cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.an37mc
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alpinia leptostachya Valeton"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000199-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zq7x6d
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alpinia macroura K.Schum."
 ]
}

 The dataset includes 16 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000225-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3hkfya
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Procapra picticaudata Hodgson, 1846"
 ]
}

 The dataset includes 89 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000251-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.99dse5
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Brachycephalus alipioi Pombal & Gasparini, 2006"
 ]
}

 The dataset includes 15 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018161-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p5v8p4
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Allmania multiflora V.S.A.Kumar, V.Suresh, Sindhu Arya & Iamonico"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000213-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/independent-c69-30836
title: ABA disciplinary enforcement report, 1991-05

description: Report of the Commission on Evaluation of Disciplinary Enforcement. American Bar Assocation, 1991 May. 10.24. Independent Commission on Los Angeles Police Department / general counsel - American Bar Assocation (Disciplinary enforcement), 1991 May. PART OF A SERIES: The series pertains to the work of Munger, Tolles & Olson, the firm responsible for coordinating the activities and proceedings of the Independent Commission's legal counsel. Included are legal briefs, court transcripts, and memoranda; press releases and article clippings about the Rodney King incident and the subsequent creation of the Independent Commission; summaries of witness interviews; analyses of legislation regarding the role and purview of peace officers; work plans and outlines developed by Commission staff; draft copies developed for all sections of the Commission report; supplemental materials for the report, including a copy of the Rodney King arrest log, law enforcement literature, and public opinion surveys; materials distributed and reviewed during Commission meetings; letters of interest submitted to Chairman Christopher from prospective Commission members and organizations; and lists of bills and Commission expenditures.
DOI: 10.15468/dl.97423q
title: Occurrence Download

description: A dataset containing 67536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Switzerland",
 "DatasetKey is United Herbaria of the University and ETH Zurich",
 "GadmGid is CHE.23_1"
 ]
}

 The dataset includes 67536 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014866-240202131308920/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p29t6m
title: Occurrence Download

description: A dataset containing 26395 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Tecoma Juss."
 ]
}

 The dataset includes 26395 records from 624 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056079-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/70e29cf5-8502-4e8d-bf32-2953431a83ff
title: Code and data underlying the publication: Social-aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving through Roundabouts as a Case Study

description: This is the code and data related to the publication:L. Zhang, Y. Dong, H. Farah and B. van Arem, "Social-Aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving Through Roundabouts as a Case Study," 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu, Oahu, HI, USA, 2023, pp. 3297-3304, doi: 10.1109/SMC53992.2023.10394462.<br>https://doi.org/10.1109/SMC53992.2023.10394462<br>keywords: {Trajectory planning;Predictive models;Prediction algorithms;Robustness;Planning;Predictive control;Vehicles;Automated vehicles;Planning and control;Social-aware driving;Roundabouts;Driving Risk Field;Model Predictive Contouring Control},<br>The implementation is based on Python and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs’ deployment and safe driving under various manoeuvres. Previous research mostly focuses on the trajectory planning of AVs using Model Predictive Control or other relevant methods, while seldom considering the integrated planning and control of AVs altogether to simplify the whole pipeline architecture. Furthermore, there are very limited studies on social-aware driving that makes AVs understandable and expected by human drivers, and none when it comes to the challenging manoeuvre of driving through roundabouts. To fill these research gaps, this paper develops an integrated social-aware planning and control algorithm for AVs’ driving through roundabouts based on Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), i.e., DRF-SVO-MPCC. The proposed method is tested and verified with simulation on the open-sourced <em>highway-env</em> platform. Compared with the baseline method using purely Nonlinear Model Predictive Control, the DRF-SVO-MPCC can achieve better performance under various maneuvers of driving through roundabouts with and without surrounding HDVs.<br><br>
DOI: 10.4121/70e29cf5-8502-4e8d-bf32-2953431a83ff.v1
title: Code and data underlying the publication: Social-aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving through Roundabouts as a Case Study

description: This is the code and data related to the publication:L. Zhang, Y. Dong, H. Farah and B. van Arem, "Social-Aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving Through Roundabouts as a Case Study," 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu, Oahu, HI, USA, 2023, pp. 3297-3304, doi: 10.1109/SMC53992.2023.10394462.<br>https://doi.org/10.1109/SMC53992.2023.10394462<br>keywords: {Trajectory planning;Predictive models;Prediction algorithms;Robustness;Planning;Predictive control;Vehicles;Automated vehicles;Planning and control;Social-aware driving;Roundabouts;Driving Risk Field;Model Predictive Contouring Control},<br>The implementation is based on Python and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs’ deployment and safe driving under various manoeuvres. Previous research mostly focuses on the trajectory planning of AVs using Model Predictive Control or other relevant methods, while seldom considering the integrated planning and control of AVs altogether to simplify the whole pipeline architecture. Furthermore, there are very limited studies on social-aware driving that makes AVs understandable and expected by human drivers, and none when it comes to the challenging manoeuvre of driving through roundabouts. To fill these research gaps, this paper develops an integrated social-aware planning and control algorithm for AVs’ driving through roundabouts based on Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), i.e., DRF-SVO-MPCC. The proposed method is tested and verified with simulation on the open-sourced <em>highway-env</em> platform. Compared with the baseline method using purely Nonlinear Model Predictive Control, the DRF-SVO-MPCC can achieve better performance under various maneuvers of driving through roundabouts with and without surrounding HDVs.<br><br>
DOI: 10.15468/dl.j8xjub
title: Occurrence Download

description: A dataset containing 1151227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.3_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1151227 records from 457 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002906-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9uzmqw
title: Occurrence Download

description: A dataset containing 50016 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echium plantagineum L.",
 "Country is Australia",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 50016 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002917-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14898783
title: Meteorological Observations and WRF Model Corrections Dataset

description: 该数据集以 CSV 文件 ('Data.csv“) 的形式提供,其中包含 2015 年的每小时气象观测和模型输出。 数据覆盖全国 359 个气象站,提供了该地区的全面空间和时间覆盖。
DOI: 10.5281/zenodo.14898784
title: Meteorological Observations and WRF Model Corrections Dataset

description: 该数据集以 CSV 文件 ('Data.csv“) 的形式提供,其中包含 2015 年的每小时气象观测和模型输出。 数据覆盖全国 359 个气象站,提供了该地区的全面空间和时间覆盖。
DOI: 10.5281/zenodo.14899470
title: Meteorological Observations and WRF Model Corrections Dataset

description: 该数据集以 CSV 文件 ('Data.csv“) 的形式提供,其中包含 2015 年的每小时气象观测和模型输出。 数据覆盖全国 359 个气象站,提供了该地区的全面空间和时间覆盖。
DOI: 10.4121/ba5805cb-a909-4185-97b0-296739df7def
title: Code underlying the publication: A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection

description: Codes for the paper<strong>Dong, Y.</strong>, Patil, S., van Arem, B., & Farah, H. (2023). A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection. <em>Computer-Aided Civil and Infrastructure Engineering</em>, <em>38</em>(1), pp.67–86.<br>Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial–temporal (ST) sequence-to-one deep learning architecture. This architecture makes full use of the ST information in multiple continuous image frames to detect the lane markings in the very last frame. Specifically, the hybrid model integrates the following aspects: (a) the single image feature extraction module equipped with the spatial convolutional neural network; (b) the ST feature integration module constructed by ST recurrent neural network; (c) the encoder–decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model architecture can effectively handle challenging driving scenes and outperforms available state-of-the-art methods.<br>
DOI: 10.4121/ba5805cb-a909-4185-97b0-296739df7def.v1
title: Code underlying the publication: A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection

description: Codes for the paper<strong>Dong, Y.</strong>, Patil, S., van Arem, B., & Farah, H. (2023). A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection. <em>Computer-Aided Civil and Infrastructure Engineering</em>, <em>38</em>(1), pp.67–86.<br>Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial–temporal (ST) sequence-to-one deep learning architecture. This architecture makes full use of the ST information in multiple continuous image frames to detect the lane markings in the very last frame. Specifically, the hybrid model integrates the following aspects: (a) the single image feature extraction module equipped with the spatial convolutional neural network; (b) the ST feature integration module constructed by ST recurrent neural network; (c) the encoder–decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model architecture can effectively handle challenging driving scenes and outperforms available state-of-the-art methods.<br>
DOI: 10.5281/zenodo.14899479
title: Bipartite-Fluctuations-and-Charge-Fractionalization
DOI: 10.5281/zenodo.14899480
title: Bipartite-Fluctuations-and-Charge-Fractionalization
DOI: 10.15468/dl.u4wza3
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.3_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Colias croceus (Geoffroy, 1785)"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002919-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/f72387bc-25ca-40e9-a6a1-6b2d0b659e30
title: Data underlying the publication: Meat analogues: the relationship between mechanical anisotropy, macrostructure, and microstructure

description: Data underlying the publication: Meat analogues: the relationship between mechanical anisotropy, macrostructure, and microstructure.This study described the relationships between macrostructure, microstructure, and mechanical anisotropy in meat analogues produced with the shear cell technology. Mechanical properties were studied with tensile testing, microstructure was studied using X-ray tomography and confocal laser scanning microscopy, and macrostructure was quantified using the Fiberlyzer tool. The data include complete dataset to perform correlation analyses, all raw macrostructure images, all raw microstructure images, all separate datasets to produce figures for the publication, and scripts used to perform the data analysis.<br>
DOI: 10.4121/f72387bc-25ca-40e9-a6a1-6b2d0b659e30.v1
title: Data underlying the publication: Meat analogues: the relationship between mechanical anisotropy, macrostructure, and microstructure

description: Data underlying the publication: Meat analogues: the relationship between mechanical anisotropy, macrostructure, and microstructure.This study described the relationships between macrostructure, microstructure, and mechanical anisotropy in meat analogues produced with the shear cell technology. Mechanical properties were studied with tensile testing, microstructure was studied using X-ray tomography and confocal laser scanning microscopy, and macrostructure was quantified using the Fiberlyzer tool. The data include complete dataset to perform correlation analyses, all raw macrostructure images, all raw microstructure images, all separate datasets to produce figures for the publication, and scripts used to perform the data analysis.<br>
DOI: 10.15468/dl.tt7362
title: Occurrence Download

description: A dataset containing 1631 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balaenoptera bonaerensis Burmeister, 1867",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 1631 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002920-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.86fumc
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Mongolia)",
 "TaxonKey is Cistanche deserticola Ma"
 ]
}

 The dataset includes 58 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002921-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kz2abw
title: Occurrence Download

description: A dataset containing 335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pavo cristatus Linnaeus, 1758",
 "Year 2025-2025"
 ]
}

 The dataset includes 335 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002922-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j8m4wc
title: Occurrence Download

description: A dataset containing 4382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balaenoptera borealis Lesson, 1828",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 4382 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002923-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15129/85d05367-43ed-465c-ba5d-c570d6228450
title: Data for: PhD work from the study of “Polymer self-assemblies: microfluidic production and nucleobase-functionalities”

description: The data collection encompasses various sources including laboratory notebooks, electronic laboratory notebooks, experiment descriptions and planning, raw and processed data, and Python scripts used for data analysis. All numerical data is provided in .txt or .csv (universal) format wherever possible, except for non-numerical data, such as confocal images, photos and videos.
 
The enclosed dataset pertains to the characterization of advanced block copolymers and their associated chemical functionalities, synthesized to generate functional self-assemblies:
Nuclear Magnetic Resonance (NMR) Spectroscopy: Utilized for characterizing all synthesized organic compounds, including small organic molecules, monomers, block copolymers, polymers, modified nucleobases, and functionalized polymers. Employed for in operando assessment of the conversion of active esters to amides.
Fourier-Transform Infrared (FTIR) Spectroscopy: Used to analyse the content of active esters and amides within the polymers. Employed for in operando monitoring of the conversion of active esters to amides.
Gel Permeation Chromatography (GPC): Used to determine the molecular weight and dispersity of the synthesized polymers.
Transmission Electron Microscopy (TEM): Utilized to characterise the morphology and size of polymer particles produced via microfluidic mixing.
Dynamic Light Scattering (DLS)/ZetaSizer: Used to measure the size and polydispersity index (PDI) of particles produced through microfluidic mixing.
Small-Angle X-ray Scattering (SAXS): Employed for an in-depth characterization of the particles produced via the microfluidic mixer.
Fluorimeter and UV-Vis Spectroscopy: Used for the quantification of encapsulated and released fluorescent probes.
Confocal Microscopy: Utilized to evaluate micrometre-sized particles/vesicles produced with nucleobase-functionalized polymers. Used to assess the impact of external stimuli (force, temperature, and thymine oligomers) on these vesicles.

This dataset was acquired during research work supported by the Swiss National Science Foundation through the National Centre of Competence in Research Bio-Inspired Materials (NCCR Bio-Inspired Materials, Grant No. 51NF40-182881)
DOI: 10.60941/gma4-mg86
title: Huge chromium-induced oxidative damage in endocrine and non-endocrine tissues; chromium oxidation state-dependent and tissue-dependent protective effects of antioxidants

description: <p><span style="color:black">Thyroids, ovaries, kidneys, and livers were collected from 20 porcine animals. Tissue homogenates were incubated with Cr(VI) (0.05–10.0 mM) or Cr(III) (5.0–200.0 mM), with or without melatonin (5 mM), IPA (5 mM), or 17β-estradiol (1 mM). The malondialdehyde &#43; 4-hydroxyalkenals (MDA &#43; 4-HDA) concentration, an index of lipid peroxidation (LPO), was measured spectrophotometrically at 586 nm using a 10 mM 4-hydroxynonenal (4-HNE) solution as a standard.</span></p>