cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23141801.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.23141813
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.23141813.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.23141927
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.23141927.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: 10.3886/e117876v1
title: Process data for the AEA Pre-publication Verification Service

description: The data in this repository is administrative data from the processing of pre-publication manuscripts at the American Economic Association's journal. The AEA Data Editor maintains a service to verify the reproducibility of supplementary materials. The data have been anonymized: references to the identify of the replicator, as well as the manuscript identifier of the specific article have been removed at this time. <br>
DOI: 10.3886/e117876v2
title: Process data for the AEA Pre-publication Verification Service

description: The data in this repository is administrative data from the processing of pre-publication manuscripts at the American Economic Association's journal. The AEA Data Editor maintains a service to verify the reproducibility of supplementary materials. The data have been anonymized: references to the identify of the replicator, as well as the manuscript identifier of the specific article have been removed at this time. <br>
DOI: 10.3886/e117876v3
title: Process data for the AEA Pre-publication Verification Service

description: The data in this repository is administrative data from the processing of pre-publication manuscripts at the American Economic Association's journal. The AEA Data Editor maintains a service to verify the reproducibility of supplementary materials. The data have been anonymized: references to the identify of the replicator, as well as the manuscript identifier of the specific article have been removed at this time. <br>
DOI: 10.48623/aperta.252427
title: Radio Listening Trends Research Data

description: The purpose of this research is to examine the activities of the World Health Organization, which stands out with its international studies in the pandemic, and health ministries in countries with the highest number of cases are seen, from the perspective of digital communication, during the period when the vaccination against the Covid-19 epidemic,which started to show its effect all over the world since March 2019. In the epidemic period, digital platforms have gained importance and been used more than ever by minimizing physical interaction between people and creating communication environments independent of time and place. Governments have also turned to digital platforms that offer instant, unmediated interaction opportunities and have realized the flow of information through these platforms in delivering their policies to the public. In this context, the shares of the World Health Organization and the first two countries with the highest number of cases due to the pandemic, the United States and India's ministries of health, on Twitter, which is a social sharing platform, will be comparatively analyzed using text mining methods. Text mining is an artificial intelligence (AI) technology that uses natural language processing (NLP) to make unstructured text in documents and databases suitable for analysis, in other words, into structured data. RStudio programming language, which is a free and open source software program for data science, will be used in data collection. In the study, firstly, the most frequently used words in the posts will be found, and then the emotions in the statements will be examined by making an analysis of the posts. Sntiment analysis is the process of determining the emotional tone behind a series of words used to ensure understanding of attitudes, ideas and emotions expressed verbally on digital platforms. With the findings obtained, the language used in digital communication activities of the World Health Organization and the ministries of health, which is a public institution, will be systematically defined and analyzed comparatively.
DOI: 10.5281/zenodo.7966347
title: Cassandra Replication Kit

description: The replication kit for the research paper "An Automated Approach to Identify Source Code Files Affected by Architectural Technical Debt" includes the necessary resources to replicate the study. It contains the following components: Steps of the Approach: This document outlines the step-by-step procedure followed in the research to identify source code files affected by architectural technical debt. It provides a detailed explanation of the methodology, techniques, and tools used in the study. Data: The replication kit includes the dataset used in the research. It consists of a collection of source code files from different software projects, which were analyzed to identify architectural technical debt. The dataset may be provided in a specific format or structure for ease of replication. Scripts: The kit contains the scripts used to analyze the source code files and detect architectural technical debt. These scripts may include code snippets, software tools, or custom algorithms developed for the research. They enable the automation of the identification process and facilitate reproducibility. Prototype: The replication kit may include a prototype or a sample implementation of the automated approach to identify source code files affected by architectural technical debt. This prototype serves as an example to demonstrate the practical application of the approach and provides insights into its functionality. By providing these components, the replication kit enables other researchers and practitioners to reproduce the study, validate the findings, and further extend the research in the area of identifying and managing architectural technical debt in source code files.
DOI: 10.5281/zenodo.7966346
title: Cassandra Replication Kit

description: The replication kit for the research paper "An Automated Approach to Identify Source Code Files Affected by Architectural Technical Debt" includes the necessary resources to replicate the study. It contains the following components: Steps of the Approach: This document outlines the step-by-step procedure followed in the research to identify source code files affected by architectural technical debt. It provides a detailed explanation of the methodology, techniques, and tools used in the study. Data: The replication kit includes the dataset used in the research. It consists of a collection of source code files from different software projects, which were analyzed to identify architectural technical debt. The dataset may be provided in a specific format or structure for ease of replication. Scripts: The kit contains the scripts used to analyze the source code files and detect architectural technical debt. These scripts may include code snippets, software tools, or custom algorithms developed for the research. They enable the automation of the identification process and facilitate reproducibility. Prototype: The replication kit may include a prototype or a sample implementation of the automated approach to identify source code files affected by architectural technical debt. This prototype serves as an example to demonstrate the practical application of the approach and provides insights into its functionality. By providing these components, the replication kit enables other researchers and practitioners to reproduce the study, validate the findings, and further extend the research in the area of identifying and managing architectural technical debt in source code files.
DOI: 10.6084/m9.figshare.23141204
title: Image_Collection.xlsx

description: Description of each image in the image collection and relative metadata, like location and author.
DOI: 10.6084/m9.figshare.23141204.v1
title: Image_Collection.xlsx

description: Description of each image in the image collection and relative metadata, like location and author.
DOI: 10.6084/m9.figshare.23137640
title: Complexity_Survey_results.xlsx

description: Raw results from the online survey.
DOI: 10.5281/zenodo.7966406
title: Current situation of the hospitalization of persons without family in Japan and related medical challenges

description: This study aims to determine the approximate number of hospitalizations of persons without family and the medical challenges they encounter in hospitals across Japan. Self-administered questionnaires were mailed to 4,000 randomly selected hospitals nationwide to investigate the actual conditions and problems, decision-making processes, and use of the government-recommended Guidelines for the hospitalization of, and decision-making support for, persons without family.
DOI: 10.5281/zenodo.7966405
title: Current situation of the hospitalization of persons without family in Japan and related medical challenges

description: This study aims to determine the approximate number of hospitalizations of persons without family and the medical challenges they encounter in hospitals across Japan. Self-administered questionnaires were mailed to 4,000 randomly selected hospitals nationwide to investigate the actual conditions and problems, decision-making processes, and use of the government-recommended Guidelines for the hospitalization of, and decision-making support for, persons without family.
DOI: 10.5281/zenodo.7966264
title: Short-Term Effects of Moderate Severity Disturbances on Forest Canopy Structure- Derived Data

description: NEON plot-level derived tables used in this study. All the derived data were openly available from The National Science Foundation's National Ecological Observatory Network (NEON). 01: R codes for 01) LiDAR-derived canopy metrics calculation; 02) Modelling 02: Input ground survey data with removed duplicate rows (tree_rm_duplicates.csv); CSV files - NEON vegestructure data (disturbance_structural_diversity_data_2022_1109_den4.csv) All NEON’s Discrete return LiDAR point cloud (DP1.30003.001) (NEON 2021a) and Vegetation structure (DP1.10098.001) (NEON 2022) data are available on NEON data portal as follows: https://data.neonscience.org/data-products/DP1.30003.001 (https://doi.org/10.48443/6E8K-3343), and https://data.neonscience.org/data-products/DP1.10098.001/RELEASE-2022 (https://doi.org/10.48443/RE8N-TN87), respectively. R scripts for calculating canopy metrics (Hardiman et al. 2021) are available on Environmental Data Initiative (EDI): https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd Reference Hardiman, B.S., E.A. LaRue, J.R. Foster, R. Fahey, and J. Hatala Matthes. 2021. Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd (Accessed 2023-05-24). NEON. 2021a. Discrete return LiDAR point cloud (DP1.30003.001). National Ecological Observatory Network (NEON). doi: https://doi.org/10.48443/6E8K-3343. NEON. 2022. Vegetation structure (DP1.10098.001). National Ecological Observatory Network (NEON). doi: https://doi.org/10.48443/RE8N-TN87.
DOI: 10.5281/zenodo.7966263
title: Short-Term Effects of Moderate Severity Disturbances on Forest Canopy Structure- Derived Data

description: NEON plot-level derived tables used in this study. All the derived data were openly available from The National Science Foundation's National Ecological Observatory Network (NEON). 01: R codes for 01) LiDAR-derived canopy metrics calculation; 02) Modelling 02: Input ground survey data with removed duplicate rows (tree_rm_duplicates.csv); CSV files - NEON vegestructure data (disturbance_structural_diversity_data_2022_1109_den4.csv) All NEON’s Discrete return LiDAR point cloud (DP1.30003.001) (NEON 2021a) and Vegetation structure (DP1.10098.001) (NEON 2022) data are available on NEON data portal as follows: https://data.neonscience.org/data-products/DP1.30003.001 (https://doi.org/10.48443/6E8K-3343), and https://data.neonscience.org/data-products/DP1.10098.001/RELEASE-2022 (https://doi.org/10.48443/RE8N-TN87), respectively. R scripts for calculating canopy metrics (Hardiman et al. 2021) are available on Environmental Data Initiative (EDI): https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd Reference Hardiman, B.S., E.A. LaRue, J.R. Foster, R. Fahey, and J. Hatala Matthes. 2021. Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd (Accessed 2023-05-24). NEON. 2021a. Discrete return LiDAR point cloud (DP1.30003.001). National Ecological Observatory Network (NEON). doi: https://doi.org/10.48443/6E8K-3343. NEON. 2022. Vegetation structure (DP1.10098.001). National Ecological Observatory Network (NEON). doi: https://doi.org/10.48443/RE8N-TN87.
DOI: 10.5281/zenodo.7966440
title: SciExpeM - 10.5281/zenodo.7966440

description: Marrodán, Lorena, 0000-0002-7767-3057; Monge, Fabiola; Millera, Ángela; Bilbao, Rafael; Alzueta, María U. - Combustion Science And Technology, 2016, (188), 719-729
DOI: 10.5281/zenodo.7966439
title: SciExpeM - 10.5281/zenodo.7966440

description: Marrodán, Lorena, 0000-0002-7767-3057; Monge, Fabiola; Millera, Ángela; Bilbao, Rafael; Alzueta, María U. - Combustion Science And Technology, 2016, (188), 719-729
DOI: 10.5281/zenodo.7966442
title: SciExpeM - 10.5281/zenodo.7966442

description: Marrodán, Lorena, 0000-0002-7767-3057; Monge, Fabiola; Millera, Ángela; Bilbao, Rafael; Alzueta, María U. - Combustion Science And Technology, 2016, (188), 719-729
DOI: 10.5281/zenodo.7966441
title: SciExpeM - 10.5281/zenodo.7966442

description: Marrodán, Lorena, 0000-0002-7767-3057; Monge, Fabiola; Millera, Ángela; Bilbao, Rafael; Alzueta, María U. - Combustion Science And Technology, 2016, (188), 719-729
DOI: 10.5281/zenodo.7966444
title: SciExpeM - 10.5281/zenodo.7966444

description: Marrodán, Lorena, 0000-0002-7767-3057; Monge, Fabiola; Millera, Ángela; Bilbao, Rafael; Alzueta, María U. - Combustion Science And Technology, 2016, (188), 719-729
DOI: 10.5281/zenodo.7966443
title: SciExpeM - 10.5281/zenodo.7966444

description: Marrodán, Lorena, 0000-0002-7767-3057; Monge, Fabiola; Millera, Ángela; Bilbao, Rafael; Alzueta, María U. - Combustion Science And Technology, 2016, (188), 719-729