cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10473509
title: UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2022

description: UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2022

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





Gross Primary Production (GPP)




Ecosystem Respiration (RECO)




Net Ecosystem Exchange (NEE)




Sensible Heat Flux (H)




Latent Energy Flux (LE)



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: 10.15468/dl.vqpngk
title: Occurrence Download

description: A dataset containing 9665 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trapezia Latreille, 1828"
 ]
}

 The dataset includes 9665 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000003-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rkk8mx
title: Occurrence Download

description: A dataset containing 137079 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erica arborea L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 137079 records from 372 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000076-250225202704447/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kyv7g3
title: Occurrence Download

description: A dataset containing 407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Accipiter poliogaster (Temminck, 1824)"
 ]
}

 The dataset includes 407 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000005-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c9q3sd
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "StateProvince is one of (Puebla (State), Teziutlan)",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 37 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000008-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ec2hc4
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Naraboryctes philcreaseri Archer et al., 2010"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000011-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.74c56w
title: Occurrence Download

description: A dataset containing 2768 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anabacerthia amaurotis (Temminck, 1823)"
 ]
}

 The dataset includes 2768 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000009-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4x4vfh
title: Occurrence Download

description: A dataset containing 8307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Agelasticus cyanopus (Vieillot, 1819)"
 ]
}

 The dataset includes 8307 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000010-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14927496
title: training data for SoleNNoID protein annotation software

description: This dataset represents an edited and upgraded version of the REPETITA dataset which was used to train and test the SOLeNNoID convolutional neural network for solenoid residue classification. The original dataset comprised PDB files with full or partial protein structures for solenoid and non-solenoid proteins, as well as residue spans (e.g. residue 224-280) for solenoid regions.

In this version of the dataset, we include the original PDB files, distance matrices derived from these PDB files, which account for the missing residues in the PDB files, as well as revised labels by mapping and editing the original residue spans to produce a per-residue label file to match each structure/distance matrix. Additionally, extra beta-solenoid entries were manually added to the dataset.

Finally, a new test dataset was curated from reviewed solenoid entries in the RepeatsDB database.

The dataset is split into training_validation_dataset and test_dataset directories.

The training_validation_dataset directory comprises the original REPETITA dataset, split into alpha-, alpha/beta-, beta-, and non-solenoid directories. Each of these directories contains subdirectories with distance matrices, labels, and PDB structures. In addition, the beta_additional directory contains subdirectories with the distance matrices, labels and PDB structures of further manually added beta-solenoid entries.

The test_dataset directory comprises directories with non-solenoid and solenoid structures in mmCIF format, as well as a directory with the ground truth labels, and labels predicted by the TAPO, RepeatsDB-Lite and PRIGSA2 methods.

References and links:

REPETITA: Luca Marsella, Francesco Sirocco, Antonio Trovato, Flavio Seno, Silvio C.E. Tosatto, REPETITA: detection and discrimination of the periodicity of protein solenoid repeats by discrete Fourier transform, Bioinformatics, Volume 25, Issue 12, June 2009, Pages i289–i295, https://doi.org/10.1093/bioinformatics/btp232

RAPHAEL: Ian Walsh, Francesco G. Sirocco, Giovanni Minervini, Tomás Di Domenico, Carlo Ferrari, Silvio C. E. Tosatto, RAPHAEL: recognition, periodicity and insertion assignment of solenoid protein structures, Bioinformatics, Volume 28, Issue 24, December 2012, Pages 3257–3264, https://doi.org/10.1093/bioinformatics/bts550

SOLeNNoID: Nikov, Georgi and Pretorius, Daniella and Murray, James W., SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures, bioRxiv, 2024

REPETITA/RAPHAEL dataset link: http://old.protein.bio.unipd.it/raphael/precompiled.html
DOI: 10.5281/zenodo.14927497
title: training data for SoleNNoID protein annotation software

description: This dataset represents an edited and upgraded version of the REPETITA dataset which was used to train and test the SOLeNNoID convolutional neural network for solenoid residue classification. The original dataset comprised PDB files with full or partial protein structures for solenoid and non-solenoid proteins, as well as residue spans (e.g. residue 224-280) for solenoid regions.

In this version of the dataset, we include the original PDB files, distance matrices derived from these PDB files, which account for the missing residues in the PDB files, as well as revised labels by mapping and editing the original residue spans to produce a per-residue label file to match each structure/distance matrix. Additionally, extra beta-solenoid entries were manually added to the dataset.

Finally, a new test dataset was curated from reviewed solenoid entries in the RepeatsDB database.

The dataset is split into training_validation_dataset and test_dataset directories.

The training_validation_dataset directory comprises the original REPETITA dataset, split into alpha-, alpha/beta-, beta-, and non-solenoid directories. Each of these directories contains subdirectories with distance matrices, labels, and PDB structures. In addition, the beta_additional directory contains subdirectories with the distance matrices, labels and PDB structures of further manually added beta-solenoid entries.

The test_dataset directory comprises directories with non-solenoid and solenoid structures in mmCIF format, as well as a directory with the ground truth labels, and labels predicted by the TAPO, RepeatsDB-Lite and PRIGSA2 methods.

References and links:

REPETITA: Luca Marsella, Francesco Sirocco, Antonio Trovato, Flavio Seno, Silvio C.E. Tosatto, REPETITA: detection and discrimination of the periodicity of protein solenoid repeats by discrete Fourier transform, Bioinformatics, Volume 25, Issue 12, June 2009, Pages i289–i295, https://doi.org/10.1093/bioinformatics/btp232

RAPHAEL: Ian Walsh, Francesco G. Sirocco, Giovanni Minervini, Tomás Di Domenico, Carlo Ferrari, Silvio C. E. Tosatto, RAPHAEL: recognition, periodicity and insertion assignment of solenoid protein structures, Bioinformatics, Volume 28, Issue 24, December 2012, Pages 3257–3264, https://doi.org/10.1093/bioinformatics/bts550

SOLeNNoID: Nikov, Georgi and Pretorius, Daniella and Murray, James W., SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures, bioRxiv, 2024

REPETITA/RAPHAEL dataset link: http://old.protein.bio.unipd.it/raphael/precompiled.html
DOI: 10.15468/dl.f9xh43
title: Occurrence Download

description: A dataset containing 434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Oceania",
 "Country is New Zealand",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Syzygium maire (A.Cunn.) Sykes & Garn.-Jones"
 ]
}

 The dataset includes 434 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000006-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fnkuzv
title: Occurrence Download

description: A dataset containing 1742 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pipile jacutinga (Spix, 1825)"
 ]
}

 The dataset includes 1742 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000007-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3xag2x
title: Occurrence Download

description: A dataset containing 1114 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-117.72128 17.914,-104.50345 17.914,-104.50345 32.08727,-117.72128 32.08727,-117.72128 17.914))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Chelonia mydas (Linnaeus, 1758)"
 ]
}

 The dataset includes 1114 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000013-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sueq5f
title: Occurrence Download

description: A dataset containing 286 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "DatasetKey is Carnegie Museum of Natural History - Mollusks",
 "GadmGid is USA.49_1",
 "TaxonKey is Gastropoda"
 ]
}

 The dataset includes 286 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000012-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/132ea1c1d9929acc9e40b6cfd1d88b48
title: Algal microbial mat biomass measurements from the McMurdo Dry Valleys and Cape Royds, Antarctica (1994-2025, ongoing)

description: As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This dataset contains microbial biomass concentrations found in algal mats located in streams throughout the McMurdo Dry Valleys as well as in ponds of the nearby Cape Royds. Microbial mat biomass has been collected as part of the McMurdo LTER since the 1993-1994 field season and measured as ash-free dry mass (AFDM) and chlorophyll-a (Chl-a).
DOI: 10.15468/dl.wtas9d
title: Occurrence Download

description: A dataset containing 257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-115.27682 21.01833,-105.5958 21.01833,-105.5958 31.25366,-115.27682 31.25366,-115.27682 21.01833))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhincodon typus Smith, 1828"
 ]
}

 The dataset includes 257 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000014-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ene2qz
title: Occurrence Download

description: A dataset containing 230842 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Buddleja davidii Franch.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 230842 records from 1559 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000073-250225202704447/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00004833.3
title: Available IPD datapackage for study 'A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease Who Have Failed or Are Intolerant to TNF Antagonist Therapy (UNITI-1)'

description: This study (UNITI-1) will compare the effects (both positive and negative) of an initial treatment with ustekinumab to placebo over 8 weeks, in patients with moderately to severely active Crohn's disease who have either failed or could not tolerate at least one TNF-antagonist medications in the past (specifically, infliximab, adalimumab, or certolizumab pegol).
DOI: 10.5281/zenodo.14246583
title: Rural population count at 1 km for 2000-2020 based on WorldPop and GHS-SMOD urbanization level

description: Rural population count at 1 km grid in EPSG:4326 for 2000-2020 (annual). This is only an estimate of the rural population. This probably misses many rural areas, especially in the tropics. The maps were derived using two data sources:



WorldPop population counts at 1 km;

GHS-SMOD urbanization levels at 1 km;


Rural population is estimated using the following translation rules for GHS-SMOD (note: these are arbitrary rules based on the GHS-SMOD documentation):



Class 30: “Urban Centre grid cell” = 0% rural

Class 23: “Dense Urban Cluster grid cell” = 0.5% rural

Class 22: “Semi-dense Urban Cluster grid cell” = 2% rural

Class 21: “Suburban or per-urban grid cell” = 15% rural

Class 13: “Rural cluster grid cell” = 95% rural

Class 12: “Low Density Rural grid cell” = 100% rural

Class 11: “Very low density rural grid cell” = 100% rural


The nighttime images are based on: https://doi.org/10.5281/zenodo.7750174



Schiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: http://data.europa.eu/89h/a0df7a6f-49de-46ea-9bde-563437a6e2ba
DOI: 10.5281/zenodo.14246584
title: Rural population count at 1 km for 2000-2020 based on WorldPop and GHS-SMOD urbanization level

description: Rural population count at 1 km grid in EPSG:4326 for 2000-2020 (annual). This is only an estimate of the rural population. This probably misses many rural areas, especially in the tropics. The maps were derived using two data sources:



WorldPop population counts at 1 km;

GHS-SMOD urbanization levels at 1 km;


Rural population is estimated using the following translation rules for GHS-SMOD (note: these are arbitrary rules based on the GHS-SMOD documentation):



Class 30: “Urban Centre grid cell” = 0% rural

Class 23: “Dense Urban Cluster grid cell” = 0.5% rural

Class 22: “Semi-dense Urban Cluster grid cell” = 2% rural

Class 21: “Suburban or per-urban grid cell” = 15% rural

Class 13: “Rural cluster grid cell” = 95% rural

Class 12: “Low Density Rural grid cell” = 100% rural

Class 11: “Very low density rural grid cell” = 100% rural


The nighttime images are based on: https://doi.org/10.5281/zenodo.7750174



Schiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: http://data.europa.eu/89h/a0df7a6f-49de-46ea-9bde-563437a6e2ba
DOI: 10.15468/dl.d5ebmh
title: Occurrence Download

description: A dataset containing 20767 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Pinus sylvestris L.",
 "Year 1985-2004"
 ]
}

 The dataset includes 20767 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007198-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kxsxxb
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Veraequa achne Jordan & Starks, 1904",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007199-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.btfj2d
title: Occurrence Download

description: A dataset containing 6219 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phthorimaea operculella (Zeller, 1873)"
 ]
}

 The dataset includes 6219 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007201-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2zxxjg
title: Occurrence Download

description: A dataset containing 199 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trachycephalus resinifictrix (Goeldi, 1907)"
 ]
}

 The dataset includes 199 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007202-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/3310089901-fra
title: Langues utilisées par les entreprises ou les organismes lors de la prestation de services ou la réalisation d’activités au cours des 12 derniers mois quatrième trimestre de 2024

description: Langues utilisées par les entreprises ou les organismes lors de la prestation de services ou la réalisation d’activités au cours des 12 derniers mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2024.
DOI: 10.25934/pr00004834.2
title: Available IPD datapackage for study 'A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease (UNITI-2)'

description: This study (UNITI-2) will compare the effects (both positive and negative) of an initial treatment with ustekinumab to a placebo over 8 weeks in patients with moderately to severely active Crohn's disease.
DOI: 10.15468/dl.avzw2k
title: Occurrence Download

description: A dataset containing 235 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Meretrix lusoria (Röding, 1798)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 235 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007203-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.