cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17750460
title: Predictive ability and RMSE data from regularized neural networks
DOI: 10.48459/bnqe-yv67
title: New Dimensions - ARIEBUKTA, Svalbard
DOI: 10.15468/dl.uag4jg
title: Occurrence Download

description: A dataset containing 5093 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Megasternum concinnum"
 ]
}

 The dataset includes 5093 records from 164 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014063-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29400842
title: Data from: "<b>Shifts in assembly rules and loss of zooplankton functional diversity across hypereutrophic fishponds"</b>

description: Freshwater species are facing massive declines, often driven by eutrophication. Identifying which facets of biodiversity are sensitive is crucial, as species loss does not always translate to reduced ecosystem functioning and functional diversity. We examined how assembly rules shape zooplankton functional diversity in hypereutrophic fishponds. Higher eutrophication was hypothesized to cause functional homogenization through reduced functional diversity, habitat filtering, and trait convergence. Higher eutrophication indeed reduced functional diversity metrics, while species richness was kept stable. Functional richness, dispersion, and dissimilarity shifted from limiting similarity, where niche partitioning and competition shape community structure, to random (incidence data) and habitat filtering (biomass) with increasing eutrophication. Functional divergence transitioned from random to habitat filtering, while redundancy increased at higher trophic states. Trait convergence was the dominant process, with the environment selecting species with similar traits. Biodiversity assessments and managers should consider how functional diversity and ecosystem functions respond to anthropogenic and environmental changes.
DOI: 10.6084/m9.figshare.29400842.v1
title: Data from: "<b>Shifts in assembly rules and loss of zooplankton functional diversity across hypereutrophic fishponds"</b>

description: Freshwater species are facing massive declines, often driven by eutrophication. Identifying which facets of biodiversity are sensitive is crucial, as species loss does not always translate to reduced ecosystem functioning and functional diversity. We examined how assembly rules shape zooplankton functional diversity in hypereutrophic fishponds. Higher eutrophication was hypothesized to cause functional homogenization through reduced functional diversity, habitat filtering, and trait convergence. Higher eutrophication indeed reduced functional diversity metrics, while species richness was kept stable. Functional richness, dispersion, and dissimilarity shifted from limiting similarity, where niche partitioning and competition shape community structure, to random (incidence data) and habitat filtering (biomass) with increasing eutrophication. Functional divergence transitioned from random to habitat filtering, while redundancy increased at higher trophic states. Trait convergence was the dominant process, with the environment selecting species with similar traits. Biodiversity assessments and managers should consider how functional diversity and ecosystem functions respond to anthropogenic and environmental changes.
DOI: 10.5281/zenodo.17716864
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model CanESM5, scenario ssp245 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716865
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model CanESM5, scenario ssp245 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.48459/k34e-tt91
title: New Dimensions - ARKEOLOGVIKA, Svalbard
DOI: 10.5281/zenodo.17716874
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model CanESM5, scenario ssp370 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716873
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model CanESM5, scenario ssp370 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.48459/bsca-pq53
title: New Dimensions - BETTYBUKTA, Svalbard
DOI: 10.5281/zenodo.17716893
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model CanESM5, scenario ssp585 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716892
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model CanESM5, scenario ssp585 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716903
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model EC-EARTH3 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716902
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model EC-EARTH3 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.15468/s7cjuk
title: Water vole records from 1 January 1999 – 1 January 2025 across Lindsey Marsh Drainage Board, Trent Valley IDB, Isle of Axholme and North Nottinghamshire Water Level Management Board and Doncaster East IDB

description: Species are recorded pre, during and post works to drainage ditches. Works usually include flail mowing, in-channel weed removal, culverting and de-silting.
DOI: 10.5281/zenodo.17716925
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model MPI-ESM1-2-HR -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716926
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 2m, Global model MPI-ESM1-2-HR -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716950
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario Historical -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716949
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario Historical -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716962
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp126 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716963
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp126 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716970
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp245 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716969
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp245 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716980
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp370 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716979
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp370 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.48459/xj2z-ng51
title: New Dimensions - BJØRNBEINFLYA, Svalbard
DOI: 10.5281/zenodo.17741301
title: Vitamin K scRNA mouse dataset

description: Seurat objects described and used in the manuscript.
DOI: 10.5281/zenodo.17716988
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp585 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17716987
title: DISTENDER Climate Simulations: Statistical Downscaling for Euraf_D2, Vertical level 50m, Global model CanESM5, scenario ssp585 -  Dehesas(Spain) and Montados(Portugal)(EURAF)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.14278/rodare.4134
title: Data publication: Spatiotemporal temperature measurements in low‑Prandtl‑number Rayleigh–Bénard convection at Γ = 5

description: Temperature data from an FBG sensor in Rayleigh–Bénard convection.
DOI: 10.14278/rodare.4133
title: Data publication: Spatiotemporal temperature measurements in low‑Prandtl‑number Rayleigh–Bénard convection at Γ = 5

description: Temperature data from an FBG sensor in Rayleigh–Bénard convection.
DOI: 10.5281/zenodo.17750462
title: Indexes of Real Incomes of the Population of the Regions of Russia

description: This dataset contains the "Indexes of Real Incomes of the Population of the Regions of Russia" for the period 2002-2023. The data is sourced from official government statistics, specifically the EMISS website for the cost of a fixed set of consumer goods and services (in rubles) and the Rosstat website for average per capita incomes of the population in the regions of Russia.

The key indicator in this dataset is the "Index of Real Incomes of the Population," defined for each region and year as:

The index of real incomes of the population of the regions of Russia is used as a basic indicator, defined for each region and year as:

where:



 — average per capita monetary income of the region's population per year t.

 — the cost of a fixed set of consumer goods and services per person in the region r in the year t.

This index reflects the real purchasing power of household incomes in different regions, adjusted for the local price level of a standard consumer set. The data can be useful for studies in economics, regional development, and income distribution across Russian regions.

Keywords: Russia, real income index, regional income, consumer goods, purchasing power, economic indicators, Rosstat, EMISS, regional economics
DOI: 10.5281/zenodo.17750461
title: Indexes of Real Incomes of the Population of the Regions of Russia

description: This dataset contains the "Indexes of Real Incomes of the Population of the Regions of Russia" for the period 2002-2023. The data is sourced from official government statistics, specifically the EMISS website for the cost of a fixed set of consumer goods and services (in rubles) and the Rosstat website for average per capita incomes of the population in the regions of Russia.

The key indicator in this dataset is the "Index of Real Incomes of the Population," defined for each region and year as:

The index of real incomes of the population of the regions of Russia is used as a basic indicator, defined for each region and year as:

where:



 — average per capita monetary income of the region's population per year t.

 — the cost of a fixed set of consumer goods and services per person in the region r in the year t.

This index reflects the real purchasing power of household incomes in different regions, adjusted for the local price level of a standard consumer set. The data can be useful for studies in economics, regional development, and income distribution across Russian regions.

Keywords: Russia, real income index, regional income, consumer goods, purchasing power, economic indicators, Rosstat, EMISS, regional economics
DOI: 10.48459/7r2z-br68
title: New Dimensions - FJØRHOLMEN, DUNØYANE, Svalbard
DOI: 10.48459/4xsx-p154
title: New Dimensions - NORDRE DUNØYA, DUNØYANE, Svalbard
DOI: 10.5281/zenodo.17745655
title: TRAX GPP: Global 0.05° Monthly GPP Dataset (2001–2024) Derived from a Hybrid LSTM Framework

description: 1. Overview

Gross primary production (GPP) represents the total amount of carbon fixed through photosynthesis and is a fundamental variable for understanding terrestrial carbon and energy cycles. Persistent discrepancies among existing global GPP products—particularly in their seasonal dynamics, interannual variability, and long-term trends—highlight the need for datasets with improved temporal robustness and spatial detail. To address this gap, we developed TRAX GPP, a global GPP dataset with 0.05° spatial resolution and monthly coverage from 2001 to 2024.

The dataset is derived using a hybrid LSTM-based modelling framework that incorporates Robust Seasonal–Trend decomposition (RSTL), an interannual-difference–enhanced loss function, and continuous XCO2 fields to better represent CO₂ fertilization effects. These components jointly improve the model’s ability to reproduce seasonal patterns, capture long-term trends, and ensure temporal consistency.

Evaluation against eddy-covariance flux tower measurements demonstrates that TRAX GPP achieves strong agreement with observed GPP and provides more reliable seasonal and interannual variations than standard LSTM baselines and several existing GPP products. As a result, TRAX GPP reduces known biases in global totals and trend behaviors.

2. Authors

Shaoyang Chen, Xinjie Liu, Qizhi Han, Yanhong Wu, Liangyun Liu

3. NetCDF Format

The dataset is provided at a 0.05° spatial resolution, corresponding to a global grid of 3600 rows × 7200 columns in latitude–longitude coordinates. The dataset covers the period from January 2001 to December 2024, with monthly temporal resolution.

Each NetCDF file contains a single GPP variable representing monthly gross primary production. Missing or invalid pixels are assigned a fill value of –9999. A scale factor of 0.01 is applied to the stored integer values, and users should multiply by this factor to obtain GPP in units of g C m⁻² d⁻¹.

The dataset follows a standard NetCDF structure, including spatial coordinates (lat, lon), a time dimension, and variable attributes documenting units, fill values, and scaling conventions.