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opencc-by-4.0Jul 2024View details →

Intermediate field-induced phase of the honeycomb magnet BaCo2(AsO4)2

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2018View details →

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL field pea

This is model output from LPJmL for field pea as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set. The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019). References: Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015 Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017 Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8

opencc-by-3.0-usOct 2022View details →

CHiMaD Phase Field Benchmark 8a r=0.99r*

Testing out new PFHub upload mechanism

opencc-zeroNov 2022View details →

Supplementary materials for: CNN-based surrogate for the phase field damage mode

We investigate the generalization of a CNN-based surrogate for the phase field model in predicting both damage and maximum/peak load, given the image of an arbitrary 2D microstructure of a unidirectional fiber-reinforced composite. We first discuss the phase field model and the numerical procedure to generate training and test data from synthetic microstructures with different volume fractions and fiber radii. We next present a two-stage approach for predicting peak load, achieved by first transforming a given fiber-encoded microstructure image to a continuous damage field; and second, predicting peak load from the damage field. A key finding is that the direct approach for predicting peak load from the microstructure image using a standard regression model fails to generalize. Instead, the damage field, even if imperfectly predicted, provides valuable cues for the CNN in generalizing across new microstructures. We describe several case studies to demonstrate the capability of the surrogate model to predict damage and peak load and to interpolate over fiber radii and volume fractions.

opencc-by-4.0Dec 2022View details →

A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture

Abstract: (from [1]) <blockquote> For the smeared approximation of a discrete crack, phase-field fracture simulations of brittle materials require suitable finite element meshes in regions where crack propagation is expected to get an accurate resolution of the phase-field function. The intuitive option is to pre-refine the mesh in regions of the expected crack paths. However, this could lead to very computationally intensive simulations due to the high number of elements. Alternatively, adaptive spatial refinement of the finite element mesh is utilized based on appropriate error indicators to obtain the required accuracy in the areas of crack propagation. Different error indicators can be used: the most common one for phase-field fracture simulations is the threshold-based approach, in which elements are refined depending on the value of the phase-field function. Alternatively, the Kelly error indicator can be used as a criterion for spatial adaptivity. It considers the jumps in the gradients of the phase-field function between the elements. We additionally introduce here an error indicator based on configurational forces, that depend on the Eshelby stress tensor. For mode I loading in linear elastic fracture mechanics, the configurational forces have a close connection to the \(\mathscr{J}\)-Integral and the critical fracture energy \(\mathrm{G}_\mathrm{c}\) , respectively. Therefore, a suitable norm of the configurational forces is introduced as an error indicator here. These three error indicators are introduced and compared to each other in terms of accuracy and efficiency by means of numerical examples for crack growth in the single edge notched shear test. </blockquote> Contact: Maurice Rohracker Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All phase-field fracture simulations were performed with deal.II [2], version 9.2.0, on the HPC cluster Meggie of NHR@FAU. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The hardware is funded by the German Research Foundation (DFG). License: Creative Commons Attribution 4.0 International Context: Dataset supplementing preprint: [1] M.Rohracker, P.Kumar, J.Mergheim, "A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture", Forces in Mechanics, 2022, <a href="https://doi.org/10.1016/j.finmec.2022.100157">10.1016/j.finmec.2022.100157</a>. This dataset contains the complete results presented in [1], which include global variables, field variables, and meshes. File structure: The file structure is explained in more detail in the shipped README.md in the dataset folder. References: [1] M.Rohracker, P.Kumar, J.Mergheim, "A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture", Forces in Mechanics, 2022, <a href="https://doi.org/10.1016/j.finmec.2022.100157">10.1016/j.finmec.2022.100157</a>. [2] D. Arndt, W. Bangerth, B. Blais, T. C. Clevenger, M. Fehling, A. V. Grayver, T. Heister, L. Heltai, M. Kronbichler, M. Maier, P. Munch, J.-P. Pelteret, R. Rastak, I. Thomas, B. Turcksin, Z. Wang, D. Wells, The deal.II Library, Version 9.2 Journal of Numerical Mathematics, vol. 28, p. 131-146, 2020.

opencc-by-4.0Jan 2023View details →

Supplementary Data to "Simulation of dendritic-eutectic growth with the phase-field method" by Seiz et al.

Video files for several simulations conducted for the paper, showing more of the dynamic time evolution than possible in the paper itself.   Update 24/04/2023: A few additional simulations were conducted to test for the applicability of the theory delineating the dendritic-eutectic regime from the eutectic regime. Videos of these plus some additional data is deposited at   https://zenodo.org/record/7858461   All videos show the Cu composition field, with the color ranging from 0.02 (pure black) to 0.33 (pure white). Thus black represents the fcc Al crystal, whitish-grey the Al2Cu intermetallic phase and grey shades in between the liquid melt, with lighter shades being richer in Cu. Excluding the complete directional solidification videos (full*webm), all videos show regions of 280x250um^2, with the far-field to the right being cut off to emphasize the structure. {close,far}_d+e.webm: Complete simulations resulting in a eutectic structure either growing close to the dendrite tip or far from it, cropped to slightly above the solidification front. The same speed v=160um/s and melt composition c_0=0.12 are used, but two different gradients: 99K/mm for close growth and 24.7K/mm for far growth; at the even smaller gradient the eutectic is no longer in the moving window. traveling_oscillation.webm: Complete simulation resulting in a eutectic with traveling oscillations. (v=160um/s, c_0=0.13, G=6.18K/mm) jump_d+e_e.webm: Jumps from v = 160um/s to 320um/s at simulation start in order to move from a dendritic-eutectic morphology to a eutectic morphology. After a eutectic morphology is obtained, the jump is reversed (around 17s into the video) and only a coarsening of the eutectic is observed. jump_e_d+e.webm: Jumps from v=320um/s to 20um/s at simulation start in order to move from a eutectic morphology to a dendritic-eutectic morphology. full_cropped*webm: Complete directional solidification for different alloy compositions and processing conditions yielding different structures. Cropped to slightly above the final maximum position of any solid phase, showing a 970x500um^2 domain. A scaling to 50% of the original resolution is performed as some players/browsers have trouble with large resolutions. e: primarily eutectic (v=320um/s, G=24.7K/mm, c_0 = 0.12) d+e: dendritic-eutectic (v=160um/s, G=24.7K/mm, c_0 = 0.12) full_d.webm: Same as above, only non-cropped as the structure fills the entire simulation box (1500x500um^2). (v=320um/s, G=24.7K/mm, c_0 = 0.08)

opencc-by-4.0Sep 2022View details →

Relative Phase Data to 'Experimental observation of curved light-cones in a quantum field simulator', arXiv:2209.09132

Relative phase profiles and averaged density profiles for the results shown in  arXiv:2209.09132 Each file "phase_and_mean_density_scan_X.mat" contains data for a measurement presented in the manuscript, where "X" is the corresponding scan number. The following table shows the relevant scan number to measurement descriptions mentioned in the manuscript (see Table S1 in the SI Appendix). <table align="center"> <thead> <tr> <th scope="col">Measurement description</th> <th scope="col">Scan number</th> </tr> </thead> <tbody> <tr> <td>     Homogeneous (main text) </td> <td>    9185</td> </tr> <tr> <td>     Inhomogeneous with sharp edges  </td> <td>    10419</td> </tr> <tr> <td>     Inhomogeneous with smoothed edges </td> <td>    8935</td> </tr> <tr> <td>     Homogeneous 2 (SI Appendix) </td> <td>    10455</td> </tr> </tbody> </table>   File Contents Each file contains the following variables: <ul> <li>"phase": A MATLAB cell containing all the phase profiles for every time step. Thus, "phase{t_ind}" is a matrix where rows represent experimental realizations and columns the spatial grid points. For example, "phase{5}(1,:)" would be a one-dimensional phase profile, representing the first realization of the fifth time step. To learn more about the extraction of phase profiles, read Section 2 and see Fig. S5 in SI Appendix.</li> <li>"z_grid_phase_si": Vector. Grid points for phase profiles in SI units (m).</li> <li>"averaged_density_si": Vector. Averaged initial linear density in SI units (m^-1). See Fig. 1(a).</li> <li>"z_grid_density_si": Vector. Grid points for averaged density in SI units (m).</li> <li>"times_si": Vector. Time points in SI units (s).</li> </ul>   Matlab script calculating the velocity field In addition to the data, a MATLAB script (velocity_field_calculation.m) loads a data file and calculates the velocity field and its correlations following the equations in the manuscript: <ul> <li>"u": MATLAB cell. Velocity field for every time step.</li> <li>"u_u_corr": MATLAB cell. Second-order correlations of the velocity field for every time step.</li> <li>"std_u_u_corr": MATLAB cell. Standard deviation of second-order correlations of the velocity field for every time-step.</li> </ul> Finally, the script plots "u_u_corr" for all the time steps and plots the averaged linear density.

opencc-by-4.0Mar 2023View details →

Supplementary Material to "An improved grand-potential phase-field model of solid-state sintering for many particles"

Supplementary Material to the publication "An improved grand-potential phase-field model of solid-state sintering for many particles" by Seiz, Hierl and Nestler. This contains the pre-study for determining the effective stiffness for the rigid-body velocity calculation and video files showing the 3D evolution of the green body in more detail than possible in the paper itself.   slicethru_{start,end}.webm: Moving slices through the 400^3 nm green body at t=0.045ms and t=1.8ms representing the start and end of the simulation respectively. White/transparency indicates the surrounding vapor, with the colourmap showing different grains. Any interfaces are shown as black lines.   greenbody_400.webm : Time evolution of the 400^3 nm green body based on the solid-vapor interface. White/transparent indicates the surrounding vapor, with the brownish material indicating the grains.   prestudy.zip: Contains the notebook and data used for the pre-study for determining the effective stiffness. A binder is available at <pre>https://mybinder.org/v2/git/https%3A%2F%2Fgit.scc.kit.edu%2Fxt5201%2Fsupplementary-material-for-improved-pf-sintering-model/master?labpath=eval-kvar.ipynb</pre>

opencc-by-4.0May 2023View details →

Exact large-scale fluctuations of the phase field in the sine-Gordon model

Raw data and Mathematica Notebooks for the thermodynamics and phase correlations in the sine-Gordon model It can be found: <ol> <li>A transfer matrix code for equilibrium correlation functions.</li> <li>A solver for the classical Thermodynamic Bethe Ansatz and the correlation functions of the phase field.</li> <li>A solver of the Thermodynamic Bethe Ansatz of the quantum sine-Gordon at the reflectionless points, and the computation of cumulants of the phase field.</li> <li>Monte Carlo data presented in the paper: cumulants of the phase field at unequal times on homogeneous thermal background and phase correlation on the inhomogeneous background mimicking the tunnel-coupled experiments.</li> <li>Mathematica notebooks that read the data and produce the figures shown in the paper.</li> </ol>

opencc-zeroSep 2023View details →

Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing

Description: The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip.  (A) data_features_and_metric.zip: The following are the contents of this folder (i) grainTheta.csv file : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:  gnid = grain id number "n", ntheta = orientation angle of nth grain (o); nltheta = orientation angle of grain to the left of nth grain (o); nrtheta = orientation angle of grain to the right of nth grain (o); j = current density (A/m2); t = time (s); area = area of nth grain (m2); tl = horizontal length of the top edge of grain "n" (m) ; bl = horizontal length of the bottom edge of grain "n" (m)  The features  gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of  the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations.  (ii) imc_theta.ipynb, imc_theta.py and  imc_theta.html files: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file.  (iii) comparison_mse.csv: This data file includes the information about  mean square error for training data (tmse) and mean square error for validation data (vmse)  at Epoch = 199  resulting from  10 different artificial neural network (ANN) models  distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are modelno, lr, tmse and vmse.  (iv) mse_comparison.gnu: This file consists the codes required to output a png image from the data provided in comparison_mse.csv.  (v) train_loss.csv and val_loss.csv: These files consist  of the data of tmse and vmse at all points of Epochs  for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file.    (vi) mse_lr2p5e-4.gnu : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv.  (B) grain_area_prediction.zip: Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files: initial_area.csv file: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle. predicted_result_00_5e4.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 0o and 0o respectively, and for applied current density of 5.0E+4 J/m2  . predicted_result_00_5e5.csv:  This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 0o and 0o respectively, and for applied current density of 5.0E+5 J/m2  . predicted_result_9090_5e4.csv:  This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 90o and 90o respectively, and for applied current density of 5.0E+4 J/m2  . predicted_result_9090_5e5.csv: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for  grain 3 and grain 5 at orientation angles of 90o and 90o respectively, and for applied current density of 5.0E+5 J/m2  . area_00_adj.gnu : This gnu file contains the code to produce the png image from the data contained in predicted_result_00_5e4.csv and predicted_result_00_5e5.csv . The information about the initial area of grain 4 is obtained from initial_area.csv file by the code. area_9090_adj.gnu : This gnu file contains the code to produce the png image from the data contained in predicted_result_9090_5e4.csv and predicted_result_9090_5e5.csv . The information about the initial area of grain 4 is obtained from initial_area.csv file by the code.

opencc-zeroOct 2023View details →

Field sampling and DNA-barcoding of fig pollinator wasps across host species and host developmental phase and on non-Ficus controls

To better understand factors that might contribute to this observed range of specificity, we used sticky traps to capture fig-pollinating wasp individuals at 13 Ficus species, sampling at different stages of the reproductive cycle of the host figs (e.g. trees with receptive inflorescences, or vegetative trees, bearing only leaves). We also sampled at other tree species, using them as non-Ficus controls. DNA barcoding allowed us to identify the wasps to species, and therefore assign their presence and abundance to host fig species and the developmental stage of that individual tree. Here we upload the data and the R scripts used to analyze these data.

opencc-by-4.0Aug 2024View details →

A Non-Isothermal Phase-Field Crystal Model with Lattice Expansion: Analysis and Benchmarks

<h1>Non-isothermal pase-field crystal simulations with lattice expansion</h1> Openly available Matlab simulation files used to produce the phase-field crystal and temperature results. Files are named by the corresponding figures. Simulations can be started by runnning the Figure*.m files. <h2><a href="#figure1_2_3_dendrite"></a>Figure1_2_3_dendrite</h2> Dendritic solidification with heat flux and lattice expansion Parameter studies for figures 2 and 3 can be obtained by setting the respective parameter values in /simulation/Pre/Parameter/Pre_modelParameters <h2><a href="#figure4_opensystems"></a>Figure4_openSystems</h2> Results for solidification in open systems controlled by applied heat flux <h2><a href="#disclaimer"></a>Disclaimer</h2> The software is released here under the MIT license. We kindly ask to refer to/cite for any usage and extension. The authors are thankful for any advice considering typos, mistakes, and/or discussions around the code/implementation or the topic of the related publication in general. Please do not hesitate to contact the main author, Maik Punke, via: <a href="mailto:maik.punke@tu-dresden.de">maik.punke@tu-dresden.de</a>

ClinicalTrials.gov36/100

Phase 4 Field Trial of the Unicirc Instrument With Tissue Adhesive

ClinicalTrials.gov study NCT02091726. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

publicOct 2023View details →

Field sampling and DNA-barcoding of fig pollinator wasps across host species and host developmental phase and on non-Ficus controls

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publicNov 2022View details →

Supplementary materials for: CNN-based surrogate for the phase field damage mode

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opencc-by-4.0Dec 2020View details →

Data supplement for "Phase-field crystal description of active crystallites: Elastic and inelastic collisions"

This dataset contains data related to the publication: Lukas Ophaus and Johannes Kirchner and Svetlana V. Gurevich and Uwe Thiele, "Phase-field crystal description of active crystallites: Elastic and inelastic collisions" Chaos 30, 123149 (2020); <a href="https://doi.org/10.1063/5.0019426">https://doi.org/10.1063/5.0019426</a> The set contains (i) all figures in pdf format and (ii) complete data files accompanied by python plot scripts for selected figures.

opencc-by-4.0Dec 2023View details →

Data for "Advanced momentum sampling and Maslov phases for a precise semiclassical model of strong-field ionization"

Data and plot scripts used to produce the figures in "Advanced momentum sampling and Maslov phases for a precise semiclassical model of strong-field ionization", available in preprint on arXiv <a href="https://doi.org/10.48550/arXiv.2311.01845">https://doi.org/10.48550/arXiv.2311.01845.</a>Abstract: Recollision processes are fundamental to strong-field physics and attoscience, thus models connecting recolliding trajectories to quantum amplitudes are a crucial part in furthering understanding of these processes. We report developments in the semiclassical path-integral-based Coulomb quantum-orbit strong-field approximation model for strong-field ionization by including an additional phase known as Maslov's phase and implementing a new solution strategy via Monte-Carlo-style sampling of the initial momenta. In doing so, we obtain exceptional agreement with solutions to the time-dependent Schrödinger equation for hydrogen, helium, and argon. We provide an in-depth analysis of the resulting photoelectron momentum distributions for these targets, facilitated by the quantum-orbits arising from the solutions to the saddle-point equations. The analysis yields a new class of rescattered trajectories that includes the well-known laser-driven long and short trajectories, along with novel Coulomb-driven rescattered trajectories. By virtue of the precision of the model, it opens the door to detailed investigations of a plethora of strong-field phenomena such as photoelectron holography, laser-induced electron diffraction and high-order above threshold ionization.For more information on the files and their content, see the README file.

openMar 2024View details →

Impact of defects on the phase diagram and field response of ferroelectric (Ba,Sr)TiO3

This repository contains the simulation results for (Ba,Sr)TiO3 using coarse-grained molecular dynamics package <a title="Feram" href="https://loto.sourceforge.net/feram/" target="_blank" rel="noopener">Feram</a>. These data can be visualized with scripts in the <a href="https://gitlab.ruhr-uni-bochum.de/tengssh/p1-defect_study/" target="_blank" rel="noopener">RUB gitlab</a> repository and are supplementary for an associated publication. The publication link will be provided after publishing. All files (1: data.avg, 2: *.dipoRavg, 3: *.coord, 4: *.hl, 5: *.defects) use the space-separated format. (1) data.avg columns: T: temperature in Kelvin Ex Ey Ez: external_E_field along x,y,z in V/Angstrom. exx eyy ezz eyz ezx exy: strain tensor ux uy uz: dipole displacements in Angstrom uxux uyuy uzuz uyuz uzux uxuy: cross-terms of dipole displacements in Angstrom^2 dk: dipo_kinetic in eV/u.c. lr: long_range in eV/u.c. dEf: dipole_E_field in V/Angstrom unhar: unharmonic in eV/u.c. s_ho: homo_strain in eV/u.c. c_ho: homo_coupling in eV/u.c. s_inho: inho_strain in eV/u.c. c_inho: inho_coupling in eV/u.c. etot: total energy in eV/u.c. HNP: H_Nose_Poincare in eV/u.c. e2: e2 dkt: dipo_kinetic_true in eV/u.c. ak: acuou_kinetic in eV/u.c. sr: short_range in eV/u.c. mod: inho_modulation in eV/u.c. px py pz: px py pz ppx ppy ppz ppyz ppzx ppxy: ppx ppy ppz ppyz ppzx ppxy mx my mz: <ux>, <uy>, <uz> in Angstrom amx amy amz: <|ux|>, <|uy|>, <|uz|> in Angstrom (2) *.dipoRavg columns: x y z: coordinates ux uy uz: dipole displacements in Angstrom (3) *.coord columns: x y z: coordinates ux uy uz: dipole displacements in Angstrom ppx ppy ppz: dipoP ddx ddy ddz: dVddi arx ary arz: acouR apx apy apz: acouP (4) *.hl columns: step: timestep T: temperature in Kelvin Ex Ey Ez: external_E_field in V/Angstrom  exx eyy ezz eyz ezx exy: strain tensor ux uy uz: dipole displacements in Angstrom (5) *.defects columns: x y z: coordinates ux uy uz: dipole displacements in Angstrom

opencc-by-4.0Jan 2022View details →

Snow and sea ice observations during the field phase of MOSAiC

Measurements of discrete and continous snow and sea ice measurements during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. The tables contain the observational dates from various methods as described in Nicolaus et al. (2021). The dates refer to Figure 3 in this publication.   Reference: Nicolaus M, Perovich D, Spreen G, Granskog M, Albedyll L, Angelopoulos M, Anhaus P, Arndt S, Belter H, Bessonov V, Birnbaum G, Brauchle J, Calmer R, Cardellach E, Cheng B, Clemens-Sewall D, Dadic R, Damm E, Boer G, Demir O, Dethloff K, Divine D, Fong A, Fons S, Frey M, Fuchs N, Gabarró C, Gerland S, Goessling H, Gradinger R, Haapala J, Haas C, Hamilton J, Hannula H-R, Hendricks S, Herber A, Heuzé C, Hoppmann M, Høyland K, Huntemann M, Hutchings J, Hwang B, Itkin P, Jacobi H-W, Jaggi M, Jutila A, Kaleschke L, Katlein C, Kolabutin N, Krampe D, Kristensen S, Krumpen T, Kurtz N, Lampert A, Lange B, Lei R, Light B, Linhardt F, Liston G, Loose B, Macfarlane A, Mahmud M, Matero I, Maus S, Morgenstern A, Naderpour R, Nandan V, Niubom A, Oggier M, Oppelt N, Pätzold F, Perron C, Petrovsky T, Pirazzini R, Polashenski C, Rabe B, Raphael I, Regnery J, Rex M, Ricker R, Riemann-Campe K, Rinke A, Rohde J, Salganik E, Scharien R, Schiller M, Schneebeli M, Semmling M, Shimanchuk E, Shupe M, Smith M, Smolyanitsky V, Sokolov V, Stanton T, Stroeve J, Thielke L, Timofeeva A, Tonboe R, Tavri A, Tsamados M, Wagner D, Watkins D, Webster M, Wendisch M. 2021. Overview of the MOSAiC expedition – Snow and sea ice. Elementa Science of the Anthropocene 9. doi:10.1525/elementa.2021.000046.