cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7705794
title: Dataset for "Infiltration and resuspension of dilute particle suspensions in micro cavity flow "

description: This dataset contains the numerical simulation data describing the migration behaviour of particles. Detailed study on the infiltration and resuspension of particle in a cavity flow is presented in a journal paper entitled "Infiltration and resuspension of dilute particle suspensions in micro cavity flow ". We provide all the essential data in a single excel file with the description below: Dataset 1- Particle trajectory for different releasing positions. The parameters are <em>Re</em>=56.9, <em>h</em>=32, <em>l</em>=80. The particle densities are <em>ρ<sub>p</sub></em>=1.3 in plot (Fig. 7a) and <em>ρ<sub>p</sub></em>=2.0 in plot (Fig. 7b). (Data of Fig. 7) Dataset 2 - Particle trajectory for different periodic domain lengths. The parameters are <em>ρ<sub>p</sub></em>=1.3, <em>Re</em>=56.9, <em>h</em>=32 and <em>l</em>=80. (Data of Fig. 8) Dataset 3 - The time evolution of (Fig. 9a) the vertical position <em>z</em>, (Fig. 9b) the horizontal velocity <em>u<sub>x</sub></em>, and (Fig. 9c) the vertical velocity <em>u<sub>z</sub></em> of a single particle with the same Renolds number <em>Re</em>=56.9 and cavity size of <em>h</em>=32 and <em>l</em>=80, but different particle densities. (Data of Fig. 9) Dataset 4 - The trajectories of a single particle over the cavity with: (Fig. 10a) different particle densities but the same Renolds number <em>Re</em>=85.3 and cavity size of <em>h</em>=16 and <em>l</em>=80; (Fig. 10b) different Reynolds numbers but the same particle density <em>ρ<sub>p</sub></em>=1.3 and cavity size of <em>h</em>=32 and <em>l</em>=80; (Fig. 10c) different cavity sizes but the same Renolds number<em> Re</em>=56.9 and particle density <em>ρ<sub>p</sub></em>=1.3. The dashed lines represent the boundary of the cavity. (Data of Fig. 10) Dataset 5 - Typical particle trajectories in the cavity. The dashed lines represent the boundary of the trap. (Data of Fig. 12) Dataset 6 - The variation of trap efficiency with particle density for (Fig. 13a) different cavity sizes with the same <em>Re</em>=56.9, and (Fig. 13b) different Reynolds numbers with the same cavity size <em>h</em>=32, <em>l</em>=80, respectively. (Data of Fig. 13) Dataset 7 - The critical entering distance as a function of the scaling parameter <em>T<sub>h</sub></em>. The fitting line is performed with an exponential function with the same form as Eq. (19). (Data of Fig.14) Dataset 8 - The variation of the trap efficiency with the trap number. (Data of Fig.15)
description: the associated paper can be accessed via https://www.sciencedirect.com/science/article/pii/S0032591021008597
DOI: 10.6084/m9.figshare.14845023
title: Time-course plot of transcriptome data of BMA64-1A and S288C strains.

description: - Time-course data of BMA64-1A (an HT strain) and S288C (an LT strain) (both plots and csv file); - Control and treatment samples were collected at 2h and 4h; - The percentage of EtOH used was 30% for BMA64-1A and 18% for S288C strain; - The plots are related to control and treatment conditions at 1h, 2h, and 4h; - In the csv files, 1C, 2C, and 4C means 1h, 2h, and 4h under control condition, respectively. The 1T, 2T, and 4T means 1h, 2h, and 4h under treatment condition, respectively; - Coding and lncRNA genes are included in the dataset. <br>
DOI: 10.5281/zenodo.7705793
title: Dataset for "Infiltration and resuspension of dilute particle suspensions in micro cavity flow "

description: This dataset contains the numerical simulation data describing the migration behaviour of particles. Detailed study on the infiltration and resuspension of particle in a cavity flow is presented in a journal paper entitled "Infiltration and resuspension of dilute particle suspensions in micro cavity flow ". We provide all the essential data in a single excel file with the description below: Dataset 1- Particle trajectory for different releasing positions. The parameters are <em>Re</em>=56.9, <em>h</em>=32, <em>l</em>=80. The particle densities are <em>ρ<sub>p</sub></em>=1.3 in plot (Fig. 7a) and <em>ρ<sub>p</sub></em>=2.0 in plot (Fig. 7b). (Data of Fig. 7) Dataset 2 - Particle trajectory for different periodic domain lengths. The parameters are <em>ρ<sub>p</sub></em>=1.3, <em>Re</em>=56.9, <em>h</em>=32 and <em>l</em>=80. (Data of Fig. 8) Dataset 3 - The time evolution of (Fig. 9a) the vertical position <em>z</em>, (Fig. 9b) the horizontal velocity <em>u<sub>x</sub></em>, and (Fig. 9c) the vertical velocity <em>u<sub>z</sub></em> of a single particle with the same Renolds number <em>Re</em>=56.9 and cavity size of <em>h</em>=32 and <em>l</em>=80, but different particle densities. (Data of Fig. 9) Dataset 4 - The trajectories of a single particle over the cavity with: (Fig. 10a) different particle densities but the same Renolds number <em>Re</em>=85.3 and cavity size of <em>h</em>=16 and <em>l</em>=80; (Fig. 10b) different Reynolds numbers but the same particle density <em>ρ<sub>p</sub></em>=1.3 and cavity size of <em>h</em>=32 and <em>l</em>=80; (Fig. 10c) different cavity sizes but the same Renolds number<em> Re</em>=56.9 and particle density <em>ρ<sub>p</sub></em>=1.3. The dashed lines represent the boundary of the cavity. (Data of Fig. 10) Dataset 5 - Typical particle trajectories in the cavity. The dashed lines represent the boundary of the trap. (Data of Fig. 12) Dataset 6 - The variation of trap efficiency with particle density for (Fig. 13a) different cavity sizes with the same <em>Re</em>=56.9, and (Fig. 13b) different Reynolds numbers with the same cavity size <em>h</em>=32, <em>l</em>=80, respectively. (Data of Fig. 13) Dataset 7 - The critical entering distance as a function of the scaling parameter <em>T<sub>h</sub></em>. The fitting line is performed with an exponential function with the same form as Eq. (19). (Data of Fig.14) Dataset 8 - The variation of the trap efficiency with the trap number. (Data of Fig.15)
description: the associated paper can be accessed via https://www.sciencedirect.com/science/article/pii/S0032591021008597
DOI: 10.6084/m9.figshare.14845023.v1
title: Time-course plot of transcriptome data of BMA64-1A and S288C strains.

description: - Time-course data of BMA64-1A (an HT strain) and S288C (an LT strain) (both plots and csv file); - Control and treatment samples were collected at 2h and 4h; - The percentage of EtOH used was 30% for BMA64-1A and 18% for S288C strain; - The plots are related to control and treatment conditions at 1h, 2h, and 4h; - In the csv files, 1C, 2C, and 4C means 1h, 2h, and 4h under control condition, respectively. The 1T, 2T, and 4T means 1h, 2h, and 4h under treatment condition, respectively; - Coding and lncRNA genes are included in the dataset. <br>
DOI: 10.5281/zenodo.7705392
title: Risk Factors that modify the familial aggregation for ischemic cardiovascular illness

description: <strong>Introduction:</strong> Ischemic cardiovascular disease is a global health problem. <strong>Objective:</strong> To determine the risk factors that modified the familial aggregation for ischemic cardiovascular disease in affected individuals. <strong>Material and Methods:</strong> An observational, analytical, longitudinal and retrospective case/control study was carried out from the population of the Banes municipality, Holguín province during May 2020-May 2022. The universe included all the diagnosed individuals and their families. By simple random sampling, the sample (149 cases) was obtained and the control group was formed at a ratio of 3:1 (447 individuals with no history of disease). The bioethical requirements were met. Inclusion/exclusion criteria were applied. The statistics were used: Chi square, Odd Ratio (OR), including p and confidence interval. The variables were operationalized: age, degree of consanguinity and risk factors. The family tree was obtained. <strong>Results:</strong> The relatives of first and second degree of consanguinity showed the highest incidence of disease. The age group 60-69 years was more affected. Familial aggregation for the disease was demonstrated (X<sup>2</sup>=45.93 OR=5.85 99.9% CI (3.29; 10.41)). Ischemic cardiomyopathy (43.3%) and arrhythmias (20.7%) were notable forms of presentation. The risk factors showed an association for the disease (X<sup>2</sup>=67.11 p ≤0.001). Arterial hypertension (X<sup>2</sup>=57.9 OR=3.04 99.9% CI (2.27; 4.06)) and smoking (X<sup>2</sup>=45.7 OR=2.66 99.9% CI (2.3 ; 5,6)) expressed a highly significant association for ischemic cardiovascular disease. <strong>Conclusions:</strong> The risk factors: arterial hypertension, smoking and family history modified the family aggregation for ischemic cardiovascular disease.
DOI: 10.5281/zenodo.7705393
title: Risk Factors that modify the familial aggregation for ischemic cardiovascular illness

description: <strong>Introduction:</strong> Ischemic cardiovascular disease is a global health problem. <strong>Objective:</strong> To determine the risk factors that modified the familial aggregation for ischemic cardiovascular disease in affected individuals. <strong>Material and Methods:</strong> An observational, analytical, longitudinal and retrospective case/control study was carried out from the population of the Banes municipality, Holguín province during May 2020-May 2022. The universe included all the diagnosed individuals and their families. By simple random sampling, the sample (149 cases) was obtained and the control group was formed at a ratio of 3:1 (447 individuals with no history of disease). The bioethical requirements were met. Inclusion/exclusion criteria were applied. The statistics were used: Chi square, Odd Ratio (OR), including p and confidence interval. The variables were operationalized: age, degree of consanguinity and risk factors. The family tree was obtained. <strong>Results:</strong> The relatives of first and second degree of consanguinity showed the highest incidence of disease. The age group 60-69 years was more affected. Familial aggregation for the disease was demonstrated (X<sup>2</sup>=45.93 OR=5.85 99.9% CI (3.29; 10.41)). Ischemic cardiomyopathy (43.3%) and arrhythmias (20.7%) were notable forms of presentation. The risk factors showed an association for the disease (X<sup>2</sup>=67.11 p ≤0.001). Arterial hypertension (X<sup>2</sup>=57.9 OR=3.04 99.9% CI (2.27; 4.06)) and smoking (X<sup>2</sup>=45.7 OR=2.66 99.9% CI (2.3 ; 5,6)) expressed a highly significant association for ischemic cardiovascular disease. <strong>Conclusions:</strong> The risk factors: arterial hypertension, smoking and family history modified the family aggregation for ischemic cardiovascular disease.
DOI: 10.5281/zenodo.7705883
title: Risk Factors that modify the familial aggregation for ischemic cardiovascular illness

description: <strong>Introduction:</strong> Ischemic cardiovascular disease is a global health problem. <strong>Objective:</strong> To determine the risk factors that modified the familial aggregation for ischemic cardiovascular disease in affected individuals. <strong>Material and Methods:</strong> An observational, analytical, longitudinal and retrospective case/control study was carried out from the population of the Banes municipality, Holguín province during May 2020-May 2022. The universe included all the diagnosed individuals and their families. By simple random sampling, the sample (149 cases) was obtained and the control group was formed at a ratio of 3:1 (447 individuals with no history of disease). The bioethical requirements were met. Inclusion/exclusion criteria were applied. The statistics were used: Chi square, Odd Ratio (OR), including p and confidence interval. The variables were operationalized: age, degree of consanguinity and risk factors. The family tree was obtained. <strong>Results:</strong> The relatives of first and second degree of consanguinity showed the highest incidence of disease. The age group 60-69 years was more affected. Familial aggregation for the disease was demonstrated (X<sup>2</sup>=45.93 OR=5.85 99.9% CI (3.29; 10.41)). Ischemic cardiomyopathy (43.3%) and arrhythmias (20.7%) were notable forms of presentation. The risk factors showed an association for the disease (X<sup>2</sup>=67.11 p ≤0.001). Arterial hypertension (X<sup>2</sup>=57.9 OR=3.04 99.9% CI (2.27; 4.06)) and smoking (X<sup>2</sup>=45.7 OR=2.66 99.9% CI (2.3 ; 5,6)) expressed a highly significant association for ischemic cardiovascular disease. <strong>Conclusions:</strong> The risk factors: arterial hypertension, smoking and family history modified the family aggregation for ischemic cardiovascular disease.
DOI: 10.6084/m9.figshare.14845041
title: Divergencies between BMA64-1A and S288C essential pathways.

description: This file contains several networks in the .cys (Cytoscape) format to allow an interative data visualization. <br> - Information flow analyzes from raw pathway integrated network (PINET): selected KEGG essential pathways integrated in a single network before the information flow analyzes starting from each pathway; - Networks after clustering using the time-course data: the divergencies observed between networks were obtained based on the time-course data of BMA64-1A and S288C EtOH stressed cells integrated onto PINET.
DOI: 10.6084/m9.figshare.14845041.v1
title: Divergencies between BMA64-1A and S288C essential pathways.

description: This file contains several networks in the .cys (Cytoscape) format to allow an interative data visualization. <br> - Information flow analyzes from raw pathway integrated network (PINET): selected KEGG essential pathways integrated in a single network before the information flow analyzes starting from each pathway; - Networks after clustering using the time-course data: the divergencies observed between networks were obtained based on the time-course data of BMA64-1A and S288C EtOH stressed cells integrated onto PINET.
DOI: 10.6084/m9.figshare.21551676.v2
title: Dataset - Plasticity of Hypoxia Tolerance of P. degeni

description: Compilation with all R scripts and datasets for analysis of respiratory traits of <em>P. degeni</em> and their plasticity throughout normoxia exposure.
DOI: 10.25386/genetics.22197457
title: Supplemental Material for Morton et al., 2023

description: Whole genome sequencing was performed on <em>C. elegans</em> strains with different rDNA copy numbers. <strong>CB3740_eDp20_WS235_sort.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain CB3740 (eDf24 I; eDp20 (I,II); mnT12 (IV,X)) <br> <strong>CB3740_eDP20_WS235_sort_depth.txt</strong> – Read depth analysis file for whole genome sequencing <em>of C. elegans</em> strain CB3740 (eDf24 I; eDp20 (I,II); mnT12 (IV,X)) <br> <strong>eDP20_chrI_13mil.txt</strong> – Read depth data for right arm of ChrI of <em>C. elegans</em> strain CB3740 <br> <strong>N2_chrI_13mil.txt</strong> – Read depth data for right arm of ChrI of<em> C. elegans </em>wild type strain N2 <br> <strong>SEA296_MY1_130E_chrI_merge_RG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA296 (mIs13[myo-2p::GFP + pes-10p::GFP + F22B7.9p::GFP] I, catIR8[I:, N2&gt;MY1]). Homozygous for 64-copy rDNA array. <br> <strong>SEA296_MY1_130E_merge.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA296 (mIs13[myo-2p::GFP + pes-10p::GFP + F22B7.9p::GFP] I, catIR8[I:, N2&gt;MY1]). Homozygous for 64-copy rDNA array. <br> <strong>SEA300_duprm_RG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA300 (catIR12[I:?-end , MY1&gt;N2]). Homozygous for 417-copy rDNA array. <br> <strong>SEA300_duprm_RG.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA300 (catIR12[I:?-end , MY1&gt;N2]). Homozygous for 417-copy rDNA array. <br> <strong>SEA302_S2_WS230_duprumRG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA302 (catIR14[I:~13500000-end, JU775&gt;N2]). Homozygous for 81-copy rDNA array. <br> <strong>SEA302_S2_WS230_duprmRG.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA302 (catIR14[I:~13500000-end, JU775&gt;N2]). Homozygous for 81-copy rDNA array. <br> <strong>SEA305_S5_WS230_duprmRG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA305 (catIR17[I:~3600000-end, MY16&gt;N2]) Homozygous for 73-copy rDNA array. <br> <strong>SEA305_S5_WS230_duprmRG.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA305 (catIR17[I:~3600000-end, MY16&gt;N2]) Homozygous for 73-copy rDNA array. <br>
DOI: 10.25386/genetics.22197457.v1
title: Supplemental Material for Morton et al., 2023

description: Whole genome sequencing was performed on <em>C. elegans</em> strains with different rDNA copy numbers. <strong>CB3740_eDp20_WS235_sort.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain CB3740 (eDf24 I; eDp20 (I,II); mnT12 (IV,X)) <br> <strong>CB3740_eDP20_WS235_sort_depth.txt</strong> – Read depth analysis file for whole genome sequencing <em>of C. elegans</em> strain CB3740 (eDf24 I; eDp20 (I,II); mnT12 (IV,X)) <br> <strong>eDP20_chrI_13mil.txt</strong> – Read depth data for right arm of ChrI of <em>C. elegans</em> strain CB3740 <br> <strong>N2_chrI_13mil.txt</strong> – Read depth data for right arm of ChrI of<em> C. elegans </em>wild type strain N2 <br> <strong>SEA296_MY1_130E_chrI_merge_RG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA296 (mIs13[myo-2p::GFP + pes-10p::GFP + F22B7.9p::GFP] I, catIR8[I:, N2&gt;MY1]). Homozygous for 64-copy rDNA array. <br> <strong>SEA296_MY1_130E_merge.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA296 (mIs13[myo-2p::GFP + pes-10p::GFP + F22B7.9p::GFP] I, catIR8[I:, N2&gt;MY1]). Homozygous for 64-copy rDNA array. <br> <strong>SEA300_duprm_RG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA300 (catIR12[I:?-end , MY1&gt;N2]). Homozygous for 417-copy rDNA array. <br> <strong>SEA300_duprm_RG.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA300 (catIR12[I:?-end , MY1&gt;N2]). Homozygous for 417-copy rDNA array. <br> <strong>SEA302_S2_WS230_duprumRG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA302 (catIR14[I:~13500000-end, JU775&gt;N2]). Homozygous for 81-copy rDNA array. <br> <strong>SEA302_S2_WS230_duprmRG.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA302 (catIR14[I:~13500000-end, JU775&gt;N2]). Homozygous for 81-copy rDNA array. <br> <strong>SEA305_S5_WS230_duprmRG.bam</strong> – Aligned whole genome sequence data for <em>C. elegans</em> strain SEA305 (catIR17[I:~3600000-end, MY16&gt;N2]) Homozygous for 73-copy rDNA array. <br> <strong>SEA305_S5_WS230_duprmRG.g.vcf</strong> – VCF for sequence variants in <em>C. elegans</em> strain SEA305 (catIR17[I:~3600000-end, MY16&gt;N2]) Homozygous for 73-copy rDNA array. <br>
DOI: 10.5281/zenodo.7705893
title: dataset for "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator"

description: This dataset contains the numerical simulation data describing the conveying behaviour of shaped-particle systems. Detailed study on the conveying behaviour is presented in a journal paper entitled "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator". We provide all the essential data in a single excel file with the description below: Dataset 1- Average particle speed (Fig. 7a) and XYZ components (Fig. 7b) for: Sphere at t = 2.1 s, Cube at t = 2.85 s, Biluna at t = 3.1 s, HexP at t = 3.45 s. (Data of Fig. 7) Dataset 2 - XYZ coordinates of a typical particle trapped in the granulator. (Data of Fig. 9) Dataset 3 - Particle retention numbers for different shaped particles. (Data of Fig. 10). Dataset 4 - Average particle speeds for different shaped particles. (Data of Fig. 11). Dataset 5 - Average axial particle speeds for different shaped particles (Data of Fig. 12). Dataset 6 - Residence time distributions for various shaped particles. (Data of Fig. 13) Dataset 7 - Normalised residence time distributions for various shaped particles. (Data of Fig. 14) Dataset 8 - Mean residence time for various shaped particles. (Data of Fig. 15) Dataset 9 - The variance of RTD for different shaped particles. (Data of Fig. 16) Dataset 10 - Cumulative exit age distribution for various shaped particles. (Data of Fig. 17) Dataset 11 - Normalised cumulative exit age distribution for various shaped particles. (Data of Fig. 18) Dataset 12 - Evolution of the cumulative power consumption for various shaped particles. (Data of Fig.19) Dataset 13 - Power consumption distribution for various shaped particles. (Data of Fig.20)
description: the associated paper can be accessed here: https://www.sciencedirect.com/science/article/pii/S1674200121000754
DOI: 10.5281/zenodo.7705892
title: dataset for "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator"

description: This dataset contains the numerical simulation data describing the conveying behaviour of shaped-particle systems. Detailed study on the conveying behaviour is presented in a journal paper entitled "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator". We provide all the essential data in a single excel file with the description below: Dataset 1- Average particle speed (Fig. 7a) and XYZ components (Fig. 7b) for: Sphere at t = 2.1 s, Cube at t = 2.85 s, Biluna at t = 3.1 s, HexP at t = 3.45 s. (Data of Fig. 7) Dataset 2 - XYZ coordinates of a typical particle trapped in the granulator. (Data of Fig. 9) Dataset 3 - Particle retention numbers for different shaped particles. (Data of Fig. 10). Dataset 4 - Average particle speeds for different shaped particles. (Data of Fig. 11). Dataset 5 - Average axial particle speeds for different shaped particles (Data of Fig. 12). Dataset 6 - Residence time distributions for various shaped particles. (Data of Fig. 13) Dataset 7 - Normalised residence time distributions for various shaped particles. (Data of Fig. 14) Dataset 8 - Mean residence time for various shaped particles. (Data of Fig. 15) Dataset 9 - The variance of RTD for different shaped particles. (Data of Fig. 16) Dataset 10 - Cumulative exit age distribution for various shaped particles. (Data of Fig. 17) Dataset 11 - Normalised cumulative exit age distribution for various shaped particles. (Data of Fig. 18) Dataset 12 - Evolution of the cumulative power consumption for various shaped particles. (Data of Fig.19) Dataset 13 - Power consumption distribution for various shaped particles. (Data of Fig.20)
description: the associated paper can be accessed here: https://www.sciencedirect.com/science/article/pii/S1674200121000754
DOI: 10.6084/m9.figshare.15052347
title: Metabolic efficiency affects the seminal relationship between pathogen growth rate and virulence - Dataset.xlsx

description: Dataset, R code for data analysis, Excel Figures, and Code and dataset for Supplementary Information for the manuscript "<strong>Metabolic efficiency affects the seminal relationship between pathogen growth rate and virulence</strong>" <em>Ecology Letters </em>2023
DOI: 10.6084/m9.figshare.15052347.v2
title: Metabolic efficiency affects the seminal relationship between pathogen growth rate and virulence - Dataset.xlsx

description: Dataset, R code for data analysis, Excel Figures, and Code and dataset for Supplementary Information for the manuscript "<strong>Metabolic efficiency affects the seminal relationship between pathogen growth rate and virulence</strong>" <em>Ecology Letters </em>2023
DOI: 10.5281/zenodo.7705882
title: Original Data for Publication: Exceptionally Stable Dimers and Trimers of Au25 Clusters Linked with a Bidentate Dithiol: Synthesis, Structure and Chirality Study.

description: Original Data for Publication: Exceptionally Stable Dimers and Trimers of Au25 Clusters Linked with a Bidentate Dithiol: Synthesis, Structure and Chirality Study. Angewandte Chemie, 2023
description: Funding by Swiss National Science Foundation: Grant Number: CRSII5_173720