cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15780244
title: Transformer-based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches

description: This Zenodo repository provides the trained models and prediction outputs used in the study "Transformer-based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches". The study evaluates and compares the performance of several deep learning-based super-resolution (SR) methods, including Swin2SR, DeepESD, and U-Net, for enhancing the spatial resolution of reanalysis temperature data from ERA5 (0.25º) to CERRA (0.05º) over the Iberian Peninsula.

🔹 File: predictions_2019-2020.zip

This archive contains super-resolved 2-meter temperature predictions (t2m) over the Iberian Peninsula for the years 2019 and 2020, generated by each of the downscaling methods evaluated in the paper. All files are in NetCDF (.nc) format, structured by model and downscaling strategy. Each file provides 3-hourly temperature fields at ~5.5 km resolution (CERRA grid, 0.05º).

Structure:





full-domain/ – Predictions from models trained over the entire domain:





bicubic/predictions.nc: Bicubic interpolation baseline.




deepesd/predictions.nc: DeepESD model predictions.




swin2sr/predictions.nc: Swin2SR (Transformer) full-domain predictions.




unet2d/predictions.nc: U-Net full-domain predictions.






tiles/weighted-sampling/ – Predictions from models using domain tiling with weighted merging:





swin2sr-p/predictions.nc: Swin2SR trained with patch-based sampling (40x40 overlapping tiles moving 20x20).




swin2sr-t/predictions.nc: Swin2SR trained using a tiled layout (40x40 non-overlapping tiles).





🔹 File: models.zip

This archive contains all trained model artifacts used to generate the predictions. It includes model weights (in PyTorch or ONNX format), configuration files, coordinate reference grids, and reproducibility metadata.

Structure:





full-domain/ – Models trained on the entire Iberian Peninsula:





deepesd/, swin2sr/, unet2d/:





model.onnx or model.pth: Trained model weights.




config_data.yml, config_model.yml: Data and model configuration.




features_coordinates.nc, labels_coordinates.nc: Input/output spatial grids.








tiles/weighted-sampling/ – Tiling-based training strategies:





swin2sr-p/ and swin2sr-t/:





model.pth: Trained model weights in PyTorch.




Full config files and coordinate NetCDFs as above.




Additional files:





highres_static_covariables.nc, lowres_static_covariables.nc: Auxiliary inputs (e.g., elevation, land cover) used in training.









These models allow researchers to:





Reproduce the prediction results in predictions_2019-2020.zip,




Analyze different training strategies (full vs tiled),




Fine-tune or reuse the models on new domains or variables,




Compare architectures in a standardized setting.
DOI: 10.5281/zenodo.15780245
title: Transformer-based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches

description: This Zenodo repository provides the trained models and prediction outputs used in the study "Transformer-based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches". The study evaluates and compares the performance of several deep learning-based super-resolution (SR) methods, including Swin2SR, DeepESD, and U-Net, for enhancing the spatial resolution of reanalysis temperature data from ERA5 (0.25º) to CERRA (0.05º) over the Iberian Peninsula.

🔹 File: predictions_2019-2020.zip

This archive contains super-resolved 2-meter temperature predictions (t2m) over the Iberian Peninsula for the years 2019 and 2020, generated by each of the downscaling methods evaluated in the paper. All files are in NetCDF (.nc) format, structured by model and downscaling strategy. Each file provides 3-hourly temperature fields at ~5.5 km resolution (CERRA grid, 0.05º).

Structure:





full-domain/ – Predictions from models trained over the entire domain:





bicubic/predictions.nc: Bicubic interpolation baseline.




deepesd/predictions.nc: DeepESD model predictions.




swin2sr/predictions.nc: Swin2SR (Transformer) full-domain predictions.




unet2d/predictions.nc: U-Net full-domain predictions.






tiles/weighted-sampling/ – Predictions from models using domain tiling with weighted merging:





swin2sr-p/predictions.nc: Swin2SR trained with patch-based sampling (40x40 overlapping tiles moving 20x20).




swin2sr-t/predictions.nc: Swin2SR trained using a tiled layout (40x40 non-overlapping tiles).





🔹 File: models.zip

This archive contains all trained model artifacts used to generate the predictions. It includes model weights (in PyTorch or ONNX format), configuration files, coordinate reference grids, and reproducibility metadata.

Structure:





full-domain/ – Models trained on the entire Iberian Peninsula:





deepesd/, swin2sr/, unet2d/:





model.onnx or model.pth: Trained model weights.




config_data.yml, config_model.yml: Data and model configuration.




features_coordinates.nc, labels_coordinates.nc: Input/output spatial grids.








tiles/weighted-sampling/ – Tiling-based training strategies:





swin2sr-p/ and swin2sr-t/:





model.pth: Trained model weights in PyTorch.




Full config files and coordinate NetCDFs as above.




Additional files:





highres_static_covariables.nc, lowres_static_covariables.nc: Auxiliary inputs (e.g., elevation, land cover) used in training.









These models allow researchers to:





Reproduce the prediction results in predictions_2019-2020.zip,




Analyze different training strategies (full vs tiled),




Fine-tune or reuse the models on new domains or variables,




Compare architectures in a standardized setting.
DOI: 10.15468/dl.s8x4e3
title: Occurrence Download

description: A dataset containing 218 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Semaprochilodus kneri (Pellegrin, 1909)"
 ]
}

 The dataset includes 218 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084079-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgupye
title: Occurrence Download

description: A dataset containing 3388 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Veronica serpyllifolia L.",
 "Geometry POLYGON((-8.00174339329464 54.1998526673513, 0.202120938926891 54.1998526673513, 0.202120938926891 61.2628565887411, -8.00174339329464 61.2628565887411, -8.00174339329464 54.1998526673513))",
 "CoordinateUncertaintyInMeters is 0,101",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3388 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084077-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8r7rdq
title: Occurrence Download

description: A dataset containing 6198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Polyphylla fullo (Linnaeus, 1758), Melolontha fullo (Linnaeus, 1758), Polyphylla luctuosa Mulsant, 1842, Polyphylla marmorata Mulsant, 1847, Scarabaeus fullo Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 6198 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084076-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/0tn-3ak-wwc
title: Geocerthia serrana serrana

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.34847/nkl.764c4oq9
title: 03_artefatto_agrinion_0023
title: 271.0

description: 1.0
description: Source: Socal, Michela
description: Provenance: Agrinio
description: Provenance: Museo Archeologico
description: modified: 43172.0
DOI: 10.5285/9ab763bf-e197-4981-b1c2-db05ba8a61a0
title: Cyclic thermal loading and triaxial strength test data from the Whitby Mudstone Formation

description: The accompanying dataset contains geomechanical strength data of mudstone samples collected from the Grey Shale Member of the Whitby Mudstone Formation of the Lias Group.
Testing includes cyclic thermo-mechanical loading completed at the British Geological Survey (BGS).
All sample preparation, preservation and testing were completed to the specification outlined by the ISRM (ISRM, 1978b; ISRM, 1978a; Bieniawski and Bernede, 1979; ISRM, 1985 for determining the indirect tensile strength, triaxial strength, UCS and point load strength respectively) unless otherwise stated.
Each test was comprised of three main stages: 1) A heating stage where the sample is heated to a set temperature loading scheme under pressure conditions of 1-1.4 MPa axial stress and 0.5 MPa confining pressure throughout the heating stage 2) A preloading stage, where the confining pressure is increased to 5 MPa which was held throughout the triaxial compression test. 3) Triaxial compression test, during the active deformation phase, the samples were axially loaded using a constant displacement rate of 0.0012 mm s-1.

The data are separated into individual Microsoft Excel files, with each file representing a single test. Each file contains time, force, stress, displacement, and strain data.
DOI: 10.17028/rd.lboro.29446436
title: Supplementary information files for "Combining virtual reality and tactile stimulation to investigate embodied finger-based numerical representations"

description: Supplementary files for article "Combining virtual reality and tactile stimulation to investigate embodied finger-based numerical representations"<br><br>Finger-based representation of numbers is a high-level cognitive strategy to assist numerical and arithmetic processing in children and adults. It is unclear whether this paradigm builds on simple perceptual features or comprises several attributes through embodiment. Here we describe the development and initial testing of an experimental setup to study embodiment during a finger-based numerical task using Virtual Reality (VR) and a low-cost tactile stimulator that is easy to build. Using VR allows us to create new ways to study finger-based numerical representation using a virtual hand that can be manipulated in ways our hand cannot, such as decoupling tactile and visual stimuli. The goal is to present a new methodology that can allow researchers to study embodiment through this new approach, maybe shedding new light on the cognitive strategy behind the finger-based representation of numbers. In this case, a critical methodological requirement is delivering precisely targeted sensory stimuli to specific effectors while simultaneously recording their behavior and engaging the participant in a simulated experience. We tested the device’s capability by stimulating users in different experimental configurations. Results indicate that our device delivers reliable tactile stimulation to all fingers of a participant’s hand without losing motion tracking quality during an ongoing task. This is reflected by an accuracy of over 95% in participants detecting stimulation of a single finger or multiple fingers in sequential stimulation as indicated by experiments with sixteen participants. We discuss possible application scenarios, explain how to apply our methodology to study the embodiment of finger-based numerical representations and other high-level cognitive functions, and discuss potential further developments of the device based on the data obtained in our testing.<br><br>© The Author(s), CC-BY 4.0
DOI: 10.17028/rd.lboro.29446436.v1
title: Supplementary information files for "Combining virtual reality and tactile stimulation to investigate embodied finger-based numerical representations"

description: Supplementary files for article "Combining virtual reality and tactile stimulation to investigate embodied finger-based numerical representations"<br><br>Finger-based representation of numbers is a high-level cognitive strategy to assist numerical and arithmetic processing in children and adults. It is unclear whether this paradigm builds on simple perceptual features or comprises several attributes through embodiment. Here we describe the development and initial testing of an experimental setup to study embodiment during a finger-based numerical task using Virtual Reality (VR) and a low-cost tactile stimulator that is easy to build. Using VR allows us to create new ways to study finger-based numerical representation using a virtual hand that can be manipulated in ways our hand cannot, such as decoupling tactile and visual stimuli. The goal is to present a new methodology that can allow researchers to study embodiment through this new approach, maybe shedding new light on the cognitive strategy behind the finger-based representation of numbers. In this case, a critical methodological requirement is delivering precisely targeted sensory stimuli to specific effectors while simultaneously recording their behavior and engaging the participant in a simulated experience. We tested the device’s capability by stimulating users in different experimental configurations. Results indicate that our device delivers reliable tactile stimulation to all fingers of a participant’s hand without losing motion tracking quality during an ongoing task. This is reflected by an accuracy of over 95% in participants detecting stimulation of a single finger or multiple fingers in sequential stimulation as indicated by experiments with sixteen participants. We discuss possible application scenarios, explain how to apply our methodology to study the embodiment of finger-based numerical representations and other high-level cognitive functions, and discuss potential further developments of the device based on the data obtained in our testing.<br><br>© The Author(s), CC-BY 4.0
DOI: 10.15468/dl.trv2fp
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Madagascar",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bruguiera gymnorhiza (L.) Lam.",
 "Year 1980-2025"
 ]
}

 The dataset includes 142 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084082-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data359
title: PANACEA Italian automatically acquired lexicon for ENV domain: Subcategorization Frames (V-SUBCAT)

description: The PANACEA_SCF_IT_ENV is an automatically created lexicon of verbal subcategorisation frames for 26 verb lemmas. The language is Italian and the domain is Environment. The lexicon was acquired from a monolingual corpus automatically crawled, normalised, cleaned, and NLP processed (Corpus name: PANACEA Annotated Italian Environment Corpus Version 2, PANACEA_MCv2_IT_ENV).
DOI: 10.15468/dl.8da3km
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salvinia ×molesta D.S.Mitch.",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 60 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084084-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mncj8j
title: Occurrence Download

description: A dataset containing 356 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cichla orinocensis Humboldt, 1821"
 ]
}

 The dataset includes 356 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084085-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data360
title: PANACEA Spanish automatically acquired lexicon for LAB domain: Lexical Semantic classes for nouns

description: -
description: This is a domain-specific lexicon of for Spanish for labour (LAB) domain. This lexicon contains the a set of nouns classified into nine different semantic classes. It has been automatically created using the PANACEA web services for noun classification and the crawled data for this domain and language, previously annotated with FreeLing tagger.
DOI: 10.15468/dl.7pj76r
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salvinia ×molesta D.S.Mitch.",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 120 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084087-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6pqztu
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Madagascar",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ceriops tagal (Perr.) C.B.Rob.",
 "Year 1980-2025"
 ]
}

 The dataset includes 64 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084089-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xtzvbw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salvinia ×molesta D.S.Mitch.",
 "Country is Pakistan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084088-250525065834625/datasets/export for details.
DOI: 10.15468/dl.fj3sqk
title: Occurrence Download

description: A dataset containing 13064051 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is one of (Africa, Asia, Oceania, Europe, North America, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tracheophyta",
 "Year 2000-2024"
 ]
}

 The dataset includes 13064051 records from 1581 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000449-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1088844.10fu
title: SH1088844.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1088845.10fu
title: SH1088845.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.18105450
title: Observasi Proses Pembelajaran Pada Anak Usia Dini ( PAUD/Kelompok Bermain )
DOI: 10.5281/zenodo.18105451
title: Observasi Proses Pembelajaran Pada Anak Usia Dini ( PAUD/Kelompok Bermain )
DOI: 10.15156/bio/sh1088849.10fu
title: SH1088849.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1088850.10fu
title: SH1088850.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.