cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.18865106.v1
title: Health risk assessment of potentially toxic elements in Maros karst groundwater: a Monte Carlo simulation approach

description: This study investigated potentially toxic elements (PTEs) pollution in groundwater across the Maros karst area. The groundwater quality was evaluated using three indexing methods, the heavy metal pollution index (HPI), the heavy metal evaluation index (HMEI) and the contamination index (Cd). The levels of PTEs were calculated to determine the noncancer and cancer risks to the residents through ingestion and dermal adsorption. To obtain high-level accuracy in cancer risk estimation, a Monte Carlo simulation model and sensitivity analysis were performed. The mean values of PTEs in rainy and summer season were followed the order of Cr &gt; Pb &gt; Zn &gt; Cu and Cr &gt; Zn &gt; Pb &gt; Cu, respectively. The high level of Pb and Cr were exceeded the permissible limit of the World Health Organization (WHO) and Indonesian Standards. However, TDS, pH, EC and temperature were still in accordance with WHO guidelines. The geostatistical interpolation of HPI, HMEI and Cd revealed that the groundwater quality around Maros karst is low, particularly in Tukamasea and Leang-Leang village. Hazard index values were lower than one, implying no possibility of noncancer risk. The Monte Carlo simulation results with 95% confidence demonstrated children and adults are at risk for developing cancer due to PTE exposure.
DOI: 10.13145/bacdive120506.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.19354958.v1
title: A geotechnical index for landslide dam stability assessment

description: Effective disaster countermeasures of landslide dams hinge on the stability prediction in the emergency and mid-long-term stages. Variable morphological characteristics and multiple geomorphic indices as well as complicated geotechnical parameters pose significant challenges to a quick stability assessment. A particle size distribution (PSD) database was established based on 83 landslide deposits, including damming and non-damming landslides from around the world. Characteristic parameter <i>K</i> for quantitatively measuring landslide dam material properties were suggested on the basis of statistical analysis of deposit PSDs. Here we proposed a geotechnical index <i>MMI</i> (morphometry & material index) including dam volume, height and width, as well as characteristic parameter <i>K</i>, grain size <i>d</i><sub>90</sub> and lake volume that helps to estimate landslide dam stability. Besides, landslide dams in our inventory were divided into four levels based on their material composition, longevity and spillway form. With this we achieved the stability classification criterion according to the relation between these historical cases stability levels and <i>MMI</i> values. This study underlines the need to include geotechnical aspects such as grain size distribution for assessing dam stability, because the impact of material conditions on landslide dam evolution cannot be ignored.
DOI: 10.13145/bacdive120506.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.19354958
title: A geotechnical index for landslide dam stability assessment

description: Effective disaster countermeasures of landslide dams hinge on the stability prediction in the emergency and mid-long-term stages. Variable morphological characteristics and multiple geomorphic indices as well as complicated geotechnical parameters pose significant challenges to a quick stability assessment. A particle size distribution (PSD) database was established based on 83 landslide deposits, including damming and non-damming landslides from around the world. Characteristic parameter <i>K</i> for quantitatively measuring landslide dam material properties were suggested on the basis of statistical analysis of deposit PSDs. Here we proposed a geotechnical index <i>MMI</i> (morphometry & material index) including dam volume, height and width, as well as characteristic parameter <i>K</i>, grain size <i>d</i><sub>90</sub> and lake volume that helps to estimate landslide dam stability. Besides, landslide dams in our inventory were divided into four levels based on their material composition, longevity and spillway form. With this we achieved the stability classification criterion according to the relation between these historical cases stability levels and <i>MMI</i> values. This study underlines the need to include geotechnical aspects such as grain size distribution for assessing dam stability, because the impact of material conditions on landslide dam evolution cannot be ignored.
DOI: 10.6084/m9.figshare.20517783
title: Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India

description: Floods have received global significance in contemporary times due to their destructive behavior, which may wreak tremendous ruin on infrastructure and civilization. The present research employed an integration of the Geographic information system (GIS) and Analytical Hierarchy Process (AHP) method for identifying the flood susceptibility zonation (FSZ), flood vulnerability zonation (FVZ), and flood risk zonation (FRZ) of the humid subtropical Uttar Dinajpur district in India. The study combined a large number of thematic layers (<i>N</i> = 12 for FSZ and <i>N</i> = 9 for FVZ) to achieve reliable accuracy and included the multicollinearity analysis of these variables to overcome the issues related to highly correlated variables. According to the findings, 27.04, 15.62, and 4.59% of the area were classified as medium, high, and very high FRZ, respectively. The ROC-AUC, MAE, MSE, and RMSE of the model exhibited a good prediction accuracy of 0.73, 0.15, 0.16, and 0.21, respectively. The performance of the AHP model has been evaluated using sensitivity analyses. It also highly recommends that persistent improvement in this subject, such as sensitivity studies on modifying criteria thresholds, changing the relative significance of criteria, and changing the desired matrix, will permit GIS and MCDA to be progressively adapted to real hazard-management issues.
DOI: 10.6084/m9.figshare.20517783.v1
title: Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India

description: Floods have received global significance in contemporary times due to their destructive behavior, which may wreak tremendous ruin on infrastructure and civilization. The present research employed an integration of the Geographic information system (GIS) and Analytical Hierarchy Process (AHP) method for identifying the flood susceptibility zonation (FSZ), flood vulnerability zonation (FVZ), and flood risk zonation (FRZ) of the humid subtropical Uttar Dinajpur district in India. The study combined a large number of thematic layers (<i>N</i> = 12 for FSZ and <i>N</i> = 9 for FVZ) to achieve reliable accuracy and included the multicollinearity analysis of these variables to overcome the issues related to highly correlated variables. According to the findings, 27.04, 15.62, and 4.59% of the area were classified as medium, high, and very high FRZ, respectively. The ROC-AUC, MAE, MSE, and RMSE of the model exhibited a good prediction accuracy of 0.73, 0.15, 0.16, and 0.21, respectively. The performance of the AHP model has been evaluated using sensitivity analyses. It also highly recommends that persistent improvement in this subject, such as sensitivity studies on modifying criteria thresholds, changing the relative significance of criteria, and changing the desired matrix, will permit GIS and MCDA to be progressively adapted to real hazard-management issues.
DOI: 10.13145/bacdive120507.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.20291160
title: Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

description: Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
DOI: 10.6084/m9.figshare.20291160.v1
title: Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

description: Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
DOI: 10.13145/bacdive120507.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120508.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.20967878
title: Wildfire risk management across diverse bioregions in a changing climate

description: Wildfire risk-management needs to consider interrelated factors that influence fire regimes, including changing climate and sometimes conflicting stakeholder priorities. With wildfires increasing in size and intensity over recent decades, wildfire risk management is becoming more important and more complex. For southwest Australia, wildfire risk-management is predicated on a longitudinal study of the relationship between prescribed burns and wildfires from 1953–2004 over a subset of this biodiverse region. Our study replicates the methodology of the longitudinal study, applying it to the wider region and extending the analysis to 2021. We found the extrapolation of the longitudinal study’s findings to the wider region invalid, as was extrapolation beyond 2004. In particular, the area of prescribed burns generally had negligible influence on wildfire area. However, more spatially complex fire history was strongly correlated with lower probability of large wildfires (independent of area burned). This highlights the limitation of extrapolating wildfire risk-management policies to areas of differing vegetation and/or climate, including changing climate over time. The potential of indigenous-led practices for wildfire risk and biodiversity conservation, particularly for areas with high spatial variability, is apparent as is the need for alternative strategies to prescribed burning as the primary tool in wildfire-risk mitigation.
DOI: 10.13145/bacdive120508.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.20967878.v1
title: Wildfire risk management across diverse bioregions in a changing climate

description: Wildfire risk-management needs to consider interrelated factors that influence fire regimes, including changing climate and sometimes conflicting stakeholder priorities. With wildfires increasing in size and intensity over recent decades, wildfire risk management is becoming more important and more complex. For southwest Australia, wildfire risk-management is predicated on a longitudinal study of the relationship between prescribed burns and wildfires from 1953–2004 over a subset of this biodiverse region. Our study replicates the methodology of the longitudinal study, applying it to the wider region and extending the analysis to 2021. We found the extrapolation of the longitudinal study’s findings to the wider region invalid, as was extrapolation beyond 2004. In particular, the area of prescribed burns generally had negligible influence on wildfire area. However, more spatially complex fire history was strongly correlated with lower probability of large wildfires (independent of area burned). This highlights the limitation of extrapolating wildfire risk-management policies to areas of differing vegetation and/or climate, including changing climate over time. The potential of indigenous-led practices for wildfire risk and biodiversity conservation, particularly for areas with high spatial variability, is apparent as is the need for alternative strategies to prescribed burning as the primary tool in wildfire-risk mitigation.
DOI: 10.13145/bacdive120509.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120509.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120510.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120510.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120511.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120511.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120512.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120512.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120513.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120513.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120514.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120514.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120515.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120515.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120516.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120516.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120517.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120517.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120518.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120518.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120519.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120519.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120520.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120520.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120521.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120521.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120522.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120522.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120523.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120523.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120524.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120524.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120525.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120525.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120526.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120526.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120527.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120527.20221219.7.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120528.20220920.7
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.