETF (WGMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: United States Lime & Minerals, Inc. (USLM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sprott Junior Uranium Miners ETF (URNJ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sprott Nickel Miners ETF (NIKL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sprott Lithium Miners ETF (LITP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Ishares Lithium Miners And Producers ETF (ILIT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Research data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach"
<div><strong>Research Data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em></strong></div> <div> </div> <div>Dear reader,</div> <div> </div> <div>reasearch data are provided for the research article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em> (https://doi.org/10.1016/j.advwatres.2024.104763). The authors hope that the research data allows for a better understanding of the modeling workflow. The research data covers the following files:</div> <div> <ul> <li>Python scripts to create the models <ul> <li>Model scripts using FloPy (Bakker et al., 2016) are stored as .py files in './model_data/flopy_scripts/', named 'model_variant_vXYZ.py', where 'XYZ' is a wildcard for the model number. </li> <li>--> Note that model numbers correspond to the different model variants as referred to in the article, see overview below.</li> <li>The model scripts require postfix files, stored in './model_data/flopy_scripts/postfix/', a PHREEQC database file, stored in './model_data/flopy_scripts/template_database/', as well as spreadsheets that contain the initial concentrations as well as reaction rate parameters needed by PHT3D, stored as .xlsx files in './model_data/flopy_scripts/', to create the models.</li> <li>Note that the .xlsx files are used by PHT3D-FSP in the model scripts to generate relevant PHT3D input files (compare https://doi.org/10.5281/zenodo.7559750 for more details).</li> </ul> </li> <li>SEAWAT/PHT3D input files <ul> <li>Original SEAWAT and PHT3D input files, which were created with the corresponding model scripts previously (see step before).</li> <li>Input files are stored in './model_data/model_files/vXYZ/model_files/' for each model variant, where 'XYZ' is a wildcard for the model number.</li> <li>SEAWAT/PHT3D executables can directly run the model files files. Thus, the files don't need to be re-created via the previous step.</li> </ul> </li> <li>Model outputs <ul> <li>Model output data is stored as NumPy arrays in './model_data/model_files/vXYZ/npy_arrays/', where 'XYZ' is a wildcard for the model number.</li> <li>The script './model_data/flopy_scripts/template_output/pht3d_output_hpc_v006.py' was used to generate the output files.</li> <li>2-D species concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/species/', where 'XYZ' is a wildcard for the model number.</li> <li>Species min./max. concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/min_max/', where 'XYZ' is a wildcard for the model number.</li> <li>2-D water budget arrays (CH & WEL boundaries) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/budgets/', where 'XYZ' is a wildcard for the model number.</li> <li>Model discretization information (ncol, nrow, nlay etc.) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/discretization/', where 'XYZ' is a wildcard for the model number.</li> </ul> </li> <li>Figure files <ul> <li>Original figure files as well as the corresponding Python scripts to create the figures are stored in the subfolder'./figures'.</li> </ul> </li> </ul> <p>Numbering of the model variants is as follows:<br><br>v401 --> VAR-conservative<br>v402 --> VAR-OM<br>v403 --> VAR-C/I<br>v404 --> VAR-C/I/S<br>v405 --> VAR-C/I/P<br>v406 --> VAR-C/I/P/H<br>v407 --> VAR-C/I/P/V<br>v408 --> VAR-C/I/P-Co<br>v409 --> VAR-all<br>v410 --> VAR-all (no C)</p> </div> <div> </div> <div>Literature:</div> <div> </div> <div>Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J.J. and Fienen, M.N., 2016. Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), pp.733-739. https://doi.org/10.1111/gwat.12413</div> <div> </div> <div>Seibert, S.L., Massmann, G., Meyer, R., Post, V.E.A., Greskowiak, J., 2024. Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2024.104763</div> <div> </div> <div><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Vincent E.A. Post (vincent@edinsi.nl), Rena Meyer (rena.meyer@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</div>
Fig. 2 in Testate Amoeba Diversity of a Poor Fen on Mineral Soil in the Hilly Area of Central Honshu, Japan
Fig. 2. Sample-based testate amoeba species accumulation curve for all three samples collected in the sampling site of the poor fen on mineral soil. The bars are standard deviations.
FIGURE 1. Translucent 3D in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 1. Translucent 3D-models of Cambarellus diminutus sample C7tank in combination with 3D-models of calcite clusters, which precipitated inside the carcass during its decomposition in freshwater. 1.1 3D-model without calcite clusters on day 1. 1.2 3D-model on day 2 showing a small amount of calcite clusters inside the cephalothorax and the first tergite. 1.3 3D-model on day 4 showing a lot of calcite clusters inside the antennules, the left major propodus, the rostrum, the cephalothorax, the tergites, the uropods, and the telson. 1.4 3D-model on day 7, showing widespread calcite clusters at the inner side of the carapace of the carcass except the dorsal side of the cephalothorax and the tergites (see also Figure.4.1). 3D-models were reconstructed based on µ-CT data.
FIGURE 6. 3D in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 6. 3D-models and SEM-images of sample C3tank. 6.1 3D-model of the whole crayfish in dorso-lateral view. 6.2 3D-model of the chela of the first left pereiopod in combination with a SEM-image of the calcified muscle of the dactyl. 6.3 SEM-image of a calcified muscle from the inside of the dactyl of the chela of the first left pereiopod. 3Dmodels were reconstructed based on µ-CT data.
FIGURE 5 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 5. SEM-images of several diverse calcite structures which precipitated inside the carcasses. 5.1 Bispherical structure with mineralized setae and a part of the cuticle layers. 5.2 and 5.3 Spherical structures. 5.4 Elliptical structure which is tapering at the left side. 5.5 Complex structure. 5.6 Bispherical structure with mineralized setae and a part of the cuticle layers.
FIGURE 8 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 8. Hypothetical scenarios of calcium dissolution and precipitation of calcite clusters inside decomposing crayfish without (8.1-2) and with gastroliths in tank water (8.3-4). 8.1 Low pH-values around and inside the carcass caused by an enzymatic self-digestion (autolysis) and bacterial activity release dissolved calcium ions which migrate out of the carapace into the body cavity and into the environment (red arrows). 8.2 Increase of the pH-value inside the carcass caused by microbial activities during the putrefaction result in a precipitation of calcite clusters at the inner side of the carapace, consisting of previously dissolved calcium ions out of the cuticle layers. 8.3 Low pH-values around and inside the carcass caused by enzymatic self-digestion (autolysis) and bacterial activity resulted in an accumulation of dissolved calcium ions (red arrows). In addition, low pH conditions inside the stomach and decay of the "gastrolith-cavity-membrane" resulted in dissolving calcium ions from the gastroliths. 8.4 An increase of the pHvalue inside the carcass, along the inner side of the carapace, caused by microbial activities during the putrefaction resulted in a precipitation of calcite clusters by previously dissolved calcium ions out of the cuticle layers and gastroliths.
FIGURE 7 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 7. Representative Raman spectra of a mineralized muscle of Cambarellus diminutus (sample C3tank) and observed crystal clusters compared to Raman reference spectra of crystalline calcite and apatite, taken from the RRUFF Raman data base (*R040170, #R060070, Laetsch and Downs, 2006). Raman spectra of the mineralized muscle as well as of the crystal cluster exhibit all main Raman bands typically observed in well crystallized calcite, including the lattice modes, which are absent in amorphous calcium carbonate (Wang et al., 2011).
Figure 2 in Root deformation affects mineral nutrition but not leaf gas exchange and growth of Genipa americana seedlings during the recovery phase after soil flooding
Figure 2. Concentrations of P in leaves for G. americana seedlings without or with root deformation (RD) after 28 days of soil drainage (recovery). N = 3. Means followed by the same letter are not significantly different according to Tukey's test (p <0.05). Capital letters represent comparisons water effects within root conditions and lower case letters represent comparisons of roots effects within water conditions.
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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.