cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.g37wz8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Brachypodium pinnatum glaucovirens, Brachypodium pinnatum sylvaticum, Brachypodium pinnatum sylvaticum, Brachypodium pinnatum sylvaticum, Brachypodium scaberrimum, Brachypodium sylvaticum majus, Brachypodium sylvaticum asperum, Brachypodium sylvaticum balcanicum, Brachypodium sylvaticum breviglume, Brachypodium sylvaticum formosanum)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043237-250811113504898/datasets/export for details.
DOI: 10.15468/dl.mn7p7y
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Geranium molle",
 "Country is Bulgaria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 32 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043241-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3rrbhbggxz
title: Frequency-tagging EEG reveals spontaneous categorical discrimination of visual self-identity

description: All the data of the three experiments
DOI: 10.17632/3rrbhbggxz.1
title: Frequency-tagging EEG reveals spontaneous categorical discrimination of visual self-identity

description: All the data of the three experiments
DOI: 10.17632/vwyt2nw4ys
title: LINF_250028800

description: Clp1/Grc3-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/vwyt2nw4ys.1
title: LINF_250028800

description: Clp1/Grc3-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.rkxbz4
title: Occurrence Download

description: A dataset containing 76997 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Material citation)",
 "Continent is one of (North America, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Coordinate reprojected)",
 "OccurrenceStatus is Present",
 "TaxonKey is Felidae"
 ]
}

 The dataset includes 76997 records from 276 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043209-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jggxmf8g4h
title: LINF_250028900

description: Sec1-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/jggxmf8g4h.1
title: LINF_250028900

description: Sec1-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/8kbjt58r6j
title: LINF_250029000

description: DnaJ domain-containing protein | JDP32 | J32; Leishmania infantum (strain JPCM5)
DOI: 10.17632/8kbjt58r6j.2
title: LINF_250029000

description: DnaJ domain-containing protein | JDP32 | J32; Leishmania infantum (strain JPCM5)
DOI: 10.17632/szfbjtx4z5
title: Forest fire risk projection for Yunnan Plateau, China under future climate scenarios

description: Dataset of baseline (2000–2020) and CMIP6-based future (2020–2100) environmental drivers and derived layers for mapping and projecting forest fire risk across the Yunnan Plateau, China (CRS: WGS84/UTM 47N).
DOI: 10.17632/szfbjtx4z5.1
title: Forest fire risk projection for Yunnan Plateau, China under future climate scenarios

description: Dataset of baseline (2000–2020) and CMIP6-based future (2020–2100) environmental drivers and derived layers for mapping and projecting forest fire risk across the Yunnan Plateau, China (CRS: WGS84/UTM 47N).
DOI: 10.17632/zvzd53sszj
title: Ranking and sorting the Pareto front in optimal shell structure design: integration of the AHP method

description: Here is a spreadsheet that formulates the AHP method for a three-by-three matrix. The inputs are DM’s preferences over the objective functions (only three elements should be determined, and the other elements are determined based on the relation between elements, as mentioned in the method section). This spreadsheet calculates the CR, CI, λ_max, the maximum eigenvalue, M_i, and w_i for the matrix and performs the required checks. Additionally, this spreadsheet will calculate the normalized weight coefficient (w_i-norm.)

Additionally, in another spreadsheet, the result (Pareto front) of Case Study I of the published paper at the Journal of Frontier of Architectural Research is reported. The users can construct a judgment matrix and calculate the weights using the first spreadsheet, then utilize it to rank and sort the Pareto front (with the second spreadsheet) based on their preferences. (There are 125 unique matrices feasible for this case study.) Moreover, this spreadsheet allows users to choose between three different methods of normalizing the values (objective functions), as discussed in Section 4.5. 

In addition, the pseudocode for the integration of multi-objective optimization and the AHP method (the proposed methodology in this journal paper) is provided.

Any other data related to this research will be available upon request.
DOI: 10.17632/zvzd53sszj.1
title: Ranking and sorting the Pareto front in optimal shell structure design: integration of the AHP method

description: Here is a spreadsheet that formulates the AHP method for a three-by-three matrix. The inputs are DM’s preferences over the objective functions (only three elements should be determined, and the other elements are determined based on the relation between elements, as mentioned in the method section). This spreadsheet calculates the CR, CI, λ_max, the maximum eigenvalue, M_i, and w_i for the matrix and performs the required checks. Additionally, this spreadsheet will calculate the normalized weight coefficient (w_i-norm.)

Additionally, in another spreadsheet, the result (Pareto front) of Case Study I of the published paper at the Journal of Frontier of Architectural Research is reported. The users can construct a judgment matrix and calculate the weights using the first spreadsheet, then utilize it to rank and sort the Pareto front (with the second spreadsheet) based on their preferences. (There are 125 unique matrices feasible for this case study.) Moreover, this spreadsheet allows users to choose between three different methods of normalizing the values (objective functions), as discussed in Section 4.5. 

In addition, the pseudocode for the integration of multi-objective optimization and the AHP method (the proposed methodology in this journal paper) is provided.

Any other data related to this research will be available upon request.
DOI: 10.15468/dl.gyw7qn
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Microglanis xylographicus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043242-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5b7p4rgt6j
title: Multi-year Long COVID study

description: In February 2022, C-support, together with Erasmus MC, started a large-scale multi-year study into Long COVID. People who had been registered with C-support up to January 2022 with long-term symptoms after a COVID-19 infection and who completed the 1th questionnaire are invited annually for an extensive questionnaire. So far there have been three rounds of questionnaires: more than 11.000 people have completed the 1th questionnaire and approximately 8.000 people completed the 2nd and/or 3rd questionnaire.

An overview of the topics per questionnaire and more information is available on our website: https://en.c-support.nu/data-aanvraag-meerjarig-long-covid-onderzoek/

You can request a portion of this data to use for your own research or analyses. We can also perform analyses for you, such as creating customized summary tables and/or graphs.

About C-support: C-support supports people with long-term symptoms after COVID-19 or a COVID-19 vaccination. In addition, C-support shares knowledge with (healthcare) professionals and promotes research, for example by making data available.
DOI: 10.17632/5b7p4rgt6j.1
title: Multi-year Long COVID study

description: In February 2022, C-support, together with Erasmus MC, started a large-scale multi-year study into Long COVID. People who had been registered with C-support up to January 2022 with long-term symptoms after a COVID-19 infection and who completed the 1th questionnaire are invited annually for an extensive questionnaire. So far there have been three rounds of questionnaires: more than 11.000 people have completed the 1th questionnaire and approximately 8.000 people completed the 2nd and/or 3rd questionnaire.

An overview of the topics per questionnaire and more information is available on our website: https://en.c-support.nu/data-aanvraag-meerjarig-long-covid-onderzoek/

You can request a portion of this data to use for your own research or analyses. We can also perform analyses for you, such as creating customized summary tables and/or graphs.

About C-support: C-support supports people with long-term symptoms after COVID-19 or a COVID-19 vaccination. In addition, C-support shares knowledge with (healthcare) professionals and promotes research, for example by making data available.
DOI: 10.5281/zenodo.16942930
title: Exploring the Relationship between Financial Development and Entrepreneurship using GMM Insights from the MENA Region

description: The dataset covers 12 countries from the MENA region—Algeria, Morocco, Tunisia, Egypt, Türkiye, United Arab Emirates, Saudi Arabia, Oman, Bahrain, Jordan, Kuwait, and Qatar—over the period 2006 to 2020. This panel structure provides both cross-country and time-series variation, allowing for a robust econometric analysis of the relationship between financial development and entrepreneurship.

The Excel file contains country-level observations, with each country represented annually throughout the 15-year period. The data includes a dependent variable (entrepreneurship), two main independent variables (financial development indicators), and several control variables, as outlined in Table 1.





Dependent Variable:





Entrepreneurship (ENT): Measured as new business density per 1,000 people ages 15–64, obtained from the World Bank.






Independent Variables:





Financial Development Index (FDI): Computed as the average of the Financial Institutions Index (FII) and Financial Markets Index (FMI), sourced from the IMF.




Financial Institutions Index (FII): Captures depth, access, and efficiency of financial institutions.




Financial Markets Index (FMI): Captures depth, access, and efficiency of financial markets.






Control Variables:





GDP (Log): The natural logarithm of gross domestic product (World Bank).




Foreign Direct Investment (FRN): Net inflows as a percentage of GDP (World Bank).




Inflation (INF): Annual GDP deflator, representing price changes in the economy (World Bank).




Natural Resource Rents (NRR): Total natural resource rents as a percentage of GDP (World Bank).




Political Stability (PLT): Index measuring political stability and absence of violence/terrorism (World Bank).




GDP Growth (GDPG): Annual GDP growth rate in percentage terms (World Bank).





The word document provide the Stata commands used and the results.
DOI: 10.5281/zenodo.16942931
title: Exploring the Relationship between Financial Development and Entrepreneurship using GMM Insights from the MENA Region

description: The dataset covers 12 countries from the MENA region—Algeria, Morocco, Tunisia, Egypt, Türkiye, United Arab Emirates, Saudi Arabia, Oman, Bahrain, Jordan, Kuwait, and Qatar—over the period 2006 to 2020. This panel structure provides both cross-country and time-series variation, allowing for a robust econometric analysis of the relationship between financial development and entrepreneurship.

The Excel file contains country-level observations, with each country represented annually throughout the 15-year period. The data includes a dependent variable (entrepreneurship), two main independent variables (financial development indicators), and several control variables, as outlined in Table 1.





Dependent Variable:





Entrepreneurship (ENT): Measured as new business density per 1,000 people ages 15–64, obtained from the World Bank.






Independent Variables:





Financial Development Index (FDI): Computed as the average of the Financial Institutions Index (FII) and Financial Markets Index (FMI), sourced from the IMF.




Financial Institutions Index (FII): Captures depth, access, and efficiency of financial institutions.




Financial Markets Index (FMI): Captures depth, access, and efficiency of financial markets.






Control Variables:





GDP (Log): The natural logarithm of gross domestic product (World Bank).




Foreign Direct Investment (FRN): Net inflows as a percentage of GDP (World Bank).




Inflation (INF): Annual GDP deflator, representing price changes in the economy (World Bank).




Natural Resource Rents (NRR): Total natural resource rents as a percentage of GDP (World Bank).




Political Stability (PLT): Index measuring political stability and absence of violence/terrorism (World Bank).




GDP Growth (GDPG): Annual GDP growth rate in percentage terms (World Bank).





The word document provide the Stata commands used and the results.
DOI: 10.17632/8c6h2thfpy
title: probiotics

description: 16S rRNA gene sequencing and Single-Cell Sequencing probiotics.
DOI: 10.17632/8c6h2thfpy.1
title: probiotics

description: 16S rRNA gene sequencing and Single-Cell Sequencing probiotics.
DOI: 10.17632/72ntnfsn34
title: A Rescheduling Framework for the Customer Visit Scheduling Problem of a Salesperson – Benchmark Instances

description: This dataset contains the data for the benchmark instances used in the computational study of the article titled “A Rescheduling Framework for the Customer Visit Scheduling Problem of a Salesperson”.
DOI: 10.17632/72ntnfsn34.1
title: A Rescheduling Framework for the Customer Visit Scheduling Problem of a Salesperson – Benchmark Instances

description: This dataset contains the data for the benchmark instances used in the computational study of the article titled “A Rescheduling Framework for the Customer Visit Scheduling Problem of a Salesperson”.
DOI: 10.17632/5bp6y6xs8m
title: A Comprehensive Dataset on Field Crop Planted Area, Harvested Area, and Production in Jordan (1994-2023).

description: Agricultural data for 16 key field crops across 16 cities and regions in Jordan
DOI: 10.17632/5bp6y6xs8m.1
title: A Comprehensive Dataset on Field Crop Planted Area, Harvested Area, and Production in Jordan (1994-2023).

description: Agricultural data for 16 key field crops across 16 cities and regions in Jordan
DOI: 10.5281/zenodo.16944179
title: Identifying Graft Incompatible Rootstocks for Sweet Cherry Through Machine Learning Algorithms(Dataset)
DOI: 10.5281/zenodo.16944180
title: Identifying Graft Incompatible Rootstocks for Sweet Cherry Through Machine Learning Algorithms(Dataset)
DOI: 10.17632/8rv9w9vc9t
title: probiotics2

description: 16S rRNA gene sequencing and Single-Cell Sequencing.
DOI: 10.17632/8rv9w9vc9t.1
title: probiotics2

description: 16S rRNA gene sequencing and Single-Cell Sequencing.
DOI: 10.17632/8rrg8htkx3
title: Study of the proliferation mechanism of PEDV in a porcine intestinal organoid and lymphocyte co-culture system

description: The raw data of the study is available.
DOI: 10.17632/8rrg8htkx3.1
title: Study of the proliferation mechanism of PEDV in a porcine intestinal organoid and lymphocyte co-culture system

description: The raw data of the study is available.
DOI: 10.15468/dl.fymqc7
title: Occurrence Download

description: A dataset containing 1776 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material citation, Specimen)",
 "Country is Mexico",
 "Geometry POLYGON((-158.38159 -51.50602,-24.39619 -51.50602,-1.19254 82.66193,-75.59254 84.96301,-158.38159 77.91843,-173.58889 65.61573,-158.38159 -51.50602))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Coordinate reprojected)",
 "OccurrenceStatus is Present",
 "TaxonKey is Campylorhynchus brunneicapillus"
 ]
}

 The dataset includes 1776 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043245-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mnc8hy
title: Occurrence Download

description: A dataset containing 196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Paraguay",
 "TaxonKey is Handroanthus impetiginosum"
 ]
}

 The dataset includes 196 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043248-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16944163
title: "Datasets for BayesMetaNMA: An Interactive R/Shiny Application

description: : Short explanation that these datasets reproduce analyses in your paper.
DOI: 10.5281/zenodo.16944162
title: "Datasets for BayesMetaNMA: An Interactive R/Shiny Application

description: : Short explanation that these datasets reproduce analyses in your paper.
DOI: 10.15468/dl.pdgy4g
title: Occurrence Download

description: A dataset containing 6824 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Colombia",
 "DatasetKey is EOD – eBird Observation Dataset",
 "OccurrenceStatus is Present",
 "TaxonKey is Grallaria quitensis"
 ]
}

 The dataset includes 6824 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043243-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/m56v5jjytc
title: SEM-EDS Microstructural Characterization of Open-Cell SnSb11Cu6 Alloy Material

description: Individual Image Descriptions:
Image 1:
Title: "BSE-SEM image of SnSb11Cu6 open-cell alloy (1000×)"
Description: Backscattered electron SEM micrograph of open-cell SnSb11Cu6 alloy material. Acquisition parameters: 20 kV accelerating voltage, 1000× magnification, 6 mm working distance, high vacuum conditions. Scale bar: 50 μm. Sample ID: BS9. Date acquired: July 11, 2024, 10:55. 
Image 2:
Title: "BSE-SEM image of SnSb11Cu6 open-cell alloy (1000×) - Region 2"
Description: Backscattered electron SEM micrograph of SnSb11Cu6 alloy material showing alternative microstructural region. Acquisition parameters: 20 kV accelerating voltage, 1000× magnification, 6 mm working distance, high vacuum conditions. Scale bar: 50 μm. Sample ID: BS9. Date acquired: July 11, 2024, 11:10.
Image 3:
Title: "BSE-SEM image of SnSb11Cu6 open-cell alloy (500×) - Overview"

EDS analysis: energy-dispersive X-ray spectroscopy for elemental composition and phase identification (data in Doc1.docx).
DOI: 10.17632/m56v5jjytc.1
title: SEM-EDS Microstructural Characterization of Open-Cell SnSb11Cu6 Alloy Material

description: Individual Image Descriptions:
Image 1:
Title: "BSE-SEM image of SnSb11Cu6 open-cell alloy (1000×)"
Description: Backscattered electron SEM micrograph of open-cell SnSb11Cu6 alloy material. Acquisition parameters: 20 kV accelerating voltage, 1000× magnification, 6 mm working distance, high vacuum conditions. Scale bar: 50 μm. Sample ID: BS9. Date acquired: July 11, 2024, 10:55. 
Image 2:
Title: "BSE-SEM image of SnSb11Cu6 open-cell alloy (1000×) - Region 2"
Description: Backscattered electron SEM micrograph of SnSb11Cu6 alloy material showing alternative microstructural region. Acquisition parameters: 20 kV accelerating voltage, 1000× magnification, 6 mm working distance, high vacuum conditions. Scale bar: 50 μm. Sample ID: BS9. Date acquired: July 11, 2024, 11:10.
Image 3:
Title: "BSE-SEM image of SnSb11Cu6 open-cell alloy (500×) - Overview"

EDS analysis: energy-dispersive X-ray spectroscopy for elemental composition and phase identification (data in Doc1.docx).
DOI: 10.17632/6m7337hjhy
title: CommitSovereignDefault2025

description: Xavier Mateos-Planas, Sean McCrary, Jose-Victor Rios-Rull and Adrien Wicht (2025), “Commitment in the Canonical Sovereign Default Model," Journal of International Economics. The readme text files contain the details of the replication procedure.
DOI: 10.17632/6m7337hjhy.2
title: CommitSovereignDefault2025

description: Xavier Mateos-Planas, Sean McCrary, Jose-Victor Rios-Rull and Adrien Wicht (2025), “Commitment in the Canonical Sovereign Default Model," Journal of International Economics. The readme text files contain the details of the replication procedure.
DOI: 10.17632/djmgs9zm86
title: Data for "Iron sulfidation suppresses S3ˉ ion in subduction zone fluids"

description: This dataset is for manuscript entitled "Iron sulfidation suppresses S3ˉ ion in subduction zone fluids" by Zhu et al. Including origin data of Raman spectra, Table 1, and supplementary tables S1-S3.
DOI: 10.17632/djmgs9zm86.1
title: Data for "Iron sulfidation suppresses S3ˉ ion in subduction zone fluids"

description: This dataset is for manuscript entitled "Iron sulfidation suppresses S3ˉ ion in subduction zone fluids" by Zhu et al. Including origin data of Raman spectra, Table 1, and supplementary tables S1-S3.
DOI: 10.17632/g5y6pswdnj
title: FDI, temperature and civil conflict

description: Synthetic version of the original data for testing the relationship between temperature and civil conflict by allowing for the role of inward foreign direct investment (FDI) and international trade
DOI: 10.17632/g5y6pswdnj.1
title: FDI, temperature and civil conflict

description: Synthetic version of the original data for testing the relationship between temperature and civil conflict by allowing for the role of inward foreign direct investment (FDI) and international trade
DOI: 10.15468/dl.9cmv6w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cleptornis marchei",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043252-250811113504898/datasets/export for details.