cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25505/fiz.icsd.cc2d6jcj
title: CSD 2210807: Experimental Crystal Structure Determination

description: Related Article: Rongqing Shang, Allan He, Elizabeth L. Kunz Wille, Na Hyun Jo, James C. Fettinger, Paul C. Canfield, Susan M. Kauzlarich|2023|Inorg.Chem.|62|2694|doi:10.1021/acs.inorgchem.2c03817
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2d6jdk
title: CSD 2210808: Experimental Crystal Structure Determination

description: Related Article: Rongqing Shang, Allan He, Elizabeth L. Kunz Wille, Na Hyun Jo, James C. Fettinger, Paul C. Canfield, Susan M. Kauzlarich|2023|Inorg.Chem.|62|2694|doi:10.1021/acs.inorgchem.2c03817
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2d6jfl
title: CSD 2210809: Experimental Crystal Structure Determination

description: Related Article: Rongqing Shang, Allan He, Elizabeth L. Kunz Wille, Na Hyun Jo, James C. Fettinger, Paul C. Canfield, Susan M. Kauzlarich|2023|Inorg.Chem.|62|2694|doi:10.1021/acs.inorgchem.2c03817
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ddngy
title: CSD 2216700: Experimental Crystal Structure Determination

description: Related Article: Haozhe Wang, Madalynn Marshall, Zhen Wang, Kemp W. Plumb, Martha Greenblatt, Yimei Zhu, David Walker, Weiwei Xie|2023|Inorg.Chem.|62|2161|doi:10.1021/acs.inorgchem.2c03835
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2ddd8h
title: CCDC 2216446: Experimental Crystal Structure Determination

description: Related Article: Yesen Tan, Ying Lv, Liyun Xu, Qinzhen Li, Jinsong Chai, Sha Yang, Haizhu Yu, Manzhou Zhu|2023|J.Am.Chem.Soc.|145|4238|doi:10.1021/jacs.2c13075
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ddnhz
title: CSD 2216701: Experimental Crystal Structure Determination

description: Related Article: Haozhe Wang, Madalynn Marshall, Zhen Wang, Kemp W. Plumb, Martha Greenblatt, Yimei Zhu, David Walker, Weiwei Xie|2023|Inorg.Chem.|62|2161|doi:10.1021/acs.inorgchem.2c03835
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ddxtk
title: CSD 2216959: Experimental Crystal Structure Determination

description: Related Article: Philip Yox, Frank Cerasoli, Arka Sarkar, Victoria Kyveryga, Gayatri Viswanathan, Davide Donadio, Kirill Kovnir|2023|J.Am.Chem.Soc.|145|4638|doi:10.1021/jacs.2c12435
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ddxvl
title: CSD 2216960: Experimental Crystal Structure Determination

description: Related Article: Philip Yox, Frank Cerasoli, Arka Sarkar, Victoria Kyveryga, Gayatri Viswanathan, Davide Donadio, Kirill Kovnir|2023|J.Am.Chem.Soc.|145|4638|doi:10.1021/jacs.2c12435
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2cwvty
title: CSD 2201521: Experimental Crystal Structure Determination

description: Related Article: Achintya Lakshan, Krishnendu Buxi, Arnab Dutta, Fei Wang, Partha Pratim Jana|2023|Inorg.Chem.|62|748|doi:10.1021/acs.inorgchem.2c02928
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2cwvsx
title: CSD 2201520: Experimental Crystal Structure Determination

description: Related Article: Achintya Lakshan, Krishnendu Buxi, Arnab Dutta, Fei Wang, Partha Pratim Jana|2023|Inorg.Chem.|62|748|doi:10.1021/acs.inorgchem.2c02928
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c30bv
title: CCDC 2177636: Experimental Crystal Structure Determination

description: Related Article: Jingjing Pang|2022|CSD Communication|||
description: Related Article: Jing‐Jing Pang, Zhao‐Quan Yao, Kuo Zhang, Quan‐Wen Li, Zi‐Xuan Fu, Ran Zheng, Wei Li, Jian Xu, Xian‐He Bu|2023|Angew.Chem.,Int.Ed.|62|e202217456|doi:10.1002/anie.202217456
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c30cw
title: CCDC 2177637: Experimental Crystal Structure Determination

description: Related Article: Jingjing Pang|2022|CSD Communication|||
description: Related Article: Jing‐Jing Pang, Zhao‐Quan Yao, Kuo Zhang, Quan‐Wen Li, Zi‐Xuan Fu, Ran Zheng, Wei Li, Jian Xu, Xian‐He Bu|2023|Angew.Chem.,Int.Ed.|62|e202217456|doi:10.1002/anie.202217456
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c30fy
title: CCDC 2177639: Experimental Crystal Structure Determination

description: Related Article: Jingjing Pang|2022|CSD Communication|||
description: Related Article: Jing‐Jing Pang, Zhao‐Quan Yao, Kuo Zhang, Quan‐Wen Li, Zi‐Xuan Fu, Ran Zheng, Wei Li, Jian Xu, Xian‐He Bu|2023|Angew.Chem.,Int.Ed.|62|e202217456|doi:10.1002/anie.202217456
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c30gz
title: CCDC 2177640: Experimental Crystal Structure Determination

description: Related Article: Jingjing Pang|2022|CSD Communication|||
description: Related Article: Jing‐Jing Pang, Zhao‐Quan Yao, Kuo Zhang, Quan‐Wen Li, Zi‐Xuan Fu, Ran Zheng, Wei Li, Jian Xu, Xian‐He Bu|2023|Angew.Chem.,Int.Ed.|62|e202217456|doi:10.1002/anie.202217456
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c30h0
title: CCDC 2177641: Experimental Crystal Structure Determination

description: Related Article: Jingjing Pang|2022|CSD Communication|||
description: Related Article: Jing‐Jing Pang, Zhao‐Quan Yao, Kuo Zhang, Quan‐Wen Li, Zi‐Xuan Fu, Ran Zheng, Wei Li, Jian Xu, Xian‐He Bu|2023|Angew.Chem.,Int.Ed.|62|e202217456|doi:10.1002/anie.202217456
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24635505
title: Supplementary Material for: Diagnostic immunocytostaining with Peroxisome Proliferator-Activated Receptor-Gamma in urine cytology samples

description: Introduction: Urine cytology is a common method for detection of urothelial carcinoma (UC), however, is not high sensitivity. Improvement of the accuracy of cytodiagnosis using immunocytostaining as an auxiliary method is needed. This study aimed to determine the cyto-diagnostic usefulness of peroxisome proliferator-activated receptor-gamma (PPAR-γ) immunocytostaining in urine cytology for the detection of urothelial carcinomas, particularly low-grade urothelial carcinomas (LGUC).
Methods: PPAR-γ immunocytostaining was performed for 37 urothelial carcinoma (UC) cases and 26 benign cases. Among the UC cases, 22 cases were of the papillary proliferation type, not including the mixed type comprising both papillary and flat growth. Fifteen LGUC cases of all papillary proliferation types were included. For comparison, the same samples were also immunocytostained for p53 and Ki-67.
Results: Of the UC cases, 25 of 37 were positive for PPAR-γ, while 24 of the 26 benign cases were PPAR-γ-negative. Regardless of histological grading, 13 of the 22 UC cases with papillary proliferation were PPAR-γ-positive. In particular, PPAR-γ immunocytostaining showed higher sensitivity for LGUC cases than that of the other biomarkers. Regarding LGUC specifically, 4 of 10 cases not identified by primary cytology were detected by PPAR-γ immunocytostaining.
Conclusion: PPAR-γ immunocytostaining enhances the accuracy of urine cytodiagnosis. Furthermore, PPAR-γ is a more useful immunobiomarker in urine cytology than p53 and Ki-67, the commonly used immunobiomarkers for malignant cell detection.
DOI: 10.6084/m9.figshare.24635505.v1
title: Supplementary Material for: Diagnostic immunocytostaining with Peroxisome Proliferator-Activated Receptor-Gamma in urine cytology samples

description: Introduction: Urine cytology is a common method for detection of urothelial carcinoma (UC), however, is not high sensitivity. Improvement of the accuracy of cytodiagnosis using immunocytostaining as an auxiliary method is needed. This study aimed to determine the cyto-diagnostic usefulness of peroxisome proliferator-activated receptor-gamma (PPAR-γ) immunocytostaining in urine cytology for the detection of urothelial carcinomas, particularly low-grade urothelial carcinomas (LGUC).
Methods: PPAR-γ immunocytostaining was performed for 37 urothelial carcinoma (UC) cases and 26 benign cases. Among the UC cases, 22 cases were of the papillary proliferation type, not including the mixed type comprising both papillary and flat growth. Fifteen LGUC cases of all papillary proliferation types were included. For comparison, the same samples were also immunocytostained for p53 and Ki-67.
Results: Of the UC cases, 25 of 37 were positive for PPAR-γ, while 24 of the 26 benign cases were PPAR-γ-negative. Regardless of histological grading, 13 of the 22 UC cases with papillary proliferation were PPAR-γ-positive. In particular, PPAR-γ immunocytostaining showed higher sensitivity for LGUC cases than that of the other biomarkers. Regarding LGUC specifically, 4 of 10 cases not identified by primary cytology were detected by PPAR-γ immunocytostaining.
Conclusion: PPAR-γ immunocytostaining enhances the accuracy of urine cytodiagnosis. Furthermore, PPAR-γ is a more useful immunobiomarker in urine cytology than p53 and Ki-67, the commonly used immunobiomarkers for malignant cell detection.
DOI: 10.5281/zenodo.5730153
title: Data set for "Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape"

description: This data set is a compilation of forest stand characteristics, forest-floor environmental conditions, soil properties, ecosystem carbon stocks, and annual forest-floor CO2 fluxes. The data set is composed by 3-year mean annual values obtained from biometric- and chamber-based flux measurements conducted during the period 2016–2018. Negative values of forest-floor CO2 fluxes indicate carbon uptake and positive values indicate carbon release. Data were collected in 50 forest stands within the Krycklan Catchment Study (https://www.slu.se/Krycklan), a multi-scale long-term monitored boreal catchment spanning 68 km2 in northern Sweden. Selected forest stands encompassed different soil types (sediment vs. till), dominant tree species (pine vs. spruce), and age classes (from initiation to old-growth stands).


Variables, units, and definitions are found in the 1_metadata_Martínez-García_et_al._forest-floor_CO2_fluxes.xlsx file


More details can be found in Martínez-García et al. (2022) Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape. Agricultural and Forest Meteorology. 318: 108916. https://doi.org/10.1016/j.agrformet.2022.108916


Contact information:


Ph.D. Eduardo Martínez García (eduardo.martinez@slu.se, edu.martinez.garcia@gmail.com)


Professor Matthias Peichl (matthias.peichl@slu.se)


Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden
DOI: 10.5281/zenodo.5730154
title: Data set for "Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape"

description: This data set is a compilation of forest stand characteristics, forest-floor environmental conditions, soil properties, ecosystem carbon stocks, and annual forest-floor CO2 fluxes. The data set is composed by 3-year mean annual values obtained from biometric- and chamber-based flux measurements conducted during the period 2016–2018. Negative values of forest-floor CO2 fluxes indicate carbon uptake and positive values indicate carbon release. Data were collected in 50 forest stands within the Krycklan Catchment Study (https://www.slu.se/Krycklan), a multi-scale long-term monitored boreal catchment spanning 68 km2 in northern Sweden. Selected forest stands encompassed different soil types (sediment vs. till), dominant tree species (pine vs. spruce), and age classes (from initiation to old-growth stands).


Variables, units, and definitions are found in the 1_metadata_Martínez-García_et_al._forest-floor_CO2_fluxes.xlsx file


More details can be found in Martínez-García et al. (2022) Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape. Agricultural and Forest Meteorology. 318: 108916. https://doi.org/10.1016/j.agrformet.2022.108916


Contact information:


Ph.D. Eduardo Martínez García (eduardo.martinez@slu.se, edu.martinez.garcia@gmail.com)


Professor Matthias Peichl (matthias.peichl@slu.se)


Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden
DOI: 10.6084/m9.figshare.24805710
title: Supplementary Material for: Screening second victims for emotional distress: Assessment of the clinimetric properties of the WITHSTAND-PSY Questionnaire (WS-PSY-Q)

description: Introduction: Adverse events (AEs) are a leading cause of patient morbidity and mortality, greatly impacting healthcare providers’ well-being (second victim (SV) phenomenon). Since it is not accurately captured by existing psychometric instruments, we developed a clinimetric instrument for assessing SVs’ emotional distress before and after an AE.
Methods: Content validity and clinical utility of the WITHSTAND-PSY Questionnaire (WS-PSY-Q) were examined using cognitive interviews. Rasch analysis (n=284) was applied for clinimetric assessment (i.e., construct, concurrent, and clinical validity, internal consistency), considering two crucial psychological facets of the SV phenomenon (1st: Emotional impact of the adverse event, 2nd: Current emotional state). 
Results: The Rasch partial credit model was used. The 1st facet demonstrated overall acceptable clinimetric properties with the subscale anxiety meeting clinimetric threshold values (e.g., all items with ordered thresholds, Loevinger’s coefficient h≥0.40; Person Separation Reliability Index (PSI)=0.7). The 2nd facet showed overall better clinimetric properties for both subscales (e.g., h ≥0.40, PSI=0.82 and 0.79, respectively; ROC area of 0.80 and 0.86, respectively). For both datasets, item fit statistics, except those for item 19, were within the critical range (z-score&lt;±2.5) and meaningful differential functioning analysis was observed for only 4 (out of 24) items. Local dependency was not observed, except for two item couples in the depression subscales. 
 Conclusions: The WS-PSY-Q is the first clinimetric tool assessing SVs’ emotional distress. It should be regarded as part of the armamentarium used by clinicians to assess in-depth healthcare providers’ psychological reactions in the aftermath of an AE to mitigate burnout and allostatic overload.
DOI: 10.20350/digitalcsic/8567
title: Dataset of the article entitled Dietary canthaxanthin reduces xanthophyll uptake and red coloration in adult red-legged partridges

description: Excel Dataset including "initial" and "final" values for red coloration, plasma carotenoids and vitamins of partridges treated with diquat or control (diquat row) and receiving different carotenoids in the diet (group). The dataset also includes values from terminal tissue samples (ornaments, liver, heart and fat) and the carotenoid and vitamin content in the egg yolk. Finally, it also includes initial values as residuals from mixed models controlling for random factors (e.g. lab session) and confounding covariates (red chip hue in leg pictures). These residuals were used as covariates in model testing experimental treatment effects on final values.
DOI: 10.6084/m9.figshare.24828399.v1
title: Supplementary Material for: Performance of a diagnostic model for the presence of unruptured intracranial aneurysms in the general population

description: Introduction: The prevalence of unruptured intracranial aneurysms (UIAs) in the general population is 3%. Aneurysmal subarachnoid hemorrhage (aSAH) can be prevented by screening for UIAs followed by monitoring and, if needed, preventive neurosurgical or endovascular treatment of identified UIAs. Therefore, we developed a diagnostic model for presence of UIAs in the general population to help identify persons at high risk of having UIAs.
Methods: Between 2005-2015, participants from the population-based Rotterdam Study underwent brain magnetic resonance imaging at 1.5 Tesla, on which presence of incidental UIAs was evaluated. We developed a multivariable logistic regression model using candidate diagnostic markers that were selected based on the literature, including sex, age, hypertension, smoking, hypercholesterolemia, diabetes, alcohol, and their interactions. We corrected for overfitting using bootstrapping. Model performance was assessed with discrimination, calibration, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results: 5835 persons were included (55.0% women, mean age 64.9 ± 10.9 years) with a 2.2% UIA prevalence. Sex, age, hypertension, smoking, diabetes, and interactions of sex with age, hypertension, and smoking were independent diagnostic markers. The resulting model had a c-statistic of 0.65 (95% confidence interval [CI] 0.60 – 0.68) and 56% sensitivity, 52% specificity, 98% PPV, and 3% NPV for UIA presence at a cut-off value of 4%. Because of interactions with sex, additional models for men and women separately were developed. The model for men had a c-statistic of 0.70 (95% CI 0.62 – 0.78) with age, hypertension, and smoking as diagnostic markers and comparable additional performance values as for the full model. The model for women had a c-statistic of 0.58 (95% CI 0.52 – 0.63) with smoking as the only diagnostic marker.
Conclusion: Our diagnostic model had insufficient performance to help identify persons at high risk of having UIAs in the general population. Rather, it provides insight in risk factors contributing to UIA risk and shows that these may be in part sex-specific.