cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24534777.v1
title: Script 1: virtual flight path and azimuth visualisation (Figure 1) from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: The process of calculating and visualising migratory tracks and the estimated azimuths of different compensation strategies, demonstrated in R Markdown. Dependencies: Sun position detail.csv.
DOI: 10.6084/m9.figshare.24534780
title: Radar weighting data.csv from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: Daily abundance of hoverflies in September detected by vertical-looking radar in three locations in the UK: Chilbolton, Malvern and Rothamsted from 2000-2003. Data are 0-padded to include all day hours.
DOI: 10.6084/m9.figshare.24534780.v1
title: Radar weighting data.csv from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: Daily abundance of hoverflies in September detected by vertical-looking radar in three locations in the UK: Chilbolton, Malvern and Rothamsted from 2000-2003. Data are 0-padded to include all day hours.
DOI: 10.6084/m9.figshare.24534786
title: Sun position detail.csv from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: The compiled sun azimuth and sun elevation data used in this study, which was downloaded from sunearthtools.com. Includes data from September and October in four locations: Falsterbo peninsula (Sweden), Puerto de Bujaruelo (France), Maghreb (Morocco) and Lake Gatun (Panama).
DOI: 10.6084/m9.figshare.24534786.v1
title: Sun position detail.csv from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: The compiled sun azimuth and sun elevation data used in this study, which was downloaded from sunearthtools.com. Includes data from September and October in four locations: Falsterbo peninsula (Sweden), Puerto de Bujaruelo (France), Maghreb (Morocco) and Lake Gatun (Panama).
DOI: 10.6084/m9.figshare.24534789
title: Script 2: flight simulation (Figure 2) from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: The main migration simulation function, results and visualisation, demonstrated in R Markdown. Dependencies: Sun position detail.csv, Radar weighting data.csv.
DOI: 10.6084/m9.figshare.24534789.v1
title: Script 2: flight simulation (Figure 2) from The efficiency of varying methods and degrees of time-compensation for the solar azimuth

description: The main migration simulation function, results and visualisation, demonstrated in R Markdown. Dependencies: Sun position detail.csv, Radar weighting data.csv.
DOI: 10.34740/kaggle/dsv/6931086
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/6931087
title: Mahanagar Yatayat Transport Dataset
DOI: 10.6084/m9.figshare.24534792
title: Data from Embryonic exposure to artificial light at night impairs learning abilities and their covariance with behavioural traits in teleost fish

description: Datasets supporting the study.
DOI: 10.5517/ccdc.csd.cc2gnjf3
title: CCDC 2283845: Experimental Crystal Structure Determination

description: Related Article: Fumihiro Kannen, Manato Nishimura, Kenji Yoza, Takahiro Kusukawa|2023|Tetrahedron|149|133710|doi:10.1016/j.tet.2023.133710
description: Related Article: Takahiro Kusukawa, Seiji Shibata, Fumihiro Kannen, Kenji Yoza|2022|Tetrahedron|111|132735|doi:10.1016/j.tet.2022.132735
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.cc2gglbw
title: CCDC 2278138: Experimental Crystal Structure Determination

description: Related Article: Wen-Ze Li, Yu-Shu Sheng, Xiao-Sa Zhang, Yu Liu, Jing Li, Jian Luan|2023|CrystEngComm|25|6067|doi:10.1039/D3CE00899A
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.cc2ggldy
title: CCDC 2278140: Experimental Crystal Structure Determination

description: Related Article: Wen-Ze Li, Yu-Shu Sheng, Xiao-Sa Zhang, Yu Liu, Jing Li, Jian Luan|2023|CrystEngComm|25|6067|doi:10.1039/D3CE00899A
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.cc2gglcx
title: CCDC 2278139: Experimental Crystal Structure Determination

description: Related Article: Wen-Ze Li, Yu-Shu Sheng, Xiao-Sa Zhang, Yu Liu, Jing Li, Jian Luan|2023|CrystEngComm|25|6067|doi:10.1039/D3CE00899A
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.cc2gglfz
title: CCDC 2278141: Experimental Crystal Structure Determination

description: Related Article: Wen-Ze Li, Yu-Shu Sheng, Xiao-Sa Zhang, Yu Liu, Jing Li, Jian Luan|2023|CrystEngComm|25|6067|doi:10.1039/D3CE00899A
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.cc2f13d0
title: CCDC 2235391: Experimental Crystal Structure Determination

description: Related Article: T.E. Gorelik, P. Lukat, C. Kleeberg, W. Blankenfeldt, R. Mueller|2023|Acta Crystallogr.,Sect.A: Found. Adv.|79|504|doi:10.1107/S2053273323008458
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.24534790.v1
title: Untitled ItemRaw data of the manuscript: Facebook users' engagement with dental caries misinformation in Brazilian Portuguese

description: Data table referring to 500 Facebook posts related to dental caries, in Brazilian Portuguese, obtained through the CrowdTangle platform. Two independent investigators characterized the posts by their time of availability, author’s profile, motivation, the aim of content, content facticity, and sentiment. To determine differences and associations between dichotomized characteristics, the statistical analysis was performed by Mann-Whitney U and Chi-Square tests, and multiple logistic regression models. P values &lt;0.05 were considered significant.
DOI: 10.6084/m9.figshare.24504205
title: Supplementary Material for: Interference of Herbal Medicine with Axitinib in Metastatic Renal Cell Cancer Treatment: a Case Study

description: Introduction: The awareness and the clinical relevance of the potential interactions between standard and complementary medicine are increasing in medical oncology. Nonetheless, the research and experience of the efficacy, safety and toxicity of herbal substances is poorly documented. Case presentation: Here we report the case of a 68-year-old female patient who had been diagnosed with advanced renal cell cancer (mRCC) with metastasis in the liver and pancreas and had undergone surgical resection with hemi-hepatectomy and resection of metastasis in the pancreas in November 2021. Thereafter, chemotherapy was immediately initiated with three-weekly infusions of pembrolizumab and daily intake of the tyrosine kinase inhibitor (TKI) axitinib. Surprisingly, three months after initiation of systemic treatment, the patient developed early progression and metastasis in the liver, which was then treated with selective internal radiotherapy (SIRT). Despite continued axitinib and pembrolizumab treatment, a short-term follow-up in November 2022 revealed another metastatic lesion in her pancreas. Due to the presumed lack of response to treatment, the plasma concentration of axitinib was measured and found to demonstrate sub-therapeutic levels of exposure. Upon extended anamnesis, the patient reported regular intake of herbal substances prescribed by her oncology acupuncturist for gastrointestinal complaints associated with the primary operation. Conclusion: Further clinical-pharmacological workup strikingly demonstrated a reduction of the therapeutic concentration of axitinib of about 90%, likely caused by herbal drugs such as Dang gui and Bai zhu.
DOI: 10.6084/m9.figshare.24356551
title: Supplementary Material for: Thickened Perirenal Fat Predicts Poor Renal Outcome in Patients with IgA Nephropathy: A Population-Based Retrospective Cohort Study

description: Introduction: Perirenal fat is a pad that fills the retroperitoneal space outside the kidney, which affects kidney function in various ways. However, the association between perirenal fat and IgA nephropathy (IgAN) has not yet been elucidated. This study aimed to investigate the role of perirenal fat in predicting IgAN progression. Methods: A total of 473 patients with biopsy-proven IgAN were recruited, and perirenal fat thickness (PFT) was measured using color Doppler ultrasonography. Patients were divided into two groups according to the median PFT: low PFT group (PFT≤1.34 cm, n=239) and high PFT group (PFT&gt;1.35 cm, n=234). A total of 473 healthy participants were included in the control group. Basic clinical characteristics were assessed at the time of renal biopsy, and the relationship between PFT and combined endpoints was analyzed. The combined endpoints were defined as a two-fold increase in blood creatinine level, end-stage renal disease (ESRD), and dialysis over three months. KaplanMeier survival analysis was used to explore the role of PFT in the progression of IgAN. Three clinicopathological models of multivariate Cox regression analysis were established to evaluate the association between PFT and renal prognosis in patients with IgAN. Results: Compared to healthy subjects, patients with IgAN showed significantly higher PFT. After a median follow-up of 50 months, 75 of 473 patients (15.9%) with IgAN reached renal combined endpoints. Among these, 13 in 239 patients (5.4%) were in the low PFT group and 62 of 234 patients (26.5%) were in the high PFT group (p&lt;0.001). The results of three Cox regression models (including demographics, pathological and clinical indicators, and PFT) demonstrated that a higher PFT was significantly associated with a higher risk of reaching combined endpoints in patients with IgAN. Conclusion: This study indicated a positive relationship between PFT and renal progression in patients with IgAN, suggesting that perirenal fat might act as a marker of poor prognosis in patients with IgAN.
DOI: 10.6084/m9.figshare.24356551.v1
title: Supplementary Material for: Thickened Perirenal Fat Predicts Poor Renal Outcome in Patients with IgA Nephropathy: A Population-Based Retrospective Cohort Study

description: Introduction: Perirenal fat is a pad that fills the retroperitoneal space outside the kidney, which affects kidney function in various ways. However, the association between perirenal fat and IgA nephropathy (IgAN) has not yet been elucidated. This study aimed to investigate the role of perirenal fat in predicting IgAN progression. Methods: A total of 473 patients with biopsy-proven IgAN were recruited, and perirenal fat thickness (PFT) was measured using color Doppler ultrasonography. Patients were divided into two groups according to the median PFT: low PFT group (PFT≤1.34 cm, n=239) and high PFT group (PFT&gt;1.35 cm, n=234). A total of 473 healthy participants were included in the control group. Basic clinical characteristics were assessed at the time of renal biopsy, and the relationship between PFT and combined endpoints was analyzed. The combined endpoints were defined as a two-fold increase in blood creatinine level, end-stage renal disease (ESRD), and dialysis over three months. KaplanMeier survival analysis was used to explore the role of PFT in the progression of IgAN. Three clinicopathological models of multivariate Cox regression analysis were established to evaluate the association between PFT and renal prognosis in patients with IgAN. Results: Compared to healthy subjects, patients with IgAN showed significantly higher PFT. After a median follow-up of 50 months, 75 of 473 patients (15.9%) with IgAN reached renal combined endpoints. Among these, 13 in 239 patients (5.4%) were in the low PFT group and 62 of 234 patients (26.5%) were in the high PFT group (p&lt;0.001). The results of three Cox regression models (including demographics, pathological and clinical indicators, and PFT) demonstrated that a higher PFT was significantly associated with a higher risk of reaching combined endpoints in patients with IgAN. Conclusion: This study indicated a positive relationship between PFT and renal progression in patients with IgAN, suggesting that perirenal fat might act as a marker of poor prognosis in patients with IgAN.
DOI: 10.6084/m9.figshare.24270466
title: Supplementary Material for: Real-world Data on the Diagnosis, Treatment and Management of Hepatocellular Carcinoma in the Asia-Pacific: The INSIGHT study

description: Introduction: Hepatocellular carcinoma (HCC) is the sixth most commonly diagnosed cancer and the third leading cause of cancer death worldwide. While there has been rapid evolution in the treatment paradigm of HCC across the past decade, the extent to which these newly approved therapies are utilised in clinical practice in the real world is however, unknown. The INSIGHT study was an investigator-initiated, multi-site longitudinal cohort study conducted to reflect real-world epidemiology and clinical practice in Asia-Pacific in the immediate 7-year period after the conclusion of the BRIDGE study. Methods: Data was collected both retrospectively (planned 30% of total cohort size) and prospectively (planned 70%) from January 2013 to December 2019 from eligible patients newly diagnosed with HCC from 33 participating sites across 9 Asia-Pacific countries. Results: A total of 2,533 newly diagnosed HCC patients (1052 in retrospective cohort and 1481 in prospective cohort) were enrolled. The most common risk factor was hepatitis B in all countries except Japan, Australia and New Zealand, where the prevalence of Hepatitis C and diabetes were more common. The top three comorbidities reported in the INSIGHT study include cirrhosis, hypertension and diabetes. We observe high heterogeneity in the first-line treatment recorded across countries and across disease stage, which significantly affects survival outcomes. Stratification by factors such as etiologies, tumor characteristics, presence of extrahepatic metastases or macrovascular invasion and the use of subsequent lines of treatment were performed. Conclusion: The INSIGHT study describes a wide spectrum of clinical management practices in HCC, where patient demographics, differential costs and patient access to therapies may lead to wide geographical variations through the patient’s treatment cycle, from diagnosis to clinical outcome. The high heterogeneity in patient outcomes demonstrates the need for more robust and clinical management strategies to be designed and adopted to bring about better patient outcomes.
DOI: 10.6084/m9.figshare.24167959
title: Robust Leave-One-Out Cross-Validation for High-Dimensional Bayesian Models

description: Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose asymptotic variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Bayesian LOO-CV criteria. Our method retains the simplicity and computational convenience of classical approaches, while guaranteeing finite asymptotic variance of the resulting estimators. Both theoretical and numerical results are provided to illustrate the improved robustness and efficiency. The computational benefits are particularly significant in high-dimensional problems, allowing to perform Bayesian LOO-CV for a broader range of models, and datasets with highly influential observations. The proposed methodology is easily implementable in standard probabilistic programming software and has a computational cost roughly equivalent to fitting the original model once. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24167959.v2
title: Robust Leave-One-Out Cross-Validation for High-Dimensional Bayesian Models

description: Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose asymptotic variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Bayesian LOO-CV criteria. Our method retains the simplicity and computational convenience of classical approaches, while guaranteeing finite asymptotic variance of the resulting estimators. Both theoretical and numerical results are provided to illustrate the improved robustness and efficiency. The computational benefits are particularly significant in high-dimensional problems, allowing to perform Bayesian LOO-CV for a broader range of models, and datasets with highly influential observations. The proposed methodology is easily implementable in standard probabilistic programming software and has a computational cost roughly equivalent to fitting the original model once. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24167959.v1
title: Robust leave-one-out cross-validation for high-dimensional Bayesian models

description: Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose asymptotic variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Bayesian LOO-CV criteria. Our method retains the simplicity and computational convenience of classical approaches, while guaranteeing finite asymptotic variance of the resulting estimators. Both theoretical and numerical results are provided to illustrate the improved robustness and efficiency. The computational benefits are particularly significant in high-dimensional problems, allowing to perform Bayesian LOO-CV for a broader range of models, and datasets with highly influential observations. The proposed methodology is easily implementable in standard probabilistic programming software and has a computational cost roughly equivalent to fitting the original model once.
DOI: 10.6084/m9.figshare.24127002
title: HAC Covariance Matrix Estimation in Quantile Regression

description: This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroscedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimator and provide an optimal bandwidth selection rule. The quantile sample autocorrelation coefficient is biased toward zero in finite sample which adversely affects the optimal bandwidth estimation. We propose a simple alternative estimator that effectively reduces the finite sample bias. Numerical simulations provide evidence that the proposed HAC covariance matrix estimator significantly improves the size distortion problem. To illustrate the usefulness of the proposed robust standard error, we examine the impacts of the expansion of renewable energy resources on electricity prices. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24127002.v1
title: HAC Covariance Matrix Estimation in Quantile Regression*

description: This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroskedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimator and provide an optimal bandwidth selection rule. The quantile sample autocorrelation coefficient is biased toward zero in finite sample which adversely affects the optimal bandwidth estimation. We propose a simple alternative estimator that effectively reduces the finite sample bias. Numerical simulations provide evidence that the proposed HAC covariance matrix estimator significantly improves the size distortion problem. To illustrate the usefulness of the proposed robust standard error, we examine the impacts of the expansion of renewable energy resources on electricity prices.
DOI: 10.57745/qrwbfc
title: 20231106_PLM_parameters_MY.RDS

description: Table de paramètres d'un des modèles utilisés dans l'article
DOI: 10.57745/ozy2l5
title: 20231106_MY_modelled_curves.RDS

description: Données modélisées de production laitière
DOI: 10.57745/vklll4
title: 20231106_BW_modelled_curves.RDS

description: Données modélisées de poids vif
DOI: 10.57745/rt5fu4
title: 20231106_Parity_table.RDS

description: Table décrivant les animaux (Identifiant, parité, race, ferme d'origine)
DOI: 10.57745/up4qmi
title: 20231109_Diversity_of_performance_pattern_in_dairy_goats_multi_scale_analysis_of_MY_BCS_and_BW_R_script_PCI_.Rmd

description: Script R associé au papier
DOI: 10.6084/m9.figshare.24534694
title: Files of instrument responses

description: The files of instrument response were requested via JWEED (https://ds.iris.edu/ds/nodes/dmc/software/downloads/jweed/) in April 2021. They were used in Yang et al. (2021, https://doi.org/10.1785/0220210232), but some of them have been officially updated. Therefore, we provided the older version of the instrument responses for readers to reproduce our results, especially the values of correction for instrument response (6th column of Table S2 in Yang et al., 2021).