cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4766424
title: CReSCENT: CanceR Single Cell ExpressioN Toolkit (CReSCENT) v2.0 PBMC example infiles and commands

description: CReSCENT: CanceR Single Cell ExpressioN Toolkit (CReSCENT) v2.0<br> Example PBMC infiles and parameters (c) Pugh Lab. Under GNU General Public License v3.0 <br> ##############<br> ## CONTENTS<br> ############## A) File crescent_v2.0_pbmc_example_infiles_and_commands.tar.bz2,<br> folder INPUT_MTX contains four scRNA-seq PBMC datasets obtained from 10X<br> https://www.10xgenomics.com/resources/datasets/<br> Two of them originally had ~8,000 and ~10,000 cells, and were downsampled to 1,000 B) File crescent_v2.0_pbmc_example_infiles_and_commands.tar.bz2,<br> folder LISTS_AND_COMMANDS contains the tables with input parameters for the integration of the four<br> datasets above, using the four CReSCENT scRNA-seq one-line-command R scripts:<br> 1) Runs_Seurat_v3_MultiDatasets_QC_Normalization.R<br> 2) Runs_Seurat_v3_MultiDatasets_Integration.R<br> 3) Runs_Seurat_v3_MultiDatasets_PCA_Clustering_DimReduction.R<br> 4) Runs_Seurat_v3_MultiDatasets_DGE.R<br> Which can be obtained from:<br> https://github.com/pughlab/crescent/tree/master/bin/in_use For help on how to run these scripts, in a Console/Terminal type:<br> `Rscript Runs_Seurat_v3_MultiDatasets_QC_Normalization.R -h`<br> Each script code has a section 'Required libraries'<br> For Bug Reports and Feature Requests, please fill out a GitHub ticket:<br> https://github.com/pughlab/crescent/issues C) File crescent_v2.0_pbmc_example_infiles_and_commands.tar.bz2,<br> folder METADATA contains two tables with cell-level metadata to colour UMAP/TSNE plots<br> and to compare class_1 vs. class_2 for Differential Gene Expression detection D) File crescent_v2.0_pbmc_example_outfiles.tar.bz2,<br> folder SEURAT contains the results obtained by running the scripts using CReSCENT scripts<br> GitHub version 5a78fe9.<br> See sub-folder 'SEURAT/LOG_FILES' for R library versions and commands used for each script E) File crescent_gsva_pbmcs_example_infiles_and_commands.tar.bz2,<br> folder INFILES contains example infiles to run Runs_GSVA.R F) File crescent_gsva_pbmcs_example_outfiles_and_commands.tar.bz2,<br> folder GSVA contains the results obtained by running script Runs_GSVA.R<br> folder LOG_FILES contains log files, including commands used for the run G) File crescent_infercnv_glio_example_infiles_and_commands.tar.bz2,<br> folder INPUTS contains example infiles to run Runs_InferCNV.R H) File crescent_infercnv_glio_example_outfiles_and_commands.tar.bz2,<br> folder INFERCNV contains the results obtained by running script Runs_InferCNV.R<br> folder LOG_FILES contains log files, including commands used for the run
DOI: 10.5281/zenodo.4642758
title: CReSCENT: CanceR Single Cell ExpressioN Toolkit (CReSCENT) v2.0 PBMC example infiles and commands

description: CReSCENT: CanceR Single Cell ExpressioN Toolkit (CReSCENT) v2.0<br> Example PBMC infiles and parameters (c) Pugh Lab. Under GNU General Public License v3.0 <br> ##############<br> ## CONTENTS<br> ############## A) File crescent_v2.0_pbmc_example_infiles_and_commands.tar.bz2,<br> folder INPUT_MTX contains four scRNA-seq PBMC datasets obtained from 10X<br> https://www.10xgenomics.com/resources/datasets/<br> Two of them originally had ~8,000 and ~10,000 cells, and were downsampled to 1,000 B) File crescent_v2.0_pbmc_example_infiles_and_commands.tar.bz2,<br> folder LISTS_AND_COMMANDS contains the tables with input parameters for the integration of the four<br> datasets above, using the four CReSCENT scRNA-seq one-line-command R scripts:<br> 1) Runs_Seurat_v3_MultiDatasets_QC_Normalization.R<br> 2) Runs_Seurat_v3_MultiDatasets_Integration.R<br> 3) Runs_Seurat_v3_MultiDatasets_PCA_Clustering_DimReduction.R<br> 4) Runs_Seurat_v3_MultiDatasets_DGE.R<br> Which can be obtained from:<br> https://github.com/pughlab/crescent/tree/master/bin/in_use For help on how to run these scripts, in a Console/Terminal type:<br> `Rscript Runs_Seurat_v3_MultiDatasets_QC_Normalization.R -h`<br> Each script code has a section 'Required libraries'<br> For Bug Reports and Feature Requests, please fill out a GitHub ticket:<br> https://github.com/pughlab/crescent/issues C) File crescent_v2.0_pbmc_example_infiles_and_commands.tar.bz2,<br> folder METADATA contains two tables with cell-level metadata to colour UMAP/TSNE plots<br> and to compare class_1 vs. class_2 for Differential Gene Expression detection D) File crescent_v2.0_pbmc_example_outfiles.tar.bz2,<br> folder SEURAT contains the results obtained by running the scripts using CReSCENT scripts<br> GitHub version 5a78fe9.<br> See sub-folder 'SEURAT/LOG_FILES' for R library versions and commands used for each script E) File crescent_gsva_pbmcs_example_infiles_and_commands.tar.bz2,<br> folder INFILES contains example infiles to run Runs_GSVA.R F) File crescent_gsva_pbmcs_example_outfiles_and_commands.tar.bz2,<br> folder GSVA contains the results obtained by running script Runs_GSVA.R<br> folder LOG_FILES contains log files, including commands used for the run G) File crescent_infercnv_glio_example_infiles_and_commands.tar.bz2,<br> folder INPUTS contains example infiles to run Runs_InferCNV.R H) File crescent_infercnv_glio_example_outfiles_and_commands.tar.bz2,<br> folder INFERCNV contains the results obtained by running script Runs_InferCNV.R<br> folder LOG_FILES contains log files, including commands used for the run
DOI: 10.6084/m9.figshare.14603017.v1
title: Additional file 2 of Phylogenetic history influences convergence for a specialized ecology: comparative skull morphology of African burrowing skinks (Squamata; Scincidae)

description: Additional file 2. Table of classifiers used in data analysis.
DOI: 10.6084/m9.figshare.14603017
title: Additional file 2 of Phylogenetic history influences convergence for a specialized ecology: comparative skull morphology of African burrowing skinks (Squamata; Scincidae)

description: Additional file 2. Table of classifiers used in data analysis.
DOI: 10.6084/m9.figshare.14603086
title: Additional file 1 of Prospective and detailed behavioral phenotyping in DDX3X syndrome

description: Additional file 1: Table S1. DDX3X variants in the cohort. Table S2: Individual scores on neuropsychological evaluation. Table S3: Medical comorbidities. Table S4: Brain MRI findings. Table S5: Neurological examination. Table S6: Summary of dysmorphic features. Table S7: Statistics and Cohen’s D for genotype-phenotype analysis.
DOI: 10.6084/m9.figshare.14603086.v1
title: Additional file 1 of Prospective and detailed behavioral phenotyping in DDX3X syndrome

description: Additional file 1: Table S1. DDX3X variants in the cohort. Table S2: Individual scores on neuropsychological evaluation. Table S3: Medical comorbidities. Table S4: Brain MRI findings. Table S5: Neurological examination. Table S6: Summary of dysmorphic features. Table S7: Statistics and Cohen’s D for genotype-phenotype analysis.
DOI: 10.6084/m9.figshare.14483133
title: Travel-mode classification based on GPS-trajectory data and geographic information

description: the dataset and codes used in the study "Travel-mode classification based on GPS-trajectory data and XGBoost classifier"
DOI: 10.6084/m9.figshare.14483133.v4
title: Travel mode classification based on GPS trajectory data and XGBoost classifie

description: the dataset and codes used in the study "Travel mode classification based on GPS trajectory data and XGBoost classifier"
DOI: 10.6084/m9.figshare.14602983
title: Inflation of Okmok volcano during 2008 to 2020 from PS analyses and source inversion with finite element models

description: All the files are saved as matlab .mat file. InSAR results are named as "Sensor"_"Path". For each track, derived deformation time series for each identified PS are presented, along with corresponding look angle, longitude and latitude. Source parameter inversion results are saved as "parm_InSAR" and "parm_GPS". Modeling for InSAR results contains source locations and strengths. For the GPS results, the source locations are fixed thus only the source strengths are inverted.
DOI: 10.6084/m9.figshare.14602983.v1
title: Inflation of Okmok volcano during 2008 to 2020 from PS analyses and source inversion with finite element models

description: All the files are saved as matlab .mat file. InSAR results are named as "Sensor"_"Path". For each track, derived deformation time series for each identified PS are presented, along with corresponding look angle, longitude and latitude. Source parameter inversion results are saved as "parm_InSAR" and "parm_GPS". Modeling for InSAR results contains source locations and strengths. For the GPS results, the source locations are fixed thus only the source strengths are inverted.
DOI: 10.6084/m9.figshare.14603115
title: Data set for paper 'Causal Pattern Test and Cloze Test: A Study of L2 Learners Using Distributed Models of Conceptual Representation'

description: Data set featured in the manuscript 'Causal Pattern Test and Cloze Test: A Study of L2 Learners Using Distributed Models of Conceptual Representation'
DOI: 10.6084/m9.figshare.14603115.v1
title: Data set for paper 'Causal Pattern Test and Cloze Test: A Study of L2 Learners Using Distributed Models of Conceptual Representation'

description: Data set featured in the manuscript 'Causal Pattern Test and Cloze Test: A Study of L2 Learners Using Distributed Models of Conceptual Representation'
DOI: 10.5281/zenodo.4766490
title: Dataset for the Transcriptome sequencing of Malaysian Mahseer

description: Trinity and Transdecoder Output for the Malaysian Mahseer transcriptome dataset. BUSCO assessment was performed on the filtered transcriptome and predicted proteome (clustered at 98% amino acid similarity with cd-hit).
DOI: 10.5281/zenodo.4766489
title: Dataset for the Transcriptome sequencing of Malaysian Mahseer

description: Trinity and Transdecoder Output for the Malaysian Mahseer transcriptome dataset. BUSCO assessment was performed on the filtered transcriptome and predicted proteome (clustered at 98% amino acid similarity with cd-hit).
DOI: 10.5517/ccdc.csd.cc1ysbln
title: CCDC 1810852: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbmp
title: CCDC 1810853: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbnq
title: CCDC 1810854: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbpr
title: CCDC 1810855: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbqs
title: CCDC 1810856: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbrt
title: CCDC 1810857: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbsv
title: CCDC 1810858: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc1ysbtw
title: CCDC 1810859: Experimental Crystal Structure Determination

description: Related Article: Ritesh Dubey, KaKing Yan, Takashi Kikuchi, Shiho Sairenji, Anouk Rossen, Shermin S. Goh, Ben L. Feringa, Makoto Fujita|2021|Angew.Chem.,Int.Ed.|60|11809|doi:10.1002/anie.202102559
DOI: 10.5517/ccdc.csd.cc20sk8n
title: CCDC 1870641: Experimental Crystal Structure Determination

description: Related Article: Zhong-Liang Gong, Kun Tang, Yu-Wu Zhong|2021|Inorg.Chem.|60|6607|doi:10.1021/acs.inorgchem.1c00403
DOI: 10.6084/m9.figshare.6815699.v6
title: JARVIS-DFT 3D dataset (jdft_3d.json)

description: Website: https://www.ctcms.nist.gov/~knc6/JVASP.html<br><br>#########################################Lading the dataset:<br>import os, jsonimport pandas as pdimport numpy as npf = open(os.path.join(os.getcwd(),'jdft_3d-4-26-2020.json'),'r')data3d=json.load(f)f.close()df=pd.DataFrame(data3d) form_enp=np.array(df['formation_energy_peratom'].replace('na',np.nan).dropna().values,dtype='float')#########################################For more details about using the dataset, use the jupyter-notebooks:https://github.com/usnistgov/jarvis<br><br>The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
DOI: 10.5281/zenodo.4766475
title: Wave and tidally driven flow dynamics within a coral reef atoll off northwestern Australia

description: The dataset contains a combination of field observation and numerical modeling output files used to study the interaction between waves and tides on a coral reef atoll off the North West Shelf of Australia. Data included here is mostly raw and unprocessed. Please see the 'Metadata_Grimaldi_etal_JGR_Oceans.pdf' file or contact camille.grimaldi@research.uwa.edu.au for further information.
DOI: 10.5281/zenodo.4760048
title: Wave and tidally driven flow dynamics within a coral reef atoll off northwestern Australia

description: The dataset contains a combination of field observation and numerical modeling output files used to study the interaction between waves and tides on a coral reef atoll off the North West Shelf of Australia. Data included here is mostly raw and unprocessed. Please see the 'Metadata_Grimaldi_etal_JGR_Oceans.pdf' file or contact camille.grimaldi@research.uwa.edu.au for further information.
DOI: 10.5281/zenodo.4756529
title: Computational synthesis of cortical dendritic morphologies

description: Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles, and investigating structural alterations associated with diseased brain states. However, a lack of understanding of the principles underlying neuron morphologies has hindered attempts to computationally synthesize morphologies for decades. We introduce a novel synthesis algorithm based on a topological descriptor of neurons that we presented in an earlier publication, which overcomes the limitations of neuronal reconstruction techniques. This topology-guided synthesis generates dendrites that are indistinguishable from biological reconstructions in terms of both morpho-electrical and connectivity properties and enables the rapid digital reconstruction of entire brain regions from relatively few reference cells. This topological synthesis method offers an unprecedented opportunity to investigate the links between neuronal morphology and brain function across different spatio-temporal scales. Synthesized cortical networks based on structurally altered dendrites, like those associated with diverse brain pathologies, revealed principles linking branching properties to the structure of large-scale networks. We provide here the original biological reconstructions, the artificially generated cells and related data (electrical traces, connectivity of artificial networks) that were used for the analysis of the paper "Computational synthesis of cortical dendritic morphologies".
DOI: 10.15151/esrf-es-446736780
title: Structure of hyaluronic acid gel loaded with NPs under the stress.

description: We will measure the structure of hyaluronic acid gel, loaded with gold and iron oxide particles, under stress.
DOI: 10.5281/zenodo.4766686
title: Dataset related to article "Inhaled nitric oxide in mechanically ventilated patients with COVID-19"

description: This record contains raw data related to article "Inhaled nitric oxide in mechanically ventilated patients with COVID-19"
DOI: 10.5281/zenodo.4766685
title: Dataset related to article "Inhaled nitric oxide in mechanically ventilated patients with COVID-19"

description: This record contains raw data related to article "Inhaled nitric oxide in mechanically ventilated patients with COVID-19"
DOI: 10.6084/m9.figshare.14603835
title: Nomophobia in medical students from Peru.xlsx

description: We carry out a cross-sectional and analytical study. A survey of 90 questions was applied to human medicine students from 20 Peruvian universities who are studying between the first year and the sixth year during the year 2020. A convenience sampling was carried out. Four instruments were used: structured sociodemographic questionnaire, hopkins symptom questionnaire, Pittsburgh sleep quality index, and nmp-q.
DOI: 10.48370/ofd/ubgsgy/9x2xzl
title: HMW_Polygonum_aviculare_L_H_3854.jpg

description: :unav
DOI: 10.48370/ofd/ubgsgy
title: Rdest ptasi (Polygonum aviculare L.), 3854

description: Herbarium
DOI: 10.5281/zenodo.4766730
title: Dataset related to article "Early detection of elevated cardiac biomarkers to optimise risk stratification in patients with COVID-19"

description: This record contains raw data related to article “Early detection of elevated cardiac biomarkers to optimise risk stratification in patients with COVID-19" <strong>Objective: </strong> Risk stratification is crucial to optimise treatment strategies in patients with COVID-19. We aimed to evaluate the impact on mortality of an early assessment of cardiac biomarkers in patients with COVID-19. <strong>Methods: </strong> Humanitas Clinical and Research Hospital (Rozzano-Milan, Lombardy, Italy) is a tertiary centre that has been converted to the management of COVID-19. Patients with confirmed COVID-19 were entered in a dedicated database for cohort observational analyses. Outcomes were stratified according to elevated levels (ie, above the upper level of normal) of high-sensitivity cardiac troponin I (hs-TnI), B-type natriuretic peptide (BNP) or both measured within 24 hours after hospital admission. The primary outcome was all-cause mortality. <strong>Results: </strong> A total of 397 consecutive patients with COVID-19 were included up to 1 April 2020. At the time of hospital admission, 208 patients (52.4%) had normal values for cardiac biomarkers, 90 (22.7%) had elevated both hs-TnI and BNP, 59 (14.9%) had elevated only BNP and 40 (10.1%) had elevated only hs-TnI. The rate of mortality was higher in patients with elevated hs-TnI (22.5%, OR 4.35, 95% CI 1.72 to 11.04), BNP (33.9%, OR 7.37, 95% CI 3.53 to 16.75) or both (55.6%, OR 18.75, 95% CI 9.32 to 37.71) as compared with those without elevated cardiac biomarkers (6.25%). A multivariate analysis identified concomitant elevation of both hs-TnI and BNP as a strong independent predictor of all-cause mortality (OR 3.24, 95% CI 1.06 to 9.93). <strong>Conclusions: </strong> An early detection of elevated hs-TnI and BNP predicts mortality in patients with COVID-19. Cardiac biomarkers should be systematically assessed in patients with COVID-19 at the time of hospital admission in order to optimise risk stratification.
DOI: 10.5281/zenodo.4766729
title: Dataset related to article "Early detection of elevated cardiac biomarkers to optimise risk stratification in patients with COVID-19"

description: This record contains raw data related to article “Early detection of elevated cardiac biomarkers to optimise risk stratification in patients with COVID-19" <strong>Objective: </strong> Risk stratification is crucial to optimise treatment strategies in patients with COVID-19. We aimed to evaluate the impact on mortality of an early assessment of cardiac biomarkers in patients with COVID-19. <strong>Methods: </strong> Humanitas Clinical and Research Hospital (Rozzano-Milan, Lombardy, Italy) is a tertiary centre that has been converted to the management of COVID-19. Patients with confirmed COVID-19 were entered in a dedicated database for cohort observational analyses. Outcomes were stratified according to elevated levels (ie, above the upper level of normal) of high-sensitivity cardiac troponin I (hs-TnI), B-type natriuretic peptide (BNP) or both measured within 24 hours after hospital admission. The primary outcome was all-cause mortality. <strong>Results: </strong> A total of 397 consecutive patients with COVID-19 were included up to 1 April 2020. At the time of hospital admission, 208 patients (52.4%) had normal values for cardiac biomarkers, 90 (22.7%) had elevated both hs-TnI and BNP, 59 (14.9%) had elevated only BNP and 40 (10.1%) had elevated only hs-TnI. The rate of mortality was higher in patients with elevated hs-TnI (22.5%, OR 4.35, 95% CI 1.72 to 11.04), BNP (33.9%, OR 7.37, 95% CI 3.53 to 16.75) or both (55.6%, OR 18.75, 95% CI 9.32 to 37.71) as compared with those without elevated cardiac biomarkers (6.25%). A multivariate analysis identified concomitant elevation of both hs-TnI and BNP as a strong independent predictor of all-cause mortality (OR 3.24, 95% CI 1.06 to 9.93). <strong>Conclusions: </strong> An early detection of elevated hs-TnI and BNP predicts mortality in patients with COVID-19. Cardiac biomarkers should be systematically assessed in patients with COVID-19 at the time of hospital admission in order to optimise risk stratification.
DOI: 10.48370/ofd/hqdwps/epnugk
title: HMW_Potamogeton_crispus_L_H_3169.jpg

description: :unav
DOI: 10.48370/ofd/hqdwps
title: Rdestnica kędzierzawa (Potamogeton crispus L.), 3169

description: Herbarium
DOI: 10.15468/k4sxyn
title: Studies on Neotropical crickets: New species and notes on the classification of Field Crickets genera Anurogryllus and Gryllus (Orthoptera: Gryllidae: Gryllinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Cadena-Castañeda, Oscar J., Díaz, Carlos Julio Arango, López, Víctor Hugo Grande, Cárdenas, Andrea Del Pilar Floréz (2021): Studies on Neotropical crickets: New species and notes on the classification of Field Crickets genera Anurogryllus and Gryllus (Orthoptera: Gryllidae: Gryllinae). Zootaxa 4970 (3): 515-532, DOI: https://doi.org/10.11646/zootaxa.4970.3.4
DOI: 10.5517/ccdc.csd.cc24wp91
title: CCDC 1992813: Experimental Crystal Structure Determination

description: Related Article: Jhonny Willians de Oliveira Maciel, Maykon Alves Lemes, Ana Karoline Valdo, Renato Rabelo, Felipe Terra Martins, Lauro June Queiroz Maia, Ricardo Costa de Santana, Francesc Lloret, Miguel Julve, Danielle Cangussu|2021|Inorg.Chem.|60|6176|doi:10.1021/acs.inorgchem.0c03226
DOI: 10.5517/ccdc.csd.cc24wpb2
title: CCDC 1992814: Experimental Crystal Structure Determination

description: Related Article: Jhonny Willians de Oliveira Maciel, Maykon Alves Lemes, Ana Karoline Valdo, Renato Rabelo, Felipe Terra Martins, Lauro June Queiroz Maia, Ricardo Costa de Santana, Francesc Lloret, Miguel Julve, Danielle Cangussu|2021|Inorg.Chem.|60|6176|doi:10.1021/acs.inorgchem.0c03226