cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.9umcat
title: Occurrence Download

description: A dataset containing 4148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "TaxonKey is Amaranthus L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4148 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011422-200127171203522/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.1uvre3
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Croton scordioides Lam."
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013603-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bjenvg
title: Occurrence Download

description: A dataset containing 1635456 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.61218 -4.63228,-78.10181 -5.44131,-78.00842 -3.72041,-75.88806 -2.40205,-74.71802 -0.3876,-77.79419 1.41416,-79.77722 1.66289,-80.22766 0.79497,-81.04065 -1.03975,-81.13953 -2.24824,-80.61218 -4.63228))",
 "HasCoordinate is true",
 "TaxonKey is Aves",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1635456 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011354-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z2kmjy
title: Occurrence Download

description: A dataset listing the 209 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Guam",
 "OccurrenceStatus is Present",
 "TaxonKey is Coleoptera"
 ]
}

 The dataset's 209 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068822-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.toouqd
title: Occurrence Download

description: A dataset containing 752365 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Genlisea tuberosa Rivadavia, Gonella & A.Fleischm., Genlisea repens Benj., Genlisea pygmaea A.St.-Hil., Genlisea filiformis A.St.-Hil., Genlisea aurea A.St.-Hil., Arundo donax L., Durio zibethinus Murray, Momordica charantia L., Cucurbita moschata (Duchesne) Duchesne ex Poir., Pimpinella rhomboidea Diels, Salix babylonica L., Populus mexicana Wesm. ex A.DC., Populus tremula L., Erigeron bonariensis L., Oryza sativa L., Fritillaria meleagroides Patrin ex Schult. & Schult.f., Fritillaria persica L., Fritillaria eduardii A.Regel ex Regel, Trapa incisa Siebold & Zucc., Broussonetia ×kazinoki Siebold, Haematococcus lacustris (Girod-Chantrans) Rostafinski, Magnolia ×alba (DC.) Figlar, Magnolia dandyi Gagnep., Magnolia conifera (Dandy) V.S.Kumar, Magnolia duclouxii (Finet & Gagnep.) Hu, Magnolia aromatica (Dandy) V.S.Kumar, Fokienia hodginsii (Dunn) A.Henry & H.H.Thomas, Amomum compactum Sol. ex Maton, Tetragonia tetragonioides (Pallas) Kuntze, Boehmeria clidemioides Miq., Syringa vulgaris L., Schrebera arborea A.Chev., Olea exasperata Jacq., Nestegis apetala (Vahl) L.A.S.Johnson, Forestiera isabelae Hammel & Cornejo, Chionanthus rupicola (Lingelsh.) Kiew, Chionanthus parkinsonii (Hutch.) Bennet & Raizada, Commiphora wightii (Arn.) Bhandari, Dalbergia hainanensis Merr. & Chun, Tapiscia sinensis Oliv., Paphiopedilum dianthum Tang & F.T.Wang, Scrophularia henryi Hemsl., Scrophularia dentata Royle ex Benth., Aquilaria yunnanensis S.C.Huang, Cupressus chengiana S.Y.Hu, Quercus tungmaiensis Y.T.Chang, Amomum krervanh Pierre, Oryza coarctata Roxb., Elaeagnus mollis Diels, Quercus glauca Thunb.)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 752365 records from 1184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013603-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8gfczw3th7
title: abc
DOI: 10.17632/8gfczw3th7.1
title: abc
DOI: 10.17632/rvs2ssrcps
title: Slope unit-based environmental variables for refined rainfall thresholds in Zhuji, China

description: Slope unit-based environmental variables and refined rainfall threshold results in Zhuji, China
DOI: 10.17632/rvs2ssrcps.1
title: Slope unit-based environmental variables for refined rainfall thresholds in Zhuji, China

description: Slope unit-based environmental variables and refined rainfall threshold results in Zhuji, China
DOI: 10.26023/awmz-6k3t-t003
title: NCAR S-PolKa Radar Reflectivity Data: DYNAMO Legacy Collection. Version 1.0

description: This data set contains the 15 minute resolution gridded radar reflectivity data from the NCAR/EOL S-PolKa S-band Doppler radar that was located on Addu Atoll of the Maldive Islands during DYNAMO. The data are in NetCDF format.
DOI: 10.17632/mynjx8hjvt
title: The Three-Dimensional Hormonal Profile in Patients with PitNETs

description: This dataset contains clinical and pathological information from 1096 surgical patients with PitNETs (Pituitary Neuroendocrine Tumors). It includes the following fields: patient number, admission date, discharge date, pathology report version (categorized as pre-2017 or 2017/2022 versions), gender, age at admission, pathological classification according to WHO 2022, hormone pattern reflected in the discharge diagnosis, hormone pattern reflected in the pathological report, and hormone pattern reflected in the ICD-10 coding. This dataset is suitable for analyzing the epidemiological characteristics of PitNETs, the consistency between pathological classification and hormone expression, the accuracy of diagnostic coding, and the classification differences between the different pathology versions.
DOI: 10.17632/mynjx8hjvt.1
title: The Three-Dimensional Hormonal Profile in Patients with PitNETs

description: This dataset contains clinical and pathological information from 1096 surgical patients with PitNETs (Pituitary Neuroendocrine Tumors). It includes the following fields: patient number, admission date, discharge date, pathology report version (categorized as pre-2017 or 2017/2022 versions), gender, age at admission, pathological classification according to WHO 2022, hormone pattern reflected in the discharge diagnosis, hormone pattern reflected in the pathological report, and hormone pattern reflected in the ICD-10 coding. This dataset is suitable for analyzing the epidemiological characteristics of PitNETs, the consistency between pathological classification and hormone expression, the accuracy of diagnostic coding, and the classification differences between the different pathology versions.
DOI: 10.17632/dt4xgr73df
title: Cryptocurrency investment intention of two countries

description: This data set includes the cryptocurrency investment intention of two countries.
DOI: 10.34740/kaggle/dsv/13228509
title: Earthquakes in Indonesia
DOI: 10.17632/dt4xgr73df.1
title: Cryptocurrency investment intention of two countries

description: This data set includes the cryptocurrency investment intention of two countries.
DOI: 10.15468/dl.5h5g3a
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphorbia neriifolia",
 "Country is Costa Rica",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036732-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dd5urk
title: Occurrence Download

description: A dataset containing 243 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Chondracanthus chamissoi"
 ]
}

 The dataset includes 243 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036733-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atu2g3
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphorbia neriifolia",
 "Country is Cuba",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036734-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.27961.84324
title: JAMMATRIA IZJ XKY Form.
DOI: 10.26023/xrgw-nce1-a60h
title: NCAR S-PolKa Radar Rhohv Data: DYNAMO Legacy Collection. Version 1.0

description: This data set contains the 15 minute resolution gridded correlation coefficient (rhohv) data from the NCAR/EOL S-PolKa S-band Doppler radar that was located on Addu Atoll of the Maldive Islands during DYNAMO. The data are in NetCDF format.
DOI: 10.13140/rg.2.2.17476.08328
title: Proyecto de Producción -Prototipo Media-Prótesis
DOI: 10.17632/brxp657w22
title: Differences in Survival Improvement Between Known and Unknown Primary Merkel Cell Carcinoma in the Era of Immune Checkpoint Inhibitors

description: Supplementary materials for the article titled “Differences in Survival Improvement Between Known and Unknown Primary Merkel Cell Carcinoma in the Era of Immune Checkpoint Inhibitors”

Patient Consent: All data were de-identified and publicly available, thus the study was exempt from institutional review board approval and informed consent.

Finding: Overall, our findings suggest that ICIs may confer greater benefit in KPMCC than in UPMCC.
DOI: 10.17632/brxp657w22.1
title: Differences in Survival Improvement Between Known and Unknown Primary Merkel Cell Carcinoma in the Era of Immune Checkpoint Inhibitors

description: Supplementary materials for the article titled “Differences in Survival Improvement Between Known and Unknown Primary Merkel Cell Carcinoma in the Era of Immune Checkpoint Inhibitors”

Patient Consent: All data were de-identified and publicly available, thus the study was exempt from institutional review board approval and informed consent.

Finding: Overall, our findings suggest that ICIs may confer greater benefit in KPMCC than in UPMCC.
DOI: 10.13140/rg.2.2.29220.13449
title: ED STEPS Analysis of PERS036-1S4 161-1 36 Real World Data by the Persian Curve (PC) ED STEP 07 PERSIAN QUANTILE COMPLETE
DOI: 10.17632/8wf3t29jbd
title: Digital Asset Funding Rates as a Distinct Risk Factor: Evidence from a Systematic Harvesting Approach

description: Research Hypothesis: Digital asset funding rates represent a distinct, harvestable risk premium that operates independently from traditional investment categories through delta-neutral cash-and-carry strategies.

Key Findings:
Performance: 10.56% annualized returns with 1.50% volatility over August 2020-August 2025
Correlation: Near-perfect decoupling with correlations bounded within ±0.10 versus traditional assets
Independence: PCA shows 95.6% loading on independent third principal component
Regression: Traditional factors explain only 0.13% of return variation (adjusted R²)
Structural Analysis: Significant regime shift post-FTX collapse (November 2022):

Pre-FTX: 13.64% returns, 1.96% volatility 
Post-FTX: 8.14% returns, 0.97% volatility
Portfolio Impact: Mean-variance analysis shows 38bp additional return at same volatility level or 182bp risk reduction at same return level. Risk parity assigns median 91.7% allocation to the factor.

Theoretical Implications: Identifies crypto-native risk-free rate statistically orthogonal to conventional risk dimensions, challenging traditional monetary policy paradigms and supporting digital assets as distinct asset class.
DOI: 10.15468/dl.rvtcx7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphorbia neriifolia",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036735-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8wf3t29jbd.1
title: Digital Asset Funding Rates as a Distinct Risk Factor: Evidence from a Systematic Harvesting Approach

description: Research Hypothesis: Digital asset funding rates represent a distinct, harvestable risk premium that operates independently from traditional investment categories through delta-neutral cash-and-carry strategies.

Key Findings:
Performance: 10.56% annualized returns with 1.50% volatility over August 2020-August 2025
Correlation: Near-perfect decoupling with correlations bounded within ±0.10 versus traditional assets
Independence: PCA shows 95.6% loading on independent third principal component
Regression: Traditional factors explain only 0.13% of return variation (adjusted R²)
Structural Analysis: Significant regime shift post-FTX collapse (November 2022):

Pre-FTX: 13.64% returns, 1.96% volatility 
Post-FTX: 8.14% returns, 0.97% volatility
Portfolio Impact: Mean-variance analysis shows 38bp additional return at same volatility level or 182bp risk reduction at same return level. Risk parity assigns median 91.7% allocation to the factor.

Theoretical Implications: Identifies crypto-native risk-free rate statistically orthogonal to conventional risk dimensions, challenging traditional monetary policy paradigms and supporting digital assets as distinct asset class.
DOI: 10.17632/jc8dtvv6kk
title: kinesiophobia in aortic dissection survivors

description: It collected data from 309 patients one month after aortic dissection surgery across three university-affiliated hospitals between September 2024 and July 2025. A general information survey, Tampa Scale for Kinesiophobia Heart, Self-Efficacy for Exercise Scale, Family Apgar Index, and Medical Coping Modes Questionnaire were used for data collection. It can be used to evaluate the current status and influencing factors of exercise fear, exercise self-efficacy , family support and coping style of survivors with aortic dissection.
DOI: 10.17632/jc8dtvv6kk.1
title: kinesiophobia in aortic dissection survivors

description: It collected data from 309 patients one month after aortic dissection surgery across three university-affiliated hospitals between September 2024 and July 2025. A general information survey, Tampa Scale for Kinesiophobia Heart, Self-Efficacy for Exercise Scale, Family Apgar Index, and Medical Coping Modes Questionnaire were used for data collection. It can be used to evaluate the current status and influencing factors of exercise fear, exercise self-efficacy , family support and coping style of survivors with aortic dissection.
DOI: 10.17632/vsmfshkcmf
title: Industry-level Emissions from Fuel Combustion in the Russian Economy (2016, Rosstat Form 4-TER)

description: This dataset provides benchmark emissions from fuel combustion in the Russian economy for 2016, based on Form No. 4-TER “Information on the use of fuel and energy resources.” It was developed as background data for computable general equilibrium (CGE) modeling and climate-policy analysis. Emissions are disaggregated by fuel type (coal, natural gas, coking coal, petroleum products, and manufactured/distributed gas) and by industry, covering more than 40 production sectors and final demand categories.

What the data show.
Reported values include CO₂, CH₄, and N₂O emissions converted into CO₂-equivalents using IPCC global warming potentials. Total benchmark emissions from combustion reach 1,351 MtCO₂e in 2016. The largest contributors are electricity generation (~599 MtCO₂e), metallurgy (~160), and pipeline transport (~85). These estimates represent ~84% of Russia’s officially reported energy-related emissions, excluding direct household fuel use.

How the data were produced.
Fuel consumption data from Form 4-TER were converted from tons of coal equivalent (tce) into terajoules (0.0293076 TJ/tce). Emissions were calculated by applying default and country-specific factors from the National Inventory Report (Romanovskaya et al., 2022).

How to interpret/use.
The dataset provides consistent benchmark emissions by industry and fuel type suitable for calibrating CGE models, deriving sectoral emission intensities (kgCO₂e/RUB), and supporting scenario analyses. It was developed for the research presented in Burova et al. (Forthcoming). Transition to a Low-carbon Economy and its Implications for Financial Stability in Russia. Emerging Markets Review. Users should cite both Form 4-TER as the source of activity data and this dataset for processed emission benchmarks.

File List

1. Burova_et_al_Emissions_data.xlsx
* Includes:
-1. Table A.1. List of industries
-2. Table A.2. List of commodities 
-3. Table A.8. Benchmark emissions from combustion by fuel type in 2016, mln tCO2e
-4. Table A.9. Emission factors by fuel type, kgCO2/RUB
-5. Table E.2.1 Correspondence between IOT 2016 activities (columns) and industries in the CGE model
-6. Table E.2.2 Correspondence between IOT 2016 product groups (rows) and goods and services in the CGE model
DOI: 10.26023/8qf0-gwf8-ky13
title: NCAR S-PolKa Radar Spectrum Width Data: DYNAMO Legacy Collection. Version 1.0

description: This data set contains the 15 minute resolution gridded spectrum width data from the NCAR/EOL S-PolKa S-band Doppler radar that was located on Addu Atoll of the Maldive Islands during DYNAMO. The data are in NetCDF format.
DOI: 10.17632/vsmfshkcmf.1
title: Industry-level Emissions from Fuel Combustion in the Russian Economy (2016, Rosstat Form 4-TER)

description: This dataset provides benchmark emissions from fuel combustion in the Russian economy for 2016, based on Form No. 4-TER “Information on the use of fuel and energy resources.” It was developed as background data for computable general equilibrium (CGE) modeling and climate-policy analysis. Emissions are disaggregated by fuel type (coal, natural gas, coking coal, petroleum products, and manufactured/distributed gas) and by industry, covering more than 40 production sectors and final demand categories.

What the data show.
Reported values include CO₂, CH₄, and N₂O emissions converted into CO₂-equivalents using IPCC global warming potentials. Total benchmark emissions from combustion reach 1,351 MtCO₂e in 2016. The largest contributors are electricity generation (~599 MtCO₂e), metallurgy (~160), and pipeline transport (~85). These estimates represent ~84% of Russia’s officially reported energy-related emissions, excluding direct household fuel use.

How the data were produced.
Fuel consumption data from Form 4-TER were converted from tons of coal equivalent (tce) into terajoules (0.0293076 TJ/tce). Emissions were calculated by applying default and country-specific factors from the National Inventory Report (Romanovskaya et al., 2022).

How to interpret/use.
The dataset provides consistent benchmark emissions by industry and fuel type suitable for calibrating CGE models, deriving sectoral emission intensities (kgCO₂e/RUB), and supporting scenario analyses. It was developed for the research presented in Burova et al. (Forthcoming). Transition to a Low-carbon Economy and its Implications for Financial Stability in Russia. Emerging Markets Review. Users should cite both Form 4-TER as the source of activity data and this dataset for processed emission benchmarks.

File List

1. Burova_et_al_Emissions_data.xlsx
* Includes:
-1. Table A.1. List of industries
-2. Table A.2. List of commodities 
-3. Table A.8. Benchmark emissions from combustion by fuel type in 2016, mln tCO2e
-4. Table A.9. Emission factors by fuel type, kgCO2/RUB
-5. Table E.2.1 Correspondence between IOT 2016 activities (columns) and industries in the CGE model
-6. Table E.2.2 Correspondence between IOT 2016 product groups (rows) and goods and services in the CGE model
DOI: 10.13140/rg.2.2.23348.10889
title: Transcripts for Boli dataset
DOI: 10.17632/tg72rp84dd
title: Landsat for tidal creek extraction

description: This dataset is for visual interpretation of tidal creeks.
DOI: 10.17632/tg72rp84dd.1
title: Landsat for tidal creek extraction

description: This dataset is for visual interpretation of tidal creeks.
DOI: 10.17632/hcptcjhncm
title: Field-measured RTK points for landscape samples in 2021

description: This dataset served to establish reference samples for landscape classification and tidal creek extraction.
DOI: 10.17632/hcptcjhncm.1
title: Field-measured RTK points for landscape samples in 2021

description: This dataset served to establish reference samples for landscape classification and tidal creek extraction.
DOI: 10.26023/qryt-fs6r-nt00
title: NCAR S-PolKa Radar ZDR Data: DYNAMO Legacy Collection. Version 1.0

description: This data set contains the 15 minute resolution gridded ZDR (differential reflectivity) data from the NCAR/EOL S-PolKa S-band Doppler radar that was located on Addu Atoll of the Maldive Islands during DYNAMO. The data are in NetCDF format.
DOI: 10.15468/dl.hpc7y9
title: Occurrence Download

description: A dataset containing 23223 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Geonoma"
 ]
}

 The dataset includes 23223 records from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036736-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/47czpmy7hg
title: Effects of dust events on primary hospital admissions in Amarillo, Texas, USA.

description: Online resources
DOI: 10.17632/47czpmy7hg.2
title: Effects of dust events on primary hospital admissions in Amarillo, Texas, USA.

description: Online resources
DOI: 10.26023/6myz-5n0n-2t0d
title: SMART-R Radar Echo Top Height Data: DYNAMO Legacy Collection. Version 1.0

description: This data set contains the 10 minute resolution gridded echo top height data from the SMART-R1 Doppler radar that was located on Addu Atoll of the Maldive Islands during DYNAMO. The data are in NetCDF format.