cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.s8gd7r
title: Occurrence Download

description: A dataset listing the 93 species recorded in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cyclopidae",
 "GadmGid is CAN.9_1",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 93 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0255967-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.69mnqq
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Americorophium salmonis (Stimpson, 1857)"
 ]
}

 The dataset includes 26 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112443-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epspd8
title: Occurrence Download

description: A dataset containing 707 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Monachus tropicalis (Gray, 1850), Cyclopes didactylus (Linnaeus, 1758))"
 ]
}

 The dataset includes 707 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0161051-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r2nsn3
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Martyringa xeraula Meyrick, 1910",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0192249-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7sv84b
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Conringia grandiflora Boiss. & Heldr."
 ]
}

 The dataset includes 34 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0149029-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aw24gp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lanocira rotundicauda Stebbing, 1904"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0122302-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8u7mn7
title: Occurrence Download

description: A dataset containing 659983 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, Svalbard and Jan Mayen, Norway, Sweden, Finland, Estonia, Latvia, Denmark, Portugal, Spain, France, Germany, Netherlands, Switzerland, Austria, Italy, Greece, Türkiye, Russian Federation, Belarus, Ukraine, Poland, Czechia, Slovakia, Hungary, Romania, Bulgaria, Albania, Serbia, Slovenia, Bosnia and Herzegovina)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Agrostis stolonifera L."
 ]
}

 The dataset includes 659983 records from 2125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066101-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-04.3084546/2551587
title: Data for EMSL Project 51287 from April 2024
DOI: 10.34740/kaggle/dsv/11347627
title: Description Matching
DOI: 10.25582/data.2024-04.3084547/2551588
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3084548/2551589
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3084549/2551590
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3084550/2551591
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.beqxxh
title: Occurrence Download

description: A dataset containing 7067 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= -12.166598047573723
 AND decimallatitude &lt;= -8.098015256935714
 AND decimallongitude &gt;= -75.89001057151437
 AND decimallongitude &lt;= -70.277640503748
 AND GBIF_Within('POLYGON ((-70.277641 -12.166598, -70.277641 -8.098015, -75.890011 -8.098015, -75.890011 -12.166598, -70.277641 -12.166598))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 7067 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017991-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18514037
title: Data Set of "URBAN IDENTITY FORMATION OF JAKARTA STREET VENDORS' FURNITURE BASED ON EVERYDAY CULTURAL ANALYSIS AND VISUAL REPRESENTATION"
title: Kumpulan Data "PEMBENTUKAN IDENTITAS URBAN PEDAGANG JALANAN DI JAKARTA BERDASARKAN ANALISIS BUDAYA SEHARI-HARI DAN REPRESENTASI VISUAL"
DOI: 10.5281/zenodo.18514036
title: Data Set of "URBAN IDENTITY FORMATION OF JAKARTA STREET VENDORS' FURNITURE BASED ON EVERYDAY CULTURAL ANALYSIS AND VISUAL REPRESENTATION"
title: Kumpulan Data "PEMBENTUKAN IDENTITAS URBAN PEDAGANG JALANAN DI JAKARTA BERDASARKAN ANALISIS BUDAYA SEHARI-HARI DAN REPRESENTASI VISUAL"
DOI: 10.15468/dl.v4ssba
title: Occurrence Download

description: A dataset containing 307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Adenochloa hymeniochila",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 307 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017995-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8nbu3
title: Occurrence Download

description: A dataset containing 5485 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "OccurrenceStatus is Present",
 "TaxonKey is Macropus fuliginosus fuliginosus",
 "Year 2005-2026"
 ]
}

 The dataset includes 5485 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017996-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aj2aaz
title: Occurrence Download

description: A dataset containing 387368 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Leptonychotes",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 387368 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017973-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18513501
title: SuperDARN data in netCDF format (2016-May)

description: 2016-May SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6423466
title: SuperDARN data in netCDF format (2016-May)

description: 2016-May SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.k9zryh
title: Occurrence Download

description: A dataset containing 421 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aegilops tauschii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 421 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017997-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26afux
title: Occurrence Download

description: A dataset containing 599 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Distribución histórica, prospección actual y áreas potenciales para reintroducir lobo mexicano (Canis lupus baileyi) en Durango, sur de la Sierra Madre Occidental, México"
 ]
}

 The dataset includes 599 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018000-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.36ywhj
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aeluropus pungens",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 46 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018002-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18513078
title: McLean & DeLone Model Modifications Model with Psychological Factors variables in Shopee Indonesia e-commerce

description: This study employs 33 measurement indicators using a 5-point Likert scale to analyze users’ behavior and experience in Shopee Fashion. System Quality (X1) is measured by four indicators: fast page loading (X1_1), easy navigation (X1_2), system reliability with minimal errors (X1_3), and secure transactions (X1_4). Information Quality (X2) includes accurate and understandable product descriptions (X2_1), complete details on size, material, and price (X2_2), product photos that reflect actual conditions (X2_3), and updated stock availability (X2_4). Service Quality (X3) is measured through responsive customer service (X3_1), easy return/refund procedures (X3_2), on-time delivery (X3_3), and clear service policies (X3_4). System Use (Y1) covers opening Shopee Fashion more than once per week (Y1_1), frequent use of search and recommendation features (Y1_2), spending long time browsing fashion products (Y1_3), and regularly joining promotions or flash sales (Y1_4). Customer Satisfaction (Y2) includes overall satisfaction (Y2_1), experience meeting expectations (Y2_2), feeling happy after shopping (Y2_3), and rarely feeling disappointed (Y2_4). Impulse Buying (Y3) is assessed by unplanned purchases (Y3_1), sudden promotions triggering purchases (Y3_2), attractive product appearance (Y3_3), spontaneous decisions (Y3_4), and promotional notifications encouraging impulsive buying (Y3_5). Post-Purchase Regret (Y4) consists of feeling regret after purchase (Y4_1), realizing the decision was too quick (Y4_2), disappointment when products do not meet expectations (Y4_3), and feeling the purchase should not have been made (Y4_4). Finally, Repurchase Intention (Z) includes planning to buy again (Z_1), choosing Shopee for future purchases (Z_2), recommending Shopee Fashion to friends (Z_3), and continuing to use Shopee in the future (Z_4).
DOI: 10.5281/zenodo.18513079
title: McLean & DeLone Model Modifications Model with Psychological Factors variables in Shopee Indonesia e-commerce

description: This study employs 33 measurement indicators using a 5-point Likert scale to analyze users’ behavior and experience in Shopee Fashion. System Quality (X1) is measured by four indicators: fast page loading (X1_1), easy navigation (X1_2), system reliability with minimal errors (X1_3), and secure transactions (X1_4). Information Quality (X2) includes accurate and understandable product descriptions (X2_1), complete details on size, material, and price (X2_2), product photos that reflect actual conditions (X2_3), and updated stock availability (X2_4). Service Quality (X3) is measured through responsive customer service (X3_1), easy return/refund procedures (X3_2), on-time delivery (X3_3), and clear service policies (X3_4). System Use (Y1) covers opening Shopee Fashion more than once per week (Y1_1), frequent use of search and recommendation features (Y1_2), spending long time browsing fashion products (Y1_3), and regularly joining promotions or flash sales (Y1_4). Customer Satisfaction (Y2) includes overall satisfaction (Y2_1), experience meeting expectations (Y2_2), feeling happy after shopping (Y2_3), and rarely feeling disappointed (Y2_4). Impulse Buying (Y3) is assessed by unplanned purchases (Y3_1), sudden promotions triggering purchases (Y3_2), attractive product appearance (Y3_3), spontaneous decisions (Y3_4), and promotional notifications encouraging impulsive buying (Y3_5). Post-Purchase Regret (Y4) consists of feeling regret after purchase (Y4_1), realizing the decision was too quick (Y4_2), disappointment when products do not meet expectations (Y4_3), and feeling the purchase should not have been made (Y4_4). Finally, Repurchase Intention (Z) includes planning to buy again (Z_1), choosing Shopee for future purchases (Z_2), recommending Shopee Fashion to friends (Z_3), and continuing to use Shopee in the future (Z_4).
DOI: 10.5281/zenodo.18514168
title: Data for the manuscript "Hydrologically-linked permafrost-thaw collapse accelerates lateral sediment and carbon loss"

description: These files provide the key data of the hydrologically-linked permafrost collapse features (PCFs) on the Tibetan Plateau and the associated sediment and carbon loss from these PCFs. 

1_PCF_inventory folder includes the .shp files of PCF inventory on the Tibetan Plateau, including a total of 5512 PCFs. 

2_Permafrost_data folder includes the simulated permafrost data by SHAW model. QTP_point_feature.txt records the key factors of permafrost dynamics on the Tibetan Plateau in a 1-km spatial resolution.

3_River_permafrost folder includes the difference between waterbody temperature and air temperature on the plateau and their impacts on permafrost. TP_water_T_change subfolder includes the water temperatures across the plateau and the surrounding air temperatures. Inundated_permafrost subfolder includes the modeling experiment of permafrost responses to river water temperature change at a section of Tuotuohe river. Bank_erosion subfolder includes the modeling experiment of permafrost bank erosion and collapse due to water temperature change.

4_PCF_prediction folder includes the predicted PCF risk regions and the disturbed area by random forest model on the Tibetan Plateau. QTP_PCF&predictors.txt records the predictors of each 1-km grid on the plateau. PCF_Risk subfolder includes the predictions of PCF risk (risk map, model accuracy, and predictor importance). PCF_Area subfolder includes the predictions of disturbed area in risk grids (disturbed area and volume map, optimal model, and predictor importance). Codes subfolder includes the codes for train and prediction.

5_PCF_sedment&carbon_loss folder includes the estimated sediment, soil organic carbon, and vegetation biomass losses from existing PCFs and potential PCF risk. Current subfolder includes the losses of each region due to existing PCFs. NewPCF_16-22 subfolder includes the losses from newly formed PCFs in each year from 2016 to 2022. Potential subfolder includes the predicted losses from the potential PCF risk and the associated codes. 

6_PCF_river_transport folder includes the SSC and SSF data of the representative PCF-free upstream reach and PCF-abundant downstream reach at headwater Yangtze River. 

For more information and relative codes, kindly reach out to the authors at yi.zhao@pku.edu.cn or yizhao_cryo@outlook.com.

Yi Zhao, on behalf of all authors
DOI: 10.5281/zenodo.17254551
title: Data for the manuscript "Hydrologically-linked permafrost-thaw collapse accelerates lateral sediment and carbon loss"

description: These files provide the key data of the hydrologically-linked permafrost collapse features (PCFs) on the Tibetan Plateau and the associated sediment and carbon loss from these PCFs. 

1_PCF_inventory folder includes the .shp files of PCF inventory on the Tibetan Plateau, including a total of 5512 PCFs. 

2_Permafrost_data folder includes the simulated permafrost data by SHAW model. QTP_point_feature.txt records the key factors of permafrost dynamics on the Tibetan Plateau in a 1-km spatial resolution.

3_River_permafrost folder includes the difference between waterbody temperature and air temperature on the plateau and their impacts on permafrost. TP_water_T_change subfolder includes the water temperatures across the plateau and the surrounding air temperatures. Inundated_permafrost subfolder includes the modeling experiment of permafrost responses to river water temperature change at a section of Tuotuohe river. Bank_erosion subfolder includes the modeling experiment of permafrost bank erosion and collapse due to water temperature change.

4_PCF_prediction folder includes the predicted PCF risk regions and the disturbed area by random forest model on the Tibetan Plateau. QTP_PCF&predictors.txt records the predictors of each 1-km grid on the plateau. PCF_Risk subfolder includes the predictions of PCF risk (risk map, model accuracy, and predictor importance). PCF_Area subfolder includes the predictions of disturbed area in risk grids (disturbed area and volume map, optimal model, and predictor importance). Codes subfolder includes the codes for train and prediction.

5_PCF_sedment&carbon_loss folder includes the estimated sediment, soil organic carbon, and vegetation biomass losses from existing PCFs and potential PCF risk. Current subfolder includes the losses of each region due to existing PCFs. NewPCF_16-22 subfolder includes the losses from newly formed PCFs in each year from 2016 to 2022. Potential subfolder includes the predicted losses from the potential PCF risk and the associated codes. 

6_PCF_river_transport folder includes the SSC and SSF data of the representative PCF-free upstream reach and PCF-abundant downstream reach at headwater Yangtze River. 

For more information and relative codes, kindly reach out to the authors at yi.zhao@pku.edu.cn or yizhao_cryo@outlook.com.

Yi Zhao, on behalf of all authors
DOI: 10.15468/dl.pvszhc
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Afrotrichloris martinii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 13 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018003-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dw96mk
title: Occurrence Download

description: A dataset containing 487 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prioneris philonome"
 ]
}

 The dataset includes 487 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018004-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.14707.80162
title: Atlas of Hydro-Morphological Characteristics of the River Jhelum and Active Flood Plains.
DOI: 10.15468/dl.m7q8hp
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lasippa viraja"
 ]
}

 The dataset includes 50 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018007-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8uy4mj
title: Occurrence Download

description: A dataset containing 386 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Agenium leptocladum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 386 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018005-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.