cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13746725.v1
title: Construction and demolition waste management in developing countries: a diagnosis from 265 construction sites in the Lima Metropolitan Area

description: The construction industry is a driver of economic and social development in developing countries; however, it is also one of the primary sources of entropic elements such as construction and demolition waste (CDW). Since construction is expected to continue growing, it is essential to study the CDW status under these scenarios. To the best of the author’s knowledge, this is the first academic research in Peru that deals with CDW. The information for the research is collected from primary and secondary sources, which includes 265 surveys, interviews, and visits to construction and disposal sites. The results are obtained from a cross-analysis of the influence of different aspects (experience and education level of the construction site manager, dimension, or socio-economical level of the site, building use, among others) on CDW management. Further, significant findings related to each stage of the CDW cycle are presented. The obtained findings provide information for effective decision making in terms of policies and regulations on CDW, which will help improve its management, reduce environmental impact, and provide economic benefits, especially in developing countries.
DOI: 10.5281/zenodo.7126105
title: Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

description: The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas. Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information. <strong>Contents of this repository</strong> <em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the Cybertronica phytosensing and phytoactuating system. <em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code. <em>dl-4-tsc:</em> Deep learning framework developed by Fawaz et. al (Deep learning for time series classification: a review) and adapted to our use case. <em>DeepClassifier: </em>Trained deep learning time series classifier. <em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.
DOI: 10.5281/zenodo.7126104
title: Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

description: The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas. Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information. <strong>Contents of this repository</strong> <em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the Cybertronica phytosensing and phytoactuating system. <em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code. <em>dl-4-tsc:</em> Deep learning framework developed by Fawaz et. al (Deep learning for time series classification: a review) and adapted to our use case. <em>DeepClassifier: </em>Trained deep learning time series classifier. <em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.
DOI: 10.21227/87yd-jk77
title: Yahoo finance asset price dataset

description: yahoo finance price dataset including Apple inc, Amazon inc and Intel inc.
DOI: 10.26302/sshade/experiment_zed_20220831_001
title: Vis-NIR BRDF spectra of serpentine pellets irradiated by Ar+ and He+.

description: Vis-NIR (0.4 - 4 µm) BRDF spectra of Rawhide serpentine pellet non-irradiated and irradiated by Ar+ and He+
DOI: 10.5281/zenodo.7688601
title: Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"

description: This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor. Each file has its header, describing each column.
DOI: 10.5281/zenodo.7688602
title: Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"

description: This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor. Each file has its header, describing each column.
DOI: 10.6084/m9.figshare.22219591
title: Supplementary Material for: Triglycerides and Renal Outcomes according to Albuminuria and in Consideration of other Metabolic Syndrome Components in Diabetic US Veterans

description: Objective: Hypertriglyceridemia, a component of the metabolic syndrome, is a known independent predictor of albuminuria and chronic kidney disease (CKD) in the general population. Previous studies have shown that the relationship of triglycerides with outcomes changes across stages of CKD. Our objective is to examine the association of triglycerides independent of other metabolic syndrome components with renal outcomes in diabetic patients with or without CKD. Research Design and Methods: This retrospective cohort study included diabetic US veteran patients with valid data on triglycerides (TG), estimated glomerular filtration rate (eGFR) and albuminuria (UACR), between fiscal years 2004 to 2006. Using Cox models adjusted for clinical characteristics and laboratory markers, we evaluated the relationship of TG with incident albuminuria (stratified by eGFR category) and based on eGFR (stratified by baseline albuminuria categories). To evaluate the relationship of TG with time to end-stage renal disease (ESRD), we stratified models by baseline CKD stage (eGFR category) and baseline albuminuria stage ascertained at time of TG measurement. Results: In a cohort of 138,675 diabetic veterans, the mean±SD age was 65±11 years old, and included 3% females and 14% African Americans. The cohort also included 28% of patients with non-dialysis dependent CKD (eGFR&lt;60 mL/min/173m2), as well as 28% of patients with albuminuria (≥30 mg/g). The median[IQR] of serum TG was 148[100, 222] mg/dL. We observed a slight positive linear association between TG and incident CKD after adjustment for case-mix and laboratory variables among non-albuminuric and microalbuminuric patients. High TG levels were associated with ESRD in CKD 3A non-albuminuric patients and in CKD 3A and 4/5 in patients with microalbuminuria. Conclusions: In a large cohort, we have shown that elevated TG are associated with all kidney outcomes tested independently of other metabolic syndrome components in diabetic patients with normal eGFR and normal albumin excretion rate, but the association is weaker in some groups of diabetic patients with pre-existing renal complications.
DOI: 10.6084/m9.figshare.22219591.v1
title: Supplementary Material for: Triglycerides and Renal Outcomes according to Albuminuria and in Consideration of other Metabolic Syndrome Components in Diabetic US Veterans

description: Objective: Hypertriglyceridemia, a component of the metabolic syndrome, is a known independent predictor of albuminuria and chronic kidney disease (CKD) in the general population. Previous studies have shown that the relationship of triglycerides with outcomes changes across stages of CKD. Our objective is to examine the association of triglycerides independent of other metabolic syndrome components with renal outcomes in diabetic patients with or without CKD. Research Design and Methods: This retrospective cohort study included diabetic US veteran patients with valid data on triglycerides (TG), estimated glomerular filtration rate (eGFR) and albuminuria (UACR), between fiscal years 2004 to 2006. Using Cox models adjusted for clinical characteristics and laboratory markers, we evaluated the relationship of TG with incident albuminuria (stratified by eGFR category) and based on eGFR (stratified by baseline albuminuria categories). To evaluate the relationship of TG with time to end-stage renal disease (ESRD), we stratified models by baseline CKD stage (eGFR category) and baseline albuminuria stage ascertained at time of TG measurement. Results: In a cohort of 138,675 diabetic veterans, the mean±SD age was 65±11 years old, and included 3% females and 14% African Americans. The cohort also included 28% of patients with non-dialysis dependent CKD (eGFR&lt;60 mL/min/173m2), as well as 28% of patients with albuminuria (≥30 mg/g). The median[IQR] of serum TG was 148[100, 222] mg/dL. We observed a slight positive linear association between TG and incident CKD after adjustment for case-mix and laboratory variables among non-albuminuric and microalbuminuric patients. High TG levels were associated with ESRD in CKD 3A non-albuminuric patients and in CKD 3A and 4/5 in patients with microalbuminuria. Conclusions: In a large cohort, we have shown that elevated TG are associated with all kidney outcomes tested independently of other metabolic syndrome components in diabetic patients with normal eGFR and normal albumin excretion rate, but the association is weaker in some groups of diabetic patients with pre-existing renal complications.
DOI: 10.6084/m9.figshare.19846112
title: A short-form version of the Australian English Communicative Development Inventory

description: <i>Purpose:</i> The Australian English Communicative Development Inventory (OZI) is a 558-item parent report tool for assessing language development at 12–30 months. Here, we introduce the short form (OZI-SF), a 100-item, picture-supported, online instrument with substantially lower time and literacy demands. <i>Method:</i> In tool development (Study 1), 95 items were drawn from the OZI to match its item distribution by age of acquisition and semantic categories. Five items were added from four other semantic categories, plus 12 gestures and six games/routines. Simulations computed OZI-SF scores from existing long-form OZI norm data, and OZI and projected OZI-SF scores were correlated. In an independent norming sample (Study 2), parents (<i>n</i> = 230) completed the OZI-SF for their children aged 12–30 months. Child scores were analysed by age and sex. <i>Result:</i> OZI-SF and OZI scores correlate highly across age and language development levels. Vocabulary scores (receptive, expressive) correlate with age and the median for girls is higher until 24 months. By 24 months, 50% of the sample combine words “often”. The median time to OZI-SF completion was 12 minutes. <i>Conclusion:</i> Fitted percentiles permit working guidelines for typical (median) performance and lower cut-offs for children who may be behind on age-based expectations and/or at risk for a communication difficulty. The OZI-SF is a short-form of the OZI that has promise for research and clinical/educational use with Australian families.
DOI: 10.6084/m9.figshare.19846112.v1
title: A short-form version of the Australian English communicative development inventory

description: <i>Purpose:</i> The Australian English Communicative Development Inventory (OZI) is a 558-item parent report tool for assessing language development at 12–30 months. Here, we introduce the short form (OZI-SF), a 100-item, picture-supported, online instrument with substantially lower time and literacy demands. <i>Method:</i> In tool development (Study 1), 95 items were drawn from the OZI to match its item distribution by age of acquisition and semantic categories. Five items were added from four other semantic categories, plus 12 gestures and six games/routines. Simulations computed OZI-SF scores from existing long-form OZI norm data, and OZI and projected OZI-SF scores were correlated. In an independent norming sample (Study 2), parents (<i>n</i> = 230) completed the OZI-SF for their children aged 12–30 months. Child scores were analysed by age and sex. <i>Result:</i> OZI-SF and OZI scores correlate highly across age and language development levels. Vocabulary scores (receptive, expressive) correlate with age and the median for girls is higher until 24 months. By 24 months, 50% of the sample combine words “often”. The median time to OZI-SF completion was 12 minutes. <i>Conclusion:</i> Fitted percentiles permit working guidelines for typical (median) performance and lower cut-offs for children who may be behind on age-based expectations and/or at risk for a communication difficulty. The OZI-SF is a short-form of the OZI that has promise for research and clinical/educational use with Australian families.
DOI: 10.6084/m9.figshare.19846112.v2
title: A short-form version of the Australian English Communicative Development Inventory

description: <i>Purpose:</i> The Australian English Communicative Development Inventory (OZI) is a 558-item parent report tool for assessing language development at 12–30 months. Here, we introduce the short form (OZI-SF), a 100-item, picture-supported, online instrument with substantially lower time and literacy demands. <i>Method:</i> In tool development (Study 1), 95 items were drawn from the OZI to match its item distribution by age of acquisition and semantic categories. Five items were added from four other semantic categories, plus 12 gestures and six games/routines. Simulations computed OZI-SF scores from existing long-form OZI norm data, and OZI and projected OZI-SF scores were correlated. In an independent norming sample (Study 2), parents (<i>n</i> = 230) completed the OZI-SF for their children aged 12–30 months. Child scores were analysed by age and sex. <i>Result:</i> OZI-SF and OZI scores correlate highly across age and language development levels. Vocabulary scores (receptive, expressive) correlate with age and the median for girls is higher until 24 months. By 24 months, 50% of the sample combine words “often”. The median time to OZI-SF completion was 12 minutes. <i>Conclusion:</i> Fitted percentiles permit working guidelines for typical (median) performance and lower cut-offs for children who may be behind on age-based expectations and/or at risk for a communication difficulty. The OZI-SF is a short-form of the OZI that has promise for research and clinical/educational use with Australian families.
DOI: 10.6084/m9.figshare.16655399
title: The role of speech-language pathology weekend service in inpatient subacute care: A national survey

description: Limited evidence is available on speech-language pathology (SLP) weekend services within the subacute inpatient setting. This study aimed to describe Australian SLP weekend services for stroke adult inpatients in subacute units and to explore clinicians’ perspectives on factors that influence the delivery of the service. Speech-language pathologists who worked in Australian subacute hospital-based units participated in an online cross-sectional survey. Descriptive statistics and content analysis were undertaken to analyse quantitative and qualitative data. From the total of 83 participating clinicians, 20 (24%) worked in rehabilitation units that provided an SLP weekend service. Assessments and reviews were the main clinical activities provided to stroke inpatients by this service. Workforce and evidence were commonly cited as facilitators and barriers to both the implementation of a new service and the continuation of an existing service. The “ideal” SLP weekend subacute service to stroke inpatients was typically described as a combination of assessment, therapy, education and training. Findings from this study’s sample indicate that an SLP weekend inpatient rehabilitation service to stroke inpatients was not standard practice. Different perspectives regarding the purpose and need of an SLP weekend service were reported.
DOI: 10.6084/m9.figshare.16655399.v3
title: The role of speech-language pathology weekend service in inpatient subacute care: A national survey

description: Limited evidence is available on speech-language pathology (SLP) weekend services within the subacute inpatient setting. This study aimed to describe Australian SLP weekend services for stroke adult inpatients in subacute units and to explore clinicians’ perspectives on factors that influence the delivery of the service. Speech-language pathologists who worked in Australian subacute hospital-based units participated in an online cross-sectional survey. Descriptive statistics and content analysis were undertaken to analyse quantitative and qualitative data. From the total of 83 participating clinicians, 20 (24%) worked in rehabilitation units that provided an SLP weekend service. Assessments and reviews were the main clinical activities provided to stroke inpatients by this service. Workforce and evidence were commonly cited as facilitators and barriers to both the implementation of a new service and the continuation of an existing service. The “ideal” SLP weekend subacute service to stroke inpatients was typically described as a combination of assessment, therapy, education and training. Findings from this study’s sample indicate that an SLP weekend inpatient rehabilitation service to stroke inpatients was not standard practice. Different perspectives regarding the purpose and need of an SLP weekend service were reported.
DOI: 10.6084/m9.figshare.16655399.v2
title: The role of speech-language pathology weekend service in inpatient subacute care: A national survey

description: Limited evidence is available on speech-language pathology (SLP) weekend services within the subacute inpatient setting. This study aimed to describe Australian SLP weekend services for stroke adult inpatients in subacute units and to explore clinicians’ perspectives on factors that influence the delivery of the service. Speech-language pathologists who worked in Australian subacute hospital-based units participated in an online cross-sectional survey. Descriptive statistics and content analysis were undertaken to analyse quantitative and qualitative data. From the total of 83 participating clinicians, 20 (24%) worked in rehabilitation units that provided an SLP weekend service. Assessments and reviews were the main clinical activities provided to stroke inpatients by this service. Workforce and evidence were commonly cited as facilitators and barriers to both the implementation of a new service and the continuation of an existing service. The “ideal” SLP weekend subacute service to stroke inpatients was typically described as a combination of assessment, therapy, education and training. Findings from this study’s sample indicate that an SLP weekend inpatient rehabilitation service to stroke inpatients was not standard practice. Different perspectives regarding the purpose and need of an SLP weekend service were reported.