Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Data set for the article "Synchronous replication initiation of multiple origins"

This data set contains the data of the submitted article "Synchronous replication initiation of multiple origins". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli initiates DNA replication at multiple origins synchronously in fast growth conditions. We study by mathematical modelling under what conditions replication is initiated synchronously.

opencc-by-nc-sa-4.0Jul 2023View details →

University Helpdesk Support Data Set

This repository contains the data set presented in the publication "Identifying NFRs conflicts using quality ontologies" by Al Balushi et al. [1]. The data set has been extracted from the manuscript for easier reuse. [1] Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. SEKE 2008, 929.

opencc-by-4.0Jul 2023View details →

Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times

Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>).  For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a>

Data sets for "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al.

AMISR-14 data set used in the study entitled "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al. and published by Earth, Planets and Space, doi: 10.1186/s40623-023-01876-7.

Data Sets: Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn

This archive contains 2 data sets and a word doc with metadata and code for statistical analyses used within the manuscript entitled Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn by Sean T. Manz, Kathryn E. Sieving, Rebecca N. Brown, Page E. Klug, Bryan M. Kluever in press at Pest Management Science as of September 2023.

(EMPIR 19ENG06 HEFMAG) Data sets of measurements of magnetic loss and complex permeability on amorphous and nanocrystalline samples up to the MHz range

We measured the magnetic losses and the complex permeability of amorphous and nanocrystalline ribbons from DC to 1 GHz by combined application of fluxmetric and transmission line methods. Two transverse field annealed Co-based amorphous alloys, ~13 µm and ~25 µm tick and two nanocrystalline Finemet type alloys, ~13 µm and ~20 µm tick, endowed with defined transverse magnetic anisotropy, were characterized.

H2020 ENODISE Analytical Data Set configuration C ECL

Preliminary wake-interaction noise and steady-loading noise estimates for a contrarotating propeller system, using analytical modeling.

Test data set for CRIMAC-RAW-To-Svf-TSf

Test data set for https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf, accompanying code to the paper "Quantitative processing of broadband data as implemented in a scientific splitbeam echosounder" submitted to Ecology and Evolution (Wiley). This is the Simrad EK80 raw files collected by the Norwegian Institute of Marine Research and used in the paper, mainly through the subset json files in https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf/tree/main/Data. To reproduce echogram figures the files Zenodo-hosted files IMR-D20210507-T074652-Svf.raw and IMR-D20211215-T143432-TSf.raw must be downloaded. This research is a part of the CRIMAC - Centre for research-based innovation in marine acoustic abundance estimation and backscatter classification funded by the Research Council of Norway (grant no. 309512).

Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022

Version 2: Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added. https://doi.org/10.5194/tc-17-3667-2023   Version 1: This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere. This dataset contains annual PDD model output.

Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set

Abstract: from [1] <blockquote> 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. </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4] Post-processing Matlab R2019b License: Creative Commons Attribution 4.0 International Context: Data set supplementing  journal paper: [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,” Forces in Mechanics, vol. 12, p. 100 207, 2023. Content: structure of data set: <ul> <li>04_Equilibration folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> <li>05_UT folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul> Output quantities (columns of *.Dat files): 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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul> References: [1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

Data set on the impact of selected plant protection products on ecosystem service providers, including interactive effects

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.

Partial Data set for Karan et al 2023 (DOIs: 10.1016/j.advwatres.2023.104521 and 10.1103/PhysRevFluids.8.084502)

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" Remaining Data Available in the Repositories: 10.5281/zenodo.8353640, 10.5281/zenodo.8353673, 10.5281/zenodo.8353679

openother-openSep 2023View details →

sabasoori23/AGU-Publication-WRR: Supporting Information-Data Set 1

The Effect of the Bridge Piers and Abutment Interaction on Large Wood Accumulation Probability using a Physical Mode

Data set to 'Microplastics in aquaculture - potential impacts on inflammatory processes in Nile tilapia'

Raw and analyzed data sets to the publication 'Microplastics in aquaculture - potential impacts on inflammatory processes in Nile tilapia'

FSE2024-ID357 Data-centric API misuse data set

A data set of data-centric API misuses The Excel file contains complete misuse data and descriptive statistics. It contains multiple sheets. Their description is as follows. <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> The PDF file contains the code guide that we followed during manual annotation. 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.

opencc-by-4.0Sep 2022View details →

Flux data kit, a comprehensive data set of ecosystem fluxes for land surface modelling

<blockquote> Newer versions (>= v3.0) are released by Benjamin Stocker and can be found at his Zenodo account at: https://zenodo.org/records/10885934 </blockquote> 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). 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 should be done by the user. 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. Data included 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. Data generation 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: <a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a> The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here: <a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a> Acknowledgements 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). References: <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"

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].