cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2284rj
title: Occurrence Download

description: A dataset containing 45556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.49056 5.97398,-75.46422 5.90441,-75.45362 5.81677,-75.45362 5.81677,-75.38628 5.66245,-75.34714 5.63907,-75.30444 5.63297,-75.2112 5.6528,-75.17068 5.69702,-75.16089 5.77372,-75.16627 5.84215,-75.18795 5.90437,-75.21322 5.96945,-75.22411 5.99476,-75.23949 6.01719,-75.25773 6.05014,-75.28423 6.06714,-75.30825 6.08005,-75.32728 6.09206,-75.37708 6.10019,-75.42202 6.08361,-75.46186 6.04752,-75.48099 6.04041,-75.48482 5.99366,-75.49056 5.97398))",
 "TaxonKey is one of (Mammalia, Amphibia, Aves, Reptilia)"
 ]
}

 The dataset includes 45556 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013583-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-131560
title: Annual Holy Convocation of the Church of God in Christ (97th: 2004)

description: [description english/roman)] The souvenir journal of the Holy Convocation of the Church of God in Christ. Annually.
DOI: 10.15468/dl.cy4ksq
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cryptacanthodes bergi Lindberg, 1930"
 ]
}

 The dataset includes 35 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069283-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-131511
title: Annual Holy Convocation of the Church of God in Christ (97th: 2004)

description: [description english/roman)] The souvenir journal of the Holy Convocation of the Church of God in Christ. Annually.
DOI: 10.15468/dl.x5u545
title: Occurrence Download

description: A dataset containing 619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "TaxonKey is Chelonia mydas (Linnaeus, 1758)"
 ]
}

 The dataset includes 619 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016114-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8acvpu
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (BRA.16.228_1, BRA.16.228.1_1)",
 "TaxonKey is Araucaria angustifolia (Bertol.) Kuntze"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005273-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-131580
title: Annual Holy Convocation of the Church of God in Christ (97th: 2004)

description: [description english/roman)] The souvenir journal of the Holy Convocation of the Church of God in Christ. Annually.
DOI: 10.15468/dl.6eqvh8
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is 9439132"
 ]
}

 The dataset includes 13 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030927-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-131471
title: Annual Holy Convocation of the Church of God in Christ (97th: 2004)

description: [description english/roman)] The souvenir journal of the Holy Convocation of the Church of God in Christ. Annually.
DOI: 10.15468/dl.xmx2cn
title: Occurrence Download

description: A dataset containing 2136 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhacocleis annulata Fieber, 1853, Rhagades pruni (Denis & Schiffermüller), 1775)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 2136 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0326200-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-3689
title: Baptist, vol. [1], no. 4 (October 1917)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.azp59j
title: Occurrence Download

description: A dataset containing 11067 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sciurus griseus Ord, 1818"
 ]
}

 The dataset includes 11067 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002240-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3ap6y
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apistogramma pedunculata Mesa S. & Lasso, 2011"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0326209-210914110416597/datasets/export for details.
DOI: 10.25549/pcra-c14-3670
title: Baptist, vol. [1], no. 3 (September 1917)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.w7b8py
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Euphorbia heterophylla L."
 ]
}

 The dataset includes 109 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018412-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-3673
title: Baptist, vol. [1], no. 3 (September 1917)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.j2af4k
title: Occurrence Download

description: A dataset containing 8476 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphorbia helioscopia L.",
 "MediaType is Image",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 8476 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0408087-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ezufhh
title: Occurrence Download

description: A dataset containing 6667259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((26.7542939189847 1.2186193716968,36.9991642472184 13.6345880476356,41.544901412726 10.038706409249,37.5419388341447 2.16847489881781,35.84576825 -7.80500813595271,36.2528491901947 -16.0144737632128,34.2174444892211 -16.5572483501391,27.5006089760084 -8.95840413317107,26.7542939189847 1.2186193716968))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6667259 records from 1156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016116-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15740728
title: Data Set of pore water  DIC , Carbon Stable Isotopes and Dissolved Silica analyses  from samples collected during DOORS 2 field campaign

description: Data Set of pore water analyses for Dissolved Inorganic Carbon , Carbon Stable Isotopes and Dissolved Silica from samples collected from 4 gravity cores during DOORS 2 Black Sea field campaign (September 2023) .
DOI: 10.5281/zenodo.15740729
title: Data Set of pore water  DIC , Carbon Stable Isotopes and Dissolved Silica analyses  from samples collected during DOORS 2 field campaign

description: Data Set of pore water analyses for Dissolved Inorganic Carbon , Carbon Stable Isotopes and Dissolved Silica from samples collected from 4 gravity cores during DOORS 2 Black Sea field campaign (September 2023) .
DOI: 10.5281/zenodo.10488804
title: UFLUX 100m daily photosynthesis fluxes in southeastern UK 2022

description: UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2022

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components:





Gross Primary Production (GPP) [open access]




Ecosystem Respiration (RECO) [in progress]




Net Ecosystem Exchange (NEE) [in progress]




Sensible Heat Flux (H) [in progress]




Latent Energy Flux (LE) [in progress]



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.5281/zenodo.10488803
title: UFLUX 100m daily photosynthesis fluxes in southeastern UK 2022

description: UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2022

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components:





Gross Primary Production (GPP) [open access]




Ecosystem Respiration (RECO) [in progress]




Net Ecosystem Exchange (NEE) [in progress]




Sensible Heat Flux (H) [in progress]




Latent Energy Flux (LE) [in progress]



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.5281/zenodo.15740517
title: DIC and Stable Carbon Isotopes in sea water and pore waters  from Zelanka (Bulgarian Coast)

description: Datasets of Dissolved Inorganic Carbon and Stable Carbon Isotopes in sea water and pore waters from the two Zelenka campaigns (Bulgarian Coast)  in 2022 and 2023.
DOI: 10.5281/zenodo.15740518
title: DIC and Stable Carbon Isotopes in sea water and pore waters  from Zelanka (Bulgarian Coast)

description: Datasets of Dissolved Inorganic Carbon and Stable Carbon Isotopes in sea water and pore waters from the two Zelenka campaigns (Bulgarian Coast)  in 2022 and 2023.
DOI: 10.3886/icpsr03629.v1
title: Survey of Consumer Attitudes and Behavior, Spring 1961
title: Version 1

description: This survey was undertaken to assess consumer 
 sentiment and buying plans, as well as role conflict 
 and role ambiguity in the labor force, the financial 
 effect of unemployment in the same family over a period 
 of time, and the relationship of unemployment to mobility. 
 Open-ended questions were asked concerning evaluations and 
 expectations about price changes, employment, retirement, 
 tax reduction, government spending, recession, and the 
 national business situation. Other variables probe 
 respondents' buying intentions for a house, automobiles, 
 appliances, and other consumer durables, as well as their 
 appraisals of present market conditions for purchasing 
 these items. There are also variables that provide 
 information about job characteristics and organizational 
 settings in which people experienced conflict and ambiguity 
 and the health effects of such work-related problems. 
 Additional variables provide information on respondents' 
 attitudes toward and experience with installment credit and 
 public spending, and respondents' savings and investments, 
 as well as their assessment of their financial status 
 relative to the previous year. Demographic variables 
 provide information on age, sex, race, marital status, 
education, occupation, employment history, and family income.
description: One respondent from each family unit in the 
 dwellings sampled, usually the head of the family, or the wife. 
 The dwelling units were selected by area probability sampling 
 from 66 primary sampling units. For each dwelling unit in the 
 sample, an interview was sought with a respondent from the 
 primary family and from each secondary family (if any). The 
 head of the family (usually the husband) was the preferred 
 respondent, but the wife could substitute if the head was not 
 readily available. The employment questions were addressed to 
 respondents who reported usually working for pay and who were
 working more than 20 hours per week at the time of the survey.
 In order to draw a representative sample of the working 
 population, other employed adults in family units where the
head was also present were also interviewed.
description: Datasets: 
DS1: Main File
description: All families living in continental United States 
dwelling units, exclusive of those on military reservations.
DOI: 10.5281/zenodo.10479359
title: UFLUX 100m daily photosynthesis fluxes in southeastern UK 2021

description: UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2021

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components:





Gross Primary Production (GPP) [open access]




Ecosystem Respiration (RECO) [in progress]




Net Ecosystem Exchange (NEE) [in progress]




Sensible Heat Flux (H) [in progress]




Latent Energy Flux (LE) [in progress]



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.5281/zenodo.10479360
title: UFLUX 100m daily photosynthesis fluxes in southeastern UK 2021

description: UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2021

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components:





Gross Primary Production (GPP) [open access]




Ecosystem Respiration (RECO) [in progress]




Net Ecosystem Exchange (NEE) [in progress]




Sensible Heat Flux (H) [in progress]




Latent Energy Flux (LE) [in progress]



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266