cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/f833cdf7-4018-4abd-8f5b-f108842a3033
title: Data underlying the submission: Exploring the Learning Strategies of Drivers using Advanced Driver Assistance Systems (ADAS)

description: Interview transcripts - Conducted interviews with drivers who have used ADAS.Research objectives: Investigate ADAS learning, learning source, learning strategies, and Usage of ADAS. <br>Interview questions are as follows:<strong>Learning strategy</strong>Q1 Can you explain the [function name]? Q2 How did you learn about the system? Q3 What information did you get from a car dealership? Q4 Did you read the owner’s manual? Q5 Which information should drivers learn about the function?Q6 What instructional methods would you recommend for learning? <strong>System experience/understanding</strong>Q7 Do you keep your hands-on the steering wheel? Q8 Are you still confused about some parts of using the function?Q9 Do you have experience where the features do not work properly? Q10 Do you use the function all the time? / Do you disengage Autopilot and FSD Beta?Q11 When do you prefer to drive manually?Q12 How do you drive differently after you use driving automation?Q13 How do you use driving automation function in complex road situations such as a school zone or a construction area?<br><strong>Abstract</strong>: The integration of Advanced Driver Assistance Systems (ADAS) in vehicles marks an advancement in automotive safety and driving efficiency. However, to obtain the benefits of ADAS, drivers need to understand and utilise the systems properly. Through in-depth interviews with thirty-five drivers U.S. drivers experienced with SAE Level 2 partial driving automation, this study explores the learning strategies, including how they learn (learning channel) and what they learn (learning content) regarding ADAS. The findings show that trial-and-error is the predominant learning channel drivers use to learn about ADAS. Dealerships are critical points for providing initial information, but there is variability in the quality and timing of information provided by sales personnel. Traditional owner’s manual was rarely used to get information, but drivers relied on digital resources to gather information about ADAS. This study suggests that during the initial learning phase, it is essential to provide fundamental information, such as basic functions, operations, and limitations of ADAS while avoiding information overload. Subsequently, supporting trial-and-error learning is necessary to ensure drivers acquire accurate knowledge during practical driving. Furthermore, the study highlights the importance of integrating various learning channels, including driving schools, dealerships, digital resources, and hands-on experience through trial-and-error, into a comprehensive learning framework for ADAS.
DOI: 10.4121/f833cdf7-4018-4abd-8f5b-f108842a3033.v1
title: Data underlying the submission: Exploring the Learning Strategies of Drivers using Advanced Driver Assistance Systems (ADAS)

description: Interview transcripts - Conducted interviews with drivers who have used ADAS.Research objectives: Investigate ADAS learning, learning source, learning strategies, and Usage of ADAS. <br>Interview questions are as follows:<strong>Learning strategy</strong>Q1 Can you explain the [function name]? Q2 How did you learn about the system? Q3 What information did you get from a car dealership? Q4 Did you read the owner’s manual? Q5 Which information should drivers learn about the function?Q6 What instructional methods would you recommend for learning? <strong>System experience/understanding</strong>Q7 Do you keep your hands-on the steering wheel? Q8 Are you still confused about some parts of using the function?Q9 Do you have experience where the features do not work properly? Q10 Do you use the function all the time? / Do you disengage Autopilot and FSD Beta?Q11 When do you prefer to drive manually?Q12 How do you drive differently after you use driving automation?Q13 How do you use driving automation function in complex road situations such as a school zone or a construction area?<br><strong>Abstract</strong>: The integration of Advanced Driver Assistance Systems (ADAS) in vehicles marks an advancement in automotive safety and driving efficiency. However, to obtain the benefits of ADAS, drivers need to understand and utilise the systems properly. Through in-depth interviews with thirty-five drivers U.S. drivers experienced with SAE Level 2 partial driving automation, this study explores the learning strategies, including how they learn (learning channel) and what they learn (learning content) regarding ADAS. The findings show that trial-and-error is the predominant learning channel drivers use to learn about ADAS. Dealerships are critical points for providing initial information, but there is variability in the quality and timing of information provided by sales personnel. Traditional owner’s manual was rarely used to get information, but drivers relied on digital resources to gather information about ADAS. This study suggests that during the initial learning phase, it is essential to provide fundamental information, such as basic functions, operations, and limitations of ADAS while avoiding information overload. Subsequently, supporting trial-and-error learning is necessary to ensure drivers acquire accurate knowledge during practical driving. Furthermore, the study highlights the importance of integrating various learning channels, including driving schools, dealerships, digital resources, and hands-on experience through trial-and-error, into a comprehensive learning framework for ADAS.
DOI: 10.34808/8pvh-0r31
title: Trigeneration unit operation - Heat pump unit - glycol parameters

description: The experiment was conducted to test the operation of the air conditioning unit and heat pump. Both units were hydraulically connected. The heat pump was equipped with an extra heat exchanger (Q2) before the main evaporator. The air conditioning unit was equipped with various heat exchangers (Q3-Q6) that transfer heat between glycol prepared in the heat pump and air to use the energy as efficiently as possible. The studies were conducted for three operational states: turning on the system, stabilized operation, and a sudden change in the heat pump parameter.
DOI: 10.15468/dl.guk3fq
title: Occurrence Download

description: A dataset containing 5883 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Axis axis (Erxleben, 1777)"
 ]
}

 The dataset includes 5883 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064593-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zb7fvc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Compsopogon Montagne, 1846",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064595-250525065834625/datasets/export for details.
DOI: 10.15468/dl.axnbyj
title: Occurrence Download

description: A dataset containing 21997 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Carnivora, Chiroptera, Lagomorpha, Primates, Rodentia, Sirenia, Sirenia, Soricomorpha, Perissodactyla, Erinaceomorpha, Diprotodontia, Cingulata, Pilosa, Afrosoricida, Proboscidea, Proboscidea, Pholidota, Didelphimorphia, Tubulidentata, Hyracoidea, Dasyuromorphia, Monotremata, Dermoptera, Dermaptera, Paucituberculata, Microbiotheria, Notoryctemorphia, Peramelemorphia, Macroscelidea, Scandentia)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 "Year is greater than or equal to 1970",
 "CoordinateUncertaintyInMeters is less than 3000"
 ]
}

 The dataset includes 21997 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064594-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15719453
title: Data: Conformer specific photophysical properties of an analog of the green fluorescent protein chromophore anion
DOI: 10.5281/zenodo.15719454
title: Data: Conformer specific photophysical properties of an analog of the green fluorescent protein chromophore anion
DOI: 10.5281/zenodo.15716835
title: DT4GS Knowledge Graph

description: The Knowledge Graph (KG) is designed with a focus on IMO number, ship name, and ship type as central identifiers, ensuring that every node representing a vessel is anchored by these unique values. This design choice allows for a clear and consistent visual representation where each ship is easily identifiable, and all associated data is linked directly to these identifiers.

In the Neo4j graph, the visualization is structured so that nodes and relationships form an interconnected web of information. The graph includes nodes for CO₂ emissions, fuel usage, KPIs, time information, and basic registry details. The right-hand panel in the Neo4j interface is particularly useful; it displays detailed properties of any selected node. For example, when a user selects a node representing CO₂ emissions, the panel highlights specific data points such as annual CO₂ emissions per distance traveled and per unit of work performed. This detailed view is essential for understanding the environmental performance of a vessel. It provides stakeholders with an at-a-glance summary of key performance metrics and allows them to drill down into the data for further analysis.

The visual structure of the Knowledge Graph also reinforces the interconnected nature of the data. The graph not only displays nodes but also shows the relationships between them, which represent how different aspects of a vessel’s performance are linked. For instance, a ship node (BasicInfo) is visually connected to multiple other nodes, such as Fuel, CO₂, TimeInfo, KPI, and CargoTransport. Since IMO number, ship name, and ship type serve as the primary unique identifiers, they act as the backbone that links all related data tables and relationships within the graph. This interconnected layout makes it immediately apparent which vessels have similar performance profiles, and which operational metrics are closely correlated.

By leveraging Neo4j’s powerful visualization capabilities, the KG transforms static data into a dynamic and interactive tool for real-time decision-making.
description: In the context of a graph database such as Neo4j, a node is a fundamental data entity that encapsulates a set of properties and represents a real-world object or concept. Within the DT4GS Knowledge Graph, nodes are used to model various aspects of shipping data that are originally stored in the Knowledge Hub (KH) relational database. The transition from relational tables to graph nodes is achieved through an automated process that maps each relevant table into a corresponding node label in the KG. As described in the project, the mapping configuration is explicit: the 'ship_basic' table maps to BasicInfo nodes, 'ship_co2' maps to CO2 nodes, 'ship_fuel' maps to Fuel nodes, 'ship_time' maps to TimeInfo nodes, 'kpi' maps to KPI nodes, and 'ship_cargo_transport' maps to CargoTransport nodes.
DOI: 10.5281/zenodo.15716836
title: DT4GS Knowledge Graph

description: The Knowledge Graph (KG) is designed with a focus on IMO number, ship name, and ship type as central identifiers, ensuring that every node representing a vessel is anchored by these unique values. This design choice allows for a clear and consistent visual representation where each ship is easily identifiable, and all associated data is linked directly to these identifiers.

In the Neo4j graph, the visualization is structured so that nodes and relationships form an interconnected web of information. The graph includes nodes for CO₂ emissions, fuel usage, KPIs, time information, and basic registry details. The right-hand panel in the Neo4j interface is particularly useful; it displays detailed properties of any selected node. For example, when a user selects a node representing CO₂ emissions, the panel highlights specific data points such as annual CO₂ emissions per distance traveled and per unit of work performed. This detailed view is essential for understanding the environmental performance of a vessel. It provides stakeholders with an at-a-glance summary of key performance metrics and allows them to drill down into the data for further analysis.

The visual structure of the Knowledge Graph also reinforces the interconnected nature of the data. The graph not only displays nodes but also shows the relationships between them, which represent how different aspects of a vessel’s performance are linked. For instance, a ship node (BasicInfo) is visually connected to multiple other nodes, such as Fuel, CO₂, TimeInfo, KPI, and CargoTransport. Since IMO number, ship name, and ship type serve as the primary unique identifiers, they act as the backbone that links all related data tables and relationships within the graph. This interconnected layout makes it immediately apparent which vessels have similar performance profiles, and which operational metrics are closely correlated.

By leveraging Neo4j’s powerful visualization capabilities, the KG transforms static data into a dynamic and interactive tool for real-time decision-making.
description: In the context of a graph database such as Neo4j, a node is a fundamental data entity that encapsulates a set of properties and represents a real-world object or concept. Within the DT4GS Knowledge Graph, nodes are used to model various aspects of shipping data that are originally stored in the Knowledge Hub (KH) relational database. The transition from relational tables to graph nodes is achieved through an automated process that maps each relevant table into a corresponding node label in the KG. As described in the project, the mapping configuration is explicit: the 'ship_basic' table maps to BasicInfo nodes, 'ship_co2' maps to CO2 nodes, 'ship_fuel' maps to Fuel nodes, 'ship_time' maps to TimeInfo nodes, 'kpi' maps to KPI nodes, and 'ship_cargo_transport' maps to CargoTransport nodes.
DOI: 10.15468/dl.d66y66
title: Occurrence Download

description: A dataset containing 21112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Podarcis siculus (Rafinesque-Schmaltz, 1810)"
 ]
}

 The dataset includes 21112 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064597-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sgsypa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Compsopogon Montagne, 1846",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064598-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15652956
title: Nine-Locus HLA Allele and Haplotype Frequency Estimates in US Populations from NMDP donors

description: Volunteer donor data from the NMDP registry provide a deep sampling of HLA from US populations and allows for characterization of the complex landscape of HLA haplotype diversity for the classical HLA Class I (A, B, C) and Class II (DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1) loci. Here we present nine-locus classical HLA allele and haplotype frequency estimates for five broad and 21 detailed US populations based on approximately ten million samples with targeted genotyping by DNA-based methods. Advancements in next-generation sequencing provide extensive HLA genotyping, offering new insights into the haplotype structure and diversity of the MHC complex, expanding knowledge, especially for Class II haplotypes.
DOI: 10.15468/dl.kmytex
title: Occurrence Download

description: A dataset containing 854736 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pteridium aquilinum (L.) Kuhn"
 ]
}

 The dataset includes 854736 records from 3099 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064553-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjq2k7
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is BRA.9.164_2",
 "IucnRedListCategory is one of (CR, EN, VU)"
 ]
}

 The dataset includes 41 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005200-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bgz68p
title: Occurrence Download

description: A dataset containing 250877211 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Fossil, Specimen, Material citation, Material sample, Human Observation, Machine Observation, Observation)",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta",
 "Year 1000-2024"
 ]
}

 The dataset includes 250877211 records from 20612 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005194-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dpis0z
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megatherium americanum English",
 {
 "or" : [
 "BasisOfRecord is Fossil",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Literature Occurrence"
 ]
 }
 ]
}

 The dataset includes 179 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009645-160118175350007/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zuqa7v
title: Occurrence Download

description: A dataset containing 114 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diospyros rosei Standl."
 ]
}

 The dataset includes 114 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0156466-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6v3jr8
title: Occurrence Download

description: A dataset containing 541 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galium glaucum subsp. glaucum, Galium graecum L., Galium graecum subsp. graecum, Galium graecum subsp. pseudocanum Ehrend., Galium heldreichii Halácsy, Galium hellenicum Krendl, Galium humifusum M.Bieb., Galium idubedae (Pau & Debeaux) Pau ex Ehrend., Galium incanum Sm., Galium incanum subsp. creticum Ehrend.)",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "DatasetKey is one of (Pl@ntNet automatically identified occurrences, Pl@ntNet observations, iNaturalist Research-grade Observations, Observation.org, Nature data from around the World, naturgucker, Artportalen (Swedish Species Observation System), Norwegian Species Observation Service, Programme d'observation naturaliste participative avec l'application mobile INPN Espèces - Application INPN Espèces: Observations naturalistes, participatives et opportunistes, fondées sur des photographies, Species recordings from the Danish National portal Arter.dk, Lajitietokeskus/FinBIF - Notebook, general observations, Vascular plant records verified via iRecord, iRecord Surveys)",
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 500",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 541 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069534-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9vzwtx
title: Occurrence Download

description: A dataset containing 522959 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Triticum aestivum L."
 ]
}

 The dataset includes 522959 records from 541 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058732-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dshese
title: Occurrence Download

description: A dataset containing 11786 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "TaxonKey is Felis silvestris Schreber, 1777"
 ]
}

 The dataset includes 11786 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005437-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c3tuc7
title: Occurrence Download

description: A dataset containing 1052 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation, Material sample)",
 "Country is Mexico",
 "TaxonKey is Trimorphodon biscutatus (Duméril, Bibron & Duméril, 1854)"
 ]
}

 The dataset includes 1052 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001357-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vspvs2
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fucus vesiculosus L.",
 "HasGeospatialIssue is true",
 "HasCoordinate is true"
 ]
}

 The dataset includes 84 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014259-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nqf5aq
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Epidendrum imthurnii Ridl."
 ]
}

 The dataset includes 51 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0156465-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.