cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2g6d47
title: CCDC 2270258: Experimental Crystal Structure Determination

description: Related Article: Andrey D. Vinokurov, Taygib M. Iliyasov, Kirill A. Karpenko, Yana V. Derkach, Anton A. Ryabov, Sergey V. Gorbunov, Alexander S. Goloveshkin, Anatoly N. Vereshchagin|2023|Tetrahedron|149|133685|doi:10.1016/j.tet.2023.133685
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.cc2grmnj
title: CCDC 2286828: Experimental Crystal Structure Determination

description: Related Article: Wenhao Yan, Samantha Carter, Chi-Tien Hsieh, Jeanette A. Krause, Mu-Jeng Cheng, Shiyu Zhang, Wei Liu|2023|J.Am.Chem.Soc.|145|26152|doi:10.1021/jacs.3c08510
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.cc2grmpk
title: CCDC 2286829: Experimental Crystal Structure Determination

description: Related Article: Wenhao Yan, Samantha Carter, Chi-Tien Hsieh, Jeanette A. Krause, Mu-Jeng Cheng, Shiyu Zhang, Wei Liu|2023|J.Am.Chem.Soc.|145|26152|doi:10.1021/jacs.3c08510
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: 10.5281/zenodo.10390429
title: Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data

description: This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).

First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.



Cl Concentrations - Wang et al. (2021).zip: Original Cl concentrations field from Wang et al. (2021). 

CH4 Fluxes - Saunois et al. (2020).zip: Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).


Second, we store the processed input data generated for the purpose of our study.



CH4 Fluxes - LMDz9696.zip: Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.

d13C Signatures - LMDz9696.zip: δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.

dD Signatures - LMDz9696.zip: δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.

OH O1D Concentrations - LMDz9696-INCA.zip: OH and O1D monthly concentrations simulated with LMDz-INCA.

Masks regions.zip: Masks for the regions used for the input data and the analysis.
DOI: 10.5281/zenodo.10390430
title: Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data

description: This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).

First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.



Cl Concentrations - Wang et al. (2021).zip: Original Cl concentrations field from Wang et al. (2021). 

CH4 Fluxes - Saunois et al. (2020).zip: Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).


Second, we store the processed input data generated for the purpose of our study.



CH4 Fluxes - LMDz9696.zip: Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.

d13C Signatures - LMDz9696.zip: δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.

dD Signatures - LMDz9696.zip: δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.

OH O1D Concentrations - LMDz9696-INCA.zip: OH and O1D monthly concentrations simulated with LMDz-INCA.

Masks regions.zip: Masks for the regions used for the input data and the analysis.
DOI: 10.6084/m9.figshare.23932899.v2
title: Biomass Statistics (Excel)

description: Harmonized statistics at national and sub-national level provide the forest biomass, FAWS and increment. They provide: the forest area (ha), the forest area available for wood supply (FAWS) (ha), the forest area not available for wood supply biomass (FNAWS) (ha), the forest aboveground biomass as AGB stock (t) and AGB/ha (t/ha), the forest aboveground biomass stock available for wood supply (BAWS) (t) and the forest aboveground biomass stock not available for wood supply (BNAWS) (t) for the year 2020; and the forest area (ha), the Forest area Available for Wood Supply (FAWS) (ha), and the Gross Annual Increment (GAI), the Annual Natural Losses (ANL), the Net Annual Increment (NAI) for the forest area and FAWS area for the period 2010 - 2020 (reference year: 2015) in units of volume per year (m<sup>3</sup>/year) and volume per hectare per year (m<sup>3</sup>/ha/year). NB: In this collection, the same statistics are also provided as spatial data in three Shapefile (“Biomass_Statistics.shp”, “FAWS_Statistics.shp”, “Increment_Statistics.shp”), which which provide the biomass, FAWS and increment for different administrative or NUTS units.
DOI: 10.25318/3310073901-eng
title: Actions planned by businesses or organizations to address skills gaps or employee skill deficiencies over the next 12 months, fourth quarter of 2023

description: Actions planned by businesses or organizations to address skills gaps or employee skill deficiencies over the next 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2023.
DOI: 10.25318/3310073901-fra
title: Mesures prévues par les entreprises ou les organismes pour combler les lacunes en matière de compétences ou de personnel au cours des 12 prochains mois, quatrième trimestre de 2023

description: Mesures prévues par les entreprises ou les organismes pour combler les lacunes en matière de compétences ou de personnel au cours des 12 prochains mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2023.
DOI: 10.6084/m9.figshare.21952696
title: Antibiotic appropriateness for Gram-negative bloodstream infections: impact of infectious disease consultation

description: We investigated the role of infectious disease consultation (IDC) on therapeutic appropriateness in Gram-negative bloodstream infections (GNBSIs) in a setting with a high proportion of antibiotic resistance. Secondary outcomes were in-hospital mortality and the impact of rapid diagnostic tests (RDTs). Retrospective study on hospitalised patients with GNBSIs. Therapy was deemed appropriate if it had the narrowest spectrum considering infection and patients’ characteristics. Interventional-IDC (I-IDC) group included patients with IDC-advised first appropriate or last non-appropriate therapy. Time to first appropriate therapy and survival were evaluated by Kaplan-Meier curves. Factors associated with therapy appropriateness were assessed by multivariate Cox proportional-hazard models. 471 patients were included. High antibiotic resistance rates were detected: quinolones 45.5%, third-generation cephalosporins 37.4%, carbapenems 7.9%. I-IDC was performed in 31.6% of patients (149/471), RDTs in 70.7% (333/471). The 7-day probability of appropriate treatment was 91.9% (95% confidence interval [95%CI]: 86.4–95.8%) vs. 75.8% (95%CI: 70.9–80.4%) with and without I-IDC, respectively (<i>p</i>-value = 0.0495); 85.5% (95%CI: 81.3–89.1%) vs. 69.4% (95%CI: 61.3–77.2%) with and without RDTs, respectively (<i>p</i>-value = 0.0023). Compared to RDTs alone, the combination with I-IDC was associated with a higher proportion of appropriate therapies at day 7: 81.9% (95%CI: 76.4–86.7%) vs. 92.6% (95%CI: 86.3–96.7%). At multivariate analysis, I-IDC and RDTs were associated with time to first appropriate therapy [adjusted hazard-ratio 1.292 (95%CI: 1.014–1.647) and 1.383 (95%CI: 1.080–1.771), respectively], with no impact on mortality. In a setting with a high proportion of antibiotic resistance, IDC and RDTs were associated with earlier prescription of appropriate therapy in GNBSIs, without impact on mortality.
DOI: 10.6084/m9.figshare.21952696.v1
title: Antibiotic appropriateness for Gram-negative bloodstream infections: impact of infectious disease consultation

description: We investigated the role of infectious disease consultation (IDC) on therapeutic appropriateness in Gram-negative bloodstream infections (GNBSIs) in a setting with a high proportion of antibiotic resistance. Secondary outcomes were in-hospital mortality and the impact of rapid diagnostic tests (RDTs). Retrospective study on hospitalised patients with GNBSIs. Therapy was deemed appropriate if it had the narrowest spectrum considering infection and patients’ characteristics. Interventional-IDC (I-IDC) group included patients with IDC-advised first appropriate or last non-appropriate therapy. Time to first appropriate therapy and survival were evaluated by Kaplan-Meier curves. Factors associated with therapy appropriateness were assessed by multivariate Cox proportional-hazard models. 471 patients were included. High antibiotic resistance rates were detected: quinolones 45.5%, third-generation cephalosporins 37.4%, carbapenems 7.9%. I-IDC was performed in 31.6% of patients (149/471), RDTs in 70.7% (333/471). The 7-day probability of appropriate treatment was 91.9% (95% confidence interval [95%CI]: 86.4–95.8%) vs. 75.8% (95%CI: 70.9–80.4%) with and without I-IDC, respectively (<i>p</i>-value = 0.0495); 85.5% (95%CI: 81.3–89.1%) vs. 69.4% (95%CI: 61.3–77.2%) with and without RDTs, respectively (<i>p</i>-value = 0.0023). Compared to RDTs alone, the combination with I-IDC was associated with a higher proportion of appropriate therapies at day 7: 81.9% (95%CI: 76.4–86.7%) vs. 92.6% (95%CI: 86.3–96.7%). At multivariate analysis, I-IDC and RDTs were associated with time to first appropriate therapy [adjusted hazard-ratio 1.292 (95%CI: 1.014–1.647) and 1.383 (95%CI: 1.080–1.771), respectively], with no impact on mortality. In a setting with a high proportion of antibiotic resistance, IDC and RDTs were associated with earlier prescription of appropriate therapy in GNBSIs, without impact on mortality.
DOI: 10.25318/3310074001-eng
title: Organization receives revenue in the form of donations from the public and challenges faced in seeking donations from the public, fourth quarter of 2023

description: Organization receives revenue in the form of donations from the public and challenges faced in seeking donations from the public, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2023.
DOI: 10.6084/m9.figshare.24829902
title: Supplementary Material for: Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers

description: Objective 
We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real world population renal cancer data from National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data is used as an anchor for prevalence rate bias assessment. 
Specific gene expression and therefore biology of RCC differ by self-reported race especially between the African American and Caucasian population. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. 
Method 
Data evaluated included the gender, demographic and reported pathologic grading and cancer staging. American Urological Association risk levels were used. Poisson regression was used to estimate the population-based and sample specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. 
Result 
Compared to PUF, KiTS19 and TCGA KIRC over sampled Caucasian by 9.5% (95% CI, -3.7% to 22.7%) and 15.1% (95% CI, 1.5% to 28.8%), under sampled African American by -6.7% (95% CI, -10% to -3.3%), -5.5% (95% CI, -9.3% to -1.8%). Tertiary also under sampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely under sampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate, however heterogeneities in risk are notable. 
Conclusion 
Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.
DOI: 10.6084/m9.figshare.24829902.v1
title: Supplementary Material for: Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers

description: Objective 
We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real world population renal cancer data from National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data is used as an anchor for prevalence rate bias assessment. 
Specific gene expression and therefore biology of RCC differ by self-reported race especially between the African American and Caucasian population. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. 
Method 
Data evaluated included the gender, demographic and reported pathologic grading and cancer staging. American Urological Association risk levels were used. Poisson regression was used to estimate the population-based and sample specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. 
Result 
Compared to PUF, KiTS19 and TCGA KIRC over sampled Caucasian by 9.5% (95% CI, -3.7% to 22.7%) and 15.1% (95% CI, 1.5% to 28.8%), under sampled African American by -6.7% (95% CI, -10% to -3.3%), -5.5% (95% CI, -9.3% to -1.8%). Tertiary also under sampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely under sampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate, however heterogeneities in risk are notable. 
Conclusion 
Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.
DOI: 10.6084/m9.figshare.24829905.v1
title: Supplementary Material for: Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers

description: Objective 
We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real world population renal cancer data from National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data is used as an anchor for prevalence rate bias assessment. 
Specific gene expression and therefore biology of RCC differ by self-reported race especially between the African American and Caucasian population. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. 
Method 
Data evaluated included the gender, demographic and reported pathologic grading and cancer staging. American Urological Association risk levels were used. Poisson regression was used to estimate the population-based and sample specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. 
Result 
Compared to PUF, KiTS19 and TCGA KIRC over sampled Caucasian by 9.5% (95% CI, -3.7% to 22.7%) and 15.1% (95% CI, 1.5% to 28.8%), under sampled African American by -6.7% (95% CI, -10% to -3.3%), -5.5% (95% CI, -9.3% to -1.8%). Tertiary also under sampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely under sampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate, however heterogeneities in risk are notable. 
Conclusion 
Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.
DOI: 10.6084/m9.figshare.24829905
title: Supplementary Material for: Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers

description: Objective 
We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real world population renal cancer data from National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data is used as an anchor for prevalence rate bias assessment. 
Specific gene expression and therefore biology of RCC differ by self-reported race especially between the African American and Caucasian population. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. 
Method 
Data evaluated included the gender, demographic and reported pathologic grading and cancer staging. American Urological Association risk levels were used. Poisson regression was used to estimate the population-based and sample specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. 
Result 
Compared to PUF, KiTS19 and TCGA KIRC over sampled Caucasian by 9.5% (95% CI, -3.7% to 22.7%) and 15.1% (95% CI, 1.5% to 28.8%), under sampled African American by -6.7% (95% CI, -10% to -3.3%), -5.5% (95% CI, -9.3% to -1.8%). Tertiary also under sampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely under sampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate, however heterogeneities in risk are notable. 
Conclusion 
Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.
DOI: 10.6084/m9.figshare.23932899
title: Biomass and Increment Statistics (Excel)

description: Harmonized statistics at national and sub-national level provide the forest biomass, FAWS and increment. They provide: the forest area (ha), the forest area available for wood supply (FAWS) (ha), the forest area not available for wood supply biomass (FNAWS) (ha), the forest aboveground biomass as AGB stock (t) and AGB/ha (t/ha), the forest aboveground biomass stock available for wood supply (BAWS) (t) and the forest aboveground biomass stock not available for wood supply (BNAWS) (t) for the year 2020; and the forest area (ha), the Forest area Available for Wood Supply (FAWS) (ha), and the Gross Annual Increment (GAI), the Annual Natural Losses (ANL), the Net Annual Increment (NAI) for the forest area and FAWS area for the period 2010 - 2020 (reference year: 2015) in units of volume per year (m<sup>3</sup>/year) and volume per hectare per year (m<sup>3</sup>/ha/year). NB: In this collection, the same statistics are also provided as spatial data in three Shapefile (“Biomass_Statistics.shp”, “FAWS_Statistics.shp”, “Increment_Statistics.shp”), which which provide the biomass, FAWS and increment for different administrative or NUTS units.
DOI: 10.6084/m9.figshare.23932899.v3
title: Biomass and Increment Statistics (Excel)

description: Harmonized statistics at national and sub-national level provide the forest biomass, FAWS and increment. They provide: the forest area (ha), the forest area available for wood supply (FAWS) (ha), the forest area not available for wood supply biomass (FNAWS) (ha), the forest aboveground biomass as AGB stock (t) and AGB/ha (t/ha), the forest aboveground biomass stock available for wood supply (BAWS) (t) and the forest aboveground biomass stock not available for wood supply (BNAWS) (t) for the year 2020; and the forest area (ha), the Forest area Available for Wood Supply (FAWS) (ha), and the Gross Annual Increment (GAI), the Annual Natural Losses (ANL), the Net Annual Increment (NAI) for the forest area and FAWS area for the period 2010 - 2020 (reference year: 2015) in units of volume per year (m<sup>3</sup>/year) and volume per hectare per year (m<sup>3</sup>/ha/year). NB: In this collection, the same statistics are also provided as spatial data in three Shapefile (“Biomass_Statistics.shp”, “FAWS_Statistics.shp”, “Increment_Statistics.shp”), which which provide the biomass, FAWS and increment for different administrative or NUTS units.