cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.aftpe4
title: Occurrence Download

description: A dataset containing 45906 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Polemaetus bellicosus (Daudin, 1800)"
 ]
}

 The dataset includes 45906 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002902-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11583/dtu.24955050
title: Stream results data for FOAK-to-NOAK e-ammonia plant configurations

description: This data file provides additional simulation results for the manuscript entitled "<b>Quantifying environmental learning and scaling rates for prospective life cycle assessment of e-ammonia production</b>". This data file contains chemical process design simulation results for e-ammonia production at various scales ranging from 2 to 100 t eNH<sub>3</sub> h<sup>-1</sup>. Simulation results were generated using the ASPEN Plus V11 simulator. Each Excel tab is dedicated to one specific e-ammonia production plant configuration. FOAK = First-Of-A-Kind technology, NOAK = N<sup>th</sup>-Of-A-Kind technology. This data file specifies the stream composition of every stream defined in Figure S1 flowchart in the Supplementary information. Data were grouped to "Air separation unit (ASU) and ammonia synthesis" and "Alkaline Water Electrolysis (AWE) unit" sections. The detailed stream properties are the following: the path of stream connections, phase, temperature, pressure, molar vapor fraction, molar liquid fraction, molar solid fraction, mass vapor fraction, mass liquid fraction, mass solid fraction, molar enthalpy, mass enthalpy, molar entropy, mass entropy, molar density, mass density, enthalpy flow, average molecular weight, mole flows for components, mole fractions for components, mass flows for components, mass fractions for components, volume flow.
DOI: 10.15468/dl.m822yz
title: Occurrence Download

description: A dataset containing 2178 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TypeStatus is Type",
 "Year 2023-2025"
 ]
}

 The dataset includes 2178 records from 682 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002904-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4yfat3
title: Occurrence Download

description: A dataset containing 253341 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Crocodylia"
 ]
}

 The dataset includes 253341 records from 587 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002890-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wfd265
title: Occurrence Download

description: A dataset containing 196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aquilegia vulgaris L.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 196 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002905-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14899370
title: Plant water source of communities in the Loess Plateau of China

description: 1.这是一套中国黄土高原三个原生典型群落的植物水源数据集,结合氢氧稳定同位素技术。

2.该数据集基于 2020 年至 2021 年中国黄土高原 3 个原生典型群落的水同位素实验数据生成。

3.该数据集提供了黄土高原 3 个原生典型群落的上部乔木和下部灌木的水源数据。
DOI: 10.5281/zenodo.14899371
title: Plant water source of communities in the Loess Plateau of China

description: 1.这是一套中国黄土高原三个原生典型群落的植物水源数据集,结合氢氧稳定同位素技术。

2.该数据集基于 2020 年至 2021 年中国黄土高原 3 个原生典型群落的水同位素实验数据生成。

3.该数据集提供了黄土高原 3 个原生典型群落的上部乔木和下部灌木的水源数据。
DOI: 10.15468/dl.ux28qx
title: Occurrence Download

description: A dataset containing 1087 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arbutus unedo L.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 1087 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002907-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14899413
title: Rajniti me mahilao ki bhagidari
DOI: 10.5281/zenodo.14899414
title: Rajniti me mahilao ki bhagidari
DOI: 10.15468/dl.u5ycpa
title: Occurrence Download

description: A dataset containing 744 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.3_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Pieris napi (Linnaeus, 1758)"
 ]
}

 The dataset includes 744 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002908-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14899381
title: 20250220_Isabel_Pfeiffer

description: M1
DOI: 10.5281/zenodo.14899382
title: 20250220_Isabel_Pfeiffer

description: M1
DOI: 10.5281/zenodo.14899245
title: Global Land / Water Mask Map with 0.3 arc sec. (10 m) Resolution

description: A global Land/Water Mask Map with 0.3 arc sec. (10 m) resolution based on WorldCover map, open street map and Copernicus dem for the year 2020.
DOI: 10.5281/zenodo.14899246
title: Global Land / Water Mask Map with 0.3 arc sec. (10 m) Resolution

description: A global Land/Water Mask Map with 0.3 arc sec. (10 m) resolution based on WorldCover map, open street map and Copernicus dem for the year 2020.
DOI: 10.15468/dl.j2cs3n
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arenaria erinacea Boiss.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 26 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002909-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xe57hm
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arenaria racemosa Willk.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002911-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/b60dfda0-055a-4046-a615-e0166a356c95
title: Code and data underlying the publication: Data-driven Semi-supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection

description: This is the code and processed data related to the publication:Dong, Y., Zhang, L., Farah, H., Zgonnikov, A., & van Arem, B. (2023). Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection. arXiv preprint arXiv:2312.04610. https://arxiv.org/abs/2312.04610<br>The original data is from https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-DatasetThe codes make use of open-sourced implementation of HELM and other semi-supervised learning algorithms.<br>After setting up the folder and fetching the data, one can simply run the code with the specific function (identified by their names) get the relevant results.Details about the implementation are demonstrated in the paper.<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Detecting abnormal driving behaviour is critical for road traffic safety and the evaluation of drivers' behaviour. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behaviour detection (also referred to as anomalies). Most existing ML-based detectors rely on supervised methods, which require substantial labelled data. However, ground truth labels are not always available in the real world, and labelling large amounts of data is tedious. Thus, there is a need to explore unsupervised or semi-supervised methods to make the anomaly detection process more feasible and efficient. To fill this research gap, this study analyzes large-scale real-world data revealing several abnormal driving behaviours (e.g., sudden acceleration, rapid lane-changing) and develops a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method using partly labelled data to accurately detect the identified abnormal driving behaviours. Moreover, previous ML-based approaches predominantly utilized basic vehicle motion features (e.g., velocity and acceleration) to label and detect abnormal driving behaviours, while this study seeks to introduce event-level safety indicators as input features for ML models to improve detection performance. Results from extensive experiments demonstrate the effectiveness of the proposed semi-supervised ML model with the introduced safety indicators serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods regarding various metrics, e.g., delivering the best accuracy (99.58%) and the best F-1 measure (0.9913). The ablation study further highlights the significance of safety indicators for advancing the detection performance.<br>
DOI: 10.4121/b60dfda0-055a-4046-a615-e0166a356c95.v1
title: Code and data underlying the publication: Data-driven Semi-supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection

description: This is the code and processed data related to the publication:Dong, Y., Zhang, L., Farah, H., Zgonnikov, A., & van Arem, B. (2023). Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection. arXiv preprint arXiv:2312.04610. https://arxiv.org/abs/2312.04610<br>The original data is from https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-DatasetThe codes make use of open-sourced implementation of HELM and other semi-supervised learning algorithms.<br>After setting up the folder and fetching the data, one can simply run the code with the specific function (identified by their names) get the relevant results.Details about the implementation are demonstrated in the paper.<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Detecting abnormal driving behaviour is critical for road traffic safety and the evaluation of drivers' behaviour. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behaviour detection (also referred to as anomalies). Most existing ML-based detectors rely on supervised methods, which require substantial labelled data. However, ground truth labels are not always available in the real world, and labelling large amounts of data is tedious. Thus, there is a need to explore unsupervised or semi-supervised methods to make the anomaly detection process more feasible and efficient. To fill this research gap, this study analyzes large-scale real-world data revealing several abnormal driving behaviours (e.g., sudden acceleration, rapid lane-changing) and develops a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method using partly labelled data to accurately detect the identified abnormal driving behaviours. Moreover, previous ML-based approaches predominantly utilized basic vehicle motion features (e.g., velocity and acceleration) to label and detect abnormal driving behaviours, while this study seeks to introduce event-level safety indicators as input features for ML models to improve detection performance. Results from extensive experiments demonstrate the effectiveness of the proposed semi-supervised ML model with the introduced safety indicators serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods regarding various metrics, e.g., delivering the best accuracy (99.58%) and the best F-1 measure (0.9913). The ablation study further highlights the significance of safety indicators for advancing the detection performance.<br>
DOI: 10.15468/dl.mknwzu
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.3_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Aporia crataegi (Linnaeus, 1758)"
 ]
}

 The dataset includes 10 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002912-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/c1020a3f-0053-491f-8ead-35d18819d37e
title: Code underlying the publication: Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning

description: This is the code related to the publication:H. Yuan, P. Li, B. Van Arem, L. Kang, H. Farah and Y. Dong, "Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6074-6079, doi: 10.1109/ITSC57777.2023.10422488. <br>keywords: {Road transportation;Deep learning;Energy consumption;Merging;Reinforcement learning;Safety;Testing},<br>The implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv.<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving models. This study explores, employs, and implements various DRL algorithms, namely Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO) to instruct automated vehicles' driving through roundabouts. The driving state space, action space, and reward function are designed. The reward function considers safety, efficiency, comfort, and energy consumption to align with real-world requirements. All three tested DRL algorithms succeed in enabling automated vehicles to drive through the roundabout. To holistically evaluate the performance of these algorithms, this study establishes an evaluation methodology considering multiple indicators, i.e., safety, efficiency, comfort and energy consumption level. A method employing the Analytic Hierarchy Process is also developed to weigh these evaluation indicators. Experimental results on various testing scenarios reveal that the TRPO algorithm outperforms DDPG and PPO in terms of safety and efficiency, while PPO performs the best in terms of comfort level and energy consumption. Lastly, to verify the model's adaptability and robustness regarding other driving scenarios, this study also deploys the model trained by TRPO to a range of different testing scenarios, e.g., highway driving and merging. Experimental results demonstrate that the TRPO model trained on only roundabout driving scenarios exhibits a certain degree of proficiency in highway driving and merging scenarios. This study provides a foundation for the application of automated driving with DRL.<br>
DOI: 10.4121/c1020a3f-0053-491f-8ead-35d18819d37e.v1
title: Code underlying the publication: Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning

description: This is the code related to the publication:H. Yuan, P. Li, B. Van Arem, L. Kang, H. Farah and Y. Dong, "Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6074-6079, doi: 10.1109/ITSC57777.2023.10422488. <br>keywords: {Road transportation;Deep learning;Energy consumption;Merging;Reinforcement learning;Safety;Testing},<br>The implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv.<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving models. This study explores, employs, and implements various DRL algorithms, namely Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO) to instruct automated vehicles' driving through roundabouts. The driving state space, action space, and reward function are designed. The reward function considers safety, efficiency, comfort, and energy consumption to align with real-world requirements. All three tested DRL algorithms succeed in enabling automated vehicles to drive through the roundabout. To holistically evaluate the performance of these algorithms, this study establishes an evaluation methodology considering multiple indicators, i.e., safety, efficiency, comfort and energy consumption level. A method employing the Analytic Hierarchy Process is also developed to weigh these evaluation indicators. Experimental results on various testing scenarios reveal that the TRPO algorithm outperforms DDPG and PPO in terms of safety and efficiency, while PPO performs the best in terms of comfort level and energy consumption. Lastly, to verify the model's adaptability and robustness regarding other driving scenarios, this study also deploys the model trained by TRPO to a range of different testing scenarios, e.g., highway driving and merging. Experimental results demonstrate that the TRPO model trained on only roundabout driving scenarios exhibits a certain degree of proficiency in highway driving and merging scenarios. This study provides a foundation for the application of automated driving with DRL.<br>
DOI: 10.15468/dl.cay22n
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aristolochia castellana (E.Nardi) A.Costa",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002913-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/26e8f131-53f8-44b9-8ecf-249bfedb0154
title: Code underlying the publication: Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers

description: This is the code and data related to the publication:Y. Dong, T. Datema, V. Wassenaar, J. Van de Weg, C. T. Kopar and H. Suleman, "Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6165-6170, doi: 10.1109/ITSC57777.2023.10422159.  keywords: {Training;Deep learning;Roads;Reinforcement learning;Automobiles;Task analysis;Optimization}<br>The implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging manoeuvres. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving manoeuvres besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving manoeuvres and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving manoeuvres with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.<br>