cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15698408
title: Voluntary Versus Enforced Tax Compliance: Empirical Evidence From Digital-Based Freelancer Data

description: The Slippery Slope Framework shows that tax compliance behavior is influenced by the dimensions of trust and power that exist in the relationship between taxpayers and the government. Although the Slippery Slope Framework has been tested by many empirical studies, there is still a lack of research targeting shadow economy actors. Therefore, this study is dedicated to examining the existence of these dimensions and their influence on the perception of tax compliance among digital-based freelancers in Indonesia. A total of 408 digital-based freelancer taxpayers were obtained through an online questionnaire survey and analyzed using a stepwise linear regression model. The results showed that perceived government power was positively correlated with approval of voluntary tax compliance. However, this study could not find the effect of perceived government power or trust in government on enforced tax compliance. These findings only partially confirm the Slippery Slope Framework. This study aims to expand the literature on the influence of trust and power on voluntary or enforced tax compliance in developing countries facing a shadow economy, particularly in Asia.
DOI: 10.5281/zenodo.12800631
title: LIDAROC dataset 20m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.

description: Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.

 





This dataset is the 20m dataset, which is part of the larger LIDAROC dataset.

The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).

For the 5m and 10m datasets, please refer to the link below:


LIDAROC 5m

LIDAROC 10m


 
description: To use this dataset, please cite this paper.

G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624
DOI: 10.5281/zenodo.12800632
title: LIDAROC dataset 20m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.

description: Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.

 





This dataset is the 20m dataset, which is part of the larger LIDAROC dataset.

The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).

For the 5m and 10m datasets, please refer to the link below:


LIDAROC 5m

LIDAROC 10m


 
description: To use this dataset, please cite this paper.

G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624
DOI: 10.15468/dl.t9gd3z
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acapoeta tanganicae (Boulenger, 1900)",
 "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 ((55.9743 -6.7082,55.8366 -6.9401,56.7426 -6.9401,56.7231 -6.8387,56.294 -6.5954,55.9743 -6.7082))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057046-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ebe872
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chroodactylon ornatum (C.Agardh) Basson",
 "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 ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057047-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.12800038
title: LIDAROC dataset 5m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.

description: Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.

 



This dataset is the 5m dataset, which is part of the larger LIDAROC dataset.

The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).

For the 10m and 20m datasets, please refer to the link below:


LIDAROC 10m

LIDAROC 20m
description: To use this dataset, please cite this paper.

G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624
DOI: 10.5281/zenodo.12800039
title: LIDAROC dataset 5m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.

description: Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.

 



This dataset is the 5m dataset, which is part of the larger LIDAROC dataset.

The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).

For the 10m and 20m datasets, please refer to the link below:


LIDAROC 10m

LIDAROC 20m
description: To use this dataset, please cite this paper.

G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624
DOI: 10.5281/zenodo.12800559
title: LIDAROC dataset 10m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.

description: Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.

 





This dataset is the 10m dataset, which is part of the larger LIDAROC dataset.

The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).

For the 5m and 20m datasets, please refer to the link below:


LIDAROC 5m

LIDAROC 20m


 
description: To use this dataset, please cite this paper.

G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624
DOI: 10.5281/zenodo.12800558
title: LIDAROC dataset 10m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.

description: Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.

 





This dataset is the 10m dataset, which is part of the larger LIDAROC dataset.

The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).

For the 5m and 20m datasets, please refer to the link below:


LIDAROC 5m

LIDAROC 20m


 
description: To use this dataset, please cite this paper.

G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624
DOI: 10.15468/dl.mz3y4z
title: Occurrence Download

description: A dataset containing 2321 species occurrences available in GBIF matching the query:
{
 "SpeciesKey" : [
 "is Lepus saxatilis F.Cuvier, 1823"
 ]
}

 The dataset includes 2321 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057048-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15698442
title: Spontaneous complexity in the dynamics of slow laboratory earthquakes.

description: Mechanical data of rock deformation experiments in montmorillonite gouge.
DOI: 10.5281/zenodo.15698608
title: Spontaneous complexity in the dynamics of slow laboratory earthquakes.

description: Mechanical data of rock deformation experiments in montmorillonite gouge.
DOI: 10.3886/icpsr03788.v3
title: CBS News/New York Times Call-Back Poll #2, March 2003
title: Version 3

description: This poll is part of a continuing series of monthly surveys
that solicits public opinion on the presidency and on a range of other
political and social issues. This survey, fielded March 20, 2003, is a
call-back of the March 7-9, 2003 cohort (CBS News Monthly Poll #3, March
2003 [ICPSR 3756]), and was conducted to asses respondent approval
ratings of President George W. Bush and his handling of the situation
with Iraq. A series of questions addressed United States' military
action in Iraq. Those queried were asked whether they approved or
disapproved of the military action, and whether the United States should
accommodate the wishes of allies and the United Nations. Respondents
were asked if they had confidence in President Bush to handle the
situation with Iraq, what they thought were the motivations for the
attack on Iraq, to what degree they felt that President Bush was
attacking Iraq for his own personal reasons, whether Iraq posed a real
threat to the United States, and when the United States should take
action against Iraq. Questions also sought respondents' views on whether
or not a war in Iraq would increase terrorism and what effects (positive
or negative) a war in Iraq might have on the United States' economy. No
demographic data were gathered.
description: A variation of random-digit dialing using primary sampling
units (PSUs) was employed, consisting of blocks of 100 telephone numbers
identical through the eighth digit and stratified by geographic region,
area code, and size of place. Within households, respondents were
selected using a method developed by Leslie Kish and modified by Charles
Backstrom and Gerald Hursh (see Backstrom and Hursh, SURVEY RESEARCH.
Evanston, IL: Northwestern University Press, 1963).
description: Datasets: 
DS1: CBS News/New York Times Call-Back Poll #2, March 2003
description: Adult population of the United States aged 18 and over
having a telephone at home.
description: This collection has not been processed by ICPSR
staff. ICPSR is distributing the data and documentation for this
collection in essentially the same form in which they were received.
When appropriate, documentation has been converted to Portable Document
Format (PDF), data files have been converted to non-platform-specific
formats, and variables have been recoded to ensure respondents'
anonymity.The ASCII data file may have been replaced if the previous version was formatted with multiple records per case. A frequency file, which contains the authoritative column locations, has been added to the collection.
DOI: 10.3886/icpsr03788
title: CBS News/New York Times Call-Back Poll #2, March 2003
title: Archival Version

description: This poll is part of a continuing series of monthly surveys
that solicits public opinion on the presidency and on a range of other
political and social issues. This survey, fielded March 20, 2003, is a
call-back of the March 7-9, 2003 cohort (CBS News Monthly Poll #3, March
2003 [ICPSR 3756]), and was conducted to asses respondent approval
ratings of President George W. Bush and his handling of the situation
with Iraq. A series of questions addressed United States' military
action in Iraq. Those queried were asked whether they approved or
disapproved of the military action, and whether the United States should
accommodate the wishes of allies and the United Nations. Respondents
were asked if they had confidence in President Bush to handle the
situation with Iraq, what they thought were the motivations for the
attack on Iraq, to what degree they felt that President Bush was
attacking Iraq for his own personal reasons, whether Iraq posed a real
threat to the United States, and when the United States should take
action against Iraq. Questions also sought respondents' views on whether
or not a war in Iraq would increase terrorism and what effects (positive
or negative) a war in Iraq might have on the United States' economy. No
demographic data were gathered.
description: A variation of random-digit dialing using primary sampling
units (PSUs) was employed, consisting of blocks of 100 telephone numbers
identical through the eighth digit and stratified by geographic region,
area code, and size of place. Within households, respondents were
selected using a method developed by Leslie Kish and modified by Charles
Backstrom and Gerald Hursh (see Backstrom and Hursh, SURVEY RESEARCH.
Evanston, IL: Northwestern University Press, 1963).
description: Datasets: 
DS1: CBS News/New York Times Call-Back Poll #2, March 2003
description: Adult population of the United States aged 18 and over
having a telephone at home.
description: This collection has not been processed by ICPSR
staff. ICPSR is distributing the data and documentation for this
collection in essentially the same form in which they were received.
When appropriate, documentation has been converted to Portable Document
Format (PDF), data files have been converted to non-platform-specific
formats, and variables have been recoded to ensure respondents'
anonymity.The ASCII data file may have been replaced if the previous version was formatted with multiple records per case. A frequency file, which contains the authoritative column locations, has been added to the collection.
DOI: 10.15468/dl.5nbgeh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acapoeta tanganicae (Boulenger, 1900)",
 "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 ((55.2639 -5.3728,55.1382 -5.4732,55.1382 -6.2691,55.1926 -6.3322,55.5629 -6.3372,55.8543 -6.0446,55.8653 -5.7146,55.6359 -5.426,55.2639 -5.3728))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057049-250525065834625/datasets/export for details.
DOI: 10.57992/nmrxiv.s1223.d5220
title: linoleamidoethyl formate[1]

description: 1
DOI: 10.57992/nmrxiv.s1223.d5221
title: linoleamidoethyl formate[2]

description: 2
DOI: 10.57992/nmrxiv.s1223.d5222
title: linoleamidoethyl formate[3]

description: 3
DOI: 10.57992/nmrxiv.s1223.d5223
title: linoleamidoethyl formate[4]

description: 4
DOI: 10.57992/nmrxiv.s1223.d5224
title: linoleamidoethyl formate[5]

description: 5
DOI: 10.57992/nmrxiv.s1223.d5225
title: linoleamidoethyl formate[6]

description: 6
DOI: 10.57992/nmrxiv.s1223.d5226
title: linoleamidoethyl formate[7]

description: 7
DOI: 10.57992/nmrxiv.s1223.d5227
title: linoleamidoethyl formate[8]

description: 8
DOI: 10.57992/nmrxiv.s1223.d5228
title: linoleamidoethyl formate[9]

description: 9
DOI: 10.57992/nmrxiv.s1224.d5229
title: oleamidoethyl formate[1]

description: 1
DOI: 10.57992/nmrxiv.s1224.d5230
title: oleamidoethyl formate[10]

description: 10
DOI: 10.57992/nmrxiv.s1224.d5231
title: oleamidoethyl formate[101]

description: 101
DOI: 10.57992/nmrxiv.s1224.d5232
title: oleamidoethyl formate[102]

description: 102
DOI: 10.57992/nmrxiv.s1224.d5233
title: oleamidoethyl formate[103]

description: 103
DOI: 10.57992/nmrxiv.s1224.d5234
title: oleamidoethyl formate[104]

description: 104
DOI: 10.57992/nmrxiv.s1224.d5235
title: oleamidoethyl formate[2]

description: 2
DOI: 10.57992/nmrxiv.s1224.d5236
title: oleamidoethyl formate[3]

description: 3
DOI: 10.57992/nmrxiv.s1224.d5237
title: oleamidoethyl formate[4]

description: 4
DOI: 10.57992/nmrxiv.s1224.d5238
title: oleamidoethyl formate[5]

description: 5
DOI: 10.57992/nmrxiv.s1224.d5239
title: oleamidoethyl formate[6]

description: 6
DOI: 10.57992/nmrxiv.s1224.d5240
title: oleamidoethyl formate[7]

description: 7
DOI: 10.57992/nmrxiv.s1224.d5241
title: oleamidoethyl formate[8]

description: 8
DOI: 10.57992/nmrxiv.s1224.d5242
title: oleamidoethyl formate[9]

description: 9
DOI: 10.57992/nmrxiv.s1225
title: oleoyl ethanolamide

description: This dataset contains NMR spectra obtained for the sample -oleoyl ethanolamide date: {"year":2025,"month":4,"day":28} isFt: false name: TN001003LK_13cE6-1-1.jdf type: NMR SPECTRUM isFid: true title: title: TN001003LK / comment: 003LK single pulse decoupled gated NOE / author:datum / site: author: datum nucleus: 13C solvent: CHLOROFORM-D metadata: [object Object] dimension: 1 isComplex: true probeName: 3365 experiment: 1d sampleName: TN001003LK temperature: 293.95 baseFrequency: 150.9495124651823 digitalFilter: 19.65565013885498 fieldStrength: 14.09636928 numberOfScans: 1757 pulseSequence: carbon.jxp spectralWidth: 313.74599811902664 numberOfPoints: 32768 relaxationTime: 2 acquisitionMode: 0 acquisitionTime: 0.69206016 frequencyOffset: 15094.951246518229 originFrequency: 150.9134303938504 pulseStrength90: 15337.423312883437 spectralWidthClipped: 250936801.71722928 date: {"year":2025,"month":4,"day":26} isFt: false name: TN001003LK_1hE6-1-1.jdf type: NMR SPECTRUM isFid: true title: title: TN001003LK / comment: 003LK single_pulse / author:datum / site: author: datum nucleus: 1H solvent: CHLOROFORM-D metadata: [object Object] dimension: 1 isComplex: true probeName: 3365 experiment: 1d sampleName: TN001003LK temperature: 294.04999999999995 baseFrequency: 600.1878601986348 digitalFilter: 19.65565013885498 fieldStrength: 14.09636928 numberOfScans: 64 pulseSequence: proton.jxp spectralWidth: 18.797249325094484 numberOfPoints: 32768 relaxationTime: 5 acquisitionMode: 0 acquisitionTime: 2.9045555199999997 frequencyOffset: 3000.9393009931737 originFrequency: 600.17230460376 pulseStrength90: 37425.149700598806 spectralWidthClipped: 15037409.712245414
DOI: 10.57992/nmrxiv.s1225.d5243
title: oleoyl ethanolamide[TN001003LK_13cE6-1-1.jdf]

description: TN001003LK_13cE6-1-1.jdf
DOI: 10.57992/nmrxiv.s1225.d5244
title: oleoyl ethanolamide[TN001003LK_1hE6-1-1.jdf]

description: TN001003LK_1hE6-1-1.jdf