cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.j7q8xt
title: Occurrence Download

description: A dataset containing 24960228 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (64ee55c9-570a-42af-b7da-3f13c6b4e5a9, 9661d20d-86b6-4485-8948-f3c86b022fa7, 897198ad-bc6c-469b-98f0-a8678e08d8d9)"
 ]
}

 The dataset includes 24960228 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006822-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e195066
title: Data and Code for: The Inner Workings of a Hub-and-Spoke Cartel in the Automotive fuel Industry

description: We study the inner workings of a hub-and-spoke cartel in the Brazilian automotive-fuel industry. Based on court documents and detailed price and sales data, we discuss how gas station owners (spokes) operating inside the federal capital received help from fuel distributors (hub) to reduce dispersion of and increase gasoline's retail price. We provide evidence that distributor cartel members benefited by raising wholesale prices while keeping competitors from supplying retail cartel members. We also provide theoretical foundations and empirical evidence for a new mechanism through which hubs help spokes: smoothing cost fluctuations.
DOI: 10.3886/e195066v1
title: Data and Code for: The Inner Workings of a Hub-and-Spoke Cartel in the Automotive fuel Industry

description: We study the inner workings of a hub-and-spoke cartel in the Brazilian automotive-fuel industry. Based on court documents and detailed price and sales data, we discuss how gas station owners (spokes) operating inside the federal capital received help from fuel distributors (hub) to reduce dispersion of and increase gasoline's retail price. We provide evidence that distributor cartel members benefited by raising wholesale prices while keeping competitors from supplying retail cartel members. We also provide theoretical foundations and empirical evidence for a new mechanism through which hubs help spokes: smoothing cost fluctuations.
DOI: 10.6084/m9.figshare.28589567
title: Grattan Beach Rock-Attached Seaweed Data 02/10/2024 - 31/10/2024 (finalizedshorestack.csv)

description: This data set was recorded in October 2024, where 8 different visits to Grattan Beach, Galway, Ireland, took place to recorded several rock-attached macroalgae factors.Factors included species, latitude and longitude, maximum frond length, seaweed biomass, rock mass, total mass, depth of rock in sand, and ratios between the recorded masses. A 'storm' value was also recorded, where samples were recorded either before or after Storm Ashley occurred (18th - 21st October 2024).
DOI: 10.4121/6fb5a547-ef0e-4931-acf7-934f86586f81
title: Field test dataset on human driven vehicles' behaviour in mixed traffic with automated vehicles

description: This research is based on data gathered in 2020 in the Netherlands.<br>A controlled field test was conducted in which human drivers interacted with both human-driven vehicles (HDVs) and automated vehicles (AVs). Participants were asked to drive in their own vehicle (because of COVID-19 restrictions). During the field test, the participants interacted with an instrumented test vehicle that could be set up to appear as an AV. The instrumented test vehicle collected data on the driving behavior of the participant during their interactions. The field test was approved by the Human Research Ethics Committee of the Delft University of Technology, the Netherlands.<br>The field test was conducted on a 3 km long straight road section in Noordzeeweg near the town of Rozenburg in the Netherlands. The test route had one 3.5-m wide lane per direction separated by dashed lane markings (i.e., overtaking was allowed). The traffic intensity of the test location was very low (around 30 vehicles per hour), and the speed limit of the road section is 60 km/h. A depiction of the route is attached in this dataset information.<br>18 participants took part in the field test.14 participants were between 35 and 60 years old, and 4 participants were younger than 35 years old. Behaviors of gap acceptance, overtaking, and car-following were studied, while the scenarios varied with the appearance of the test vehicle, either as an HDV or AV.<br>More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.02.002<br><br><strong>Attached files:</strong>Processed datasetReadMe file<br><strong>File formats:</strong>Data (created and used in matlab) /.sav
DOI: 10.4121/6fb5a547-ef0e-4931-acf7-934f86586f81.v1
title: Field test dataset on human driven vehicles' behaviour in mixed traffic with automated vehicles

description: This research is based on data gathered in 2020 in the Netherlands.<br>A controlled field test was conducted in which human drivers interacted with both human-driven vehicles (HDVs) and automated vehicles (AVs). Participants were asked to drive in their own vehicle (because of COVID-19 restrictions). During the field test, the participants interacted with an instrumented test vehicle that could be set up to appear as an AV. The instrumented test vehicle collected data on the driving behavior of the participant during their interactions. The field test was approved by the Human Research Ethics Committee of the Delft University of Technology, the Netherlands.<br>The field test was conducted on a 3 km long straight road section in Noordzeeweg near the town of Rozenburg in the Netherlands. The test route had one 3.5-m wide lane per direction separated by dashed lane markings (i.e., overtaking was allowed). The traffic intensity of the test location was very low (around 30 vehicles per hour), and the speed limit of the road section is 60 km/h. A depiction of the route is attached in this dataset information.<br>18 participants took part in the field test.14 participants were between 35 and 60 years old, and 4 participants were younger than 35 years old. Behaviors of gap acceptance, overtaking, and car-following were studied, while the scenarios varied with the appearance of the test vehicle, either as an HDV or AV.<br>More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.02.002<br><br><strong>Attached files:</strong>Processed datasetReadMe file<br><strong>File formats:</strong>Data (created and used in matlab) /.sav
DOI: 10.5281/zenodo.13981334
title: Data and Code: Decreased spinal inhibition leads to undiversified locomotor patterns

description: Data and Code belonging to the article 'Increased spinal excitation causes decreased locomotor complexity'.Available as preprint at: https://www.biorxiv.org/content/10.1101/2022.04.21.489087v2

Included filesCODE: 

-        runExampleNet.m: this trains and tests an example network and plots the network outputs. The settings for the signal and the network can all be changed in this file.

-        runMFT.m: this runs the meanfield theory analysis for various levels of Imbalance and g_tot

-        createSignal.m: function to create the test and train signals, called from runExampleNet.m

-        ESN_EI.m: function for the echo state network with distinct excitatory and inhibitory populations, called from runExampleNet.m

-        getNPG.m: function to get parameter values for the desired imbalances and overall parameter levels

DATA: Myonardo (a 3D musculoskeletal model) output for walking slowly (XXX=WalkingI), walking fast (XXX=WalkingII) and running (XXX=Running), used in createSignal.

Each folder contains:

-        XXX.qtm: File with the labelled qualisys data 

-        XXX.mat: File with the recorded kinetic ('force') and kinematic ('trajectories') data 

-        modelOutput.mat: the relevant outputs of the myonardo simulation, used to create the signals in createSignal: 

-        includes: time, right heel marker, muscle length, muscle velocity and muscle activation

-        musclenames.mat: file indicating which column corresponds to what muscle, necessary for signal creation in createSignal.

 

Upon request, the model simulations results can be shared (several GB). Contact: mdegraaf@uni-muenster.de
DOI: 10.5281/zenodo.6502264
title: Data and Code: Decreased spinal inhibition leads to undiversified locomotor patterns

description: Data and Code belonging to the article 'Increased spinal excitation causes decreased locomotor complexity'.Available as preprint at: https://www.biorxiv.org/content/10.1101/2022.04.21.489087v2

Included filesCODE: 

-        runExampleNet.m: this trains and tests an example network and plots the network outputs. The settings for the signal and the network can all be changed in this file.

-        runMFT.m: this runs the meanfield theory analysis for various levels of Imbalance and g_tot

-        createSignal.m: function to create the test and train signals, called from runExampleNet.m

-        ESN_EI.m: function for the echo state network with distinct excitatory and inhibitory populations, called from runExampleNet.m

-        getNPG.m: function to get parameter values for the desired imbalances and overall parameter levels

DATA: Myonardo (a 3D musculoskeletal model) output for walking slowly (XXX=WalkingI), walking fast (XXX=WalkingII) and running (XXX=Running), used in createSignal.

Each folder contains:

-        XXX.qtm: File with the labelled qualisys data 

-        XXX.mat: File with the recorded kinetic ('force') and kinematic ('trajectories') data 

-        modelOutput.mat: the relevant outputs of the myonardo simulation, used to create the signals in createSignal: 

-        includes: time, right heel marker, muscle length, muscle velocity and muscle activation

-        musclenames.mat: file indicating which column corresponds to what muscle, necessary for signal creation in createSignal.

 

Upon request, the model simulations results can be shared (several GB). Contact: mdegraaf@uni-muenster.de
DOI: 10.4121/836a93d0-543f-42b8-8e72-aca99ebc3259
title: Driving simulator dataset on human driven vehicles' car following behaviour in mixed traffic with automated vehicles

description: This research is based on data gathered in 2023 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles (HDVs) and automated vehicles (AVs).<br>A driving simulator experiment was designed to collect data on the car-following behavior of HDVs in the different scenarios. The driving simulator used is located at the Transport & Planning department of Delft University in the Netherlands. It operates using the SCANeR (v1.9) software by AV Simulation. It is a fixed base driving simulator equipped with a Fanatec steering wheel and pedals, a dashboard mock-up, and three 4 K high-resolution screens which approximately provide 180° vision.<br>The dataset consists of 47 drivers. We categorized them into three age categories. There were 16 younger (25 - 45) drivers (8 male, 8 female), 16 middle-aged (45 – 65) drivers (8 male, 8 female), and 15 older (70+) drivers (10 male, 5 female).<br>The route in the driving simulator consisted of 3 parts. Part 1: 3 motorway on-ramps (excluding an initial on-ramp), Part 2: 3 provincial road signalized intersections, and Part 3: a straight road section. A depiction of the route is attached in this dataset information. Each driver drove four scenarios, excluding an initial familiarization scenario. The four scenarios varied in terms of the appearance of the vehicle interacting with the human driver and its driving style.<br>More details about the experiment set-up itself can be found in our paper published: TO BE ADDED<br><br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxPython scripts /.pyJupyternotebooks /.ipynb
DOI: 10.4121/836a93d0-543f-42b8-8e72-aca99ebc3259.v1
title: Driving simulator dataset on human driven vehicles' car following behaviour in mixed traffic with automated vehicles

description: This research is based on data gathered in 2023 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles (HDVs) and automated vehicles (AVs).<br>A driving simulator experiment was designed to collect data on the car-following behavior of HDVs in the different scenarios. The driving simulator used is located at the Transport & Planning department of Delft University in the Netherlands. It operates using the SCANeR (v1.9) software by AV Simulation. It is a fixed base driving simulator equipped with a Fanatec steering wheel and pedals, a dashboard mock-up, and three 4 K high-resolution screens which approximately provide 180° vision.<br>The dataset consists of 47 drivers. We categorized them into three age categories. There were 16 younger (25 - 45) drivers (8 male, 8 female), 16 middle-aged (45 – 65) drivers (8 male, 8 female), and 15 older (70+) drivers (10 male, 5 female).<br>The route in the driving simulator consisted of 3 parts. Part 1: 3 motorway on-ramps (excluding an initial on-ramp), Part 2: 3 provincial road signalized intersections, and Part 3: a straight road section. A depiction of the route is attached in this dataset information. Each driver drove four scenarios, excluding an initial familiarization scenario. The four scenarios varied in terms of the appearance of the vehicle interacting with the human driver and its driving style.<br>More details about the experiment set-up itself can be found in our paper published: TO BE ADDED<br><br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxPython scripts /.pyJupyternotebooks /.ipynb
DOI: 10.15468/dl.8w8fxk
title: Occurrence Download

description: A dataset containing 93056 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-93.30627 -2.21172,-86.27201 -2.21172,-86.27201 0.60078,-93.30627 0.60078,-93.30627 -2.21172))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2023-2024"
 ]
}

 The dataset includes 93056 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006827-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/91308654-6467-49c5-be0f-12b056e42ec7
title: Driving simulator dataset on human driven vehicles' speed compliance under variable speed limits in mixed traffic with automated vehicles

description: This research is based on data gathered in 2020 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles and connected and automated vehicles (CAVs).<br>The driving simulator used is located at the Transport & Planning department of Delft University in the Netherlands. It operates using the SCANeR (v1.9) software by AV Simulation. It is a fixed base driving simulator equipped with a Fanatec steering wheel and pedals, a dashboard mock-up, and three 4 K high-resolution screens which approximately provide 180° vision.<br>The dataset consists of 36 participants, over different ages and both males and females.<br>The route in the driving simulator consisted of a motorway with a defined speed limit of 100 km/h. On the motorway, drivers also experienced dynamic speed limit sections, where a decreasing speed limit from 100 to 70 to 50, and again back to 100 was applied. A depiction of the route is attached in this dataset information.<br>Each driver drove 7 scenarios. The scenarios were defined using two variables: the penetration rate of CAVs (0%, 10% and 40%) and the distance of speed limit information provision to CAVs (300 m, 600 m, and 900 m).<br>More details about the experiment set-up itself can be found in the master thesis: http://resolver.tudelft.nl/uuid:b2bf818d-1cc9-40fb-8515-eef9b8eb348a<br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxJupyternotebooks /.ipynb
DOI: 10.4121/91308654-6467-49c5-be0f-12b056e42ec7.v1
title: Driving simulator dataset on human driven vehicles' speed compliance under variable speed limits in mixed traffic with automated vehicles

description: This research is based on data gathered in 2020 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles and connected and automated vehicles (CAVs).<br>The driving simulator used is located at the Transport & Planning department of Delft University in the Netherlands. It operates using the SCANeR (v1.9) software by AV Simulation. It is a fixed base driving simulator equipped with a Fanatec steering wheel and pedals, a dashboard mock-up, and three 4 K high-resolution screens which approximately provide 180° vision.<br>The dataset consists of 36 participants, over different ages and both males and females.<br>The route in the driving simulator consisted of a motorway with a defined speed limit of 100 km/h. On the motorway, drivers also experienced dynamic speed limit sections, where a decreasing speed limit from 100 to 70 to 50, and again back to 100 was applied. A depiction of the route is attached in this dataset information.<br>Each driver drove 7 scenarios. The scenarios were defined using two variables: the penetration rate of CAVs (0%, 10% and 40%) and the distance of speed limit information provision to CAVs (300 m, 600 m, and 900 m).<br>More details about the experiment set-up itself can be found in the master thesis: http://resolver.tudelft.nl/uuid:b2bf818d-1cc9-40fb-8515-eef9b8eb348a<br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxJupyternotebooks /.ipynb
DOI: 10.15468/dl.4mgwv2
title: Occurrence Download

description: A dataset containing 54686 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glebionis segetum (L.) Fourr."
 ]
}

 The dataset includes 54686 records from 589 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006821-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8nm8n4
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acarospora normanii H.Magn."
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006842-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/28149b45-62fd-44f6-9c8a-fbc14a5d645c
title: Driving simulator dataset on human driven vehicles' gap acceptance behaviour in mixed traffic with automated vehicles

description: This research is based on data gathered in 2020 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles and automated vehicles (AVs).<br>The dataset consists of 95 participants of which 71 (74.7 %) were male and 24 females.<br>The route in the driving simulator consisted of several motorway sections, provincial (regional) road sections, and three priority T-intersections. Each T-intersection consisted of an urban road (the minor road) intersecting with a provincial road (the major road). The defined speed limit was 100 km/h on the motorway, 80 km/h on the provincial roads, and 50 km/h on urban roads. On the motorway, drivers also experienced dynamic speed limit sections. A depiction of the route is attached in this dataset information.<br>The experiment design aimed to separately observe the effects of AVs’ recognizability and their driving style on human driving behavior as well as their combined effects. Each participant drove four scenarios, excluding a familiarization drive. The scenarios differed in two aspects: the recognizability and the driving style of AVs.Two variables primarily varied in the experiment: the driving style of AVs, and their recognizability. The participants were assigned randomly to one of three groups: Defensive AVs, Aggressive AVs, and Mixed AVs. The group determined the driving style of AVs that a participant encountered in the experiment.<br>More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.09.018<br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxJupyternotebooks /.ipynb
DOI: 10.4121/28149b45-62fd-44f6-9c8a-fbc14a5d645c.v1
title: Driving simulator dataset on human driven vehicles' gap acceptance behaviour in mixed traffic with automated vehicles

description: This research is based on data gathered in 2020 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles and automated vehicles (AVs).<br>The dataset consists of 95 participants of which 71 (74.7 %) were male and 24 females.<br>The route in the driving simulator consisted of several motorway sections, provincial (regional) road sections, and three priority T-intersections. Each T-intersection consisted of an urban road (the minor road) intersecting with a provincial road (the major road). The defined speed limit was 100 km/h on the motorway, 80 km/h on the provincial roads, and 50 km/h on urban roads. On the motorway, drivers also experienced dynamic speed limit sections. A depiction of the route is attached in this dataset information.<br>The experiment design aimed to separately observe the effects of AVs’ recognizability and their driving style on human driving behavior as well as their combined effects. Each participant drove four scenarios, excluding a familiarization drive. The scenarios differed in two aspects: the recognizability and the driving style of AVs.Two variables primarily varied in the experiment: the driving style of AVs, and their recognizability. The participants were assigned randomly to one of three groups: Defensive AVs, Aggressive AVs, and Mixed AVs. The group determined the driving style of AVs that a participant encountered in the experiment.<br>More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.09.018<br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxJupyternotebooks /.ipynb
DOI: 10.15468/dl.9pan48
title: Occurrence Download

description: A dataset containing 133 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Monitoring the restoration of natural vegetation at the site of the former Kakhovka reservoir"
 ]
}

 The dataset includes 133 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006843-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.