cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3473137
title: Karst Sinkhole Detecting and Mapping Using Airborne LiDAR

description: Corresponding data set for Tran-SET Project No. 18GTUNM01. Abstract of the final report is stated below for reference: "The focus of this study is to detect sinkhole hazards using airborne light detection and ranging (LiDAR) data. The premise is sinkholes, particularly those close to transportation infrastructure assets, could cause substantial damages to infrastructure assets, and therefore, being able to accurately and rapidly detect them is essential. However, it is expensive, time-consuming, labor-intensive, and unsafe to survey sinkholes using conventional ground observation methods. This research project was focused on developing accurate and rapid airborne LiDAR-based sinkhole detection and mapping methods, and transfer the technologies to transportation engineers for implementation and workforce development. The project team also identified best practices for implementation of a state-level sinkhole hazard management system (SHMS). In addition, a guidebook was developed for airborne LiDAR-based sinkhole detection and mapping for professional education and training. The effectiveness of LiDAR to detect existing sinkholes has received very limited attention. Most of the research on LiDAR-based sinkhole detection postulates that morphological-based surface feature extraction methods can effectively detect sinkholes because of their geometric properties – sinkholes are oval-shaped concave depressions in the Earth’s surface. However, sinkholes have varying sizes, shapes, and appearance given various landforms, which adds even greater challenges to further improving the detection accuracy of methods that are based solely on morphology; for example, a dry stock pond may be incorrectly detected as a sinkhole. The proposed research used airborne LiDAR data in combination with auxiliary context such as site and association to improve the accuracy of the morphological-based sinkhole detection methods, and implement these by developing tools that can be used in standard geographic information systems (GIS). This methodology allows for the development of a robust LiDAR-based sinkhole detection toolset that provides an adequate degree of accuracy while maximizing the ability to assist inspectors with varying expertise."
description: Tran-SET Project No. 18GTUNM01
DOI: 10.5281/zenodo.3473138
title: Karst Sinkhole Detecting and Mapping Using Airborne LiDAR

description: Corresponding data set for Tran-SET Project No. 18GTUNM01. Abstract of the final report is stated below for reference: "The focus of this study is to detect sinkhole hazards using airborne light detection and ranging (LiDAR) data. The premise is sinkholes, particularly those close to transportation infrastructure assets, could cause substantial damages to infrastructure assets, and therefore, being able to accurately and rapidly detect them is essential. However, it is expensive, time-consuming, labor-intensive, and unsafe to survey sinkholes using conventional ground observation methods. This research project was focused on developing accurate and rapid airborne LiDAR-based sinkhole detection and mapping methods, and transfer the technologies to transportation engineers for implementation and workforce development. The project team also identified best practices for implementation of a state-level sinkhole hazard management system (SHMS). In addition, a guidebook was developed for airborne LiDAR-based sinkhole detection and mapping for professional education and training. The effectiveness of LiDAR to detect existing sinkholes has received very limited attention. Most of the research on LiDAR-based sinkhole detection postulates that morphological-based surface feature extraction methods can effectively detect sinkholes because of their geometric properties – sinkholes are oval-shaped concave depressions in the Earth’s surface. However, sinkholes have varying sizes, shapes, and appearance given various landforms, which adds even greater challenges to further improving the detection accuracy of methods that are based solely on morphology; for example, a dry stock pond may be incorrectly detected as a sinkhole. The proposed research used airborne LiDAR data in combination with auxiliary context such as site and association to improve the accuracy of the morphological-based sinkhole detection methods, and implement these by developing tools that can be used in standard geographic information systems (GIS). This methodology allows for the development of a robust LiDAR-based sinkhole detection toolset that provides an adequate degree of accuracy while maximizing the ability to assist inspectors with varying expertise."
description: Tran-SET Project No. 18GTUNM01
DOI: 10.21227/y46t-jb86
title: Real-world DNS dataset (2016)

description: · We captured ten days real-world DNS traffic from campus network comprising of 4000 hosts in peak load hours.
DOI: 10.5281/zenodo.3473255
title: Bibliotheken in den Niederlanden

description: »Wie arbeiten Bibliotheken in anderen Ländern?« lautet der Titel einer Veranstaltungsreihe des Fachbereichs Informationswissenschaften der Fachhochschule Potsdam in Zusammenarbeit mit dem Weiterbildungszentrum der Freien Universitat Berlin sowie der Botschaft des jeweiligen Landes. Auf »Bibliotheken in Finnland« und »Bibliotheken in den Vereinigten Staaten« folgte am 17. Marz 2006 das Informationsseminar »Bibliotheken in den Niederlanden - Zusammenarbeit, Reorganisation und neue Dienstleistungen«. Unter der Leitung sowie Moderation von Rolf Busch und Prof. Hans-Christoph Hobohm sprachen die Referenten diesmal über die Nationalbibliothek der Niederlande, die Universi- tätsbibliothek Filburg und die öffentlichen Bibliotheken in Almere und Utrechrecht. Vorträge zu der Webseite »bibIiotheek.nl« und der Zukunft der öffentlichen Bibliothek, die »Bibliothek 2040«, veranschaulichten weitere Projekte der niederländischen Bibliotheksarbeit.
DOI: 10.5281/zenodo.3473256
title: Bibliotheken in den Niederlanden

description: »Wie arbeiten Bibliotheken in anderen Ländern?« lautet der Titel einer Veranstaltungsreihe des Fachbereichs Informationswissenschaften der Fachhochschule Potsdam in Zusammenarbeit mit dem Weiterbildungszentrum der Freien Universitat Berlin sowie der Botschaft des jeweiligen Landes. Auf »Bibliotheken in Finnland« und »Bibliotheken in den Vereinigten Staaten« folgte am 17. Marz 2006 das Informationsseminar »Bibliotheken in den Niederlanden - Zusammenarbeit, Reorganisation und neue Dienstleistungen«. Unter der Leitung sowie Moderation von Rolf Busch und Prof. Hans-Christoph Hobohm sprachen die Referenten diesmal über die Nationalbibliothek der Niederlande, die Universi- tätsbibliothek Filburg und die öffentlichen Bibliotheken in Almere und Utrechrecht. Vorträge zu der Webseite »bibIiotheek.nl« und der Zukunft der öffentlichen Bibliothek, die »Bibliothek 2040«, veranschaulichten weitere Projekte der niederländischen Bibliotheksarbeit.
DOI: 10.5281/zenodo.3473139
title: Evaluation and Enhancement of Carbon Sequestration Potential of Existing Vegetation along Roadsides

description: Corresponding data set for Tran-SET Project No. 18HSTSA01. Abstract of the final report is stated below for reference: "The objectives of this study were to evaluate the vegetative composition and carbon sequestration potential of vegetation along a major roadway in Texas. Soil and vegetation were evaluated along IH-35 within Bexar County for composition and carbon content. Three 20 m transects were placed at each site and percent vegetative cover was estimated and above ground plant biomass, and soil was collected from three 0.25 m2 subplots along each transect. Plant and soil samples were analyzed for carbon content. Two non-native grasses, bermudagrass and King Ranch bluestem, were the dominant cover at all sites accounting for &gt; 90% coverage at several sites. Native plants were rare with only one species, western ragweed, accounting for &gt; 5% mean coverage at all sites. Carbon content of plant species was highest for bermudagrass (699 kg C ha-1) and King Ranch bluestem (401 kg C ha-1), and 6 of 7 sites contained significantly more carbon in non-native plants compared to native plants. The highest native plant carbon content was western ragweed with an average of 15 kg C ha-1. Mean soil carbon content ranged from 3.1 to 6.9 kg C m3 -1 among the sites, and significantly (P &lt; 0.05) greater amounts of carbon were recorded in the upper 0-10 cm compared to the 10-20 cm. The photosynthesis rates of Bermudagrass were significantly greater than rates recorded for King Ranch bluestem indicating the former species is highly adapted to hot semi-arid climate of central Texas, but King Ranch bluestem may gain a competitive advantage along roadways during the spring, fall and periods of increased precipitation. The total available area for the vegetation along the IH-35 highway in Bexar County is estimated to be approximately 81.7 ha (201.5 acres). We suggest the area between sites 5 and 6 (25.1 ha; 62 acres) are ideal locations for carbon sequestration using native plant communities including those with larger diameter woody stems such as trees."
description: Tran-SET Project No. 18HSTSA01
DOI: 10.5281/zenodo.3473140
title: Evaluation and Enhancement of Carbon Sequestration Potential of Existing Vegetation along Roadsides

description: Corresponding data set for Tran-SET Project No. 18HSTSA01. Abstract of the final report is stated below for reference: "The objectives of this study were to evaluate the vegetative composition and carbon sequestration potential of vegetation along a major roadway in Texas. Soil and vegetation were evaluated along IH-35 within Bexar County for composition and carbon content. Three 20 m transects were placed at each site and percent vegetative cover was estimated and above ground plant biomass, and soil was collected from three 0.25 m2 subplots along each transect. Plant and soil samples were analyzed for carbon content. Two non-native grasses, bermudagrass and King Ranch bluestem, were the dominant cover at all sites accounting for &gt; 90% coverage at several sites. Native plants were rare with only one species, western ragweed, accounting for &gt; 5% mean coverage at all sites. Carbon content of plant species was highest for bermudagrass (699 kg C ha-1) and King Ranch bluestem (401 kg C ha-1), and 6 of 7 sites contained significantly more carbon in non-native plants compared to native plants. The highest native plant carbon content was western ragweed with an average of 15 kg C ha-1. Mean soil carbon content ranged from 3.1 to 6.9 kg C m3 -1 among the sites, and significantly (P &lt; 0.05) greater amounts of carbon were recorded in the upper 0-10 cm compared to the 10-20 cm. The photosynthesis rates of Bermudagrass were significantly greater than rates recorded for King Ranch bluestem indicating the former species is highly adapted to hot semi-arid climate of central Texas, but King Ranch bluestem may gain a competitive advantage along roadways during the spring, fall and periods of increased precipitation. The total available area for the vegetation along the IH-35 highway in Bexar County is estimated to be approximately 81.7 ha (201.5 acres). We suggest the area between sites 5 and 6 (25.1 ha; 62 acres) are ideal locations for carbon sequestration using native plant communities including those with larger diameter woody stems such as trees."
description: Tran-SET Project No. 18HSTSA01
DOI: 10.21227/snk6-gd95
title: Real-world DNS dataset (2016)

description: · We captured ten days real-world DNS traffic from campus network comprising of 4000 hosts in peak load hours.
DOI: 10.5281/zenodo.3472639
title: Heart failure ECG analysis, XML files and strain output

description: Automated analysis of ECGs taken from patients with heart failure of different aetiologies
DOI: 10.5281/zenodo.3473147
title: Assessing the Impacts of Super Storm Flooding in the Transportation Infrastructure – Case Study: San Antonio, Texas

description: Corresponding data set for Tran-SET Project No. 18HSTSA02. Abstract of the final report is stated below for reference: "Flooding are likely to increase worldwide due to climate change. Large storms, referred here as superstorms, defined as events with return period equal or larger than 100 years, can lead to an increase of property damages and loss of life. The ability to predict and plan for the impacts of superstorms on transportation infrastructure is key to mitigate future damages and losses. This study analyzed 51 combinations of future projections for representative concentration pathways (RCP) 4.5 and 8.5 scenarios, which were used to calculate future 1st and 3rd quartiles, median, minimum and maximum intensity-duration-frequency curves (IDF). A HEC-HMS and GSSHA models were built for Leon Creek and Upper San Antonio watersheds. HEC-RAS 1D and 2D were used to evaluate flooding in 20 bridges and the extent of flooded area and roads in both watersheds and to test flood control scenarios. Land use modification with 5, 10 and 15% of LID areas in the watersheds were simulated. The use of levees and altering channels were evaluated. In addition, we evaluated how an increasing the storage capacity of the Olmos Dam would contribute to reduce flood impacts downstream. Results show that the 3rd quartile of projected IDF is closest to the one originated with observed precipitation, which is likely to increase in the future. The near future (2025-2049) under RCP 4.5 scenario presented the greatest increase in intensity. HEC-HMS models showed that discharge peak will increase for all future periods under both scenarios, for the 100- and 500-years storms. Flood projections generated by GSSHA for 100- and 500-years and future precipitation showed that flooded area can increase significantly. For instance, the increase in flooded roads can be more than 80% in near future for 500-year storm in Leon Creek watershed. HEC-RAS analysis showed that all 20 analyzed bridges can be flooded with 500-years storm with climate change and 15 with the 100-year storm. Simulation showed that LID implementation and the elevation of the Olmos Dam’s crest were ineffective to protect transportation infrastructure. Enhancing cross-sections of the main channels and the use of levees can mitigate the impact in some bridges. This study illustrates the need for updates in the design criteria of current and future transportation infrastructure."
description: Tran-SET Project No. 18HSTSA02
DOI: 10.5281/zenodo.3473148
title: Assessing the Impacts of Super Storm Flooding in the Transportation Infrastructure – Case Study: San Antonio, Texas

description: Corresponding data set for Tran-SET Project No. 18HSTSA02. Abstract of the final report is stated below for reference: "Flooding are likely to increase worldwide due to climate change. Large storms, referred here as superstorms, defined as events with return period equal or larger than 100 years, can lead to an increase of property damages and loss of life. The ability to predict and plan for the impacts of superstorms on transportation infrastructure is key to mitigate future damages and losses. This study analyzed 51 combinations of future projections for representative concentration pathways (RCP) 4.5 and 8.5 scenarios, which were used to calculate future 1st and 3rd quartiles, median, minimum and maximum intensity-duration-frequency curves (IDF). A HEC-HMS and GSSHA models were built for Leon Creek and Upper San Antonio watersheds. HEC-RAS 1D and 2D were used to evaluate flooding in 20 bridges and the extent of flooded area and roads in both watersheds and to test flood control scenarios. Land use modification with 5, 10 and 15% of LID areas in the watersheds were simulated. The use of levees and altering channels were evaluated. In addition, we evaluated how an increasing the storage capacity of the Olmos Dam would contribute to reduce flood impacts downstream. Results show that the 3rd quartile of projected IDF is closest to the one originated with observed precipitation, which is likely to increase in the future. The near future (2025-2049) under RCP 4.5 scenario presented the greatest increase in intensity. HEC-HMS models showed that discharge peak will increase for all future periods under both scenarios, for the 100- and 500-years storms. Flood projections generated by GSSHA for 100- and 500-years and future precipitation showed that flooded area can increase significantly. For instance, the increase in flooded roads can be more than 80% in near future for 500-year storm in Leon Creek watershed. HEC-RAS analysis showed that all 20 analyzed bridges can be flooded with 500-years storm with climate change and 15 with the 100-year storm. Simulation showed that LID implementation and the elevation of the Olmos Dam’s crest were ineffective to protect transportation infrastructure. Enhancing cross-sections of the main channels and the use of levees can mitigate the impact in some bridges. This study illustrates the need for updates in the design criteria of current and future transportation infrastructure."
description: Tran-SET Project No. 18HSTSA02
DOI: 10.5281/zenodo.3473154
title: Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events

description: Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference: "Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a fixed set of contextual factors. There is a clear need to develop traffic models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions/policies. In this project: (1) used Immersive Virtual Environment (IVE) tools for generating context-aware and high-fidelity data related to drivers’ route choice behavior, (2) developed a novel approach for developing high-fidelity route choice models with increased predictive power by augmenting existing aggregate level baseline models with information on drivers' responses to contextual factors obtained from stated choice experiments carried out in an IVE through the use of knowledge distillation. To this end, the study used a virtual driving environment designed based on I-10 in Baton Rouge, LA. Five alternate routes were introduced to the participant. Ten experimental scenarios were conducted to produce initial data about drivers’ dynamic route choice behavior, given emerging contextual factors. Experimental results have demonstrated that the predictions of the augmented models produced by our approach are much closer to reality than that of the baseline. Our study demonstrates that existing route choice models based on econometric theories cannot accurately predict behavior in real world scenarios. For high-fidelity route choice models, one needs to combine existing route choice models with information about contextual factors gleaned from SCEs."
description: Tran-SET Project No. 18ITSLSU09
DOI: 10.5281/zenodo.3473155
title: Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events

description: Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference: "Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a fixed set of contextual factors. There is a clear need to develop traffic models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions/policies. In this project: (1) used Immersive Virtual Environment (IVE) tools for generating context-aware and high-fidelity data related to drivers’ route choice behavior, (2) developed a novel approach for developing high-fidelity route choice models with increased predictive power by augmenting existing aggregate level baseline models with information on drivers' responses to contextual factors obtained from stated choice experiments carried out in an IVE through the use of knowledge distillation. To this end, the study used a virtual driving environment designed based on I-10 in Baton Rouge, LA. Five alternate routes were introduced to the participant. Ten experimental scenarios were conducted to produce initial data about drivers’ dynamic route choice behavior, given emerging contextual factors. Experimental results have demonstrated that the predictions of the augmented models produced by our approach are much closer to reality than that of the baseline. Our study demonstrates that existing route choice models based on econometric theories cannot accurately predict behavior in real world scenarios. For high-fidelity route choice models, one needs to combine existing route choice models with information about contextual factors gleaned from SCEs."
description: Tran-SET Project No. 18ITSLSU09