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3,206 results for “property (T)”
Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces: ftDFT code
<p>This is the complete ftDFT code used to generate the results reported in our Nov 2017 Nanoscale article "Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces",DOI: 10.1039/C7NR07218J. </p>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</p>
Surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect (2009 - 2019): NetCDF format
<p>This dataset is a compilation of particulate inherent optical properties (IOPs) and co-incident high performance liquid chromatography (HPLC) phytoplankton pigment concentrations measured underway on nine Atlantic Meridional Transect (AMT) cruises. The time period of data collection is 2009 - 2019, between Sep-Nov within each year, with measurements collected between approximately 50 degrees South to 50 degrees North. A separate netCDF file is provided for each cruise (AMT 19, and AMT 22-29), including particulate IOPs (absorption, scattering, beam attenuation), pigment concentrations, and associated metadata.</p> <p>A manuscript containing a full description of the dataset, including associated code, will soon be submitted to Earth System Science Data. A Jupytper notebook illustrating data access is provided at: https://github.com/tjor/AMT_ACSpaperplots/blob/main/AMT_DataAccess.ipynb.</p> <p>The data are also released in SeaBASS format: https://seabass.gsfc.nasa.gov/archive/PML/AMT</p>
Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS: The Sample
<p>Sample from "Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS". We present a sample of 74,216 M and L dwarfs constructed from two existing catalogs of cool dwarfs spectroscopically identified in the Sloan Digital Sky Survey (SDSS). We cross-matched the SDSS catalog with Gaia DR2 to obtain parallaxes and proper motions and modified the quality cuts suggested by the Gaia Collaboration to make them suitable for late-M and L dwarfs. </p>
Stability in water and electrochemical properties of the Na3V2(PO4)2F3 – Na3(VO)2(PO4)2F solid solution
<p>Graphitical Abstract of the publication "Stability in water and electrochemical properties of the Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>3</sub> – Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F solid solution" <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/science/journal/24058297/20/supp/C">Energy Storage Materials Volume 20</a>, 2019, p. 324-334 - DOI : <a href="https://doi-org.docelec.u-bordeaux.fr/10.1016/j.ensm.2019.04.010">https://doi.org/10.1016/j.ensm.2019.04.010</a></p> <p>Abstract : Polyanionic materials have been intensively studied as promising active materials for <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/engineering/positive-electrode">positive electrodes</a> in Na-ion batteries thanks to their excellent stability upon cycling and the fast ionic mobility in their structural framework. Among them, Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>3</sub> and Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F are two of the most promising ones due to their high voltages for Na<sup>+</sup>-ion extraction and their high energy densities: 500 mWh g<sup>−1</sup> and 495 mWh g<sup>−1</sup>, respectively. Here, we study the formation mechanism as well as the stability of these phases in <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/engineering/aqueous-medium">aqueous media</a> and the possible use of a washing step in water in order to remove undesirable <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/materials-science/impurity">impurities</a> formed during the synthesis. Furthermore, the origin of the extra capacity observed at the high voltage region for Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>3</sub> and Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>1.5</sub>O<sub>1.5</sub> was studied by <em>operando</em> <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/materials-science/x-ray-absorption-spectroscopy">X-ray absorption spectroscopy</a>.</p> <p> </p>
Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties
<p>This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests.</p> <p>The approach is described in <a href="http://dx.doi.org/10.1145/3297280.3297500 ">http://dx.doi.org/10.1145/3297280.3297500 </a></p> <p>DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them.</p> <p>Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out <a href="http://dx.doi.org/10.5445/IR/1000046878">http://dx.doi.org/10.5445/IR/1000046878</a> for a reference on the used specification language.</p> <p>For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.</p> <p> </p>
CMI: Sea ice properties from mesocosm oil-in-ice experiments at University of Alaska Fairbanks, 2014-15
<p>Sea ice core properties (salinity) and raw ice temperature data measurement collected during the CMI mesocosm experiment aiming at assessing the impacts of crude oil on ice biota lead at UAF in 2014 and 2015.</p>
Tensile Properties of Flax Fibre Bundles with Graphene Oxide Coating
<p>In the current datasheet, authors report the effect of graphene oxide treatment on tensile behaviour of single flax fibre bundles. As graphene oxide is hydrophilic with many hydroxyl functional groups, it is expected to bond with technical fibres and increase the stress transfer in a flax yarn.</p> <p> Graphene oxide (GO) aqueous dispersion with 1.2 wt % is prepared based on the modified Hummer’s method. GO is physically adsorbed on fibres by immersion of flax yarns into the aqueous dispersion for 24 hr. Fibres are dried at 80 C for 2 hr followed by 48 hr at 60 C. To differentiate between the effect of GO treatment and the potential loss in the tensile strength and tensile stiffness of fibres, authors report the data in 4 subclasses:</p> <ul> <li>As received flax yarns (dried at 60 C for 48 hr): labelled ‘as received’</li> <li>Kept in deionised water for 30 min: tagged ’30 min’</li> <li>Placed in deionised water for 24 hr: marked ’24 hr’</li> <li>Flax fibres immersed in 1.2 wt % GO aqueous dispersion for 24 hr: labelled ‘GO’</li> </ul> <p>Tensile test of single natural fibres is a challenging measurement. This is mainly due to the hierarchical and nonhomogenous structure of single fibres and difficulty in their extraction. The test methods are not standard, and the final data is very scattered. As an alternative method, we report the tensile properties of flax fibre bundles based on the impregnated fibre bundle test (IFBT) [1].</p> <p>Materials and brief description of the methodology can be found in the datasheet under ‘method’ tab. Flax fibre bundles were extracted from AmpliTex 5009 flax fabrics kindly provided by Bcomp. The matrix was Epikote 828 LVEL epoxy resin with Dytek DCH-99 hardener.</p> <p>Impregnated fibre bundle tests were performed with Instron 5567 and 30 kN loadcell, with 120 mm gauge length and 4% min <sup>-1</sup> strain rate. The strain was measured by a 50 mm clip-on extensometer. The abrasive paper was placed without glue in between the testing clamps and the samples. All samples were stored one week before test in a controlled environment of RH 50 % and 25 C.</p> <p>In the current datasheet, authors report the effect of graphene oxide treatment on tensile behaviour of single flax fibre bundles. As graphene oxide is hydrophilic with many hydroxyl functional groups, it is expected to bond with technical fibres and increase the stress transfer in a flax yarn.</p> <p> Graphene oxide (GO) aqueous dispersion with 1.2 wt % is prepared based on the modified Hummer’s method. GO is physically adsorbed on fibres by immersion of flax yarns into the aqueous dispersion for 24 hr. Fibres are dried at 80 C for 2 hr followed by 48 hr at 60 C. To differentiate between the effect of GO treatment and the potential loss in the tensile strength and tensile stiffness of fibres, authors report the data in 4 subclasses:</p> <ul> <li>As received flax yarns (dried at 60 C for 48 hr): labelled ‘as received’</li> <li>Kept in deionised water for 30 min: tagged ’30 min’</li> <li>Placed in deionised water for 24 hr: marked ’24 hr’</li> <li>Flax fibres immersed in 1.2 wt % GO aqueous dispersion for 24 hr: labelled ‘GO’</li> </ul> <p>Tensile test of single natural fibres is a challenging measurement. This is mainly due to the hierarchical and nonhomogenous structure of single fibres and difficulty in their extraction. The test methods are not standard, and the final data is very scattered. As an alternative method, we report the tensile properties of flax fibre bundles based on the impregnated fibre bundle test (IFBT) [1].</p> <p>Materials and brief description of the methodology can be found in the datasheet under ‘method’ tab. Flax fibre bundles were extracted from AmpliTex 5009 flax fabrics kindly provided by Bcomp. The matrix was Epikote 828 LVEL epoxy resin with Dytek DCH-99 hardener.</p> <p>Impregnated fibre bundle tests were performed with Instron 5567 and 30 kN loadcell, with 120 mm gauge length and 4% min <sup>-1</sup> strain rate. The strain was measured by a 50 mm clip-on extensometer. The abrasive paper was placed without glue in between the testing clamps and the samples. All samples were stored one week before test in a controlled environment of RH 50 % and 25 C.</p>
MOSIDEO: Sea ice properties and oil concentrations measured during the HSVA experiment during MOSIDEO
<p>Physical ice properties (porosity, permeability and brine volume fraction), and oil concentration measured on collected ice cores during the experiments.</p> <ul> <li>Oil concentrations are measured using a UV-fluorescence meter TD500TM (Turner Designs Hydrocarbon Instruments, Inc.)</li> <li>Ice temperature measured in-situ with thermocouple strings</li> <li>Porosity and permeability fields are computed from ice and temperature profiles using semi-empirical equations (Cox and Weeks, 1983; Golden et al., 2009). The oil intake and pore space saturation in oil (oil saturation) are derived from acoustic data of the oil/water and oil/ice interface position.</li> </ul>
Common biochemical and topological properties of metabolic genes recurrently dysregulated in tumors
<p>Although tumors exhibit numerous metabolic alterations, it’s unclear if common objectives and constraints underlie diverse metabolic changes. Here we interpret cancer gene expression, copy number variation, and survival data using a computational model, MetOncoFit. MetOncoFit evaluates142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topological attributes, and reaction flux. Meta-analysis of tumor databases using MetOncoFit revealed that metabolic enzymes with high catalytic activity were frequently up-regulated in many tumors and associated with poor survival. MetOncoFit also identified metabolites that were hot-spots of dysregulation. MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.</p>
Automotive Domain Property Specification Pattern Dataset
<p>This dataset contains 1000 requirements written in the Property Specification Pattern (PSP) format, specifically tailored for the automotive domain. The dataset serves as a valuable resource for researchers and practitioners working on the formal verification of automotive software, providing a comprehensive set of standardized requirements that can be used for validation, testing, and benchmarking of verification tools and methodologies.</p>
Al-Ni-Co quasicrystalline melt-spun alloy - microstructure and catalytic properties
<p>This set contains supplementary data for the work: Al-Ni-Co decagonal quasicrystal application as an energy-effective catalyst<br>for phenylacetylene hydrogenation, Sustainable Materials and Technologies 41 (2024) e01055, https://doi.org/10.1016/j.susmat.2024.e01055</p> <p> </p> <p>SEM BSE images present the microstructure of the cross-section of the ribbon.</p> <p>MS_Surf images show the surface of the ribbons acquired using an optical microscope.</p> <p>TEM images were named as follows:</p> <p>ms_ribb - melt-spun ribbon</p> <p>nabh4_ribb - ribbon cleaned with NaBH4 aqueous solution</p> <p>liq_ribb - ribbon recovered after phenylacetylene hydrogenation reaction </p> <p><a href="../api/records/13371995/draft/files/phenylacetylene%20hydrogenation%20reactions.ods/content" target="_blank" rel="noopener noreferrer">phenylacetylene hydrogenation reactions.ods</a> - Reaction course of phenylacetylene hydrogenation reactions with new portions of catalyst. Chemical composition of the reaction mixture was evaluated using the gas chromatography method.</p> <p>XPS spectra were collected for surfaces of ribbons in a melt-spun form and recovered after the phenylacetylene hydrogenation reaction. </p> <p> </p> <p>The material preparation and microstructural analyses were performed at the Institute of Metallurgy and Materials Science of the Polish Academy of Sciences.</p> <p>The experimental procedure for material preparation, instrumentation, data collection and results analysis were described in the work: https://doi.org/10.1016/j.susmat.2024.e01055</p> <p> </p> <p>Preparation of materials: Amelia Zięba</p> <p>TEM images collection (FEI Tecnai G2, ThermoFisher Titan Themis G2 200 Probe Cs-Corrected): Amelia Zięba, Lidia Lityńska-Dobrzyńska</p> <p>SEM images acquisition (FEI E-SEM XL-30): Amelia Zięba</p> <p>Catalytic performance tests: Dorota Duraczyńska</p> <p>XPS study: Mateusz Marzec</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p><strong><em>The work was financially supported by the National Science Centre (NCN), Poland, project No. 2021/41/N/ST8/02533.</em></strong></p> <p> </p>
Soil Properties Summarized by Lots in Ouro Preto D'Oeste, Rondônia - Brazil (2019 surveyed lots V2)
<p>Soil properties data, summarized (weighted average by area with the given property on the lots surveyed on 2019 - V2) over small properties lots in Ouro Preto d'Oeste, Rondônia - Brazil:<br> AWHC:Available Water Holding Capacity: Water content at “field capacity” minus Water content at wilting point. Water content is % by weight. (50% = 50% of the weight of a lump of soil is water). (%)<br> PBS: Percent Base Saturation (%)<br> CLAY: Clay content (%)</p>
Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".
<p>Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".</p> <p>David Rutherford1, Markéta Šlapal Bařinková1, Thaiskang Jamatia2, Pavol Šuly2, Martin Cvek2, Bohuslav Rezek1</p> <p><br>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16227 Prague, Czech Republic<br>2 Centre of Polymer systems, Tomas Bata University in Zlin, Trida T. Bati 5678, 760 01 Zlín, Czech Republic</p> <p><br>Dataset description:</p> <p>240909 UV-vis_capped_ZnO.xlsx UV-vis spectroscopy<br>220623 ZnO Zlin Zn ion.xlsx Zinc ion measurement<br>230511 dls_zeta_data.xlsx DLS & zeta potential measurement<br>230221 ZnO_Zlin_MIC_MASTER.xlsx Minimum inhibitory concentration</p>
Soil properties in agricultural systems affect microbial genomic traits
<p>Code and supplementary table </p>
IrLumDB: a dataset of bis-cyclometalated iridium(III) complexes luminescence properties
<h1><strong>If you use this dataset, please cite our paper</strong>: <a href="https://doi.org/10.1039/D5TC00305A">https://doi.org/10.1039/D5TC00305A</a></h1> <p>IrLumDB contains data about 1454 experimentally measured luminescence spectra of 1287 unique iridium(III) complexes reported in the 340 literature papers.</p> <p><br>The 13 columns of this dataset are explained as follows:</p> <ol> <li>L1 — SMILES representation of the L1 ligand attached to the iridium ion</li> <li>L2 — SMILES representation of the L2 ligand attached to the iridium ion</li> <li>L3 — SMILES representation of the L3 ligand attached to the iridium ion</li> <li>Counterion — SMILES representation of the counterion (if the complex molecule is charged)</li> <li>Abbreviation_in_the_article — the original abbreviation depicting the complex in the article</li> <li>Charge — the total charge of the complex molecule</li> <li>Max_wavelength(nm) — value of maximal luminescence wavelength reported in the article</li> <li>PLQY — value of quantum yield reported in the article</li> <li>tau(s*10^-6) — value of excited state lifetime reported in the article</li> <li>Solvent — solvent media for luminescence measurements reported in the article</li> <li>DOI — DOI of a data source for given values</li> <li>Notes — additional notes for presented data</li> <li>PLQY_in_train — photoluminescence quantum yields which we consider suitable for ML purposes (for all the PLQY’s for which inert atmosphere is stated in the source article “1” is stated, otherwise “0” is stated).</li> </ol> <p>An appendix to the dataset (Synthesized_complexes.csv) contains data about 33 experimentally measured luminescence spectra of 33 unique iridium(III) complexes synthesized by our research group.</p> <p><br>Additional remarks:</p> <ul> <li>The iridium(III) complexes reported in this dataset are bis-cyclometalated Ir(III) complexes, which usually contain two bidentate cyclometalated ligands and one bidentate ancillary ligand; for these L1 and L2 correspond to the cyclometalated ligands and L3 corresponds to the ancillary ligand. </li> <li>Several ligands make formally covalent bonds with the Ir(III) ion. For these a negatively charged bond-forming atom is drawn in the SMILES of corresponding ligand.</li> <li>The vast majority of quantum yield and excited state lifetime measurements were claimed to be performed in deoxygenated solutions at room temperature. If not, the conditions for measurements are presented in the ”notes” column.</li> </ul>
Physico-chemical properties of Saharan dust samples deposited across Europe in March 2022 and collected through a participatory approach
<p>A particularly long and dense episode of airborne dust transport crossed Spain, France and part of Europe from 15 to 18 March 2022. This episode led to significant dust deposits on cars, roofs, etc., which may have caused surprise or concern among the general public. Based on association reports, several media published articles stating that this dust contained an artificial radionuclide, cesium-137 (<sup>137</sup>Cs), the source of which was attributed to the French nuclear tests conducted in the Sahara early in the 1960s.</p> <p>In order to provide a solid and representative scientific basis for a better characterisation of this dust and its radionuclide content, a participatory call for the collection of about ten grams of dust in different sites in France and Europe was launched on 17 March 2022 on the social network Twitter by Olivier Evrard. It was then relayed by Germán Orizaola, generating numerous reactions and the collection of several dozen samples in France, Spain and other neighbouring countries in Europe.</p> <p>The current dataset provides general information about the location/time of sample collection (<em>n</em>=110 samples) and data about the content of dust samples in cesium-137 (<sup>137</sup>Cs), an artificial radionuclide emitted by nuclear atmospheric tests and accidents (Evrard et al., 2020). Samples (2-80g) were analysed using the ultra-low background Germanium HyperPure gamma spectrometry detectors of LSCE installed in the underground facilities at both University Paris-Saclay (Gif-sur-Yvette) and Modane (Underground Lab of Modane, France). Filters were analysed for 80,000 to 200,000 s to obtain sufficient counting statistics (for the detection of the <sup>137</sup>Cs peak at 662 keV). All results were expressed in Bq kg<sup>−1</sup> with activities decay-corrected to the sampling date. Counting efficiencies and reliability were conducted using certified International Atomic Energy Agency (IAEA) standards (IAEA-444, 135, 375, RGU-1 and RGTh-1) prepared in the same containers as the samples.</p> <p>Additional data regarding other physico-chemical properties analysed in these dust samples has been added in June 2024.</p>
Connexin 46 and connexin 50 gap junction channel properties are shaped by structural and dynamic features of their N-terminal domains
<p>Provided are reduced trajectories (.dcd) of the MD simulations -- each trajectory has 100 ps/frame with only protein and ion atoms remaining. Each set of trajectories are accompanied by a protein structure file (.psf) which is required to visualize the trajectories in VMD. Additionally, the z-trajectories of each intracellular ion (2 ps/frame) are provided in zipped files.<br> <br> To re-create the potentials of mean force (PMF) in Yue & Haddad et al., use the scripts provided with the paper (https://github.com/reichow-lab/Yue-Haddad_et-al.JPhysiol2021):<br> <br> </p> <pre><code class="language-bash">python3 GapJ_Analysis.py "Cx46_Ace_Produc-1_POT_*"</code></pre> <ul> <li>Choose a bin size in Å (3)</li> <li>Choose an output name (Cx46_Ace)</li> <li>Choose option (M)</li> <li>Choose time (ps) / frame (2)</li> <li>Choose column from file (1)</li> <li>Choose bin<sub>min</sub>/bin<sub>max </sub>(auto)</li> </ul>
Process-structure-property map for organic solar cells
<p>This archive contains:<br> - 1708 morphologies generated using Cahn-Hilliard equation for two parameters: blend ratio in range (0.5-0.63) and chi/interaction parameter (2.3-4.0). Blend ratio and chi are the processing conditions. Morphologies are given in two formats: plt files (srcdata folder) - raw data from simulations, and txt file (data folder) in the row-wise format for Graspi. For quick visualization morphologies are visualized and stored in folder figs.</p> <p>The dataset contains:<br> Five folders:<br> - srcdata: the source data with all plt files (1708 files) generated by Cahn Hilliard equation solver<br> - data: the data used by graspi to compute descriptors (these are txt files stored as row-wise array, the volume fraction has been segmented using tools of graspi)<br> - logs: 1708 log files with the descriptors generated by graspi (native C++ version)<br> - figs: 1708 png files with visualized morphologies</p> <p>Two tables (in comma separated values format):<br> - Combined PSP.csv (combined data on process-structure-property maps), where process information consists of three variables: PHI, CHI, NN (volume fraction, interaction parameter and time step index)<br> - AllPropertiesCurated.csv - File with results from EDD - see reference below for more details</p> <p>One shell script: extractDesc.sh to build CombinedPSP.csv table from three sources: filename (contains info about PHI, CHI, NN), descriptors (graspi and logs), and Jsc (from AllPropertiesCurated.csv)</p> <p>More details on the dataset: Wodo, O., J. Zola, . Pokuri, P. Du, and B. Ganapathysubramanian. "Automated, high throughput exploration of process–structure–property relationships using the mapreduce paradigm." Materials discovery 1 (2015): 21-28.</p> <p> </p>
Soil microarthropods, ground-dwelling arthropods and soil properties in mown and grazed grasslands in the Veluwe region
<p>In order to find out which factors limit the restoration of soil life and their ecosystem services under grasslands on sandy soils, we studied 40 grasslands of which 20 had agricultural and 20 nature land use, all after an agricultural history.</p> <p> </p> <p><strong>Site selection</strong></p> <p>Within the Veluwe region (The Netherlands), we selected 40 grasslands: 20 agricultural grasslands and 20 nature grasslands which were managed as new nature reserves since last tillage. Within each of these two land-use types, two types of grassland management were selected: mowing and grazing. Within each of the four combinations of land use and management we selected ten grasslands over a broad age range since last tillage. All grasslands were located on sandy soils (Typic Haploquod and Plaggeptic Haploquod; Soil Survey Staff 1999) with a deep water table to rule out dispersal of soil fauna during waterlogging (Siepel 1996; Jabbour & Barbercheck 2008).</p> <p> </p> <p><strong>Vegetation and insect surveys</strong></p> <p>Within each grassland a 5×5 meter monitoring plot was laid-out for plant cover surveys, insect and soil-microarthropod sampling and soil analyses. The vegetation surveys were carried out in 2019 at the end of May and in early June, using the Braun-Blanquet method (Braun-Blanquet 1932). In June 2019 soil-surface dwelling insects were sampled with a pitfall trap (Wiggers et al. 2015). Three pitfall traps (8 cm diameter, ca. 20 cm deep) were placed in each plot. Traps were half filled with a solution of water and glycol (3:1) and 3 % Extran soap. A plexiglass cover 20 cm above the trap prevented rainfall diluting the liquid. Traps were removed and emptied after seven days. Insects were identified and grouped at the order level, however, predator groups (carabid and staphylinid beetles, ants and spiders) were identified to the species level in order to group those by their feeding guild.</p> <p> Before analyzing the pitfall trap catches we first removed certain groups from the counts because pitfall traps are not well-suited to catch them systematically: Acari, Collembola, Psocoptera, Thysanoptera, Trichoptera, Lepidoptera, Siphonaptera, Diptera, Symphyta, Apocrita, and Parasitica. The remaining 62.0% of the caught individuals were surface-dwelling animals, and their totals (of three pitfall traps per site) were analyzed with negative-binomial generalized linear models. We also analyzed the subset of predators (73.6% of the surface dwellers).</p> <p> </p> <p><strong>Soil chemical and pesticide sampling and analysis</strong></p> <p>On 8, 9 and 16 October 2019, a bulk soil sample of 50 soil cores (0 - 10 cm) was collected from each 5×5 meter monitoring plot. After homogenization a sub-sample was analyzed for soil chemical analysis. Prior to chemical analysis, samples were oven-dried at 40 °C. Soil acidity of the oven-dried samples was measured in 1 M KCl (pH-KCl). Soil Organic Matter (SOM) was determined by loss-on-ignition (Ball 1964). Ammonium-lactate-extractable P (PAL) was determined according to the standard method (Bronswijk et al. 2003). Total potassium (K) in solution was determined using flame photometry after extraction of soil with HCl (0.1 M) and oxalic acid (0.5 M) in a 1:10 M:V ratio and filtration (Bronswijk et al. 2003). Clay (<2 μm diameter) content was determined through density fractionation (NEN 5753, 2018). Another soil sub-sample was sent to Eurofins Zeeuws-Vlaanderen for pesticide/residue analysis. Samples were freeze-dried and homogenized prior to analysis. Homogenized samples were extracted with acetone, petroleum ether and dichloro-methane using an optimized mini-Luke method. In total 664 pesticides and pesticide residues were analyzed with gas chromatography (Agilent) and liquid chromatography (LC-chromatograph (Agilent) and MSMS (Sciex)). Glyphosate, its residue AMPA and gluphosinate were analyzed using single residue analysis. The detection limit (LOD) was 0,1 mg per kg sample.</p> <p> </p> <p><strong>Soil microarthropods sampling and determination</strong></p> <p>Grasslands were sampled for microarthropods on 8, 9 and 16 October 2019, taking three cores per monitoring plot of 5×5 m. Cores were 5 cm Ø and 5 cm deep mineral soil plus upper litter. Cores were taken in the middle of the monitoring plots, 1 m apart from each other. Cores were extracted on a Tullgren funnel for 7 days. During that period temperature was increased from 35 to 45 <sup>0</sup>C. Ethanol 70% was used as conservation fluid and microarthropods obtained were put into lactic acid 30% for clarification and identification (Siepel & van de Bund 1988). Identification for the main groups is according to Weigmann (2006) for Oribatida, Karg (1993) for Gamasina and Karg (1989) for Uropodina. Nomenclature is according to Siepel et al. (2009) (Oribatida), Siepel et al. (2016) (Astigmatina) and Siepel et al. (2018) (Mesostigmata).</p> <p> </p> <p><strong>Litter decomposition</strong></p> <p>To determine the potential decomposition of soil organic matter on each grassland the Tea Bag Index (TBI) was used (Keuskamp et al. 2013). In each grassland four green tea and four rooibos tea bags were buried at 8 cm deep in May 2019 in the 5×5 meter monitoring plots. After 90 days tea bags were collected and stored at 4 ⁰C prior to drying at 70 ⁰C for 48 hours. After drying, remaining sand and (fine) plant roots were carefully removed and the teabags were weighted to determine weight loss. The decomposition rate (<em>k</em>) and the litter stabilization factor (<em>S</em>) of the tea was calculated using the Tea Bag Index (Keuskamp et al. 2013).</p> <p> </p> <p><strong>Data files</strong></p> <p><em><strong>siteData.csv</strong></em></p> <p>site: grassland ID</p> <p>landuse: agricultural or nature land use</p> <p>treat: mowing or grazing management</p> <p>yearsManaged: number of years since last tillage</p> <p>fertilization: kg available nitrogen applied per hectare</p> <p>nGrazingDaysPerHa: livestock days per hectare per year</p> <p>N: mg nitrogen per 100 g </p> <p>PAl: mg P<sub>2</sub>0<sub>5</sub> per 100 g</p> <p>organicMatter: soil organic matter percentage</p> <p>clay: soil clay percentage</p> <p>nPlantSpecies: number of plant species</p> <p>nForbSpecies: number of forb species</p> <p>nMitesSpringtails: total number of individuals of mites and springtails in three core samples</p> <p>nMitesSpringtailsSpecies: number of mite and springtail species in three core samples</p> <p>shannonMitesSpringtails: Shannon diversity index for microarthropods (mites and springtails)</p> <p>nHerboFungivorousGrazerMitesSpringtails: total number of individuals of mites and springtails that are (herbo-)fungivorous grazers, in three core samples</p> <p>nInsectsSpidersPitfall: number of ground-dwelling insect and spider individuals in pitfall traps</p> <p>nPredatorInsectsSpidersPitfall: number of ground-dwelling insect and spider individuals that are predators, in pitfall traps</p> <p>decompositionRate: decomposition rate based on the Tea Bag Index</p> <p>litterStabilisationFactor: litter stabilization factor based on the Tea Bag Index</p> <p>nPesticides: number of detected pesticides</p> <p>avicidesTotalConcentration: microgram antraquinon per kg dry soil</p> <p>fungicidesTotalConcentration: total microgram of fungicides per kg dry soil</p> <p>insecticidesTotalConcentration: total microgram of insecticides per kg dry soil</p> <p>herbicidesTotalConcentration: total microgram of herbicides per kg dry soil</p> <p>pesticidesTotalConcentration: total microgram of pesticides (avicides+fungicides+herbicides+insecticides) per kg dry soil</p> <p>nPredatorCarabids: number of predator carabid beetles in pitfall traps</p> <p>nPredatorStaphylinids: number of predator staphylinid beetles in pitfall traps</p> <p>distanceToNearestHighway: shortest distance (in meters) to the nearest highway (A-road)</p> <p>distanceToNearestNroad: shortest distance (in meters) to the nearest national road (N-road)</p> <p> </p> <p><em><strong>mitesSpringtails.csv</strong></em></p> <p>core: core ID, consisting of the site ID (number) and core-within-site ID (letter)</p> <p>species: soil mite or springtail taxon encountered in a soil core</p> <p>guild: feeding guild of the soil mite or springtail taxon:</p> <p> b: bacterivorous</p> <p> fb: fungivorous browser</p> <p> fg: fungivorous grazer</p> <p> gp: general predator</p> <p> hb: herbivorous browser</p> <p> hfg: (herbo-)fungivorous grazer</p> <p> hg: herbivorous grazer</p> <p> o: omnivore</p> <p> ohf: opportunistic herbo-fungivore</p> <p>droughtSens: drought strategy of soil mite and springtail taxa</p> <p> 1: drought avoiders</p> <p> 2: drought sensitive</p> <p> 3: drought mesotolerant</p> <p> 4: drought tolerant</p> <p>microart: number of individuals of a taxon found in a soil core</p> <p> </p> <p><em><strong>insecticideData.csv</strong></em></p> <p><em><strong>fungicideData.csv</strong></em></p> <p><em><strong>herbicideData.csv</strong></em></p> <p>site: grassland ID</p> <p>other variables: microgram of a certain pesticide per kg dry soil</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.