cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.12763/azrdrm/vx5isy
title: Monthly_rainfall_199011.tif
DOI: 10.12763/azrdrm/hg0u7b
title: Monthly_rainfall_199005.tif
DOI: 10.12763/azrdrm/1avzzg
title: Monthly_rainfall_195603.tif
DOI: 10.12763/azrdrm/x3k5z7
title: Monthly_rainfall_200307.tif
DOI: 10.12763/azrdrm/lgkenj
title: Monthly_rainfall_199603.tif
DOI: 10.12763/azrdrm/eca8hy
title: Monthly_rainfall_198509.tif
DOI: 10.12763/azrdrm/5dy17g
title: Monthly_rainfall_195005.tif
DOI: 10.12763/azrdrm/awyvuk
title: Monthly_rainfall_197612.tif
DOI: 10.12763/azrdrm/cxc9d9
title: Monthly_rainfall_201810.tif
DOI: 10.12763/azrdrm/7arqos
title: Monthly_rainfall_197606.tif
DOI: 10.12763/azrdrm/gociaz
title: Monthly_rainfall_201804.tif
DOI: 10.12763/azrdrm/orb4ks
title: Monthly_rainfall_195011.tif
DOI: 10.12763/azrdrm/az4rqu
title: Monthly_rainfall_197809.tif
DOI: 10.12763/azrdrm/p9s4su
title: Monthly_rainfall_200501.tif
DOI: 10.12763/azrdrm/slfq83
title: Monthly_rainfall_199401.tif
DOI: 10.12763/azrdrm/tluhhc
title: Monthly_rainfall_197410.tif
DOI: 10.12763/azrdrm/cwlehp
title: Monthly_rainfall_195207.tif
DOI: 10.12763/azrdrm/cuh6jy
title: Monthly_rainfall_197404.tif
DOI: 10.12763/azrdrm/grbw49
title: Monthly_rainfall_198904.tif
DOI: 10.12763/azrdrm/an82wf
title: Monthly_rainfall_198910.tif
DOI: 10.12763/azrdrm/hk0zme
title: Monthly_rainfall_201409.tif
DOI: 10.12763/azrdrm/evg9az
title: Monthly_rainfall_196901.tif
DOI: 10.12763/azrdrm/7a8pu5
title: Monthly_rainfall_200703.tif
DOI: 10.12763/azrdrm/ugnkrt
title: Monthly_rainfall_196905.tif
DOI: 10.12763/azrdrm/1schha
title: Monthly_rainfall_200707.tif
DOI: 10.12763/azrdrm/khzv4i
title: Monthly_rainfall_196911.tif
DOI: 10.12763/azrdrm/iojuvf
title: Monthly_rainfall_199411.tif
DOI: 10.12763/azrdrm/1gusnj
title: Monthly_rainfall_199405.tif
DOI: 10.12763/azrdrm/226hdu
title: Monthly_rainfall_195203.tif
DOI: 10.12763/azrdrm/bdmbfa
title: Monthly_rainfall_200908.tif
DOI: 10.12763/azrdrm/bm6end
title: Monthly_rainfall_199808.tif
DOI: 10.12763/azrdrm/tldfyz
title: Monthly_rainfall_200505.tif
DOI: 10.12763/azrdrm/pbufnp
title: Monthly_rainfall_200511.tif
DOI: 10.12763/azrdrm/hqezly
title: Monthly_rainfall_199607.tif
DOI: 10.12763/azrdrm/3e7umn
title: Monthly_rainfall_197602.tif
DOI: 10.12763/azrdrm/qcqalw
title: Monthly_rainfall_196508.tif
DOI: 10.12763/azrdrm/lm95zi
title: Monthly_rainfall_195001.tif
DOI: 10.12763/azrdrm/dgrnii
title: Monthly_rainfall_201009.tif
DOI: 10.12763/azrdrm/6mz4gi
title: Monthly_rainfall_200303.tif
DOI: 10.12763/azrdrm/wqqnjd
title: Monthly_rainfall_195808.tif
DOI: 10.12763/azrdrm/pt3vyb
title: Monthly_rainfall_199001.tif
DOI: 10.12763/azrdrm/qk0ugm
title: Monthly_rainfall_197010.tif
DOI: 10.12763/azrdrm/eersm9
title: Monthly_rainfall_195607.tif
DOI: 10.12763/azrdrm/gtnbki
title: Monthly_rainfall_197004.tif
DOI: 10.12763/azrdrm/bgntcf
title: Monthly_rainfall_200101.tif
DOI: 10.12763/azrdrm/r7wwkh
title: Monthly_rainfall_199203.tif
DOI: 10.12763/azrdrm/s74gon
title: Monthly_rainfall_198109.tif
DOI: 10.12763/azrdrm/zxiurk
title: Monthly_rainfall_195405.tif
DOI: 10.12763/azrdrm/1czgyr
title: Monthly_rainfall_197212.tif
DOI: 10.12763/azrdrm/mkezha
title: Monthly_rainfall_197206.tif
DOI: 10.12763/azrdrm/buh5wy
title: Monthly_rainfall_195411.tif
DOI: 10.12763/azrdrm/44ahk1
title: Monthly_rainfall_195410.tif
DOI: 10.12763/azrdrm/vtsenu
title: Monthly_rainfall_197207.tif
DOI: 10.12763/azrdrm/xdw1g2
title: Monthly_rainfall_195404.tif
DOI: 10.12763/azrdrm/gkaaj0
title: Monthly_rainfall_198108.tif
DOI: 10.12763/azrdrm/kz9sbk
title: Monthly_rainfall_199202.tif
DOI: 10.12763/azrdrm/ure26b
title: Monthly_rainfall_197005.tif
DOI: 10.12763/azrdrm/ymr1hd
title: Monthly_rainfall_195612.tif
DOI: 10.12763/azrdrm/t2533y
title: Monthly_rainfall_195606.tif
DOI: 10.12763/azrdrm/6wbset
title: Monthly_rainfall_197011.tif
DOI: 10.12763/azrdrm/s0vatp
title: Monthly_rainfall_195809.tif
DOI: 10.12763/azrdrm/wbtg5f
title: Monthly_rainfall_200302.tif
DOI: 10.12763/azrdrm/eua406
title: Monthly_rainfall_201008.tif
DOI: 10.12763/azrdrm/z6faqc
title: Monthly_rainfall_196509.tif
DOI: 10.12763/azrdrm/hckrkq
title: Monthly_rainfall_201801.tif
DOI: 10.12763/azrdrm/kzyglg
title: Monthly_rainfall_197603.tif
DOI: 10.12763/azrdrm/osqpdo
title: Monthly_rainfall_199606.tif
DOI: 10.12763/azrdrm/p9cti1
title: Monthly_rainfall_199612.tif
DOI: 10.12763/azrdrm/luvfty
title: Monthly_rainfall_200510.tif
DOI: 10.12763/azrdrm/uzurnp
title: Monthly_rainfall_200504.tif
DOI: 10.12763/azrdrm/0nhwix
title: Monthly_rainfall_199809.tif
DOI: 10.12763/azrdrm/lvskch
title: Monthly_rainfall_200909.tif
DOI: 10.12763/azrdrm/x9fllc
title: Monthly_rainfall_195202.tif
DOI: 10.12763/azrdrm/skwxco
title: Monthly_rainfall_197401.tif
DOI: 10.12763/azrdrm/ch7uym
title: Monthly_rainfall_199404.tif
DOI: 10.12763/azrdrm/mnsmt7
title: Monthly_rainfall_199410.tif
DOI: 10.12763/azrdrm/68j7ye
title: Monthly_rainfall_200712.tif
DOI: 10.12763/azrdrm/pxg1yo
title: Monthly_rainfall_196910.tif
DOI: 10.12763/azrdrm/hdyd4k
title: Monthly_rainfall_200706.tif
DOI: 10.12763/azrdrm/isckmw
title: Monthly_rainfall_196904.tif
DOI: 10.6084/m9.figshare.22277287
title: Supplementary tables S1 and S2

description: Supplementary Tables S1 and S2 of <strong>Nuclear genetic background influences the phenotype of the</strong><em><strong>Drosophila tko25t</strong></em> <strong>mitochondrial protein-synthesis mutant</strong> <br> Under revision at G3
DOI: 10.6084/m9.figshare.22277287.v2
title: Supplementary tables S1 and S2

description: Supplementary Tables S1 and S2 of <strong>Nuclear genetic background influences the phenotype of the</strong><em><strong>Drosophila tko25t</strong></em> <strong>mitochondrial protein-synthesis mutant</strong> <br> Under revision at G3
DOI: 10.6084/m9.figshare.22277287.v1
title: Supplementary tables S1 and S2

description: Supplementary Tables S1 and S2 of <strong>Nuclear genetic background influences the phenotype of the</strong><em><strong>Drosophila tko25t</strong></em> <strong>mitochondrial protein-synthesis mutant</strong> <br> Under revision at G3
DOI: 10.57967/hf/0451
title: GuanacoDataset
DOI: 10.34740/kaggle/dsv/5188001
title: AutoXGB
DOI: 10.5281/zenodo.3706758
title: Virtual Energy Storage - Hydropower

description: Data used to derive information of hydropower potential in Europe: Simulated runoff and covariance spectra for 3032 the potential of 3032 main river basins.
DOI: 10.5281/zenodo.3706757
title: Virtual Energy Storage - Hydropower

description: Data used to derive information of hydropower potential in Europe: Simulated runoff and covariance spectra for 3032 the potential of 3032 main river basins.
DOI: 10.26188/21127273
title: Agent Miner : Evaluation results

description: The results of the Agent Miner algorithm evaluation on the nine BPIC datasets (2011,2012,2013,2014,2015_1,2017,2018,2019,2020tpd). <br> The python code of the agent miner algorithm implementation and evaluation is also included.
DOI: 10.26188/21127273.v1
title: Agent Miner : BPIC Evaluation

description: The results of the Agent Miner algorithm evaluation on the nine BPIC datasets (2011,2012,2013,2014,2015_1,2017,2018,2019,2020tpd).
DOI: 10.34943/6141bbd2-33d2-41dc-8bd5-1dd5e1ce25d2
title: Fraser River Delta Upper Slope Junction Box Deployed 2023-03-16

description: The S30014 was deployed on 2023-03-16 at Fraser River Delta Upper Slope. This site is located on the upper slope of the Fraser River delta, where depth is approximately 100 metres. This device is a Junction Box. Junction Boxes supply power and communications to deployed instruments. Junction boxes have a number of serial and ethernet ports, including 400V ethernet ports that enable connections to other junction boxes and high-voltage instruments. Junction boxes can convert high voltages to lower voltages (15V, 24V or 48V) required by many instruments. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.25345/c5z31nz7d
title: MassIVE MSV000091506 - Quantification of adaptive immune responses against protein binding interfaces in the streptococcal M1 protein
title: The work used the M1 protein from streptococcus pyogenes as a model system. Streptococcus pyogenes (or Group A streptococcus, GAS) is a Gram-positive bacterium and a significant human pathogen that forms extensive protein interaction networks with human host proteins. Due to the high frequency of streptococcal infections, most humans have been exposed to S. pyogenes and have circulating antibodies against streptococcal antigens making it challenging to find completely naive human subjects. The presence of an existing repertoire of circulating antibodies complicates investigations focusing on the interplay between polyclonal antibody mixtures and host-pathogen protein-protein interactions. Here, we addressed this challenge by immunization of naive mice with M1 protein. The work is of relevance for as it provides new information of the molecular mechanisms behind immunodominance and subdominance which is important to understand pathogen evasion of antibody responses and for the future design vaccines that elicit antibodies against protective, evolutionarily conserved antigenic sites.
DOI: 10.34740/kaggle/dsv/5188355
title: YouTube Daily
DOI: 10.6084/m9.figshare.22298191
title: Table 1. Characteristics of participants

description: <strong> Characteristics of participants</strong>
DOI: 10.6084/m9.figshare.22298191.v1
title: Table 1. Characteristics of participants

description: <strong> Characteristics of participants</strong>
DOI: 10.5281/zenodo.7748478
title: Table

description: Table test
DOI: 10.5281/zenodo.7748477
title: Table

description: Table test
DOI: 10.5281/zenodo.7506565
title: Strong pressure dependence of the valence band maximum in tetragonal ZrO2

description: This dataset will serve the publication of a work on <strong>Strong pressure dependence of the valence band maximum in tetragonal ZrO2</strong> The archive only includes the resulting files from the workflow leading to such work. the analysis tools can be found at: https://github.com/mdforti/ZrO2-Notebookos-Newpot The file tree is somewhat complicated but we think it will be clear. several folders are available, <code>ZrO2-$factor</code> where <code>factor</code> is the scale factor applied to the prototype unit cell. Inside each folder, you will find several subfolders, each of them for the different parts of the calculation. <code>RLX2dir</code> corresponding to the relaxation at constant volume. <code>TOTEN_5dir</code> has the calculation of the DOS at high resolution, and the vaspruns and EIGENVALS there are used for BS energy isosurfaces construction. <code>BANDSdir</code> saves the calculation for the complete band structure for a standard k-path. <code>ABANDSdir</code>, <code>ZBANDSdir</code>, <code>MBANDSdir</code>, <code>TBANDSdir</code> and <code>NSMBANDSdir</code> save the results for the individual k-paths around each of the VBM candidates. We kindly acknowledge the partial support of ANPCyT - Argentina through grants PICT-2018-01671 and PICT-2018-02366.
DOI: 10.5281/zenodo.7506564
title: Strong pressure dependence of the valence band maximum in tetragonal ZrO2

description: This dataset will serve the publication of a work on <strong>Strong pressure dependence of the valence band maximum in tetragonal ZrO2</strong> The archive only includes the resulting files from the workflow leading to such work. the analysis tools can be found at: https://github.com/mdforti/ZrO2-Notebookos-Newpot The file tree is somewhat complicated but we think it will be clear. several folders are available, <code>ZrO2-$factor</code> where <code>factor</code> is the scale factor applied to the prototype unit cell. Inside each folder, you will find several subfolders, each of them for the different parts of the calculation. <code>RLX2dir</code> corresponding to the relaxation at constant volume. <code>TOTEN_5dir</code> has the calculation of the DOS at high resolution, and the vaspruns and EIGENVALS there are used for BS energy isosurfaces construction. <code>BANDSdir</code> saves the calculation for the complete band structure for a standard k-path. <code>ABANDSdir</code>, <code>ZBANDSdir</code>, <code>MBANDSdir</code>, <code>TBANDSdir</code> and <code>NSMBANDSdir</code> save the results for the individual k-paths around each of the VBM candidates. We kindly acknowledge the partial support of ANPCyT - Argentina through grants PICT-2018-01671 and PICT-2018-02366.
DOI: 10.6084/m9.figshare.22298203
title: Table 2. Descriptive analysis of study variables

description: <strong>Table 2. Descriptive analysis of study variables</strong>
DOI: 10.6084/m9.figshare.21786224
title: Multidimensional Scaling Methods Can Reconstruct Genomic DNA Loops Using Hi-C Data Properties

description: This supplemental file shows the genomic structure by MDS (v<sub>2</sub>, v<sub>3</sub>, v<sub>4</sub>), the selected DNA loops, and the list of genes contained therein. Also included are the consistency of selected DNA loops with ChIP-seq or ATAC-seq peaks, and the results of applying MDS to the raw Hi-C data.
DOI: 10.6084/m9.figshare.21786224.v2
title: Multidimensional Scaling Methods Can Reconstruct Genomic DNA Loops Using Hi-C Data Properties

description: This supplemental file shows the genomic structure by MDS (v<sub>2</sub>, v<sub>3</sub>, v<sub>4</sub>), the selected DNA loops, and the list of genes contained therein. Also included are the consistency of selected DNA loops with ChIP-seq or ATAC-seq peaks, and the results of applying MDS to the raw Hi-C data.
DOI: 10.6084/m9.figshare.21786224.v1
title: Multidimensional Scaling Methods Can Reconstruct Genomic DNA Loops Using Hi-C Data Properties

description: This supplemental file shows the genomic structure by MDS (v<sub>2</sub>, v<sub>3</sub>, v<sub>4</sub>), the selected DNA loops, and the list of genes contained therein. Also included are the consistency of selected DNA loops with ChIP-seq or ATAC-seq peaks, and the results of applying MDS to the raw Hi-C data.
DOI: 10.6084/m9.figshare.22298209
title: Table 3. Results from independent samples test

description: <strong>Table 3. Results from independent samples test</strong>
DOI: 10.6084/m9.figshare.22298209.v1
title: Table 3. Results from independent samples test

description: <strong>Table 3. Results from independent samples test</strong>
DOI: 10.5281/zenodo.7748557
title: Ca6BaP4O17:Mn5+ near-infrared emission data for luminescence thermometry

description: 1. ALLSPECTRA.zip Ca<sub>6</sub>BaP<sub>4</sub>O<sub>17</sub>:Mn<sup>5+ </sup>near-infrared emission spectra measured under 635 nm excitation 2. ALLDATA.txt.zip Data for the evaluation of luminescence thermometry perfomance
DOI: 10.5281/zenodo.7748556
title: Ca6BaP4O17:Mn5+ near-infrared emission data for luminescence thermometry

description: 1. ALLSPECTRA.zip Ca<sub>6</sub>BaP<sub>4</sub>O<sub>17</sub>:Mn<sup>5+ </sup>near-infrared emission spectra measured under 635 nm excitation 2. ALLDATA.txt.zip Data for the evaluation of luminescence thermometry perfomance
DOI: 10.6084/m9.figshare.22200976.v1
title: Willingness to Accept and Willingness to Pay on Residential Properties - A Hedonic Approach.xlsx

description: Data used in the study was obtained from Property24 August 2021 to January 2022. Property24 is a property portal where property listings for sale and to rent from leading real estate agents are advertised. Property24 helps sellers, home buyers, and renters find apartments, houses, townhouses, vacant land, and farms across South Africa. Residential properties from two leading metros in the Eastern Cape Province, Buffalo City Municipality and Nelson Mandela Bay were considered. The data obtained was limited to the data displayed on each property by Property24 that evaluations obtained The study adopted a parametric and non-parametric techniques to estimate the effects of hedonic housing characteristics on willingness to pay and willingness to accept. Hedonic regression model used multiple ordinary least squares regression to examine how each hedonic characteristic adds to the residential properties' entire worth. Ordinary least squares regression has been widely used in estimating house prices over the decades and it is well document in literature (McCord et al., 2018; My-Linh, 2020; Olamide & Adepoju, 2013; Owusu-Ansah, 2013). The study further adopted the Multilayer perceptron of the Artificial Neural Network which is a predictive model that is made up of multiple techniques that measure each self-contained component independently despite its linearity (Papadopoulos et al., 2021). The Artificial Neural Network has gained momentum recently due to its ability to ability to predict house prices (Ghorbani & Afgheh, 2017; Limsombunchai, 2004; Moreno et al., 2011). These techniques were used to estimate the effect of property attributes in influencing the willingness to accept and the willingness to pay in the Eastern Cape province of South Africa. <strong>Model specification and estimation techniques </strong> When building hedonic models, underlying assumptions of linearity, homoscedasticity, independence, normality, and correct model specification should be met. To meet these assumptions, the correct function form and the number of explanatory variables matters. The number of explanatory variables included in the model were determined after several pseudo model evaluations (Sirmans & Macpherson, 2003).
DOI: 10.6084/m9.figshare.22200976
title: Willingness to Accept and Willingness to Pay on Residential Properties - A Hedonic Approach.xlsx

description: Data used in the study was obtained from Property24 August 2021 to January 2022. Property24 is a property portal where property listings for sale and to rent from leading real estate agents are advertised. Property24 helps sellers, home buyers, and renters find apartments, houses, townhouses, vacant land, and farms across South Africa. Residential properties from two leading metros in the Eastern Cape Province, Buffalo City Municipality and Nelson Mandela Bay were considered. The data obtained was limited to the data displayed on each property by Property24 that evaluations obtained The study adopted a parametric and non-parametric techniques to estimate the effects of hedonic housing characteristics on willingness to pay and willingness to accept. Hedonic regression model used multiple ordinary least squares regression to examine how each hedonic characteristic adds to the residential properties' entire worth. Ordinary least squares regression has been widely used in estimating house prices over the decades and it is well document in literature (McCord et al., 2018; My-Linh, 2020; Olamide & Adepoju, 2013; Owusu-Ansah, 2013). The study further adopted the Multilayer perceptron of the Artificial Neural Network which is a predictive model that is made up of multiple techniques that measure each self-contained component independently despite its linearity (Papadopoulos et al., 2021). The Artificial Neural Network has gained momentum recently due to its ability to ability to predict house prices (Ghorbani & Afgheh, 2017; Limsombunchai, 2004; Moreno et al., 2011). These techniques were used to estimate the effect of property attributes in influencing the willingness to accept and the willingness to pay in the Eastern Cape province of South Africa. <strong>Model specification and estimation techniques </strong> When building hedonic models, underlying assumptions of linearity, homoscedasticity, independence, normality, and correct model specification should be met. To meet these assumptions, the correct function form and the number of explanatory variables matters. The number of explanatory variables included in the model were determined after several pseudo model evaluations (Sirmans & Macpherson, 2003).
DOI: 10.6084/m9.figshare.22200976.v2
title: Willingness to Accept and Willingness to Pay on Residential Properties - A Hedonic Approach.xlsx

description: Data used in the study was obtained from Property24 August 2021 to January 2022. Property24 is a property portal where property listings for sale and to rent from leading real estate agents are advertised. Property24 helps sellers, home buyers, and renters find apartments, houses, townhouses, vacant land, and farms across South Africa. Residential properties from two leading metros in the Eastern Cape Province, Buffalo City Municipality and Nelson Mandela Bay were considered. The data obtained was limited to the data displayed on each property by Property24 that evaluations obtained The study adopted a parametric and non-parametric techniques to estimate the effects of hedonic housing characteristics on willingness to pay and willingness to accept. Hedonic regression model used multiple ordinary least squares regression to examine how each hedonic characteristic adds to the residential properties' entire worth. Ordinary least squares regression has been widely used in estimating house prices over the decades and it is well document in literature (McCord et al., 2018; My-Linh, 2020; Olamide & Adepoju, 2013; Owusu-Ansah, 2013). The study further adopted the Multilayer perceptron of the Artificial Neural Network which is a predictive model that is made up of multiple techniques that measure each self-contained component independently despite its linearity (Papadopoulos et al., 2021). The Artificial Neural Network has gained momentum recently due to its ability to ability to predict house prices (Ghorbani & Afgheh, 2017; Limsombunchai, 2004; Moreno et al., 2011). These techniques were used to estimate the effect of property attributes in influencing the willingness to accept and the willingness to pay in the Eastern Cape province of South Africa. <strong>Model specification and estimation techniques </strong> When building hedonic models, underlying assumptions of linearity, homoscedasticity, independence, normality, and correct model specification should be met. To meet these assumptions, the correct function form and the number of explanatory variables matters. The number of explanatory variables included in the model were determined after several pseudo model evaluations (Sirmans & Macpherson, 2003).
DOI: 10.6084/m9.figshare.22298212
title: Table 4. One-way ANOVA Results

description: <strong>Table 4. One-way ANOVA Results</strong>
DOI: 10.6084/m9.figshare.22298212.v1
title: Table 4. One-way ANOVA Results

description: <strong>Table 4. One-way ANOVA Results</strong>
DOI: 10.25550/2w-0jc4
title: EPCAM+ CELLS FROM LAMBDOIDAL JUNCTIONS

description: EPCAM+ CELLS FROM LAMBDOIDAL JUNCTIONS Captured 2.000 cells approx with V2 chronium.
DOI: 10.6084/m9.figshare.22298227
title: Table 5. Correlation between BP, CAT, PQLES, Loneliness, Neglect, and PSS (N=158)

description: <strong>Table 5. Correlation between BP, CAT, PQLES, Loneliness, Neglect, and PSS (N=158)</strong>
DOI: 10.6084/m9.figshare.22298227.v1
title: Table 5. Correlation between BP, CAT, PQLES, Loneliness, Neglect, and PSS (N=158)

description: <strong>Table 5. Correlation between BP, CAT, PQLES, Loneliness, Neglect, and PSS (N=158)</strong>
DOI: 10.5281/zenodo.7743859
title: Correlative study of liquid in human bone by 3D neutron microscopy and lab-based X-ray µCT

description: X-ray (xray_data.tiff) and neutron microscopy (neutron_data.tiff) of the same piece of human cortical bone. Neutron data were collected at the neutron microscope at SINQ, PSI, Switzerland while the x-ray data were collected in house at an VERSA 620 X-ray microscope. The voxel size is in both case 2.7 µm
description: We acknowledge support from the ESS lighthouse on hard materials in 3D, SOLID, funded by the Danish Agency for Science and Higher Education, grant number 8144-00002B. We thank the Danish Agency for Science, Technology, and Innovation for funding the instrument center DanScatt. Use of the Novo Nordisk Foundation research infrastructure AXIA (grant NNF19OC0055801) and support from Dr. Nina K. Wittig is gratefully acknowledged. Technical support in sample preparation by Dr. Carsten Pedersen is gratefully acknowledged. This work is based on experiments performed at the Swiss spallation neutron source SINQ, Paul Scherrer Institute, Villigen, Switzerland.