cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fvcxl
title: CCDC 2259680: Experimental Crystal Structure Determination

description: Related Article: Peng-Da Liu, Yuan Chen, Ao-Gang Liu, Zi-Tong Chen, Bao Li|2023|J.Mater.Chem.A|11|25322|doi:10.1039/D3TA04403C
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.22939401
title: Weight change among patients engaged in medication treatment for opioid use disorder: a scoping review

description: <i>Background:</i> Medication treatment for opioid use disorder (MOUD) is an instrumental tool in combatting opioid use and overdose. Excess weight gain associated with MOUD initiation is a potential barrier that is not well understood. <i>Objectives:</i> Conduct a scoping review of available studies investigating the effect of MOUD on weight. <i>Methods:</i> Included studies consisted of adults taking any type of MOUD (e.g. methadone, buprenorphine/naloxone, naltrexone) with data on weight or body mass index for at least two time points. Evidence was synthesized using qualitative and descriptive approaches, and predictors of weight gain including demographics, comorbid substance use, and medication dose were examined. <i>Results:</i> Twenty-one unique studies were identified. Most studies were uncontrolled cohort studies or retrospective chart reviews testing the association between methadone and weight gain (<i>n</i> = 16). Studies examining 6 months of methadone treatment reported weight gain ranging from 4.2 to 23.4 pounds. Women appear to gain more weight from methadone than men, while patients using cocaine may gain less. Racial and ethnic disparities were largely unexamined. Only three case reports and two nonrandomized studies examined the effects of either buprenorphine/naloxone or naltrexone, and potential associations with weight gain were not clear. <i>Conclusion:</i> The use of methadone as an MOUD appears to be associated with mild to moderate weight gain. In contrast, there is little data supporting or refuting weight gain with buprenorphine/naloxone or naltrexone. Providers should discuss the potential risk for weight gain with patients as well as prevention and intervention methods for excess weight gain.
DOI: 10.6084/m9.figshare.22939401.v1
title: Weight change among patients engaged in medication treatment for opioid use disorder: a scoping review

description: <i>Background:</i> Medication treatment for opioid use disorder (MOUD) is an instrumental tool in combatting opioid use and overdose. Excess weight gain associated with MOUD initiation is a potential barrier that is not well understood. <i>Objectives:</i> Conduct a scoping review of available studies investigating the effect of MOUD on weight. <i>Methods:</i> Included studies consisted of adults taking any type of MOUD (e.g. methadone, buprenorphine/naloxone, naltrexone) with data on weight or body mass index for at least two time points. Evidence was synthesized using qualitative and descriptive approaches, and predictors of weight gain including demographics, comorbid substance use, and medication dose were examined. <i>Results:</i> Twenty-one unique studies were identified. Most studies were uncontrolled cohort studies or retrospective chart reviews testing the association between methadone and weight gain (<i>n</i> = 16). Studies examining 6 months of methadone treatment reported weight gain ranging from 4.2 to 23.4 pounds. Women appear to gain more weight from methadone than men, while patients using cocaine may gain less. Racial and ethnic disparities were largely unexamined. Only three case reports and two nonrandomized studies examined the effects of either buprenorphine/naloxone or naltrexone, and potential associations with weight gain were not clear. <i>Conclusion:</i> The use of methadone as an MOUD appears to be associated with mild to moderate weight gain. In contrast, there is little data supporting or refuting weight gain with buprenorphine/naloxone or naltrexone. Providers should discuss the potential risk for weight gain with patients as well as prevention and intervention methods for excess weight gain.
DOI: 10.6084/m9.figshare.23799381.v1
title: Many leveled ordinal models for frequency of alcohol and drug use

description: <i>Background:</i> The numbers of days people consume alcohol and other drugs over a fixed interval, such as 28 days, are often collected in surveys of substance use. The presence of an upper bound on these variables can result in response distributions with “ceiling effects.” Also, if some peoples’ substance use behaviors are characterized by weekly patterns of use, summaries of substance days-of-use over longer periods can exhibit multiple modes. <i>Objective:</i> To highlight advantages of ordinal models with a separate level for each distinct survey response, for bounded, and potentially multimodal, count data. <i>Methods:</i> We fitted a Bayesian proportional odds ordinal model to longitudinal cannabis days-of-use reported by 443 individuals who used illicit drugs (206 female, 214 male, 23 non-binary). We specified an ordinal level for each unique response to allow the exact numeric distribution implied by the predicted ordinal response to be inferred. We then compared the fit of the proportional odds model with binomial, negative binomial, hurdle negative binomial and beta-binomial models. <i>Results:</i> Posterior predictive checks and the leave one out information criterion both suggested that the proportional odds model gave a better fit to the cannabis days-of-use data than the other models. Cannabis use among the target population declined during the COVID-19 pandemic in Australia, with the odds of a member of the population exceeding any specified frequency of cannabis use in Wave 4 estimated to be 73% lower than in Wave 1 (median odds ratio 0.27, 90% credible interval 0.19, 0.38). <i>Conclusion:</i> Ordinal models can be suitable for complex count data.
DOI: 10.6084/m9.figshare.22960633
title: Sustainability burden or boost? examining the effect of public debt on renewable energy consumption in Sub-Saharan Africa

description: Given that the development of renewable energy is regarded as a sustainable alternative to the realization of environmental quality, it is not surprising that the discussion of the sustainability of the world’s energy sources continues to expand. While renewable energy has a negligible impact on environmental degradation, developing regions like sub-Saharan Africa (SSA) is restricted by the capital-intensive investment requirements of the burgeoning renewable energy market. To explore the significance of available funding sources on renewable energy development in the region, this study investigates the influence of public debt on renewable energy consumption (REC) in a panel of 29 SSA countries, in full and sub-regional categorizations. A combination of the instrumental variable generalized method of moment (IV-GMM) approach and the two-stage least squares estimator was applied to achieve the goal of the study. Overall, our findings indicate that public debt, carbon emission, financial development, and economic growth exert a negative and significant linkage with renewable energy, while urbanization has a positive and significant influence. Aware of the study findings, appropriate policy prescriptions are proposed to improve the debt-financed funding for the development of the renewable energy sector in SSA.
DOI: 10.6084/m9.figshare.22960633.v1
title: Sustainability burden or boost? examining the effect of public debt on renewable energy consumption in Sub-Saharan Africa

description: Given that the development of renewable energy is regarded as a sustainable alternative to the realization of environmental quality, it is not surprising that the discussion of the sustainability of the world’s energy sources continues to expand. While renewable energy has a negligible impact on environmental degradation, developing regions like sub-Saharan Africa (SSA) is restricted by the capital-intensive investment requirements of the burgeoning renewable energy market. To explore the significance of available funding sources on renewable energy development in the region, this study investigates the influence of public debt on renewable energy consumption (REC) in a panel of 29 SSA countries, in full and sub-regional categorizations. A combination of the instrumental variable generalized method of moment (IV-GMM) approach and the two-stage least squares estimator was applied to achieve the goal of the study. Overall, our findings indicate that public debt, carbon emission, financial development, and economic growth exert a negative and significant linkage with renewable energy, while urbanization has a positive and significant influence. Aware of the study findings, appropriate policy prescriptions are proposed to improve the debt-financed funding for the development of the renewable energy sector in SSA.
DOI: 10.6084/m9.figshare.11542974
title: Robust inference under r-size-biased sampling without replacement from finite population

description: The case of size-biased sampling of known order from a finite population without replacement is considered. The behavior of such a sampling scheme is studied with respect to the sampling fraction. Based on a simulation study, it is concluded that such a sample cannot be treated either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution and as the sampling fraction increases, the biasness in the sample decreases resulting in a transition from an r-size-biased sample to a random sample. A modified version of a likelihood-free method is adopted for making statistical inference for the unknown population parameters, as well as for the size of the population when it is unknown. A simulation study, which takes under consideration the sampling fraction, demonstrates that the proposed method presents better and more robust behavior compared to the approaches, which treat the r-size-biased sample either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution. Finally, a numerical example which motivates this study illustrates our results.
DOI: 10.6084/m9.figshare.11542974.v1
title: Robust inference under r-size-biased sampling without replacement from finite population

description: The case of size-biased sampling of known order from a finite population without replacement is considered. The behavior of such a sampling scheme is studied with respect to the sampling fraction. Based on a simulation study, it is concluded that such a sample cannot be treated either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution and as the sampling fraction increases, the biasness in the sample decreases resulting in a transition from an r-size-biased sample to a random sample. A modified version of a likelihood-free method is adopted for making statistical inference for the unknown population parameters, as well as for the size of the population when it is unknown. A simulation study, which takes under consideration the sampling fraction, demonstrates that the proposed method presents better and more robust behavior compared to the approaches, which treat the r-size-biased sample either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution. Finally, a numerical example which motivates this study illustrates our results.
DOI: 10.6084/m9.figshare.12337025
title: On rank distribution classifiers for high-dimensional data

description: Spatial sign and rank-based methods have been studied in the recent literature, especially when the dimension is smaller than the sample size. In this paper, a classification method based on the distribution of rank functions for high-dimensional data is considered with extension to functional data. The method is fully nonparametric in nature. The performance of the classification method is illustrated in comparison with some other classifiers using simulated and real data sets. Supporting code in R are provided for computational implementation of the classification method that will be of use to others.
DOI: 10.6084/m9.figshare.12848180
title: Assessing uncertainty of voter transitions estimated from aggregated data. Application to the 2017 French presidential election

description: Inferring electoral individual behaviour from aggregated data is a very active research area, with ramifications in sociology and political science. A new approach based on linear programming is proposed to estimate voter transitions among parties (or candidates) between two elections. Compared to other linear and quadratic programming models previously published, our approach presents two important innovations. Firstly, it explicitly deals with new entries and exits in the election census without assuming unrealistic hypotheses, enabling a reasonable estimation of vote behaviour of young electors voting for the first time. Secondly, by exploiting the information contained in the model residuals, we develop a procedure to assess the uncertainty in the estimates. This significantly distinguishes our model from other published mathematical programming methods. The method is illustrated estimating the vote transfer matrix between the first and second rounds of the 2017 French presidential election and measuring its level of uncertainty. Likewise, compared to the most current alternatives based on ecological regression, our approach is considerably simpler and faster, and has provided reasonable results in all the actual elections to which it has been applied. Interested scholars can easily use our procedure with the aid of the R-function provided in the Supplemental Material.
DOI: 10.6084/m9.figshare.12848180.v1
title: Assessing uncertainty of voter transitions estimated from aggregated data. Application to the 2017 French presidential election

description: Inferring electoral individual behaviour from aggregated data is a very active research area, with ramifications in sociology and political science. A new approach based on linear programming is proposed to estimate voter transitions among parties (or candidates) between two elections. Compared to other linear and quadratic programming models previously published, our approach presents two important innovations. Firstly, it explicitly deals with new entries and exits in the election census without assuming unrealistic hypotheses, enabling a reasonable estimation of vote behaviour of young electors voting for the first time. Secondly, by exploiting the information contained in the model residuals, we develop a procedure to assess the uncertainty in the estimates. This significantly distinguishes our model from other published mathematical programming methods. The method is illustrated estimating the vote transfer matrix between the first and second rounds of the 2017 French presidential election and measuring its level of uncertainty. Likewise, compared to the most current alternatives based on ecological regression, our approach is considerably simpler and faster, and has provided reasonable results in all the actual elections to which it has been applied. Interested scholars can easily use our procedure with the aid of the R-function provided in the Supplemental Material.
DOI: 10.6084/m9.figshare.12337025.v1
title: On rank distribution classifiers for high-dimensional data

description: Spatial sign and rank-based methods have been studied in the recent literature, especially when the dimension is smaller than the sample size. In this paper, a classification method based on the distribution of rank functions for high-dimensional data is considered with extension to functional data. The method is fully nonparametric in nature. The performance of the classification method is illustrated in comparison with some other classifiers using simulated and real data sets. Supporting code in R are provided for computational implementation of the classification method that will be of use to others.
DOI: 10.5066/p9mkq2uk
title: Upper Skagit River intrinsic potential results

description: We assessed habitat suitability for salmonids across selected tributaries upstream from three hydroelectric dams on the upper Skagit River in Whatcom County, northern Washington. We used NetMap, a commercial toolset within the ArcMap geographic information system (GIS), to analyze stream attributes based upon a synthetic stream channel network derived from digital elevation models. The GIS-derived stream attributes?including gradient, bankfull width, valley width index, elevation, and stream flow?allowed us to examine the spatial distribution and relative quality of spawning and rearing habitat for salmonids based on existing intrinsic potential (IP) models. As a first step, we created maps of potential anadromous fish distribution by identifying potential migration barriers within the synthetic stream network. Next, we applied a suite of existing IP models for steelhead, Coho, and Chinook Salmon (Oncorhynchus mykiss, O. kisutch, and O. tshawytscha, respectively) to estimate low, medium, and high IP habitat for each species. Three different IP models were used for each species, based on species preference curves from populations from coastal Oregon, northern California, Alaska, and western Washington.
DOI: 10.5281/zenodo.6616637
title: Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland

description: This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland". More details of the data are available in the upcoming data description article (manuscript can be found in this repository).<br> <br> Abstract: <br> Access to good quality green spaces in urban regions is vital for the well-being of citizens. In this article, we present data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities. <strong>Related research article:</strong> Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. <em>Applied Geography</em>, <em>157</em>, 102973. https://doi.org/10.1016/j.apgeog.2023.102973
description: The authors thank the HYVIÖ project team at the Finnish Environment Institute SYKE and Finnish Institute for Health and Welfare (THL). This work would not have been possible without open data from OpenStreetMap contributors, Finnish Environment Institute SYKE and the European Environment Agency (EEA), and open-source software from the open source developer community. Funding: This work was supported by the 'Lähiöohjelma' programme 2020-2022 at the Finnish Ministry of the Environment through the HYVIÖ project (Spatial information and residents' experiences for development of comfortable living environments, decision number VN/10679/2020-YM-3) and the Strategic Research Council (SRC) at Academy of Finland through the STYLE project (Healthy Lifestyles to Boost Sustainable Growth, project number 320402).
DOI: 10.34740/kaggle/ds/4059607
title: YOLO v4 format of the Traffic Signs dataset
DOI: 10.5281/zenodo.8058800
title: CLDF dataset derived from Oliveira's "Contribuições para a Reconstrução do Protopáno" from 2014

description: Cite the source of the dataset as:




Oliveira, Sanderson Castro Soares de. 2014. Contribuições para a reconstrução do Protopáno. Universidade de Brasília, Brasília.
DOI: 10.5281/zenodo.8058801
title: CLDF dataset derived from Oliveira's "Contribuições para a Reconstrução do Protopáno" from 2014

description: Cite the source of the dataset as: Oliveira, Sanderson Castro Soares de (2014). Contribuições para a reconstrução do Protopáno. Universidade de Brasília, Brasília, 2014.
DOI: 10.5281/zenodo.10214008
title: Occurrence data for different plant groups in southern Africa (from GBIF)
DOI: 10.5281/zenodo.10214007
title: Occurrence data for different plant groups in southern Africa (from GBIF)
DOI: 10.18112/openneuro.ds003029.v1.0.7
title: Epilepsy-iEEG-Multicenter-Dataset
DOI: 10.6084/m9.figshare.24429028
title: Data for "Role of Multifidelity Data in Sequential Active Learning Materials Discovery Campaigns: Case Study of Electronic Bandgap"

description: This Figshare repository contains data used in the study entitled "Role of Multifidelity Data in Sequential Active Learning Materials Discovery Campaigns: Case Study of Electronic Bandgap", by Ryan Jacobs, Philip Goins and Dane Morgan, to be published.
DOI: 10.6084/m9.figshare.24429028.v1
title: Data for "Role of Multifidelity Data in Sequential Active Learning Materials Discovery Campaigns: Case Study of Electronic Bandgap"

description: This Figshare repository contains data used in the study entitled "Role of Multifidelity Data in Sequential Active Learning Materials Discovery Campaigns: Case Study of Electronic Bandgap", by Ryan Jacobs, Philip Goins and Dane Morgan, to be published.
DOI: 10.25345/c5jd4q08p
title: MassIVE MSV000093518 - New strategies for archaeological textiles analysis: unique workflow for the simultaneous characterization of dyes and proteins
title: Protocol development for co-extraction of proteins and dyes from wool
DOI: 10.5281/zenodo.5745946
title: Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing

description: This deposit contains the MERFISH data used in this study.
DOI: 10.5281/zenodo.5745947
title: Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing

description: This deposit contains the MERFISH data used in this study.
DOI: 10.34740/kaggle/dsv/7075478
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/7075480
title: Mahanagar Yatayat Transport Dataset
DOI: 10.34740/kaggle/ds/4075094
title: 📝 Dataset containing 479k English words
DOI: 10.5281/zenodo.10214031
title: Citizen-centricity
DOI: 10.5281/zenodo.10214030
title: Citizen-centricity
DOI: 10.5281/zenodo.10213637
title: Projekt 975_scRNAseq in DIO+ DQ_2
DOI: 10.5281/zenodo.10213638
title: Projekt 975_scRNAseq in DIO+ DQ_2
DOI: 10.5281/zenodo.10213642
title: Projekt 975_scRNAseq in DIO+ DQ_3
DOI: 10.5281/zenodo.10213641
title: Projekt 975_scRNAseq in DIO+ DQ_3