cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8260743
title: Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"

description: Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work : <strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>. Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download here. Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded here and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021). In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification. Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products. IPython notebooks for computing and plotting global maps are also provided : <em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020). <em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid. <em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps. <em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products. <em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs. Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.
DOI: 10.5281/zenodo.8262498
title: Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"

description: Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work : <strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>. Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download here. Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded here and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021). In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification. Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products. IPython notebooks for computing and plotting global maps are also provided : <em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020). <em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid. <em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps. <em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products. <em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs. Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.
DOI: 10.6084/m9.figshare.23259263
title: Supplementary Material for: Risk Factors for Common Kidney Stones Are Correlated with Kidney Function Independent of Stone Composition

description: Background: Kidney stone type varies with age, sex, season, and medical conditions. Lower estimate glomerular filtration rate (eGFR) leads to changes in urine chemistry, and risk factors for kidney stones are thought to vary by stone type. We explore the association between eGFR, urine risk factors, and common stone compositions. Methods: This was a retrospective cohort study of 811 kidney stone patients seen at Yale Medicine between 1994-2021 with serum chemistries and 24-hour urine chemistries matched within one year of baseline stone analysis. Patients’ eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation. Demographics and medical history were compared by Chi-squared tests. 24-hour urine chemistries and stone analyses were analyzed by one-way ANOVA. Linear regressions were performed to control for demographics, comorbidities, and stone composition. Results: With lower eGFR, the proportion of calcium stones declined while uric acid stones increased. On univariable analysis, lower eGFR was associated with lower urine pH, calcium, citrate, uric acid, magnesium, phosphorus, and ammonium. On multivariable analysis, controlling for age, sex, ethnicity, BMI, comorbidities, and stone type, these factors remained significant. Stone formers with lower eGFR had elevated supersaturation for uric acid, but reduced supersaturations for calcium-containing stones. Though urine oxalate was significant on univariable analysis, it was not on multivariable analysis. Discussion/Conclusion: Changes in urine parameters are strongly correlated with eGFR regardless of stone type. Renal function may play a key role in modulating kidney stone risk factors. Strategies to mitigate stone risk may need to vary with kidney function, especially when patient urine or stone composition data is unavailable.
DOI: 10.6084/m9.figshare.23259263.v2
title: Supplementary Material for: Risk Factors for Common Kidney Stones Are Correlated with Kidney Function Independent of Stone Composition

description: Background: Kidney stone type varies with age, sex, season, and medical conditions. Lower estimate glomerular filtration rate (eGFR) leads to changes in urine chemistry, and risk factors for kidney stones are thought to vary by stone type. We explore the association between eGFR, urine risk factors, and common stone compositions. Methods: This was a retrospective cohort study of 811 kidney stone patients seen at Yale Medicine between 1994-2021 with serum chemistries and 24-hour urine chemistries matched within one year of baseline stone analysis. Patients’ eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation. Demographics and medical history were compared by Chi-squared tests. 24-hour urine chemistries and stone analyses were analyzed by one-way ANOVA. Linear regressions were performed to control for demographics, comorbidities, and stone composition. Results: With lower eGFR, the proportion of calcium stones declined while uric acid stones increased. On univariable analysis, lower eGFR was associated with lower urine pH, calcium, citrate, uric acid, magnesium, phosphorus, and ammonium. On multivariable analysis, controlling for age, sex, ethnicity, BMI, comorbidities, and stone type, these factors remained significant. Stone formers with lower eGFR had elevated supersaturation for uric acid, but reduced supersaturations for calcium-containing stones. Though urine oxalate was significant on univariable analysis, it was not on multivariable analysis. Discussion/Conclusion: Changes in urine parameters are strongly correlated with eGFR regardless of stone type. Renal function may play a key role in modulating kidney stone risk factors. Strategies to mitigate stone risk may need to vary with kidney function, especially when patient urine or stone composition data is unavailable.
DOI: 10.6084/m9.figshare.23259263.v1
title: Supplementary Material for: Risk Factors for Common Kidney Stones Are Correlated with Kidney Function Independent of Stone Composition

description: Background: Kidney stone type varies with age, sex, season, and medical conditions. Lower estimate glomerular filtration rate (eGFR) leads to changes in urine chemistry, and risk factors for kidney stones are thought to vary by stone type. We explore the association between eGFR, urine risk factors, and common stone compositions. Methods: This was a retrospective cohort study of 811 kidney stone patients seen at Yale Medicine between 1994-2021 with serum chemistries and 24-hour urine chemistries matched within one year of baseline stone analysis. Patients’ eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation. Demographics and medical history were compared by Chi-squared tests. 24-hour urine chemistries and stone analyses were analyzed by one-way ANOVA. Linear regressions were performed to control for demographics, comorbidities, and stone composition. Results: With lower eGFR, the proportion of calcium stones declined while uric acid stones increased. On univariable analysis, lower eGFR was associated with lower urine pH, calcium, citrate, uric acid, magnesium, phosphorus, and ammonium. On multivariable analysis, controlling for age, sex, ethnicity, BMI, comorbidities, and stone type, these factors remained significant. Stone formers with lower eGFR had elevated supersaturation for uric acid, but reduced supersaturations for calcium-containing stones. Though urine oxalate was significant on univariable analysis, it was not on multivariable analysis. Discussion/Conclusion: Changes in urine parameters are strongly correlated with eGFR regardless of stone type. Renal function may play a key role in modulating kidney stone risk factors. Strategies to mitigate stone risk may need to vary with kidney function, especially when patient urine or stone composition data is unavailable.
DOI: 10.6084/m9.figshare.23141795
title: Supplementary Material for: Risk Factors for Common Kidney Stones Are Correlated with Kidney Function Independent of Stone Composition

description: Background: Kidney stone type varies with age, sex, season, and medical conditions. Lower estimate glomerular filtration rate (eGFR) leads to changes in urine chemistry, and risk factors for kidney stones are thought to vary by stone type. We explore the association between eGFR, urine risk factors, and common stone compositions. Methods: This was a retrospective cohort study of 811 kidney stone patients seen at Yale Medicine between 1994-2021 with serum chemistries and 24-hour urine chemistries matched within one year of baseline stone analysis. Patients’ eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation. Demographics and medical history were compared by Chi-squared tests. 24-hour urine chemistries and stone analyses were analyzed by one-way ANOVA. Linear regressions were performed to control for demographics, comorbidities, and stone composition. Results: With lower eGFR, the proportion of calcium stones declined while uric acid stones increased. On univariable analysis, lower eGFR was associated with lower urine pH, calcium, citrate, uric acid, magnesium, phosphorus, and ammonium. On multivariable analysis, controlling for age, sex, ethnicity, BMI, comorbidities, and stone type, these factors remained significant. Stone formers with lower eGFR had elevated supersaturation for uric acid, but reduced supersaturations for calcium-containing stones. Though urine oxalate was significant on univariable analysis, it was not on multivariable analysis. Discussion/Conclusion: Changes in urine parameters are strongly correlated with eGFR regardless of stone type. Renal function may play a key role in modulating kidney stone risk factors. Strategies to mitigate stone risk may need to vary with kidney function, especially when patient urine or stone composition data is unavailable.
DOI: 10.6084/m9.figshare.23141795.v1
title: Supplementary Material for: Risk Factors for Common Kidney Stones Are Correlated with Kidney Function Independent of Stone Composition

description: Background: Kidney stone type varies with age, sex, season, and medical conditions. Lower estimate glomerular filtration rate (eGFR) leads to changes in urine chemistry, and risk factors for kidney stones are thought to vary by stone type. We explore the association between eGFR, urine risk factors, and common stone compositions. Methods: This was a retrospective cohort study of 811 kidney stone patients seen at Yale Medicine between 1994-2021 with serum chemistries and 24-hour urine chemistries matched within one year of baseline stone analysis. Patients’ eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation. Demographics and medical history were compared by Chi-squared tests. 24-hour urine chemistries and stone analyses were analyzed by one-way ANOVA. Linear regressions were performed to control for demographics, comorbidities, and stone composition. Results: With lower eGFR, the proportion of calcium stones declined while uric acid stones increased. On univariable analysis, lower eGFR was associated with lower urine pH, calcium, citrate, uric acid, magnesium, phosphorus, and ammonium. On multivariable analysis, controlling for age, sex, ethnicity, BMI, comorbidities, and stone type, these factors remained significant. Stone formers with lower eGFR had elevated supersaturation for uric acid, but reduced supersaturations for calcium-containing stones. Though urine oxalate was significant on univariable analysis, it was not on multivariable analysis. Discussion/Conclusion: Changes in urine parameters are strongly correlated with eGFR regardless of stone type. Renal function may play a key role in modulating kidney stone risk factors. Strategies to mitigate stone risk may need to vary with kidney function, especially when patient urine or stone composition data is unavailable.
DOI: 10.5517/ccdc.csd.cc2bxpzy
title: CCDC 2172541: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq00
title: CCDC 2172542: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq11
title: CCDC 2172543: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq22
title: CCDC 2172544: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq33
title: CCDC 2172545: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq44
title: CCDC 2172546: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq55
title: CCDC 2172547: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2bxq66
title: CCDC 2172548: Experimental Crystal Structure Determination

description: Related Article: Evgeny Tretyakov, Pavel Fedyushin, Nadejda Bakuleva, Alexander Korlyukov, Pavel Dorovatovskii, Nina Gritsan, Alexey Dmitriev, Anna Akyeva, Mikhail Syroeshkin, Dmitri Stass, Mikhail Zykin, Nikolay Efimov, Dominique Luneau|2022|J.Org.Chem.|88|10355|doi:10.1021/acs.joc.2c01793
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.cc2cwfk8
title: CCDC 2201110: Experimental Crystal Structure Determination

description: Related Article: Shuai-Cong Huo, Yao Li, Peng-Fei Ji, De-Xiang Zhang, Ying Yang, Herbert W. Roesky|2023|Dalton Trans.|52|10672|doi:10.1039/D3DT02094K
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.cc2cwfl9
title: CCDC 2201111: Experimental Crystal Structure Determination

description: Related Article: Shuai-Cong Huo, Yao Li, Peng-Fei Ji, De-Xiang Zhang, Ying Yang, Herbert W. Roesky|2023|Dalton Trans.|52|10672|doi:10.1039/D3DT02094K
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.23987988
title: Comparative analysis on the clinical characteristics and refraction status of Tibetan and Han strabismus patients: A single-center retrospective study

description: The relevant datas of Comparative analysis on the clinical characteristics and refraction status of Tibetan and Han strabismus patients: A single-center retrospective study
DOI: 10.6084/m9.figshare.23987988.v1
title: Comparative analysis on the clinical characteristics and refraction status of Tibetan and Han strabismus patients: A single-center retrospective study

description: The relevant datas of Comparative analysis on the clinical characteristics and refraction status of Tibetan and Han strabismus patients: A single-center retrospective study