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

description: A dataset containing 9 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Augasma aeratella (Zeller, 1839)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 9 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083868-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.stw4sk
title: Occurrence Download

description: A dataset containing 29824 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leptinotarsa decemlineata (Say, 1824)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 29824 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083870-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8e9254
title: Occurrence Download

description: A dataset containing 133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "TaxonKey is Phengaris arion (Linnaeus, 1758)"
 ]
}

 The dataset includes 133 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083871-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mm3496
title: Occurrence Download

description: A dataset containing 5295 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Limnaecia phragmitella Stainton, 1851",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 5295 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083872-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/gqjq-8p72
title: OCT

description: This dataset consists of publicly available ophthalmic OCT images, including categories such as CNV (choroidal neovascularization), DRUSEN (macular drusen), and NORMAL (normal). It serves as the raw data for the study titled Super-Resolution Reconstruction of OCT Images Based on Frequency and Spatial Information in Adversarial Neural Networks. The data is sourced from a publicly available image dataset, with reference to D. Kermany, K. Zhang, and M. Goldbaum, "Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification," Mendeley Data, vol. 2, no. 2, pp. 651, 2018.
DOI: 10.15468/dl.fts2rb
title: Occurrence Download

description: A dataset containing 694 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Callima formosella (Denis & Schiffermüller), 1775",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 694 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083874-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ezs7xm
title: Occurrence Download

description: A dataset containing 92188 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Timor-Leste"
 ]
}

 The dataset includes 92188 records from 183 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083869-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uajpev
title: Occurrence Download

description: A dataset containing 29824 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leptinotarsa decemlineata (Say, 1824)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 29824 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083873-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wd6927
title: Occurrence Download

description: A dataset containing 651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Chinese Taipei, China)",
 "Geometry POLYGON((99.5 -2.75,125 -2.75,125 26.47,99.5 26.47,99.5 -2.75))",
 "TaxonKey is Scleractinia",
 "Year 2000-2020"
 ]
}

 The dataset includes 651 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083875-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k4vg8v
title: Occurrence Download

description: A dataset containing 63 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Stephensia brunnichella (Linnaeus, 1767)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 63 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083876-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.886dby
title: Occurrence Download

description: A dataset listing the 245 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Timor-Leste",
 "DatasetKey is EOD – eBird Observation Dataset"
 ]
}

 The dataset's 245 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083858-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b9mfm3
title: Occurrence Download

description: A dataset containing 29824 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leptinotarsa decemlineata (Say, 1824)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 29824 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083877-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dfqk6w
title: Occurrence Download

description: A dataset listing the 2034 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Timor-Leste",
 "DatasetKey is iNaturalist Research-grade Observations"
 ]
}

 The dataset's 2034 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083862-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n8fgnv
title: Occurrence Download

description: A dataset containing 740 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Zelleria hepariella Stainton, 1849",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 740 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083878-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ekemuu
title: Occurrence Download

description: A dataset containing 29824 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leptinotarsa decemlineata (Say, 1824)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 29824 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083879-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xtwude
title: Occurrence Download

description: A dataset containing 120 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leucoptera sinuella Reutti, 1853",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 120 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083880-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.94nr2f
title: Occurrence Download

description: A dataset containing 95 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leucoptera lotella (Stainton, 1850)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 95 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083881-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7nnqv5
title: Occurrence Download

description: A dataset containing 29824 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)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leptinotarsa decemlineata (Say, 1824)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 29824 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083882-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.3566703
title: Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version

description: <strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version &gt;= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong> This is the data to the same-titled Data paper. The files contain: <strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal <strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper, and for algorithm at https://doi.org/10.3390/rs5010110. <strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper. The maps are GeoTIFF with integer based values: 0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata Version history: 1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper
DOI: 10.5281/zenodo.3529518
title: Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version

description: <strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version &gt;= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong> This is the data to the same-titled Data paper. Abstract: Snow cover dynamics impact a whole range of systems in mountain regions, from society to economy to ecology; and they also affect downstream regions. Monitoring and analyzing snow cover dynamics has been facilitated with remote sensing products. Here, we present a high-resolution daily snow cover data set for the entire European Alps based on MODIS covering the years 2002 to 2019, with automatic updates. Its implementation is specifically tailored to the complex terrain. Additionally, a nearly-cloud free product was created using temporal and spatial filters, which reduces average cloud cover from 41.9% to less than 0.1%. Overall accuracy was 91.9% for the original and 91.1% for the nearly cloud-free version based on a comparison with ground data from 90 stations in the province of Bolzano in Northern Italy for 17 years (2002 Jul – 2019 May). The data can be accessed online and via the R and python programming languages. Possible applications of the data include but are not limited to hydrology, cryosphere and climate.
DOI: 10.5281/zenodo.3601891
title: Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version

description: <strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version &gt;= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong> This is the data to the same-titled Data paper, which can be found at https://doi.org/10.3390/data5010001. Along with auxilary files for the cloudremoval package, and example scripts on how to access chunks of the data. The files contain: <strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal <strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python <strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R <strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (https://doi.org/10.3390/data5010001), and for algorithm at https://doi.org/10.3390/rs5010110. <strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper. The maps are GeoTIFF with integer based values: 0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata Version history: 1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts
DOI: 10.5281/zenodo.3529517
title: Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version

description: <strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version &gt;= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong> This is the data to the same-titled Data paper, which can be found at https://doi.org/10.3390/data5010001. Along with auxilary files for the cloudremoval package, and example scripts on how to access chunks of the data. The files contain: <strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal <strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python <strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R <strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (https://doi.org/10.3390/data5010001), and for algorithm at https://doi.org/10.3390/rs5010110. <strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper. The maps are GeoTIFF with integer based values: 0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata Version history: 1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts
DOI: 10.5281/zenodo.2581400
title: Tree species reference samples in southwest of France

description: <strong>Ground tree species reference samples located in southwest of France</strong> 1 262 tree species references from a temperate forest. Suitable for remote sensing applications.<br> 8 broadleaf species, and 5 coniferous. We also provide the spectral values of Formosat-2 level 2A for each one-year Satellite Image Time Series from 2006 to 2014. Paper related to this dataset : https://doi.org/10.3390/rs11212512 <strong>For more details, please consult the README.pdf file.</strong>
description: {"references": ["Karasiak et al. (2019), Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series, https://doi.org/10.3390/rs11212512"]}
DOI: 10.6084/m9.figshare.25139189
title: Sample Knows benchmark outputs in YARS-PG

description: This dataset contains a sample of YARS-PG outputs from the Knows v1.0.0 benchmark. Knows is a property graph benchmark with node and edge generation, multiple output formats, and visualization.Read more about the Knows benchmark in the Knows repository.Contents<code>README.md</code>: README file<code>LICENSE.txt</code>: A license for all files<code>10000.yarspg</code>: YARS-PG file with 10000 nodes and 16000 edges<code>20000.yarspg</code>: YARS-PG file with 20000 nodes and 32000 edges<code>30000.yarspg</code>: YARS-PG file with 30000 nodes and 48000 edges<code>40000.yarspg</code>: YARS-PG file with 40000 nodes and 64000 edges<code>50000.yarspg</code>: YARS-PG file with 50000 nodes and 80000 edges<code>60000.yarspg</code>: YARS-PG file with 60000 nodes and 96000 edges<code>70000.yarspg</code>: YARS-PG file with 70000 nodes and 112000 edges<code>80000.yarspg</code>: YARS-PG file with 80000 nodes and 128000 edges<code>90000.yarspg</code>: YARS-PG file with 90000 nodes and 144000 edges<code>100000.yarspg</code>: YARS-PG file with 100000 nodes and 160000 edgesGenerationThe dataset was generated using the following commands:knows 10000 16000 -f yarspg &gt; 10000.yarspgknows 20000 32000 -f yarspg &gt; 20000.yarspgknows 30000 48000 -f yarspg &gt; 30000.yarspgknows 40000 64000 -f yarspg &gt; 40000.yarspgknows 50000 80000 -f yarspg &gt; 50000.yarspgknows 60000 96000 -f yarspg &gt; 60000.yarspgknows 70000 112000 -f yarspg &gt; 70000.yarspgknows 80000 128000 -f yarspg &gt; 80000.yarspgknows 90000 144000 -f yarspg &gt; 90000.yarspgknows 100000 160000 -f yarspg &gt; 100000.yarspgLicense<br>The MMLKG dataset is licensed under the Creative Commons Attribution 4.0 International License.
DOI: 10.6084/m9.figshare.25139189.v1
title: Sample Knows benchmark outputs in YARS-PG

description: This dataset contains a sample of YARS-PG outputs from the Knows v1.0.0 benchmark. Knows is a property graph benchmark with node and edge generation, multiple output formats, and visualization.Read more about the Knows benchmark in the Knows repository.Contents<code>README.md</code>: README file<code>LICENSE.txt</code>: A license for all files<code>10000.yarspg</code>: YARS-PG file with 10000 nodes and 16000 edges<code>20000.yarspg</code>: YARS-PG file with 20000 nodes and 32000 edges<code>30000.yarspg</code>: YARS-PG file with 30000 nodes and 48000 edges<code>40000.yarspg</code>: YARS-PG file with 40000 nodes and 64000 edges<code>50000.yarspg</code>: YARS-PG file with 50000 nodes and 80000 edges<code>60000.yarspg</code>: YARS-PG file with 60000 nodes and 96000 edges<code>70000.yarspg</code>: YARS-PG file with 70000 nodes and 112000 edges<code>80000.yarspg</code>: YARS-PG file with 80000 nodes and 128000 edges<code>90000.yarspg</code>: YARS-PG file with 90000 nodes and 144000 edges<code>100000.yarspg</code>: YARS-PG file with 100000 nodes and 160000 edgesGenerationThe dataset was generated using the following commands:knows 10000 16000 -f yarspg &gt; 10000.yarspgknows 20000 32000 -f yarspg &gt; 20000.yarspgknows 30000 48000 -f yarspg &gt; 30000.yarspgknows 40000 64000 -f yarspg &gt; 40000.yarspgknows 50000 80000 -f yarspg &gt; 50000.yarspgknows 60000 96000 -f yarspg &gt; 60000.yarspgknows 70000 112000 -f yarspg &gt; 70000.yarspgknows 80000 128000 -f yarspg &gt; 80000.yarspgknows 90000 144000 -f yarspg &gt; 90000.yarspgknows 100000 160000 -f yarspg &gt; 100000.yarspgLicense<br>The MMLKG dataset is licensed under the Creative Commons Attribution 4.0 International License.
DOI: 10.6084/m9.figshare.23244062
title: DID_TravelBehaviour

description: Contains the following items for the paper titled '<em>Machine learning-based causal inference for travel behaviour analysis: a difference in differences framework</em>':
<br>

 An appendix with detailed results and data specifications.
 Python codes for data processing, simulation, and empirical analysis.
 Test data processed for the empirical analysis.

<br>
For original open data used in empirical case studies, please refer to Chauhan et al. (2021) and the official NHTS website (https://nhts.ornl.gov/).
<br>
Chauhan, R. S., Bhagat-Conway, M. W., Capasso da Silva, D., Salon, D., Shamshiripour, A., Rahimi, E., Khoeini, S., Mohammadian, A. (Kouros), Derrible, S., & Pendyala, R. (2021). A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States. Scientific Data, 8(1), 1–7. https://doi.org/10.1038/s41597-021-01020-8