cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4106257
title: Effective radiative forcing (ERF) experiments with EC-Earth3

description: # Effective radiative forcing (ERF) experiments with EC-Earth3 Contact: klaus.wyser@smhi.se<br> Date: 2020-10-19 The tarball contains the datasets that have been used to assess the ERF of the modified MACv2-SP forcing that has been prepared for the CovidMIP experiments. All experiments were done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth) that includes the MACv2-SP aerosol forcing including the effects on the cloud droplet activation. Following the CFMIP protocol the ERF is obtained by running the model in atmosphere-only mode with prescribed SST and sea-ice from the piControl simulation. Two experiments are needed to assess the ERF: a control experiment (mctl) in which the all forcings are kept constant at the pre-industrial level, and a sensitivity experiment in which only the MACv2-SP forcing is taken from a different scenario from a specific year. Both control and sensitivty experiment are then run for 55 years.<br> The following MACv2-SP forcings were used in the different experiments:<br> - **mctl**: all forcings as in the piControl experiment<br> - **mba2**: forcing for year 2020 from the new ssp245 baseline dataset<br> - **mba5**: forcing for year 2050 from the new ssp245 baseline dataset<br> - **mbl2**: forcing for year 2020 from the TwoYearBlip dataset<br> - **msg5**: forcing for year 2050 from the StrongGreen recovery dataset<br> - **mmg5**: forcing for year 2050 from the ModerateGreen recovery dataset<br> - **mff5**: forcing for year 2050 from the FossilFuel rebound dataset The following variables from the model output are included in the dataset:<br> - **tsr**: Top of the atmosphere (TOA) SW radiation (all sky)<br> - **tsrc**: TOA SW radiation (clear sky)<br> - **tsra**: TOA SW radiation (cloudy sky)<br> - **ttr**: TOA LW radiation (all sky)<br> - **ttrc**: TOA LW radiation (clear sky)<br> - **ttra**: TOA LW radiation (cloudy sky)<br> - **ssr**: Surface SW radiation (all sky)<br> - **ssrc**: Surface SW radiation (clear sky)<br> - **ssra**: Surface SW radiation (cloudy sky)<br> - **str**: Surface LW radiation (all sky)<br> - **strc**: Surface LW radiation (clear sky)<br> - **stra**: Surface LW radiation (cloudy sky)<br> These variables have been used to compute timeseries and maps of the different components of the ERF:<br> - **erf**: ERF at TOA (all sky)<br> - **erfc**: ERF at TOA (clear sky)<br> - **erfa**: ERF at TOA (cloudy sky)<br> - **erfsfc**: ERF at SFC (all sky)<br> - **erfsfcc**: ERF at SFC (clear sky)<br> - **erfsfca**: ERF at SFC (cloudy sky) The script **stats.sh** has been used to compute the ERF.
DOI: 10.5281/zenodo.4106413
title: Electric-Field Control of Neon Uptake and Release to and from Clathrate Hydrates

description: The thermodynamic and kinetic properties of neon uptake and release to and from a sII clathrate hydrate were evaluated using molecular dynamics simulation in the absence and presence of an applied static electric field. In the case of neon “leakage”, neon clathrate in contact with a vacuum was simulated at temperatures ranging from 200 to 225 K for 0.5 μs, with progressive neon-emptying of cages monitored. Activation energies for release and uptake were 16.4 and 14.9 kJ/mol, respectively—consistent with experimentally determined values; for neon release in a 0.7 V/nm electric field, the value declines to 6.5 kJ/mol, suggesting its use as a control agent in facilitating gas release from already-loaded clathrates. For neon uptake into an initially empty hydrate, loading free energies for 512 and 51264 cages, computed via thermodynamic integration, was lower than that previously reported for methane uptake into sI hydrates.
DOI: 10.5281/zenodo.4106412
title: Electric-Field Control of Neon Uptake and Release to and from Clathrate Hydrates

description: The thermodynamic and kinetic properties of neon uptake and release to and from a sII clathrate hydrate were evaluated using molecular dynamics simulation in the absence and presence of an applied static electric field. In the case of neon “leakage”, neon clathrate in contact with a vacuum was simulated at temperatures ranging from 200 to 225 K for 0.5 μs, with progressive neon-emptying of cages monitored. Activation energies for release and uptake were 16.4 and 14.9 kJ/mol, respectively—consistent with experimentally determined values; for neon release in a 0.7 V/nm electric field, the value declines to 6.5 kJ/mol, suggesting its use as a control agent in facilitating gas release from already-loaded clathrates. For neon uptake into an initially empty hydrate, loading free energies for 512 and 51264 cages, computed via thermodynamic integration, was lower than that previously reported for methane uptake into sI hydrates.
DOI: 10.5281/zenodo.4091167
title: Global climate fields for the early Cambrian simulated using the FOAM ocean-atmosphere general circulation model

description: These files contain the output of early Cambrian global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. The files include four hypothesized early Cambrian continental configurations, the topography-bathymetry files of which are also supplied. Global climate fields were simulated for each continental configuration at six greenhouse gas forcings: 4, 8, 16, 32, 64, and 128 times pre-industrial atmospheric level (PAL = 280 ppm). Simulations for all greenhouse gas forcings were run using present day orbital parameters. Additionally, there are cold and hot summer orbital parameter (CS and HS) and high and low obliquity orbital parameter (OH and OL) simulations at 32 PAL CO2. The reader is referred to the associated paper for a full description of the model and boundary conditions.
description: NERC (CENTA) PhD studentship NE/L0022493/1 BGS University Funding Initiative (BUFI) S266 Ghent University Special Research Fund (BOF) Fellowship 01P12419 Marie Sklodowska-Curie grant agreement No. 838373
DOI: 10.6084/m9.figshare.13110104
title: Supplementary Material for: What Should Clinicians Tell Patients about Placebo and Nocebo Effects? Practical Considerations Based on Expert Consensus

description: <b><i>Introduction:</i></b> Clinical and laboratory studies demonstrate that placebo and nocebo effects influence various symptoms and conditions after the administration of both inert and active treatments. <b><i>Objective:</i></b> There is an increasing need for up-to-date recommendations on how to inform patients about placebo and nocebo effects in clinical practice and train clinicians how to disclose this information. <b><i>Methods:</i></b> Based on previous clinical recommendations concerning placebo and nocebo effects, a 3-step, invitation-only Delphi study was conducted among an interdisciplinary group of internationally recognized experts. The study consisted of open- and closed-ended survey questions followed by a final expert meeting. The surveys were subdivided into 3 parts: (1) informing patients about placebo effects, (2) informing patients about nocebo effects, and (3) training clinicians how to communicate this information to the patients. <b><i>Results:</i></b> There was consensus that communicating general information about placebo and nocebo effects to patients (e.g., explaining their role in treatment) could be beneficial, but that such information needs to be adjusted to match the specific clinical context (e.g., condition and treatment). Experts also agreed that training clinicians to communicate about placebo and nocebo effects should be a regular and integrated part of medical education that makes use of multiple formats, including face-to-face and online modalities. <b><i>Conclusions:</i></b> The current 3-step Delphi study provides consensus-based recommendations and practical considerations for disclosures about placebo and nocebo effects in clinical practice. Future research is needed on how to optimally tailor information to specific clinical conditions and patients’ needs, and on developing standardized disclosure training modules for clinicians.
DOI: 10.6084/m9.figshare.13110104.v1
title: Supplementary Material for: What Should Clinicians Tell Patients about Placebo and Nocebo Effects? Practical Considerations Based on Expert Consensus

description: <b><i>Introduction:</i></b> Clinical and laboratory studies demonstrate that placebo and nocebo effects influence various symptoms and conditions after the administration of both inert and active treatments. <b><i>Objective:</i></b> There is an increasing need for up-to-date recommendations on how to inform patients about placebo and nocebo effects in clinical practice and train clinicians how to disclose this information. <b><i>Methods:</i></b> Based on previous clinical recommendations concerning placebo and nocebo effects, a 3-step, invitation-only Delphi study was conducted among an interdisciplinary group of internationally recognized experts. The study consisted of open- and closed-ended survey questions followed by a final expert meeting. The surveys were subdivided into 3 parts: (1) informing patients about placebo effects, (2) informing patients about nocebo effects, and (3) training clinicians how to communicate this information to the patients. <b><i>Results:</i></b> There was consensus that communicating general information about placebo and nocebo effects to patients (e.g., explaining their role in treatment) could be beneficial, but that such information needs to be adjusted to match the specific clinical context (e.g., condition and treatment). Experts also agreed that training clinicians to communicate about placebo and nocebo effects should be a regular and integrated part of medical education that makes use of multiple formats, including face-to-face and online modalities. <b><i>Conclusions:</i></b> The current 3-step Delphi study provides consensus-based recommendations and practical considerations for disclosures about placebo and nocebo effects in clinical practice. Future research is needed on how to optimally tailor information to specific clinical conditions and patients’ needs, and on developing standardized disclosure training modules for clinicians.
DOI: 10.6084/m9.figshare.13110107
title: Supplementary Material for: Cystatin C and Risk of Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

description: <b><i>Introduction:</i></b> Cystatin C (Cys C) has been found as a novel biomarker of neurodegenerative diseases, such as dementia and Alzheimer’s disease. Published studies on the role of Cys C as a biomarker of mild cognitive impairment (MCI) have not been reviewed systematically. <b><i>Objective:</i></b> Present meta-analysis was performed to elucidate the association between Cys C and risk of MCI. <b><i>Methods:</i></b> A comprehensive search was performed in PubMed, EMBASE, Cochrane Library, Trip databases, Worldwide Science, and Google Scholar from January 1, 1950, to April 30, 2020. Standardized mean difference (SMD) with 95% confidence interval (CI) using fixed or random effect models were used to calculate summary estimates. Quality of evidence was also assessed using the Diagnostic Accuracy Quality Scale (DAQS) and grading quality of evidence and strength of recommendations approach. <b><i>Results:</i></b> In our meta-analysis, 12 studies with a total of 2,433 MCI patients and 1,034 controls were included. Our findings suggest a strong association between increased levels of Cys C and risk of MCI as compared to control subjects (SMD = 2.39, 95% CI = 0.22–4.57). Subgroup analysis based on ethnicity, a significant association for the high level of Cys C with the risk of MCI was observed in the Asian population (SMD = 1.63, 95% CI = 0.44–2.82) but not in the Caucasian population (SMD = 2.80, 95% CI = [−0.66]−6.26). <b><i>Conclusion:</i></b> Cys C was associated with MCI, and it could be considered as a predictor for the risk of cognitive impairment.
DOI: 10.6084/m9.figshare.13108517.v1
title: R0310_20191105a_videos

description: Rat 0310, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5281/zenodo.4090270
title: Data and code for: Consistent population decline but idiosyncratic range shifts in Alpine orchids under global change

description: Mountains are plant biodiversity hotspots considered particularly vulnerable to multiple environmental changes. Here, we quantify population changes and range-shift dynamics along elevational gradients over the last three decades for c. two-thirds of the orchid species of the European Alps. Local extinctions were more likely for small populations, after habitat alteration, and predominated at the rear edge of species’ ranges. Except for the most thermophilic species and wetland specialists, population density decreased over time. Declines were more pronounced for rear-edge populations possibly due to multiple pressures such as climate warming, habitat alteration, and mismatched ecological interactions. Besides these demographic trends, different species exhibited idiosyncratic range shifts with more than 50% of the species lagging behind climate warming. Our study highlights the importance of long-term monitoring of populations and range distributions at fine spatial resolution to be able to fully understand the consequences of global change for orchids
DOI: 10.5281/zenodo.4090269
title: Data and code for: Consistent population decline but idiosyncratic range shifts in Alpine orchids under global change

description: Mountains are plant biodiversity hotspots considered particularly vulnerable to multiple environmental changes. Here, we quantify population changes and range-shift dynamics along elevational gradients over the last three decades for c. two-thirds of the orchid species of the European Alps. Local extinctions were more likely for small populations, after habitat alteration, and predominated at the rear edge of species’ ranges. Except for the most thermophilic species and wetland specialists, population density decreased over time. Declines were more pronounced for rear-edge populations possibly due to multiple pressures such as climate warming, habitat alteration, and mismatched ecological interactions. Besides these demographic trends, different species exhibited idiosyncratic range shifts with more than 50% of the species lagging behind climate warming. Our study highlights the importance of long-term monitoring of populations and range distributions at fine spatial resolution to be able to fully understand the consequences of global change for orchids
DOI: 10.5281/zenodo.3665734
title: Global reanalysis of riverine water levels at the river mouth

description: Dataset prepared for manuscript "The effect of surge on riverine flood hazard and impact in deltas globally" (Eilander <em>et al </em>2020) This dataset includes water level data and discharge at 3433 river mouth locations globally, including several components of nearshore still water levels based on a model framework for global compound flood simulations. We usedof runoff from tier 2 of the EartH2Observe (E2O) project (Dutra <em>et al</em> 2017, Schellekens <em>et al</em> 2017) with meteorological forcing from ERA-Interim (Dee <em>et al</em> 2011) and MSWEP v1.2 (Beck <em>et al</em> 2017), surge levels from the Global Tide and Surge Reanalysis (GTSR) based on the GTSM model (Muis <em>et al</em> 2016), and tide levels from the FES2012 model (Carrere <em>et al</em> 2012). These runoff and dynamic sea level (surge and tide) data were used to force the global river routing model CaMa-Flood (Yamazaki <em>et al</em> 2011) to simulate riverine water levels. The time series of simulated water levels is available for three different scenarios in which the sea level boundary is varied to include tide only (tide), tide and the seasonal component of surge levels (seas) and tide and total surge levels (surge).
DOI: 10.5281/zenodo.4106439
title: NEMO, HIDRA and Tide Gauge Datasets for HIDRA Machine Learning Algorithm Verification

description: Supporting sea level datasets for paper: "HIDRA 1.0: Deep-Learning-Based Ensemble Sea Level Forecastingin the Northern Adriatic" by Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer
DOI: 10.5517/ccdc.csd.cc12yjl4
title: CCDC 1011486: Experimental Crystal Structure Determination

description: Related Article: C. Alvarez-Lorenzo, A. Castiñeiras, A. Frontera, I. García-Santos, J. M. González-Pérez, J. Niclós-Gutiérrez, I. Rodríguez-González, E. Vílchez-Rodríguez, J. K. Zaręba|2020|CrystEngComm|22|6674|doi:10.1039/D0CE01064B
DOI: 10.5517/ccdc.csd.cc12yjm5
title: CCDC 1011487: Experimental Crystal Structure Determination

description: Related Article: C. Alvarez-Lorenzo, A. Castiñeiras, A. Frontera, I. García-Santos, J. M. González-Pérez, J. Niclós-Gutiérrez, I. Rodríguez-González, E. Vílchez-Rodríguez, J. K. Zaręba|2020|CrystEngComm|22|6674|doi:10.1039/D0CE01064B
DOI: 10.5517/ccdc.csd.cc12yjn6
title: CCDC 1011488: Experimental Crystal Structure Determination

description: Related Article: C. Alvarez-Lorenzo, A. Castiñeiras, A. Frontera, I. García-Santos, J. M. González-Pérez, J. Niclós-Gutiérrez, I. Rodríguez-González, E. Vílchez-Rodríguez, J. K. Zaręba|2020|CrystEngComm|22|6674|doi:10.1039/D0CE01064B
DOI: 10.5517/ccdc.csd.cc12yjq8
title: CCDC 1011490: Experimental Crystal Structure Determination

description: Related Article: C. Alvarez-Lorenzo, A. Castiñeiras, A. Frontera, I. García-Santos, J. M. González-Pérez, J. Niclós-Gutiérrez, I. Rodríguez-González, E. Vílchez-Rodríguez, J. K. Zaręba|2020|CrystEngComm|22|6674|doi:10.1039/D0CE01064B
DOI: 10.5517/ccdc.csd.cc12yjr9
title: CCDC 1011491: Experimental Crystal Structure Determination

description: Related Article: C. Alvarez-Lorenzo, A. Castiñeiras, A. Frontera, I. García-Santos, J. M. González-Pérez, J. Niclós-Gutiérrez, I. Rodríguez-González, E. Vílchez-Rodríguez, J. K. Zaręba|2020|CrystEngComm|22|6674|doi:10.1039/D0CE01064B
DOI: 10.5517/ccdc.csd.cc1312z6
title: CCDC 1013947: Experimental Crystal Structure Determination

description: Related Article: Wenchao Tong, Mei Bian, Yongan Feng, Tonglai Zhang, Shuangqi Hu, Li Yang|2020|CrystEngComm|22|6591|doi:10.1039/D0CE01008A
DOI: 10.5517/ccdc.csd.cc1q20mf
title: CCDC 1580864: Experimental Crystal Structure Determination

description: Related Article: Fan Yin, Long Wang, Xiankai Yang, Meihui Liu, Hua Geng, Yi Liao, Qing Liao, Hongbing Fu|2020|New J.Chem.|44|17552|doi:10.1039/D0NJ03297B
DOI: 10.5517/ccdc.csd.cc209r36
title: CCDC 1856407: Experimental Crystal Structure Determination

description: Related Article: Yuki Arakawa, Shunsuke Sasaki, Kazunobu Igawa, Masatoshi Tokita, Gen-ichi Konishi, Hideto Tsuji|2020|New J.Chem.|44|17531|doi:10.1039/D0NJ04426A
DOI: 10.5517/ccdc.csd.cc21mf15
title: CCDC 1895496: Experimental Crystal Structure Determination

description: Related Article: Alex J. Plajer, Felix J. Rizzuto, Hao-Che Niu, Sanha Lee, Jonathan M. Goodman, Dominic S. Wright|2019|Angew.Chem.,Int.Ed.|58|10655|doi:10.1002/anie.201905771
DOI: 10.5517/ccdc.csd.cc21mf26
title: CCDC 1895497: Experimental Crystal Structure Determination

description: Related Article: Alex J. Plajer, Felix J. Rizzuto, Hao-Che Niu, Sanha Lee, Jonathan M. Goodman, Dominic S. Wright|2019|Angew.Chem.,Int.Ed.|58|10655|doi:10.1002/anie.201905771
DOI: 10.5517/ccdc.csd.cc21v3xy
title: CCDC 1901940: Experimental Crystal Structure Determination

description: Related Article: Suat Sari, Didem Kart, Naile Öztürk, F. Betül Kaynak, Melis Gencel, Gülce Taşkor, Arzu Karakurt, Selma Saraç, Şebnem Eşsiz, Sevim Dalkara|2019|Eur.J.Med.Chem.|179|634|doi:10.1016/j.ejmech.2019.06.083
DOI: 10.5517/ccdc.csd.cc21v3yz
title: CCDC 1901941: Experimental Crystal Structure Determination

description: Related Article: Suat Sari, Didem Kart, Naile Öztürk, F. Betül Kaynak, Melis Gencel, Gülce Taşkor, Arzu Karakurt, Selma Saraç, Şebnem Eşsiz, Sevim Dalkara|2019|Eur.J.Med.Chem.|179|634|doi:10.1016/j.ejmech.2019.06.083
DOI: 10.5517/ccdc.csd.cc225m30
title: CCDC 1912021: Experimental Crystal Structure Determination

description: Related Article: Man-Ting Chen, Xiao-Dan Xie, Jin-Xiu Meng, Yong-Cong Ou, Jian-Zhong Wu, Ming-Liang Tong|2020|CrystEngComm|22|6713|doi:10.1039/D0CE01043J
DOI: 10.5517/ccdc.csd.cc225m41
title: CCDC 1912022: Experimental Crystal Structure Determination

description: Related Article: Man-Ting Chen, Xiao-Dan Xie, Jin-Xiu Meng, Yong-Cong Ou, Jian-Zhong Wu, Ming-Liang Tong|2020|CrystEngComm|22|6713|doi:10.1039/D0CE01043J
DOI: 10.5517/ccdc.csd.cc225m52
title: CCDC 1912023: Experimental Crystal Structure Determination

description: Related Article: Man-Ting Chen, Xiao-Dan Xie, Jin-Xiu Meng, Yong-Cong Ou, Jian-Zhong Wu, Ming-Liang Tong|2020|CrystEngComm|22|6713|doi:10.1039/D0CE01043J
DOI: 10.5517/ccdc.csd.cc225m74
title: CCDC 1912025: Experimental Crystal Structure Determination

description: Related Article: Man-Ting Chen, Xiao-Dan Xie, Jin-Xiu Meng, Yong-Cong Ou, Jian-Zhong Wu, Ming-Liang Tong|2020|CrystEngComm|22|6713|doi:10.1039/D0CE01043J
DOI: 10.5517/ccdc.csd.cc2322w5
title: CCDC 1938426: Experimental Crystal Structure Determination

description: Related Article: C. Alvarez-Lorenzo, A. Castiñeiras, A. Frontera, I. García-Santos, J. M. González-Pérez, J. Niclós-Gutiérrez, I. Rodríguez-González, E. Vílchez-Rodríguez, J. K. Zaręba|2020|CrystEngComm|22|6674|doi:10.1039/D0CE01064B
DOI: 10.5517/ccdc.csd.cc249bc5
title: CCDC 1975176: Experimental Crystal Structure Determination

description: Related Article: Yury V. Torubaev, Ivan V. Skabitsky|2020|CrystEngComm|22|6661|doi:10.1039/D0CE01093F
DOI: 10.5517/ccdc.csd.cc24nynf
title: CCDC 1986345: Experimental Crystal Structure Determination

description: Related Article: Sujay Kumar Nandi, Debasish Haldar|2020|New J.Chem.|44|17523|doi:10.1039/D0NJ03769A
DOI: 10.5517/ccdc.csd.cc24wgr8
title: CCDC 1992610: Experimental Crystal Structure Determination

description: Related Article: Xiuli Wang, Yu Liu, Hongyan Lin, Na Xu, Guocheng Liu, Xiang Wang, Zhihan Chang, Jianrong Li|2020|CrystEngComm|22|6626|doi:10.1039/D0CE01139H
DOI: 10.5517/ccdc.csd.cc214j1s
title: CCDC 1881174: Experimental Crystal Structure Determination

description: Related Article: Sakhiul Islam, Baishakhi Pal, Samim Khan, Suvendu Maity, Sanobar Naaz, Prasanta Ghosh, Partha Pratim Ray, Mohammad Hedayetullah Mir|2020|CrystEngComm|22|6720|doi:10.1039/D0CE00831A
DOI: 10.5517/ccdc.csd.cc24l9ln
title: CCDC 1983801: Experimental Crystal Structure Determination

description: Related Article: Mohammad c|2020|CSD Communication|||
description: Related Article: Sakhiul Islam, Baishakhi Pal, Samim Khan, Suvendu Maity, Sanobar Naaz, Prasanta Ghosh, Partha Pratim Ray, Mohammad Hedayetullah Mir|2020|CrystEngComm|22|6720|doi:10.1039/D0CE00831A
DOI: 10.5517/ccdc.csd.cc21lbxx
title: CCDC 1894469: Experimental Crystal Structure Determination

description: Related Article: E. Encarnacion-Thomas, R.D. Sommer, A. Mallia, J. Sloop|2020|IUCrData|5|x200759|doi:10.1107/S2414314620007592
DOI: 10.5517/ccdc.csd.cc25fpdq
title: CCDC 2009153: Experimental Crystal Structure Determination

description: Related Article: A.G. Amaro Hernández, T. Rodríguez Tzompantzi, Á. Dávila García, R.L. Meza-León, S. Bernès|2020|IUCrData|5|x200788|doi:10.1107/S2414314620007889
DOI: 10.5517/ccdc.csd.cc25fv9s
title: CCDC 2009305: Experimental Crystal Structure Determination

description: Related Article: M. Yoo, D. Koh|2020|IUCrData|5|x200792|doi:10.1107/S2414314620007920
DOI: 10.5517/ccdc.csd.cc25g43x
title: CCDC 2009578: Experimental Crystal Structure Determination

description: Related Article: B.R. Srinivasan, S.S. Harmalkar, L.R. D'Souza, S.N. Dhuri|2020|IUCrData|5|x200796|doi:10.1107/S2414314620007968
DOI: 10.5517/ccdc.csd.cc25g842
title: CCDC 2009703: Experimental Crystal Structure Determination

description: Related Article: T. Peppel, C. Wulf, A. Spannenberg|2020|IUCrData|5|x200768|doi:10.1107/S2414314620007683
DOI: 10.5517/ccdc.csd.cc25ggx1
title: CCDC 2009913: Experimental Crystal Structure Determination

description: Related Article: D. Arunkumar, S. Samshuddin, M. Ansar, J.T. Mague, Y. Ramli|2020|IUCrData|5|x200800|doi:10.1107/S2414314620008007