cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ssdema
title: Occurrence Download

description: A dataset containing 96816 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "GadmGid is USA.29_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 96816 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034212-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.htn9kt
title: Occurrence Download

description: A dataset containing 357777666 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 357777666 records from 10383 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041904-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.6202760
title: Dataset of the paper "A Mixed-Criticality Approach to Fault Tolerance: Integrating Schedulability and Failure Requirements"

description: Dataset for the paper "A Mixed-Criticality Approach to Fault Tolerance: Integrating Schedulability and Failure Requirements" published in RTAS'22 conference
DOI: 10.5281/zenodo.6202759
title: Dataset of the paper "A Mixed-Criticality Approach to Fault Tolerance: Integrating Schedulability and Failure Requirements"

description: Dataset for the paper "A Mixed-Criticality Approach to Fault Tolerance: Integrating Schedulability and Failure Requirements" published in RTAS'22 conference
DOI: 10.6073/pasta/ee8553d972b2d50220cec64b9d57c09f
title: Annual nutrient loading and yield to Plum Island Estuary, as measured at the Ipswich and Parker Dams

description: Nutrient concentrations for various forms of N, P, C, as well as suspended sediments, are determined from monthly grab samples taken at the Ipswich and Parker dams. These nutrient concentrations are then used in conjunction with USGS discharge data (recorded at gages in the Parker River at Byfield, MA and the Ipswich River at Ipswich, MA) to calculate annual nutrient loading to the Estuary, coming over each dam. Annual yield is also calculated for both dams.
DOI: 10.5281/zenodo.6368580
title: Surface emissivity of Mars from MGS Thermal Emission Spectrometer, sampled to Mars Odyssey THEMIS bands 3-9

description: These maps are described in Pankine et al. (submitted to Planetary and Space Science). Surface emissivity was derived from MGS TES data, and then binned at a spatial resolution of 8 pixels-per-degree. The binned surface emissivity was then convolved to THEMIS spectral bandpasses between bands 3-9 (~7.9 to 12.5 microns). Each map covers the range -180 to 180 degrees longitude, and -70 to 70 degrees latitude. Null data values are set to zero. A longitudinal offset of 0.271 degrees was applied so that the maps align with data sets that use the IAU 2000 prime meridian. These products may be uploaded as custom maps into the JMARS GIS software.
DOI: 10.5281/zenodo.6368579
title: Surface emissivity of Mars from MGS Thermal Emission Spectrometer, sampled to Mars Odyssey THEMIS bands 3-9

description: These maps are described in Pankine et al. (submitted to Planetary and Space Science). Surface emissivity was derived from MGS TES data, and then binned at a spatial resolution of 8 pixels-per-degree. The binned surface emissivity was then convolved to THEMIS spectral bandpasses between bands 3-9 (~7.9 to 12.5 microns). Each map covers the range -180 to 180 degrees longitude, and -70 to 70 degrees latitude. Null data values are set to zero. A longitudinal offset of 0.271 degrees was applied so that the maps align with data sets that use the IAU 2000 prime meridian. These products may be uploaded as custom maps into the JMARS GIS software.
DOI: 10.5517/cc5fk04
title: CCDC 161975: Experimental Crystal Structure Determination

description: Related Article: A.A.Karasik, A.V.Krashilina, A.T.Gubaidullin, I.A.Litvinov, V.K.Cherkasov, O.G.Sinyashin, G.A.Abakumov|2000|Izv.Akad.Nauk SSSR,Ser.Khim.||1806|
DOI: 10.5517/cc5fk15
title: CCDC 161976: Experimental Crystal Structure Determination

description: Related Article: A.A.Karasik, A.V.Krashilina, A.T.Gubaidullin, I.A.Litvinov, V.K.Cherkasov, O.G.Sinyashin, G.A.Abakumov|2000|Izv.Akad.Nauk SSSR,Ser.Khim.||1806|
DOI: 10.5517/ccz9gnc
title: CCDC 902833: Experimental Crystal Structure Determination

description: Related Article: Han-dong Yin, Ji-chun Cui|2005|Huaxue Shiji|27|551|
DOI: 10.5517/ccdc.csd.cc22ck12
title: CCDC 1917723: Experimental Crystal Structure Determination

description: Related Article: Tanja Keškić, Božidar Čobeljić, Maja Gruden, Katarina Anđelković, Andrej Pevec, Iztok Turel, Dušanka Radanović, Matija Zlatar|2019|Cryst.Growth Des.|19|4810|doi:10.1021/acs.cgd.9b00760
DOI: 10.5517/ccdc.csd.cc27052j
title: CCDC 2055736: Experimental Crystal Structure Determination

description: Related Article: Wenwen Yu, Chao Chen, Lei Feng, Tianqi Xia, Chen Shi, Yaxi Yang, Bing Zhou|2022|Org.Lett.|24|1762|doi:10.1021/acs.orglett.2c00029
DOI: 10.17028/rd.lboro.19351976
title: Exploring public's perceptions of the process of using COVID-19 immunity certificates

description: This collection of documents contains the dataset (anonymous) of a survey the aim of which was to explore public’s perceptions about their experiences of the immunity certification process. Specifically, we aimed to address the following two research questions: 1. What is the perceived level of confidence in performing a series of actions/tasks for demonstrating proof of immunity? 2. What is the level of agreement with a series of statements relevant to operational issues about immunity certificates? We explored the aforementioned questions both for members of the public who had previous experience in the use of immunity certificates for international travel or domestic purposes, and those who while they had proof of immunity they did not have a chance to use their certificates. To address the aforementioned research questions we ran a UK-wide questionnaire survey (November 2021). The questions of the survey were informed by the findings of a series of focus groups, participatory design workshops and interviews.<br>We gathered responses from a total of 600 individuals who were demographically representative of the UK population in terms of gender, age, and ethnicity. We excluded 28 participants who failed the attention checks, 2 incomplete responses, and 54 participants who were not fully vaccinated nor had previous COVID-19 infection. The rationale for this decision is that the present study is aimed at members of the public who are eligible to use COVID-19 immunity certificates in the UK, therefore the survey questions are only relevant for those who are immune from COVID-19.<br>
DOI: 10.17028/rd.lboro.19351976.v1
title: Exploring public's perceptions of the process of using COVID-19 immunity certificates

description: This collection of documents contains the dataset (anonymous) of a survey the aim of which was to explore public’s perceptions about their experiences of the immunity certification process. Specifically, we aimed to address the following two research questions: 1. What is the perceived level of confidence in performing a series of actions/tasks for demonstrating proof of immunity? 2. What is the level of agreement with a series of statements relevant to operational issues about immunity certificates? We explored the aforementioned questions both for members of the public who had previous experience in the use of immunity certificates for international travel or domestic purposes, and those who while they had proof of immunity they did not have a chance to use their certificates. To address the aforementioned research questions we ran a UK-wide questionnaire survey (November 2021). The questions of the survey were informed by the findings of a series of focus groups, participatory design workshops and interviews.<br>We gathered responses from a total of 600 individuals who were demographically representative of the UK population in terms of gender, age, and ethnicity. We excluded 28 participants who failed the attention checks, 2 incomplete responses, and 54 participants who were not fully vaccinated nor had previous COVID-19 infection. The rationale for this decision is that the present study is aimed at members of the public who are eligible to use COVID-19 immunity certificates in the UK, therefore the survey questions are only relevant for those who are immune from COVID-19.<br>
DOI: 10.5517/ccdc.csd.cc28khy9
title: CCDC 2102201: Experimental Crystal Structure Determination

description: Related Article: Yongxian Li, Jia-Yi Chen, Qiyi Miao, Xin Yu, Lei Feng, Rong-Zhen Liao, Shengfa Ye, Chen-Ho Tung, Wenguang Wang|2022|J.Am.Chem.Soc.|144|4365|doi:10.1021/jacs.1c08609
DOI: 10.5517/ccdc.csd.cc28kj1f
title: CCDC 2102204: Experimental Crystal Structure Determination

description: Related Article: Yongxian Li, Jia-Yi Chen, Qiyi Miao, Xin Yu, Lei Feng, Rong-Zhen Liao, Shengfa Ye, Chen-Ho Tung, Wenguang Wang|2022|J.Am.Chem.Soc.|144|4365|doi:10.1021/jacs.1c08609
DOI: 10.5517/ccdc.csd.cc28kj2g
title: CCDC 2102205: Experimental Crystal Structure Determination

description: Related Article: Yongxian Li, Jia-Yi Chen, Qiyi Miao, Xin Yu, Lei Feng, Rong-Zhen Liao, Shengfa Ye, Chen-Ho Tung, Wenguang Wang|2022|J.Am.Chem.Soc.|144|4365|doi:10.1021/jacs.1c08609
DOI: 10.5517/ccdc.csd.cc291ykw
title: CCDC 2117038: Experimental Crystal Structure Determination

description: Related Article: Xiao-Peng Sun, Zheng Tang, Jiajia Li, Pengtao Ma, Zi-Shuo Yao, Jingping Wang, Jingyang Niu, Jun Tao|2022|Inorg.Chem.|61|4240|doi:10.1021/acs.inorgchem.1c03898
DOI: 10.5517/ccdc.csd.cc292dld
title: CCDC 2117473: Experimental Crystal Structure Determination

description: Related Article: Wei-Na Wang, Ren-Ming Liu, Lei Zhang, Xiong-Li Liu, Yi-Feng Dai, Zhang-Biao Yu, Li-Jun Peng|2022|Org.Biomol.Chem.|20|2227|doi:10.1039/D1OB02432A
DOI: 10.5517/ccdc.csd.cc2937k7
title: CCDC 2118278: Experimental Crystal Structure Determination

description: Related Article: Wei-Na Wang, Ren-Ming Liu, Lei Zhang, Xiong-Li Liu, Yi-Feng Dai, Zhang-Biao Yu, Li-Jun Peng|2022|Org.Biomol.Chem.|20|2227|doi:10.1039/D1OB02432A
DOI: 10.5517/ccdc.csd.cc2937l8
title: CCDC 2118279: Experimental Crystal Structure Determination

description: Related Article: Wei-Na Wang, Ren-Ming Liu, Lei Zhang, Xiong-Li Liu, Yi-Feng Dai, Zhang-Biao Yu, Li-Jun Peng|2022|Org.Biomol.Chem.|20|2227|doi:10.1039/D1OB02432A
DOI: 10.5517/ccdc.csd.cc29hln2
title: CCDC 2130154: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlp3
title: CCDC 2130155: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlq4
title: CCDC 2130156: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlr5
title: CCDC 2130157: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hls6
title: CCDC 2130158: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlt7
title: CCDC 2130159: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlv8
title: CCDC 2130160: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlw9
title: CCDC 2130161: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlxb
title: CCDC 2130162: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlyc
title: CCDC 2130163: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hlzd
title: CCDC 2130164: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hm0g
title: CCDC 2130165: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hm1h
title: CCDC 2130166: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hm2j
title: CCDC 2130167: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5517/ccdc.csd.cc29hm3k
title: CCDC 2130168: Experimental Crystal Structure Determination

description: Related Article: Alejandro Orellana-Silla, Francisco Javier Valverde-Muñoz, M. Carmen Muñoz, Carlos Bartual-Murgui, Sacramento Ferrer, José Antonio Real|2022|Inorg.Chem.|61|4484|doi:10.1021/acs.inorgchem.2c00014
DOI: 10.5255/ukda-sn-855473
title: The Social Distancing and Development Study, 2020-2021

description: The Social Distancing and Development Study (SDDS) aimed to examine how changes in sleep, parenting style, social interactions, screen use and activities affect young children’s language and cognitive development since the Spring 2020 UK lockdown. This longitudinal study followed a cohort of nearly 900 children aged 8 to 36 months of age, enrolled in an online study at the onset or during the Spring 2020 UK lockdown, to capture changes in their environment and measure their impact on children’s vocabulary size and executive function. Since Spring 2020, we have collected data at three additional timepoints: T2 – End of the Spring 2020 lockdown, T3 – November 2020 lockdown, and T4 – One-year follow-up.
DOI: 10.6084/m9.figshare.19115781.v1
title: Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles

description: We present a Bayesian nonparametric model for conditional distribution estimation using Bayesian additive regression trees (BART). The generative model we use is based on rejection sampling from a base model. Like other BART models, our model is flexible, has a default prior specification, and is computationally convenient. To address the distinguished role of the response in our BART model, we introduce an approach to targeted smoothing of BART models which is of independent interest. We study the proposed model theoretically and provide sufficient conditions for the posterior distribution to concentrate at close to the minimax optimal rate adaptively over smoothness classes in the high-dimensional regime in which many predictors are irrelevant. To fit our model, we propose a data augmentation algorithm which allows for existing BART samplers to be extended with minimal effort. We illustrate the performance of our methodology on simulated data and use it to study the relationship between education and body mass index using data from the medical expenditure panel survey (MEPS). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19115781
title: Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles

description: We present a Bayesian nonparametric model for conditional distribution estimation using Bayesian additive regression trees (BART). The generative model we use is based on rejection sampling from a base model. Like other BART models, our model is flexible, has a default prior specification, and is computationally convenient. To address the distinguished role of the response in our BART model, we introduce an approach to targeted smoothing of BART models which is of independent interest. We study the proposed model theoretically and provide sufficient conditions for the posterior distribution to concentrate at close to the minimax optimal rate adaptively over smoothness classes in the high-dimensional regime in which many predictors are irrelevant. To fit our model, we propose a data augmentation algorithm which allows for existing BART samplers to be extended with minimal effort. We illustrate the performance of our methodology on simulated data and use it to study the relationship between education and body mass index using data from the medical expenditure panel survey (MEPS). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19294349
title: Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control

description: Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied. The target GGM is estimated by incorporating the data from similar and related auxiliary studies, where the similarity between the target graph and each auxiliary graph is characterized by the sparsity of a divergence matrix. An estimation algorithm, Trans-CLIME, is proposed and shown to attain a faster convergence rate than the minimax rate in the single-task setting. Furthermore, we introduce a universal debiasing method that can be coupled with a range of initial graph estimators and can be analytically computed in one step. A debiased Trans-CLIME estimator is then constructed and is shown to be element-wise asymptotically normal. This fact is used to construct a multiple testing procedure for edge detection with false discovery rate control. The proposed estimation and multiple testing procedures demonstrate superior numerical performance in simulations and are applied to infer the gene networks in a target brain tissue by leveraging the gene expressions from multiple other brain tissues. A significant decrease in prediction errors and a significant increase in power for link detection are observed. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19294349.v1
title: Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control

description: Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied. The target GGM is estimated by incorporating the data from similar and related auxiliary studies, where the similarity between the target graph and each auxiliary graph is characterized by the sparsity of a divergence matrix. An estimation algorithm, Trans-CLIME, is proposed and shown to attain a faster convergence rate than the minimax rate in the single-task setting. Furthermore, we introduce a universal debiasing method that can be coupled with a range of initial graph estimators and can be analytically computed in one step. A debiased Trans-CLIME estimator is then constructed and is shown to be element-wise asymptotically normal. This fact is used to construct a multiple testing procedure for edge detection with false discovery rate control. The proposed estimation and multiple testing procedures demonstrate superior numerical performance in simulations and are applied to infer the gene networks in a target brain tissue by leveraging the gene expressions from multiple other brain tissues. A significant decrease in prediction errors and a significant increase in power for link detection are observed.
DOI: 10.6084/m9.figshare.16455570
title: Animal personalities generate context dependence in the seed dispersal mutualism

description: These data and code accompany the paper: "Brehm, A.M., & Mortelliti, A. "Small mammal personalities generate context dependence in the seed dispersal mutualism". These data can be used to replicate all main analyses in the paper. These data are not intended for use in other publications. Please contact the authors if you would like to collaborate on a project using these data. If additional comments or questions arise, please contact Alessio Mortelliti (alessio.mortelliti@maine.edu) or Allison Brehm (allibrehm@gmail.com).
DOI: 10.5517/ccdc.csd.ccnchcv
title: CCDC 606867: Experimental Crystal Structure Determination

description: Related Article: Ruma Roy Choudhury, Chirantan Roy Choudhury, Jose Antonio Real, Antonio Frontera, Samiran Mitra|2017|J.Indian Chem.Soc.|94|511|
DOI: 10.5517/ccdc.csd.cc1zs351
title: CCDC 1840413: Experimental Crystal Structure Determination

description: Related Article: R. Raja|2018|CSD Communication|||
description: Related Article: K. Hemanathan, R. Raja, K. Sakthi Murugesan|2019|Int.J.ChemTech.Res.|12|277-4|doi:10.20902/ijctr.2019.120433
DOI: 10.5517/ccdc.csd.cc1zs340
title: CCDC 1840412: Experimental Crystal Structure Determination

description: Related Article: R. Raja|2018|CSD Communication|||
description: Related Article: K. Hemanathan, R. Raja, K. Sakthi Murugesan|2019|Int.J.ChemTech.Res.|12|277-4|doi:10.20902/ijctr.2019.120433
DOI: 10.5517/ccdc.csd.cc291ylx
title: CCDC 2117039: Experimental Crystal Structure Determination

description: Related Article: Xiao-Peng Sun, Zheng Tang, Jiajia Li, Pengtao Ma, Zi-Shuo Yao, Jingping Wang, Jingyang Niu, Jun Tao|2022|Inorg.Chem.|61|4240|doi:10.1021/acs.inorgchem.1c03898
DOI: 10.5517/ccdc.csd.cc27whqc
title: CCDC 2082013: Experimental Crystal Structure Determination

description: Related Article: Yojiro Kimura, Yukihiro Yoshida, Yuki Tanaka, Mitsuhiko Maesato, Tokutaro Komatsu, Hiroshi Kitagawa|2022|Inorg.Chem.|61|4453|doi:10.1021/acs.inorgchem.1c03956
DOI: 10.5517/ccdc.csd.cc29zxv2
title: CCDC 2144885: Experimental Crystal Structure Determination

description: Related Article: Xiao-Peng Sun, Zheng Tang, Jiajia Li, Pengtao Ma, Zi-Shuo Yao, Jingping Wang, Jingyang Niu, Jun Tao|2022|Inorg.Chem.|61|4240|doi:10.1021/acs.inorgchem.1c03898