cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4769420
title: Dataset for "The high frequency response correction of eddy covariance fluxes. Part 1: an experimental approach and its interdependence with the time-lag estimation"

description: This record contains codes and data to reproduce figures 3, 4 and 5 in Peltola et al. (2021) and flux time series processed with four different spectral correction methods. Please see more details in Peltola et al. (2021). Peltola, O., Aslan, T., Ibrom, A., Nemitz, E., Rannik, Ü., and Mammarella, I.: The high frequency response correction of eddy covariance fluxes. Part 2: the empirical approach and its interdependence with the time-lag estimation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-479, in review, 2021.
description: {"references": ["Peltola, O., Aslan, T., Ibrom, A., Nemitz, E., Rannik, \u00dc., and Mammarella, I.: The high frequency response correction of eddy covariance fluxes. Part 2: the empirical approach and its interdependence with the time-lag estimation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-479, in review, 2021."]}
DOI: 10.5281/zenodo.4769419
title: Dataset for "The high frequency response correction of eddy covariance fluxes. Part 1: an experimental approach and its interdependence with the time-lag estimation"

description: This record contains codes and data to reproduce figures 3, 4 and 5 in Peltola et al. (2021) and flux time series processed with four different spectral correction methods. Please see more details in Peltola et al. (2021). Peltola, O., Aslan, T., Ibrom, A., Nemitz, E., Rannik, Ü., and Mammarella, I.: The high frequency response correction of eddy covariance fluxes. Part 2: the empirical approach and its interdependence with the time-lag estimation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-479, in review, 2021.
description: {"references": ["Peltola, O., Aslan, T., Ibrom, A., Nemitz, E., Rannik, \u00dc., and Mammarella, I.: The high frequency response correction of eddy covariance fluxes. Part 2: the empirical approach and its interdependence with the time-lag estimation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-479, in review, 2021."]}
DOI: 10.5258/soton/d1817
title: Dataset for An assessment of the Performance of the Secure Remote Update Performance in Simulated Real-World Conditions

description: The dataset & analysis to accompany the paper "An assessment of the Performance of the Secure Remote Update Performance in Simulated Real-World Conditions"
DOI: 10.5258/soton/d1741
title: Dataset for: Semantic segmentation of pollen grain images generated from scattering patterns via deep learning

description: This dataset supports the publication: Grant-Jacob, James A., Praeger, Matthew, Eason, Robert W. and Ben Mills (2021). Semantic segmentation of pollen grain images generated from scattering patterns via deep learning. IOP Journal of Physics Communications
DOI: 10.5526/erdr-00000137
title: Effects of thermal acclimation on metabolic and feeding rates
DOI: 10.5523/bris.r3nggcqs5yxk2ea67587zthql
title: Acute Stroke Imaging

description: The data repository contains anonymised imaging and clinical data of hyperacute ischaemic stroke patients admitted to North Bristol NHS Trust Frenchay and Southmead Hospitals (Bristol, UK) and Queen Elizabeth Hospital (Glasgow, UK) between 2014 and 2018.
DOI: 10.6084/m9.figshare.14610102
title: Supplementary Material for: Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers

description: <b><i>Background:</i></b> Pediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers. The aim of this study was to describe at-home recovery of 3 common pediatric respiratory diseases using a questionnaire and wearable device. <b><i>Methods:</i></b> In this study, patients admitted due to pneumonia (<i>n</i> = 30), preschool wheezing (<i>n</i> = 30), and asthma exacerbation (AE; <i>n</i> = 11) were included. Patients were monitored with a smartwatch and a questionnaire during admission, with a 14-day recovery period and a 10-day “healthy” period. Median compliance was calculated, and a mixed-effects model was fitted for physical activity and heart rate (HR) to describe the recovery period, and the physical activity recovery trajectory was correlated to respiratory symptom scores. <b><i>Results:</i></b> Median compliance was 47% (interquartile range [IQR] 33–81%) during the entire study period, 68% (IQR 54–91%) during the recovery period, and 28% (IQR 0–74%) during the healthy period. Patients with pneumonia reached normal physical activity 12 days postdischarge, while subjects with wheezing and AE reached this level after 5 and 6 days, respectively. Estimated mean physical activity was closely correlated with the estimated mean symptom score. HR measured by the smartwatch showed a similar recovery trajectory for subjects with wheezing and asthma, but not for subjects with pneumonia. <b><i>Conclusions:</i></b> The digital biomarkers, physical activity, and HR obtained via smartwatch show promise for quantifying postdischarge recovery in a noninvasive manner, which can be useful in pediatric clinical trials and clinical care.
DOI: 10.6084/m9.figshare.14610102.v1
title: Supplementary Material for: Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers

description: <b><i>Background:</i></b> Pediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers. The aim of this study was to describe at-home recovery of 3 common pediatric respiratory diseases using a questionnaire and wearable device. <b><i>Methods:</i></b> In this study, patients admitted due to pneumonia (<i>n</i> = 30), preschool wheezing (<i>n</i> = 30), and asthma exacerbation (AE; <i>n</i> = 11) were included. Patients were monitored with a smartwatch and a questionnaire during admission, with a 14-day recovery period and a 10-day “healthy” period. Median compliance was calculated, and a mixed-effects model was fitted for physical activity and heart rate (HR) to describe the recovery period, and the physical activity recovery trajectory was correlated to respiratory symptom scores. <b><i>Results:</i></b> Median compliance was 47% (interquartile range [IQR] 33–81%) during the entire study period, 68% (IQR 54–91%) during the recovery period, and 28% (IQR 0–74%) during the healthy period. Patients with pneumonia reached normal physical activity 12 days postdischarge, while subjects with wheezing and AE reached this level after 5 and 6 days, respectively. Estimated mean physical activity was closely correlated with the estimated mean symptom score. HR measured by the smartwatch showed a similar recovery trajectory for subjects with wheezing and asthma, but not for subjects with pneumonia. <b><i>Conclusions:</i></b> The digital biomarkers, physical activity, and HR obtained via smartwatch show promise for quantifying postdischarge recovery in a noninvasive manner, which can be useful in pediatric clinical trials and clinical care.
DOI: 10.5281/zenodo.4575812
title: A small climate-amplifying effect of climate-carbon cycle feedback

description: This is the code for the work in the paper "A small climate-amplifying effect of climate-carbon cycle feedback" by Xuanze Zhang et al., 2021. This code is used to quantify the carbon-concentration feedback parameter (beta) and carbon-climate feedback parameter (gamma) of the global climate-carbon cycle feedback on different timescales (1/frequencies) for a time period with inputs of global CO2 emssions, atmospheric CO2 concentration and global mean surface air temperature time series.
DOI: 10.5281/zenodo.4769388
title: Data Survey PIED 2021

description: Data from the questionnaire of the project "Comprehensive Plan for Digital Education" (PIED). Composed by SQD-Scale (Synthesis of Qualitative Evidence), a self-reporting tool based on a theoretical model called SQD-Model (Tondeur et al., 2012) and "Scale for teachers' ICT integration proficiency" (ICTTIP) (Hsu, 2017). And a series of 22 items based on the Learning Design Support Environment project (Laurillard et al., 2013).
description: This research has been made possible thanks to funding granted by the Consejería de Economía, Ciencia y Agenda Digital from Junta de Extremadura (Spain) and by the European Regional Development Fund of the European Union through the reference grant IB18088.
DOI: 10.5281/zenodo.4769387
title: Data Survey PIED 2021

description: Data from the questionnaire of the project "Comprehensive Plan for Digital Education" (PIED). Composed by SQD-Scale (Synthesis of Qualitative Evidence), a self-reporting tool based on a theoretical model called SQD-Model (Tondeur et al., 2012) and "Scale for teachers' ICT integration proficiency" (ICTTIP) (Hsu, 2017). And a series of 22 items based on the Learning Design Support Environment project (Laurillard et al., 2013).
description: This research has been made possible thanks to funding granted by the Consejería de Economía, Ciencia y Agenda Digital from Junta de Extremadura (Spain) and by the European Regional Development Fund of the European Union through the reference grant IB18088.
DOI: 10.5281/zenodo.4769528
title: Mass spectrometric and quantum chemical treatments of molecular and ionic interactions of flavonoid-glycosides - a stochastic dynamic approach (SUPPORTING INFORMATION)

description: Quantum chemical <em>ab initio</em> and DFT data on charged tautomers of mass spectrometric species at m/z 111, 153 and 579 of a flavonoid-<em>O</em>-glycoside involving frequency analysis of gas-phase, ground and transition states; atomic coordinates and gas-phase thermochemistry, respectively.
DOI: 10.5281/zenodo.4765563
title: Mass spectrometric and quantum chemical treatments of molecular and ionic interactions of flavonoid-glycosides - a stochastic dynamic approach (SUPPORTING INFORMATION)

description: Quantum chemical <em>ab initio</em> and DFT data on charged tautomers of mass spectrometric species at m/z 111, 153 and 579 of a flavonoid-<em>O</em>-glycoside involving frequency analysis of gas-phase, ground and transition states; atomic coordinates and gas-phase thermochemistry, respectively.
DOI: 10.5281/zenodo.4290784
title: Dataset related to "Distribution, behavior and erosion of Uranium in vineyard soils"

description: Dataset and R-code for the data analysis of the study: "Uranium from Phosphate-based fertilizer in vineyard soils: behavior and distribution" Published in Environmental Science and Pollution Research in 2021
DOI: 10.5281/zenodo.4290783
title: Dataset related to "Distribution, behavior and erosion of Uranium in vineyard soils"

description: Dataset and R-code for the data analysis of the study: "Uranium from Phosphate-based fertilizer in vineyard soils: behavior and distribution" Published in Environmental Science and Pollution Research in 2021
DOI: 10.5519/qd.9f53bi14
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (29 records)
DOI: 10.15161/oar.it/73795
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4602864
title: Grey water footprint of pollution discharged from waste water treatment plants in the Czech Republic (2002-2018) – data set

description: The data set contains grey water footprint values for discharges from wastewater treatment plants in the Czech Republic. The grey water footprint is a qualitative indicator converting the discharged pollution into the volume of water needed to dilute pollutants to such an extent that the quality of the ambient water remains above agreed water quality standards. Information on the grey water footprint of individual sources of pollution can be used by all stakeholders involved in water quality assessment, water footprint assessment, environmental impact assessment, sustainability of water resource use, etc. The data on wastewater treatment plants registered in the water balance pursuant to Order No 431/2001 Sb. for the period 2002 to 2018 have been analysed and the water footprint of pollution of influent to treatment plants and of effluent from treatment plants has been established.
description: dot is used as a decimal separator This work was supported by the institutional resources for the long-term development of the T G Masaryk Water Research Institute, public research institution, Prague, Czech Republic (Project No. IG/2019/1644)
description: {"references": ["Ansorge, L., Stejskalov\u00e1, L., & Dlabal, J. (2020). Effect of WWTP size on grey water footprint\u2014Czech Republic case study. Environmental Research Letters, 15(10), 104020. https://doi.org/10.1088/1748-9326/aba6ae", "Ansorge, L., Stejskalov\u00e1, L., & Dlabal, J. (2020). Grey water footprint of point sources of pollution: The Czech Republic study. Journal of Urban and Environmental Engineering, 14(1), 144\u2013149. https://doi.org/10.4090/juee.2020.v14n1.144149", "Ansorge, L., Stejskalov\u00e1, L., Dlabal, J., & \u010cejka, E. (2020). Wp\u0142yw oczyszczalni \u015bciek\u00f3w na redukcj\u0119 zanieczyszcze\u0144 odprowadzanych w czeskiej cz\u0119\u015bci dorzecza Odry [Effect of wastewater treatment plants to the reduction of pollution discharged in the Czech part of the Odra river]. Scientific Review Engineering and Environmental Sciences, 29(2), 123\u2013135. https://doi.org/10.22630/PNIKS.2020.29.2.11"]}
DOI: 10.6084/m9.figshare.14610579.v1
title: RawData_FreezeThaw.csv

description: raw data of freeze-thaw in porcine eyes. filling times assessed using fluorescent canalography before and after freeze thaw
DOI: 10.6084/m9.figshare.14610579
title: RawData_FreezeThaw.csv

description: raw data of freeze-thaw in porcine eyes. filling times assessed using fluorescent canalography before and after freeze thaw
DOI: 10.5517/ccdc.csd.cc27vznr
title: CCDC 2081515: Experimental Crystal Structure Determination

description: Related Article: F.N. Naghiyev, G.Z. Mammadova, A.N. Khalilov, Z. Atioğlu, M. Akkurt, A.A. Akobirshoeva, İ.G. Mamedov|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|600|doi:10.1107/S2056989021004655
DOI: 10.5517/ccdc.csd.cc27wjwk
title: CCDC 2082049: Experimental Crystal Structure Determination

description: Related Article: T. Vu Quoc, L. Phan Thuy, D. Do Ba, D. Tran Thi Thuy, L. Nguyen Ngoc, C. Nguyen Thuy, L. Duong Khanh, H. Ha Manh, H. Thai, K. Le Van, L. Van Meervelt|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|609|doi:10.1107/S2056989021004801
DOI: 10.5517/ccdc.csd.cc27wk2s
title: CCDC 2082055: Experimental Crystal Structure Determination

description: Related Article: S.K. Mohamed, A.I. Said, J.T. Mague, M.F. Aly, M. Akkurt, S.M.I. Elgarhy|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|596|doi:10.1107/S2056989021004813
DOI: 10.5281/zenodo.4751642
title: Data and code for "Triple Andreev dot chains in semiconductor nanowires"

description: Figure Data.zip:<br> This zip file contains datasets and code necessary for generating paper figures.<br> MTX and DAT datasets are in folders named after paper figures.<br> "example.ipynb" is Jupyter notebook generating figures. Data Summary.zip:<br> Plotted extended data beyond paper figures (in PPT). Simulation Code.zip:<br> Simulation code, data, and results. Full Raw Data.zip:<br> Full Original Raw Data for all devices. Data formats:<br> MTX:<br> A simple 2D/3D matrix format developed for Spyview (https://nsweb.tn.tudelft.nl/~gsteele/spyview/). DAT:<br> Original plain-text tabular data. SET:<br> Instrument settings. PY:<br> Measurement scripts.
DOI: 10.5281/zenodo.4751641
title: Data and code for "Triple Andreev dot chains in semiconductor nanowires"

description: Figure Data.zip:<br> This zip file contains datasets and code necessary for generating paper figures.<br> MTX and DAT datasets are in folders named after paper figures.<br> "example.ipynb" is Jupyter notebook generating figures. Data Summary.zip:<br> Plotted extended data beyond paper figures (in PPT). Simulation Code.zip:<br> Simulation code, data, and results. Full Raw Data.zip:<br> Full Original Raw Data for all devices. Data formats:<br> MTX:<br> A simple 2D/3D matrix format developed for Spyview (https://nsweb.tn.tudelft.nl/~gsteele/spyview/). DAT:<br> Original plain-text tabular data. SET:<br> Instrument settings. PY:<br> Measurement scripts.
DOI: 10.6084/m9.figshare.14610630.v1
title: Emissions Characterization in FabLabs: an Additive Manufacturing Environment Issue

description: This data was obtained measuring particles and volatile organic compounds resulted from operation of different additive manufacturing machines ie Laser cutter and 3D printers placed in fabrication laboratory in an University environment.
DOI: 10.6084/m9.figshare.14610630
title: Emissions Characterization in FabLabs: an Additive Manufacturing Environment Issue

description: This data was obtained measuring particles and volatile organic compounds resulted from operation of different additive manufacturing machines ie Laser cutter and 3D printers placed in fabrication laboratory in an University environment.
DOI: 10.5281/zenodo.4769670
title: Data for the comparison of COVID-19 mortality in European and North American geographic entities

description: Data used by Melina Ribaud, Edith Gabriel, Joseph Hughes, Samuel Soubeyrand (Identifying potential significant factors impacting zero-inflated proportions data. 2021. &lt;hal-02936779v3&gt;) for comparing COVD-19-mortality dynamics in Europe and Northern America. Data are provided in two tab-delimited text files (rawFactors.txt and diffFactors.txt) and their format is described in the file data-description.pdf.
DOI: 10.5517/ccdc.csd.cc10mvm3
title: CCDC 942605: Experimental Crystal Structure Determination

description: Related Article: Daniel J. Ramírez, Luis A. Alfonso Herrera, Raúl Colorado-Peralta, Rodolfo Peña Rodríguez, P. K. Camarillo Reyes, Lidia Elena Chiñas, Mario Sánchez, José María Rivera|2021|CrystEngComm|23|3537|doi:10.1039/D0CE00741B
DOI: 10.5517/ccdc.csd.cc1ztc49
title: CCDC 1841621: Experimental Crystal Structure Determination

description: Related Article: Soumen Singha, Rajkumar Jana, Rituparna Mondal, Partha Pratim Ray, Partha Pratim Bag, Kajal Gupta, Nandan Pakhira, Corrado Rizzoli, Arabinda Mallick, Sanjay Kumar, Rajat Saha|2021|CrystEngComm|23|3510|doi:10.1039/D1CE00020A
DOI: 10.5517/ccdc.csd.cc25jzf4
title: CCDC 2012316: Experimental Crystal Structure Determination

description: Related Article: Anowar Hossain, Arka Dey, Saikat Kumar Seth, Partha Pratim Ray, Joaquín Ortega-Castro, Antonio Frontera, Subrata Mukhopadhyay|2021|CrystEngComm|23|3569|doi:10.1039/D1CE00248A
DOI: 10.5517/ccdc.csd.cc25jzh6
title: CCDC 2012318: Experimental Crystal Structure Determination

description: Related Article: Anowar Hossain, Arka Dey, Saikat Kumar Seth, Partha Pratim Ray, Joaquín Ortega-Castro, Antonio Frontera, Subrata Mukhopadhyay|2021|CrystEngComm|23|3569|doi:10.1039/D1CE00248A
DOI: 10.5517/ccdc.csd.cc25txdb
title: CCDC 2020902: Experimental Crystal Structure Determination

description: Related Article: Matilde Fondo, Julio Corredoira-Vázquez, Ana M. García-Deibe, Jesús Sanmartín-Matalobos, Silvia Gómez-Coca, Eliseo Ruiz, Enrique Colacio|2021|Inorg.Chem.Front.|8|2532|doi:10.1039/D1QI00152C
DOI: 10.5517/ccdc.csd.cc25txgd
title: CCDC 2020904: Experimental Crystal Structure Determination

description: Related Article: Matilde Fondo, Julio Corredoira-Vázquez, Ana M. García-Deibe, Jesús Sanmartín-Matalobos, Silvia Gómez-Coca, Eliseo Ruiz, Enrique Colacio|2021|Inorg.Chem.Front.|8|2532|doi:10.1039/D1QI00152C
DOI: 10.5517/ccdc.csd.cc25txmk
title: CCDC 2020909: Experimental Crystal Structure Determination

description: Related Article: Matilde Fondo, Julio Corredoira-Vázquez, Ana M. García-Deibe, Jesús Sanmartín-Matalobos, Silvia Gómez-Coca, Eliseo Ruiz, Enrique Colacio|2021|Inorg.Chem.Front.|8|2532|doi:10.1039/D1QI00152C
DOI: 10.5517/ccdc.csd.cc25z4t4
title: CCDC 2024976: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Rong Sun, Jiefeng Liang, Yuhui Fang, Zheng Liu, Shangda Jiang, Bingwu Wang, Song Gao, Wenliang Huang|2021|Inorg.Chem.Front.|8|2591|doi:10.1039/D1QI00240F
DOI: 10.5517/ccdc.csd.cc25z4v5
title: CCDC 2024977: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Rong Sun, Jiefeng Liang, Yuhui Fang, Zheng Liu, Shangda Jiang, Bingwu Wang, Song Gao, Wenliang Huang|2021|Inorg.Chem.Front.|8|2591|doi:10.1039/D1QI00240F
DOI: 10.5517/ccdc.csd.cc25z4z9
title: CCDC 2024981: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Rong Sun, Jiefeng Liang, Yuhui Fang, Zheng Liu, Shangda Jiang, Bingwu Wang, Song Gao, Wenliang Huang|2021|Inorg.Chem.Front.|8|2591|doi:10.1039/D1QI00240F
DOI: 10.5517/ccdc.csd.cc25z50c
title: CCDC 2024982: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Rong Sun, Jiefeng Liang, Yuhui Fang, Zheng Liu, Shangda Jiang, Bingwu Wang, Song Gao, Wenliang Huang|2021|Inorg.Chem.Front.|8|2591|doi:10.1039/D1QI00240F
DOI: 10.5517/ccdc.csd.cc25z51d
title: CCDC 2024983: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Rong Sun, Jiefeng Liang, Yuhui Fang, Zheng Liu, Shangda Jiang, Bingwu Wang, Song Gao, Wenliang Huang|2021|Inorg.Chem.Front.|8|2591|doi:10.1039/D1QI00240F
DOI: 10.5517/ccdc.csd.cc268z7q
title: CCDC 2034413: Experimental Crystal Structure Determination

description: Related Article: Elvin V. Salerno, Jeff W. Kampf, Vincent L. Pecoraro, Talal Mallah|2021|Inorg.Chem.Front.|8|2611|doi:10.1039/D1QI00207D
DOI: 10.5517/ccdc.csd.cc269q08
title: CCDC 2035119: Experimental Crystal Structure Determination

description: Related Article: Estefania Fernandez-Bartolome, Esther Resines-Urien, María Murillo-Vidal, Lucía Piñeiro-Lopez, José Sánchez Costa|2021|Inorg.Chem.Front.|8|2426|doi:10.1039/D1QI00059D
DOI: 10.5517/ccdc.csd.cc269q19
title: CCDC 2035120: Experimental Crystal Structure Determination

description: Related Article: Estefania Fernandez-Bartolome, Esther Resines-Urien, María Murillo-Vidal, Lucía Piñeiro-Lopez, José Sánchez Costa|2021|Inorg.Chem.Front.|8|2426|doi:10.1039/D1QI00059D
DOI: 10.5517/ccdc.csd.cc269q2b
title: CCDC 2035121: Experimental Crystal Structure Determination

description: Related Article: Estefania Fernandez-Bartolome, Esther Resines-Urien, María Murillo-Vidal, Lucía Piñeiro-Lopez, José Sánchez Costa|2021|Inorg.Chem.Front.|8|2426|doi:10.1039/D1QI00059D
DOI: 10.5517/ccdc.csd.cc26myld
title: CCDC 2044964: Experimental Crystal Structure Determination

description: Related Article: Lev E. Zelenkov, Anastasiya A. Eliseeva, Sergey V. Baykov, Vitalii V. Suslonov, Bartomeu Galmés, Antonio Frontera, Vadim Yu. Kukushkin, Daniil M. Ivanov, Nadezhda A. Bokach|2021|Inorg.Chem.Front.|8|2505|doi:10.1039/D1QI00314C