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7,195 results for “SET”
low Cerro Gordo; StL 2, Cerro Gordo sandstone; StL 3, between Cerro Gordo and Chunchullo; StL 4, Chunchullo sandstone; StL 5, bed set between Chunchullo and Tatacoa; StL 6, Tatacoa sandstone; StL 7, bed set below Cerbatana conglomerate; StL 8, Cerbatana conglomerate; StL 9, Monkey beds; StL 10, bed set above Monkey beds; StL 12, bed set above Fish bed; StL 14, bed set below La Venta red beds; StL 15, La Venta red beds; StL 16, bed set between La Venta red beds and El Cardón red beds; StL 17, El Cardón red beds; StL 18, San Francisco sandstone; StL 19, Polonia red beds; D, reconstruction of the head of Neodolodus colombianus based on the 3D model of the almost complete skull of the specimen VPPLT 1696. Abbreviations: Fm, Formation; St m, Stratigraphic meter. Reconstruction of N. colombianus made by Tatsuya Shimura. in New remains of Neotropical bunodont litopterns and the systematics of Megadolodinae (Mammalia: Litopterna)
low Cerro Gordo; StL 2, Cerro Gordo sandstone; StL 3, between Cerro Gordo and Chunchullo; StL 4, Chunchullo sandstone; StL 5, bed set between Chunchullo and Tatacoa; StL 6, Tatacoa sandstone; StL 7, bed set below Cerbatana conglomerate; StL 8, Cerbatana conglomerate; StL 9, Monkey beds; StL 10, bed set above Monkey beds; StL 12, bed set above Fish bed; StL 14, bed set below La Venta red beds; StL 15, La Venta red beds; StL 16, bed set between La Venta red beds and El Cardón red beds; StL 17, El Cardón red beds; StL 18, San Francisco sandstone; StL 19, Polonia red beds; D, reconstruction of the head of Neodolodus colombianus based on the 3D model of the almost complete skull of the specimen VPPLT 1696. Abbreviations: Fm, Formation; St m, Stratigraphic meter. Reconstruction of N. colombianus made by Tatsuya Shimura.
Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
<p><strong>Version 2:</strong></p> <p><strong>Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.</strong></p> <p><strong>https://doi.org/10.5194/tc-17-3667-2023</strong></p> <p> </p> <p>Version 1:</p> <p>This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day<br> model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere.</p> <p>This dataset contains annual PDD model output.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Environmental DNA-based detection of Batrachochytrium salamandrivorans in captive settings
<p>Detecting pathogens in the live animal trade is critical for tracking and preventing their movement, introduction, and spillover into susceptible fauna. However, the scale of the live animal trade makes individually testing animals infeasible for all but the most economically important taxa. For instance, while the fungal pathogen, <em>Batrachochytrium salamandrivorans</em> (<em>Bsal</em>), threatens amphibian, particularly caudate diversity, in Europe and the Americas, screening even a fraction of the millions of live amphibians imported into the United States, alone, is impractically laborious and expensive. A promising alternative to individual-level sampling (e.g., swabbing the skin of salamanders) is to instead collect DNA from the animals' environment (e.g., housing container or water) which allows us to screen a whole group of animals at a time. </p> <p>We used a series of experiments with <em>Bsal</em>-spiked water and substrates and experimentally infected rough-skinned newts (<em>Taricha granulosa</em>) to determine how best to collect <em>Bsal</em> environmental DNA (eDNA) samples, that is, which methods yield the greatest recovery of <em>Bsal</em> eDNA, and evaluate the capacity of these methods to detect <em>Bsal</em>-infected animals in conditions that might be found in captive settings and trade.</p> <p>We found that filtering water housing infected animals for even an hour can consistently recover detectable levels of <em>Bsal</em> eDNA, that there is little evidence of <em>Bsal </em>eDNA being clumped in housing containers or being swamped or inhibited under realistically dirty conditions, and that eDNA-based methods achieves an equivalent or higher chance of detecting <em>Bsal</em> infections in a group of co-housed newts with fewer samples than traditional methods of individually swabbing.</p> <p>By sampling the genetic materials shed or produced by a whole group of animals, eDNA-based methods are a powerful means of detecting pathogens, such as <em>Bsal,</em> in shipment and captive population. These methods bring routine pathogen surveillance into reach in many more contexts and can thus be an important tool in conservation and disease control.</p>
Data set on the impact of selected plant protection products on ecosystem service providers, including interactive effects
<p>The Excel files contain the results of ecotoxicological tests for the effects of selected insecticides on ESP species. The objective of this dataset is to provide original data from acute and semi-chronic laboratory tests on a few important beneficial species with broad geographic distribution. The data allow the evaluation of delayed effects and possible interactive effects of combined treatments for those pesticides that are commonly used in mixtures or sprayed next to each other in short time intervals, effectively exposing non-target arthropods to combined/sequential effects. Each data file contains the “Description” sheet where all details of the test and the exact meaning of data fields in the database are reported. The data files are named in a self-explanatory manner, starting with the name of the institution that produced the data (UC – University of Coimbra; UJA – Jagiellonian University), followed by the name of the tested species and names of tested products.</p>
Partial Data set for Karan et al 2023 (DOIs: 10.1016/j.advwatres.2023.104521 and 10.1103/PhysRevFluids.8.084502)
<p>This is the simulation and theory dataset for the research articles "Impact of hydrodynamic dispersion on mixing-induced reactions under radial flows P Karan, U Ghosh, Y Méheust, T Le Borgne - Advances in Water Resources, 2023" and "Effect of hydrodynamic dispersion on spherical reaction front dynamics in porous media P Karan, U Ghosh, F Brau, Y Méheust, T Le Borgne - Physical Review Fluids, 2023"</p> <p>Remaining Data Available in the Repositories: 10.5281/zenodo.8353640, 10.5281/zenodo.8353673, 10.5281/zenodo.8353679</p>
A set of positive examples consisting of short-term schedules for testing the acquisition of MiniZinc scheduling models
<p>To test the robustness of schedule model acquisition in a variety of situations, we generated 48,000 instances of schedules with variations in the following five dimensions:</p> <p>1. task description, 2. temporal constraints, 3. resource constraints, 4. the introduction or absence of noisy columns, and 5. the number of tasks and resources in a schedule.</p> <p>The file 'schedule_robustness_dimensions.pl' contains the list of constraints used to generate each table which, thus, need to be acquired.</p> <p>The dimensions are:</p> <p>1. Different ways to describe a task:<br> a. only the start time and duration columns are part of the table, the task duration is a pre-assigned input parameter,<br> b. only the start time and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> c. only the duration and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> d. all three columns are present in the table, the task duration is a pre-assigned input parameter,<br> e. the start time, duration and end time columns are all part of the table, and the task duration is calculated using a formula,<br> f. only the start time and end time columns are part of the table, and the task duration is calculated using a formula,<br> g. only the duration and end time columns are part of the table, and the task duration is calculated using a formula,<br> h. all three columns are present in the table, and the task duration is calculated using a formula.</p> <p>2. Different ways of expressing temporal constraints between task $i$ and its successor $j$<br> a. no temporal constraints at all,<br> b. $\textit{start}_i + \textit{cst} \leq \textit{start}_j$,<br> c. $\textit{start}_i + \textit{cst} \geq \textit{start}_j$,<br> d. $\textit{start}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> e. $\textit{start}_i + \textit{cst} = \textit{start}_j$,<br> f. $\textit{start}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> g. $\textit{start}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> h. $\textit{start}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{end\_time}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> i. $\textit{start}_i + \textit{cst} = \textit{end\_time}_j$,<br> j. $\textit{end\_time}_i + \textit{cst} \leq \textit{start}_j$,<br> k. $\textit{end\_time}_i + \textit{cst} \geq \textit{start}_j$,<br> l. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{end\_time}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> m. $\textit{end\_time}_i + \textit{cst} = \textit{start}_j$,<br> n. $\textit{end\_time}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> o. $\textit{end\_time}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> p. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{end\_time}_i+\textit{cst}_2\geq\textit{end\_time}_j$ ($\textit{cst}_2\neq\textit{cst}_1$),<br> q. $\textit{end\_time}_i + \textit{cst} = \textit{end\_time}_j$.<br> Note that we only generate temporal constraints that mention the start time, i.e. 2b–2m, if the start time attribute is part of the table, i.e. not in the cases 1c or 1g.</p> <p>3. Different ways of expressing resource scheduling constraints:<br> a. no scheduling constraints at all,<br> b. a DISJUNCTIVE constraint for each subset of tasks using the same resource,<br> c. a DIFFN constraint on all tasks, so that there is no overlap between tasks that will be assigned to the same resource,<br> d. a SHIFT constraint that forces the start and end times of each task to be within the same availability period, with no gap between two consecutive availability periods,<br> e. a CALENDAR constraint that forces the start and end time of each task assigned to a given resource $r$ to fall within the same period of availability of the resource $r$,<br> f. a set of DISJUNCTIVE constraints and a SHIFT constraint,<br> g. a DIFFN and a SHIFT constraint,<br> h. a set of DISJUNCTIVE constraints and a CALENDAR constraint,<br> i. a DIFFN and a CALENDAR constraint.<br> The combination of certain temporal and resource constraints may lead to infeasibility. For instance, in a temporal constraint of type 2c, the two corresponding tasks may overlap, which is incompatible with a DISJUNCTIVE constraint between these tasks, i.e. a constraint of type 3b. Therefore, we do not generate scheduling instances that mix the dimensions 2c, 2d, 2e, 2g, 2h, 2i, 2o, 2p, and 2q, with the dimensions 3b, 3c, 3f, 3g, 3h, and 3i. Note that the number of resources generated varies according to the number of tasks, as explained in Item 5.</p> <p>4. Creating noisy columns or not:<br> a. no additional noisy columns,<br> b. three extra columns with random values standing for noise.</p> <p>5. Number of tasks and resources referenced by the schedule:<br> a. 10 tasks and 2 resources,<br> b. 100 tasks and 10 resources,<br> c. 1,000 tasks and 20 resources,<br> d. 10,000 tasks and 100 resources.</p> <p><br> For each valid combination of dimensions, we generated 10 instances of schedules.</p>
sabasoori23/AGU-Publication-WRR: Supporting Information-Data Set 1
<p>The Effect of the Bridge Piers and Abutment Interaction on Large Wood Accumulation Probability using a Physical Mode</p>
Data set to 'Microplastics in aquaculture - potential impacts on inflammatory processes in Nile tilapia'
<p>Raw and analyzed data sets to the publication 'Microplastics in aquaculture - potential impacts on inflammatory processes in Nile tilapia'</p>
FSE2024-ID357 Data-centric API misuse data set
<p><strong>A data set of data-centric API misuses</strong></p> <p>The Excel file contains complete misuse data and descriptive statistics. It contains multiple sheets. Their description is as follows.</p> <ul> <li>Main datasheet: It contains misuse collection</li> <li>Misuses: Each misuses is labeled with the misuse type</li> <li>Categories: Misuse categories and descriptive statistics</li> <li>Impacts: Each misuse is labeled with the impact type</li> <li>Documented caveats: Each misuse is labelled if there exists an explicit API directive</li> <li>Error messages: Analysis of error message contents</li> <li>Stats: Summary of descriptive statistics</li> </ul> <p>The PDF file contains the code guide that we followed during manual annotation.</p> <p>The zip file contains code examples. Files are named based on the misuse ID, StackOverflow question ID, and version. For example, 1_999999_fix.py indicates that the file is the fix version of misuse ID 1 which is based os StackOverflow question id 999999.</p>
Virtual benchmarking study training time series - binary set 1
<p>A dataset with time series for training virtual sensor models for wind turbine aeroelastic loads. Data are in zipped format, each zip file contains 1000 individual time series. For the purpose of space preservation, each time series is stored in parquet binary format with "snappy" encoding.</p> <p>Reading a parquet file in Python can be done with the pandas library, with the following command sequence:</p> <p>import pandas as pd<br> Data = pd.read_parquet(filename)</p> <p> </p>
Flux data kit, a comprehensive data set of ecosystem fluxes for land surface modelling
<blockquote> <p>Newer versions (>= v3.0) are released by Benjamin Stocker and can be found at his Zenodo account at: https://zenodo.org/records/10885934</p> </blockquote> <p>The Flux data kit is an effort to expand upon the existing work by Ukolla et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).</p> <p>The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration <em>should be done by the user</em>. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukolla et al. (2022) for details.</p> <p><strong>Data included</strong></p> <p>The data included consists of three files, containing different versions of the same data. The FLUXDATAKIT_LSM.tar.gz file contains compressed netCDF files compatible with the ALMA scheme for land surface modelling. The FLUXDATAKIT_FLUXNET.tar.gz file contains data in a CSV format according to the FLUXNET specifications. And finally the rsofun_driver_data_clean.rds file is a compressed serialized R file containing data formatted for use with the `rsofun` R package.</p> <p><strong>Data generation</strong></p> <p>Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:</p> <p><a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a></p> <p>The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:</p> <p><a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a></p> <p><strong>Acknowledgements</strong></p> <p>The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).</p> <p><strong>References:</strong></p> <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>
Graphic representation of data set for the project "IRI model performance evaluation for the Mexican region"
<p>Here, we illustrate the modeling and experimental results for vertical Total Electron Content (TEC) over Mexico during the five year period 2018-2022. The results were obtained for the UCOE GNSS receiver station (geographic coordinates: 19.6°N; 101.68°W ). The calculations were made each two hours during the whole period under considerations. The modeling results were obtained using the "International Reference Ionosphere (IRI)" model, which is an empirical climatological model based on ground and space observations of the ionosphere [Bilitza et al., 2022].</p>
Zbiva, Early Medieval Data Set for the Eastern Alps. Data sub-set
<p><strong>Zbiva, Early Medieval Data Set for the Eastern Alps</strong></p><p> </p><p>Authors: Benjamin Štular, Andrej Pleterski, Mateja Belak</p><p>Institution: Znanstvenoraziskovalni center Slovenske akademije znanosti in umetnosti</p><p>Location and date: Ljubljana (Slovenia), 6 December 2021</p><p> </p><p>This dataset is a subset of Zbiva database. Zbiva is an open-access online research data base for the archaeology of the Eastern Alps in the Early Middle Ages. It consists of four parts: archaeological sites, graves, artefacts, and bibliography. Geographically, it contains data from present-day Slovenia, Austria, NW Croatia and NE Italy. For comparison purposes, it also includes selected relevant sites from neighbouring regions.</p><p>To access Zbiva directly go to https://zbiva4.zrc-sazu.si/en.</p><p>Read more about Zbiva:</p><p>Štular, B. (2019). The Zbiva Web Application: a tool for Early Medieval archaeology of the Eastern Alps . In J. D. Richards & F. Niccolucci, Eds. <i>The ARIADNE Impact;</i> (pp. 69–82). Archaeolingua, Budapest. https://doi.org/10.5281/zenodo.3476712.</p><p>Štular, B., Belak, M., Deep Data Example: Zbiva, Early Medieval Data Set for the Eastern Alps. – Research Data Journal for the Humanities and Social Sciences, September 2022; https://doi.org/10.1163/24523666-bja10024.</p><p> </p><p>This dataset is a subset of the Zbiva. It is published as a supporting material for:</p><p>Štular, B., Lozić, E., Belak, M., Rihter, J., Koch, I., Modrijan, Z., Magdič, A., Karl, S., Lehner, M., Gutjahr, Ch. 2022, Migration of Alpine Slavs and machine learning: Space-time pattern</p><p>A detailed description of the dataset including the metada is part of the downloadable dataset.</p>
Dataset (Pareto fronts and sets) for the Multi-objective pathfinding problem
<p>The dataset was used in several publications. In contains global Pareto fronts and sets for various instances of the problem.</p>
Training set of microscopy images for Park et al. Nature Methods 2023
<p>All data for key-point tracking:</p><p>keypoint-tracking-data-*.h5</p><p>Videos illustrating key-point tracking:</p><p>keypoint-tracking-videos.zip</p><p>Sample data for 3D volume tracking and videos:</p><p>volumetric-tracking-data-and-videos.zip</p>
Impact of Online Ordering on Low-Income Adults' Food Security in Online Food Pantry Settings
ClinicalTrials.gov study NCT05752721. IPD Sharing: YES. Countries: 1. Publications: 1.
Group-Based Intervention to Improve Mental Health and Adherence Among Youth Living With HIV in Low Resource Settings
ClinicalTrials.gov study NCT04024488. IPD Sharing: YES. Countries: 4. Publications: 0.
Engaging Patients in Tobacco Cessation Resources in Dental Settings
ClinicalTrials.gov study NCT03656874. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: How many specimens make a sufficient training set for automated three dimensional feature extraction?
Open the record for dataset details and reuse information.
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