cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3663548
title: EMG and Video Dataset for sensor fusion based hand gestures recognition

description: This dataset contains data for hand gesture recognition recorded with 3 different sensors. sEMG: recorded via the Myo armband that is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u DVS: Dynamic Video Sensor which is a very low power event-based camera with 128x128 resolution DAVIS: Dynamic Video Sensor which is a very low power event-based camera with 240x180 resolution that also acquires APS frames. The dataset contains recordings of 21 subjects. Each subject performed 3 sessions, where each of the 5 hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation. Note: All the information for the DVS sensor has been extracted and can be found in the *.npy files. In case the raw data (.aedat) was needed please contact enea.ceolini@ini.uzh.ch elisa@ini.uzh.ch ==== README ==== DATASET STRUCTURE: EMG, DVS and APS recordings 21 subjects 3 sessions for each subject 5 gestures in each session ('pinky', 'elle', 'yo', 'index', 'thumb') SINGLE DATASETS: - relax21_raw_emg.zip: contains raw sEMG and annotations (ground truth of gestures) in the format `subjectXX_sessionYY_ZZZ` with `XX` subject ID (01 to 21), `YY` session ID (01-03) and `ZZZ` that can be ‘emg’ or ‘ann’. - relax21_raw_dvs.zip: contains the full-frame dvs events in an array with dimensions 0 -&gt; addr_x, 1 -&gt; addr_y, 2 -&gt; timestamp, 3 -&gt; polarity. The timestamps are in seconds and synchronized with the Myo. Each file is in the format `subjectXX_sessionYY_dvs` with `XX` subject ID (01 to 21), `YY` session ID (01-03). - relax21_cropped_aps.zip: contains the 40x40 pixel aps frames for all subjects and trials in the format `subjectXX_sessionYY_Z_W_K` with `XX` subject ID (01 to 21), `YY` session ID (01-03), Z gesture ('pinky', 'elle', 'yo', 'index', 'thumb’), W trial ID (1-5), `K` frame index. - relax21_cropped_dvs_emg_spikes.pkl: spiking dataset that can be used to reproduce the results in the paper. The dataset is a dictionary with the following keys: <strong>- </strong><strong>y</strong>: array of size 1xN with the class (0-&gt;4). <strong>- </strong><strong>sub</strong>: array of size 1xN with the subject id (1-&gt;10). <strong>- </strong><strong>sess</strong>: array of size 1xN with the session id (1-&gt;3). <strong>- </strong><strong>dvs</strong>: list of length N, each object in the list is a 2d array of size 4xT_n where T_n is the number of events in the trial and the 4 dimensions rappresent: 0 -&gt; addr_x, 1 -&gt; addr_y, 2 -&gt; timestamp, 3 -&gt; polarity . <strong>- </strong><strong>emg</strong>: list of length N, each object in the list is a 2d array of size 3xT_n where T_n is the number of events in the trial and the 3 dimensions rappresent: 0 -&gt; addr, 1 -&gt; timestamp, 3 -&gt; polarity.
DOI: 10.5281/zenodo.3603538
title: Raw diffraction images of Drosophila Piwi

description: Crystal structure of Drosophila Piwi (PDB code: 6KR6). 28 mercury-bound data and 4 native data were included (see file_list.txt for details). Each dataset consists of 180° (except two 90° datasets) from single crystal and was collected using EIGER X 9M detector at a wavelength of 1 Å with helical data collection scheme using 15×10 μm beam on BL32XU, SPring-8. All diffraction images were processed using DIALS 1.10.2 through KAMO pipeline, and merged using XSCALE from XDS package with kamo.multi_merge. The crystals belong to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with a=62.1, b=115.6, c=119.9 Å. Finally 23 mercury-bound datasets were merged at 2.9 Å resolution in the published result (Yamaguchi et al. Nature Communications, 2020). Merging improved resolution and electron density of PAZ domain that was difficult to interpret with a single dataset.
DOI: 10.5281/zenodo.3603539
title: Raw diffraction images of Drosophila Piwi

description: Crystal structure of Drosophila Piwi (PDB code: 6KR6). 28 mercury-bound data and 4 native data were included (see file_list.txt for details). Each dataset consists of 180° (except two 90° datasets) from single crystal and was collected using EIGER X 9M detector at a wavelength of 1 Å with helical data collection scheme using 15×10 μm beam on BL32XU, SPring-8. All diffraction images were processed using DIALS 1.10.2 through KAMO pipeline, and merged using XSCALE from XDS package with kamo.multi_merge. The crystals belong to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with a=62.1, b=115.6, c=119.9 Å. Finally 23 mercury-bound datasets were merged at 2.9 Å resolution in the published result (Yamaguchi et al. Nature Communications, 2020). Merging improved resolution and electron density of PAZ domain that was difficult to interpret with a single dataset.
DOI: 10.5519/qd.afc8fl0m
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3663612
title: Impact of sub-core scale heterogeneity on meter-scale flow and capillary trapping in drainage by CO2

description: Simulation input and output files for generating the results in sections 2-5
DOI: 10.5281/zenodo.3663613
title: Impact of sub-core scale heterogeneity on meter-scale flow and capillary trapping in drainage by CO2

description: Simulation input and output files for generating the results in sections 2-5
DOI: 10.26302/sshade/experiment_op_20200212_001
title: Vis-NIR bidirectional reflection spectra of several ammonium salts mixed with graphite powder at 296 K

description: Mixtures of graphite powder (&lt;20 µm) and ammonium (NH4+) salts (chloride, sulfate, formate) were prepared by mixing manually these constituents in a mortar. Reflectance spectra (from 0.4 to 4 µm) of these mixtures were measured at 296 K under ambient air.
DOI: 10.26302/sshade/experiment_op_20191119_001
title: Vis-NIR bidirectional reflection spectra of several ammonium salts mixed with pyrrhotite grains in sublimate residues at 173 K

description: Dry mixtures (called "sublimate residues") of sub-micrometer size opaque pyrrhotite (Fe1-xS) grains with ammonium (NH4+) salts (chloride, sulfate, formate, carbamate, citrate) were prepared by sublimation of ice-dust mixtures. Reflectance spectra (from 0.4 to 4 µm) of these mixtures were measured at 173 K under high vacuum (pressure lower than 10^-5 mbar).
DOI: 10.5258/soton/d1233
title: Dataset for 'Long-length and thermally stable high-finesse Fabry-Perot interferometers made of hollow core optical fiber '.

description: Dataset supporting publication in Journal of Lightwave Technology entitled: ''Long-length and thermally stable high-finesse Fabry-Perot interferometers made of hollow core optical fiber.
DOI: 10.5258/soton/d1116
title: Data for "100km Single-ended Distributed Vibration Sensor based on Remotely Pumped EDFA"

description: Data for "100km Single-ended Distributed Vibration Sensor based on Remotely Pumped EDFA" published in Optics Letters Fig. 2 Measured Rayleigh backscattered signal (blue) with combined EDFA and Raman amplification using a launched power of 40mW combined with a 500mW 1480nm pump; (orange) without the use of optical amplification with 220mW launched power. (otdr.mat) Fig. 3 3D map of the strain distribution along the sensing fiber as a function of a) time and distance and b) frequency and distance. (3a.mat &3b.mat) Fig. 4 2D cross-section of the 3D diagram of Fig. 3 at a specific location on the sensing fiber where strain is applied (99.5km). a) The 2D cross-section in time-domain and, b) the 2D cross-section in the frequency-domain at fixed point and c) the 2D cross-section in the frequency-domain at fixed frequency. (time2d.mat, freq2d.mat, 4c.mat) Fig. 5 Strain response of the fiber to the PZT input voltage for 10Hz sinusoidal signal measured by the DVS. (pzt.mat)
DOI: 10.5281/zenodo.3664089
title: metaSPAdes 3.13.0 assembly of the CAMI 2 Mouse Gut Toy data set, samples 0-9, short reads

description: Assembly of the first 10 short read samples<br> <strong>Software: </strong>metaSPAdes<br> <strong>SoftwareVersion: </strong>3.13.0<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/ablab/spades<br> <strong>ShortReadsUsed:</strong> True<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> conda create -n spades3130 spades=3.13.0-0<br> <br> Sample0=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_0/reads/anonymous_reads.fq.gz<br> Sample1=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_1/reads/anonymous_reads.fq.gz<br> Sample2=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_2/reads/anonymous_reads.fq.gz<br> Sample3=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_3/reads/anonymous_reads.fq.gz<br> Sample4=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_4/reads/anonymous_reads.fq.gz<br> Sample5=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_5/reads/anonymous_reads.fq.gz<br> Sample6=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_6/reads/anonymous_reads.fq.gz<br> Sample7=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_7/reads/anonymous_reads.fq.gz<br> Sample8=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_8/reads/anonymous_reads.fq.gz<br> Sample9=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_9/reads/anonymous_reads.fq.gz<br> <br> cat $Sample0 $Sample1 $Sample2 $Sample3 $Sample4 $Sample5 $Sample6 $Sample7 $Sample8 $Sample9 &gt; Samples0-9_anonymous_reads.fq.gz<br> <br> conda activate spades3130<br> /usr/bin/time -v metaspades.py --12 Samples0-9_anonymous_reads.fq.gz -o metaSPAdes3130-Sample0-9
DOI: 10.5281/zenodo.3664090
title: metaSPAdes 3.13.0 assembly of the CAMI 2 Mouse Gut Toy data set, samples 0-9, short reads

description: Assembly of the first 10 short read samples<br> <strong>Software: </strong>metaSPAdes<br> <strong>SoftwareVersion: </strong>3.13.0<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/ablab/spades<br> <strong>ShortReadsUsed:</strong> True<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> conda create -n spades3130 spades=3.13.0-0<br> <br> Sample0=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_0/reads/anonymous_reads.fq.gz<br> Sample1=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_1/reads/anonymous_reads.fq.gz<br> Sample2=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_2/reads/anonymous_reads.fq.gz<br> Sample3=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_3/reads/anonymous_reads.fq.gz<br> Sample4=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_4/reads/anonymous_reads.fq.gz<br> Sample5=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_5/reads/anonymous_reads.fq.gz<br> Sample6=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_6/reads/anonymous_reads.fq.gz<br> Sample7=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_7/reads/anonymous_reads.fq.gz<br> Sample8=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_8/reads/anonymous_reads.fq.gz<br> Sample9=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_9/reads/anonymous_reads.fq.gz<br> <br> cat $Sample0 $Sample1 $Sample2 $Sample3 $Sample4 $Sample5 $Sample6 $Sample7 $Sample8 $Sample9 &gt; Samples0-9_anonymous_reads.fq.gz<br> <br> conda activate spades3130<br> /usr/bin/time -v metaspades.py --12 Samples0-9_anonymous_reads.fq.gz -o metaSPAdes3130-Sample0-9
DOI: 10.5258/soton/d1228
title: Dataset for: A Multifactorial Approach to the Estimation of Sex using the Facet Joints of the Spine

description: This dataset supports the thesis: Carolyn Felton (2020) A Multifactorial Approach to the Estimation of Sex using the Facet Joints of the Spine. It is data set of facet measurements.
DOI: 10.5281/zenodo.3663885
title: MEGAHIT 1.0.3, 1.1.3, 1.2.9 assembly of the CAMI 2 Mouse Gut Toy data set, samples 0-9, short reads

description: Assembly of the first 10 short read samples with different MEGAHIT versions and parameters<br> <strong>Software: </strong>MEGAHIT<br> <strong>SoftwareVersion: </strong>1.0.3, 1.1.3, 1.2.9<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/voutcn/megahit<br> <strong>ShortReadsUsed:</strong> True<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> # install bioconda and run:<br> conda create -n megahit103 megahit=1.0.3<br> conda create -n megahit113 megahit=1.1.3<br> conda create -n megahit129 megahit=1.2.9<br> <br> Sample0=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_0/reads/anonymous_reads.fq.gz<br> Sample1=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_1/reads/anonymous_reads.fq.gz<br> Sample2=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_2/reads/anonymous_reads.fq.gz<br> Sample3=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_3/reads/anonymous_reads.fq.gz<br> Sample4=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_4/reads/anonymous_reads.fq.gz<br> Sample5=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_5/reads/anonymous_reads.fq.gz<br> Sample6=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_6/reads/anonymous_reads.fq.gz<br> Sample7=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_7/reads/anonymous_reads.fq.gz<br> Sample8=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_8/reads/anonymous_reads.fq.gz<br> Sample9=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_9/reads/anonymous_reads.fq.gz<br> <br> conda activate megahit103<br> /usr/bin/time -v megahit -t 48 \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit103-Sample0-9-sLibs-default<br> <br> conda activate megahit113<br> /usr/bin/time -v megahit -t 48 \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit113-Sample0-9-sLibs-default<br> <br> /usr/bin/time -v megahit -t 48 --presets meta-sensitive \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit113-Sample0-9-sLibs-meta-sensitive<br> <br> /usr/bin/time -v megahit -t 48 --presets meta-large \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit113-Sample0-9-sLibs-meta-large<br> <br> conda activate megahit129<br> /usr/bin/time -v megahit -t 48 \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit129-Sample0-9-sLibs-default
DOI: 10.5281/zenodo.3663884
title: MEGAHIT 1.0.3, 1.1.3, 1.2.9 assembly of the CAMI 2 Mouse Gut Toy data set, samples 0-9, short reads

description: Assembly of the first 10 short read samples with different MEGAHIT versions and parameters<br> <strong>Software: </strong>MEGAHIT<br> <strong>SoftwareVersion: </strong>1.0.3, 1.1.3, 1.2.9<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/voutcn/megahit<br> <strong>ShortReadsUsed:</strong> True<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> # install bioconda and run:<br> conda create -n megahit103 megahit=1.0.3<br> conda create -n megahit113 megahit=1.1.3<br> conda create -n megahit129 megahit=1.2.9<br> <br> Sample0=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_0/reads/anonymous_reads.fq.gz<br> Sample1=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_1/reads/anonymous_reads.fq.gz<br> Sample2=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_2/reads/anonymous_reads.fq.gz<br> Sample3=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_3/reads/anonymous_reads.fq.gz<br> Sample4=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_4/reads/anonymous_reads.fq.gz<br> Sample5=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_5/reads/anonymous_reads.fq.gz<br> Sample6=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_6/reads/anonymous_reads.fq.gz<br> Sample7=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_7/reads/anonymous_reads.fq.gz<br> Sample8=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_8/reads/anonymous_reads.fq.gz<br> Sample9=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_9/reads/anonymous_reads.fq.gz<br> <br> conda activate megahit103<br> /usr/bin/time -v megahit -t 48 \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit103-Sample0-9-sLibs-default<br> <br> conda activate megahit113<br> /usr/bin/time -v megahit -t 48 \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit113-Sample0-9-sLibs-default<br> <br> /usr/bin/time -v megahit -t 48 --presets meta-sensitive \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit113-Sample0-9-sLibs-meta-sensitive<br> <br> /usr/bin/time -v megahit -t 48 --presets meta-large \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit113-Sample0-9-sLibs-meta-large<br> <br> conda activate megahit129<br> /usr/bin/time -v megahit -t 48 \<br> --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \<br> -o megahit129-Sample0-9-sLibs-default
DOI: 10.5281/zenodo.3664225
title: Scopus records

description: This spreadsheet contains the records retrieved from Scopus.
DOI: 10.17632/s256v7hd3w.1
title: Data for: Divergent effects of a 6-year warming experiment on the nutrient productivities of subtropical tree species

description: The data shows the values of N productivity and P productivity for the four tree species in subtropical forest.
DOI: 10.5281/zenodo.3629664
title: Dataset for 'Rotational dependence of turbulent transport coefficients in global convective dynamo simulations of solar-like stars'

description: For moderate and slow rotation, magnetic activity of solar-like stars is observed to strongly depend on rotation, while for rapid rotation, only a very weak or no dependency is detected. These observations do not yet have a solid explanation in terms of dynamo theory. To work towards such an explanation, we numerically investigated the rotational dependency of dynamo drivers in solar-like stars, that is, stars that have a convective envelope of similar thickness as in the Sun. We ran semi-global convection simulations of stars with rotation rates from 0 to 30 times the solar value, corresponding to Coriolis numbers, Co, of 0 to 110. We measured the turbulent transport coefficients describing the magnetic field evolution with the help of the test-field method, and compared with the dynamo effect arising from the differential rotation, self-consistently generated in the models. The trace of the <strong>\(\alpha\)</strong> tensor increases for moderate rotation rates with Co<sup>0.5</sup> and levels off for rapid rotation. This behavior is in agreement with the kinetic \(\alpha\) based on the kinetic helicity, if one takes into account the decrease of the convective scale with increasing rotation. The <strong>\(\alpha\)</strong> tensor becomes highly anisotropic for Co &gt; 1, \(\alpha_{rr}\) dominates for moderate rotation (1&lt;Co&lt;10), and \(\alpha_{\phi\phi}\) for rapid rotation (Co &gt; 10). The effective meridional flow, taking into account the turbulent pumping effects, is markedly different from the actual meridional circulation profile. Hence, the turbulent pumping effect is dominating the meridional transport of the magnetic field. Taking all dynamo effects into account, we find three distinct regimes. For slow rotation, the \(\alpha\) and Rädler effects are dominating in presence of anti-solar differential rotation. For moderate rotation, \(\alpha\) and \(\Omega\) effects are dominant, indicative of \(\alpha\Omega\) or \(\alpha^2\Omega\) dynamos in operation, producing equatorward-migrating dynamo waves with the qualitatively solar-like rotation profile. For rapid rotation, an \(\alpha^2\) mechanism, with an influence from the Rädler effect, appears to be the most probable driver of the dynamo. Our study reveals the presence of a large variety of dynamo effects beyond the classical \(\alpha\Omega\) mechanism, which need to be investigated further to fully understand the dynamos of solar-like stars. The highly anisotropic <strong>\(\alpha\)</strong> tensor might be the primary reason for the change of axisymmetric to non-axisymmetric dynamo solutions in the moderate rotation regime. For the full article see https://arxiv.org/abs/1910.06776
description: The turbulent transport coefficients are stored as ascii files for each run separately in a tar file. Descriptions, units and structure of the runs and data files are header* and Info* files.