cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nccd3h
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Spain",
 "DatasetKey is one of (DIBA-Parc Natural del Montseny, Col·lecció de plantes de l'herbari de la Universitat de Barcelona, CSIC-Real Jardín Botánico-Colección de Plantas Vasculares (MA), Institut Botanic de Barcelona (IBB), CSIC-CMCNB – BC-Plantae)",
 "Geometry POLYGON((-2.15977 43.79348,-6.72773 44.88248,-9.26548 44.41824,-10.64312 40.68094,-11.87574 37.12748,-6.07516 35.90378,-2.66732 35.96249,-0.05706 37.30071,5.96104 39.68386,4.72841 42.52416,3.06075 42.73755,-0.70963 43.00325,-2.15977 43.79348))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Senecio pterophorus"
 ]
}

 The dataset includes 113 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020193-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.svkmu4
title: Occurrence Download

description: A dataset containing 3479 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mustela putorius",
 "Country is Spain",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2015"
 ]
}

 The dataset includes 3479 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020194-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17754692
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario Historical -  Guimaraes (Portugal)

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.17754693
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario Historical -  Guimaraes (Portugal)

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.17711512
title: Dataset and Model Weights for: Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures

description: This is the dataset and the corresponding model weight needed to reporduce the results for our paper "Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures".

With the GitHub Repository put the files in the corresponding folders:



- data/

    - images.npy

    - images_filles.npy

    - train_and_val.npy

    - test_images.npy

    - test_images_filled.npy

    - test_k.npy

- models/

    - ConvNeXtTiny_500_epochs.pth

    - ConvNeXtSmall_400_epochs.pth

    - ResNet50_500_epochs.pth

    - ResNet101_400_epochs.pth

    - ViT_S16.pth

    - ViT-T16.pth

- results/

    - all_models_similarity_filled.npz
DOI: 10.5281/zenodo.17711511
title: Dataset and Model Weights for: Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures

description: This is the dataset and the corresponding model weight needed to reporduce the results for our paper "Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures".

With the GitHub Repository put the files in the corresponding folders:



- data/

    - images.npy

    - images_filles.npy

    - train_and_val.npy

    - test_images.npy

    - test_images_filled.npy

    - test_k.npy

- models/

    - ConvNeXtTiny_500_epochs.pth

    - ConvNeXtSmall_400_epochs.pth

    - ResNet50_500_epochs.pth

    - ResNet101_400_epochs.pth

    - ViT_S16.pth

    - ViT-T16.pth

- results/

    - all_models_similarity_filled.npz
DOI: 10.5281/zenodo.17754712
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp126 -  Guimaraes (Portugal)

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.17754711
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp126 -  Guimaraes (Portugal)

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.17752298
title: [Dataset & Models] Online material to publication "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints"

description: Supporting material for publication "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints" (Munch, Kemper, Khan, Helffrich, Giardini).

This repository contains:



SCEM_nmodes_catalog.xls: Compilation of normal mode centre frequencies and quality (attenuation) factors used on the study. 

SCEM_bestfit.bm: Best-fitting self-consistent Earth model (SCEM).

P-PEM_bestfit.bm: Best-fitting physically-parameterised Earth model (P-PEM).

forward_calculator.zip: Python script to compute normal mode centre frequencies and quality factors for a given 1D model (in .bm format) and compare predictions with observations (in .csv format). Running this script locally requires installation of package specnm (https://johkem1.gitlab.io/specnm/). Alternatively, the script can also be run online on the following link: https://colab.research.google.com/drive/1jaFiMHTlUW_kTOdtLCyOpN5StGxWtqk7?usp=sharing

specnm_predictions_PREM: Predicted normal mode centre frequencies and quality factors for PREM. Calculations performed using the specnm tool.

Kernel_Gallery.zip: Gallery of normal modes sensitivity kernels (pdf format).
DOI: 10.5281/zenodo.17754721
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp245 -  Guimaraes (Portugal)

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.17754722
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp245 -  Guimaraes (Portugal)

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.17754739
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp370 -  Guimaraes (Portugal)

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.17754738
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp370 -  Guimaraes (Portugal)

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/dl.tftksb
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-100600.0",
 "Country is Spain",
 "Geometry POLYGON((-9.66045 43.5056,-9.28361 41.84337,-9.66045 36.47124,-6.79642 35.92385,-1.97278 36.2892,3.0016 38.32719,5.26268 40.76768,3.9814 42.17937,-2.34963 43.28654,-7.77622 44.32002,-9.66045 43.5056))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "PublishingOrg is one of (8879085f-09ed-4780-bc1b-b69a8acaa8b9, ab5c0b99-5af3-4958-b03c-0b7e85481cc1, 799acb53-2764-4e89-9792-5fdb6f7d7d29, 04f91d3b-6ed7-4948-9baa-fc99cdcbbdaa, c0758a16-296b-4ec3-97fd-8a057d9060f3, b934f398-a2df-412c-bde2-de176590bc73, 061b4f20-f241-11da-a328-b8a03c50a862)",
 "TaxonKey is Erica multiflora",
 "VerbatimScientificName is erica multiflora"
 ]
}

 The dataset includes 58 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020196-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data1733
title: Spanish Government' Constitutional Challenges to Regional Laws 1980-2021

description: This dataset provides comprehensive information on all laws passed by Spain's autonomous parliaments from 1980 to 2021, specifying whether each law was challenged by the Spanish Government in the Constitutional Court. Additionally, it includes data on the type of government at both the regional and central levels at the time of each constitutional challenge, as well as the governing party names at these levels. The dataset further incorporates variables capturing partisan preferences along the left-right and center-periphery axes, as defined by the Party Manifesto Project and the Regional Manifesto Project. We are grateful to the finantial support of the Institut d'Estudis d'Autogovern (IEA PRE166/23/00005)
DOI: 10.5281/zenodo.17234123
title: Analysis of SUHF National Roadmap for Open Science Annual Survey 2023-2025

description: The Excel file contains an analysis of survey responses from Swedish Higher Education Institutions (HEIs) regarding their progress on implementing Open Science recommendations (Recommendations 1–10) as described in the National Roadmap for Open Science (SUHF, see 'related works'). The dataset covers the years 2023, 2024, and 2025, and includes responses on actions such as open access publishing, research assessment reform, governance structures, and data management.

The analysis is intended to support monitoring, reporting, and visualization of HEIs’ progress toward implementing Open Science policies and practices.

The powerpoint file contains a presentation (in Swedish) of some of the results from the analysis. This was presented digitally during the seminar 'Omställningen till ett öppet vetenskapligt system – hur går det?' held thursday Oct 2nd 2025, 14:00-15:15 on Zoom
DOI: 10.5281/zenodo.17776351
title: Analysis of SUHF National Roadmap for Open Science Annual Survey 2023-2025

description: The Excel file contains an analysis of survey responses from Swedish Higher Education Institutions (HEIs) regarding their progress on implementing Open Science recommendations (Recommendations 1–10) as described in the National Roadmap for Open Science (SUHF, see 'related works'). The dataset covers the years 2023, 2024, and 2025, and includes responses on actions such as open access publishing, research assessment reform, governance structures, and data management.

The analysis is intended to support monitoring, reporting, and visualization of HEIs’ progress toward implementing Open Science policies and practices.

The powerpoint file contains a presentation (in Swedish) of some of the results from the analysis. This was presented digitally during the seminar 'Omställningen till ett öppet vetenskapligt system – hur går det?' held thursday Oct 2nd 2025, 14:00-15:15 on Zoom
DOI: 10.5281/zenodo.17754752
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp585 -  Guimaraes (Portugal)

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.17754753
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 10m, Global model MPI-ESM1-2-HR, scenario ssp585 -  Guimaraes (Portugal)

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.17754766
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 2000m, Global model CanESM5, scenario Historical -  Guimaraes (Portugal)

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.17754767
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 2000m, Global model CanESM5, scenario Historical -  Guimaraes (Portugal)

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/dl.zzfajr
title: Occurrence Download

description: A dataset containing 231 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((1.7355815459530965 49.07450631489965, 1.7337943026764688 49.0832391876214, 1.729446625367772 49.091575111939896, 1.7227044858353282 49.09919345726822, 1.7138262863882987 49.105801050067555, 1.7031530359082159 49.11114350197011, 1.6910952722778865 49.115015056226845, 1.6781172294150735 49.11726656333926, 1.6647188658437302 49.117811268908135, 1.651416465448285 49.116628182692416, 1.6387225843984108 49.1137628940302, 1.627126147442253 49.10932580094135, 1.6170734900816972 49.10348782386373, 1.6089511009690887 49.09647377543297, 1.603070743735132 49.08855365055652, 1.59965753379025 49.080032182266734, 1.5988414193212397 49.07123707514212, 1.6006523735544649 49.06250637697616, 1.6050194545668408 49.054175479266206, 1.6117737365142282 49.04656424734481, 1.6206549685103109 49.03996477180016, 1.6313216799868426 49.034630205221816, 1.643364328557408 49.030765103889145, 1.656320981432902 49.02851763490021, 1.6696949365255909 49.02797393788547, 1.6829736259292665 49.029154849542415, 1.695648103265437 49.032015111590475, 1.7072323978276482 49.03644509126745, 1.7172820226788672 49.042274951089006, 1.7254109508301974 49.04928111419849, 1.7313064231339421 49.057194786156465, 1.7347410230451465 49.06571221632773, 1.7355815459530965 49.07450631489965))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "EventDate is greater than or equal to 2015-01-01",
 "LastInterpreted is greater than or equal to 2025-06-01",
 "Modified is greater than or equal to 2025-06-01"
 ]
}

 The dataset includes 231 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020197-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17754782
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 2000m, Global model CanESM5, scenario ssp126 -  Guimaraes (Portugal)

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.17754781
title: DISTENDER Climate Simulations: Statistical Downscaling for Guimaraes_D2, Vertical level 2000m, Global model CanESM5, scenario ssp126 -  Guimaraes (Portugal)

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: 10.34740/kaggle/dsv/13943623
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.26300/n7nr-ce30
title: Brown Library Training Recording and Slides for Introduction to Using SciENcv and PREVIEW Commons Forms for NIH Biosketch and Current and Pending
DOI: 10.5281/zenodo.17719914
title: WP2 - Milešovka campaign - DWD & GHO (UV and TOC)

description: For the campaign BIOSPHERE Milešovka (August-November 2024):



TOC (Total Ozone Column) in Dobson Units (DU)

UV is UV Spectra and UVInd (UV index) computed from UV spectra measured in "global" (GHI: Global Horizontal Irradiance) geeometry


-> Look at ReadMe Files for more information:



L2_TOC_BTS -> Instrument BTS of GHO (Gigahertz-Optik) -> ReadMe_TOC-BTS-GHO.txt

L2_UV_BTS -> Instrument BTS of DWD (Deutscher Wetterdienst) -> ReadMe_UV-BTS-DWD.txt
description: Metadata for Data L2_TOC_BTS

Additional information to Readme: ReadMe_TOC-BTS-GHO.txt

Instrument ownship and operating and first data processing: Gigahertz-Optik GmbH (GHO); contact: Dominik Allnoch, d.allnoch@gigahertz-optik.deL2 Data Processing and data submission: Deutscher Wetterdienst (DWD); contact: Lionel Doppler, lionel.doppler@dwd.de

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union. 
description: Metadata for Data L2_UV_BTS

Additional information to Readme: ReadMe_UV-BTS-DWD.txt

Instrument ownship and operating and data processing and data submission: Deutscher Wetterdienst (DWD); contact: Lionel Doppler, lionel.doppler@dwd.de 

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union. 
description: Acknowledgement

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union.
DOI: 10.5281/zenodo.17719915
title: WP2 - Milešovka campaign - DWD & GHO (UV and TOC)

description: For the campaign BIOSPHERE Milešovka (August-November 2024):



TOC (Total Ozone Column) in Dobson Units (DU)

UV is UV Spectra and UVInd (UV index) computed from UV spectra measured in "global" (GHI: Global Horizontal Irradiance) geeometry


-> Look at ReadMe Files for more information:



L2_TOC_BTS -> Instrument BTS of GHO (Gigahertz-Optik) -> ReadMe_TOC-BTS-GHO.txt

L2_UV_BTS -> Instrument BTS of DWD (Deutscher Wetterdienst) -> ReadMe_UV-BTS-DWD.txt
description: Metadata for Data L2_TOC_BTS

Additional information to Readme: ReadMe_TOC-BTS-GHO.txt

Instrument ownship and operating and first data processing: Gigahertz-Optik GmbH (GHO); contact: Dominik Allnoch, d.allnoch@gigahertz-optik.deL2 Data Processing and data submission: Deutscher Wetterdienst (DWD); contact: Lionel Doppler, lionel.doppler@dwd.de

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union. 
description: Metadata for Data L2_UV_BTS

Additional information to Readme: ReadMe_UV-BTS-DWD.txt

Instrument ownship and operating and data processing and data submission: Deutscher Wetterdienst (DWD); contact: Lionel Doppler, lionel.doppler@dwd.de 

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union. 
description: Acknowledgement

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union.