cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gn8tg5
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diartiger fossulatus Sharp, 1883",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 35 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0210984-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112037431
title: Cuneiform Tablet 1913.14.1688, Spurlock 1913.14.1688

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.1688; CDLI P421539 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Brownish Pink (5YR 8/1.5) Yellowish White (10YR 9/1.5) ❧ [Physical Object Note:] 4.5 x 3.4 x 1.8 cm (27 g)
DOI: 10.15468/dl.vb6hb9
title: Occurrence Download

description: A dataset containing 63449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Succisa pratensis Moench",
 "Year 1980-2022"
 ]
}

 The dataset includes 63449 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001501-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.af4fqd
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Fossil, Living Specimen, Occurrence evidence)",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Arctocephalus townsendi Merriam, 1897"
 ]
}

 The dataset includes 118 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0258644-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8utd74
title: Occurrence Download

description: A dataset containing 300 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Liothyrella neozelanica Thomson, 1918"
 ]
}

 The dataset includes 300 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057574-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qxu4nh
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cymatia apparens (Distant, 1910)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0211732-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112037509
title: Cuneiform Tablet 1913.14.1688, Spurlock 1913.14.1688

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.1688; CDLI P421539 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Brownish Pink (5YR 8/1.5) Yellowish White (10YR 9/1.5) ❧ [Physical Object Note:] 4.5 x 3.4 x 1.8 cm (27 g)
DOI: 10.15468/dl.2r2h2c
title: Occurrence Download

description: A dataset containing 228 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Helianthus agrestis Pollard"
 ]
}

 The dataset includes 228 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0238542-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112037307
title: Cuneiform Tablet 1913.14.1688, Spurlock 1913.14.1688

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.1688; CDLI P421539 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Brownish Pink (5YR 8/1.5) Yellowish White (10YR 9/1.5) ❧ [Physical Object Note:] 4.5 x 3.4 x 1.8 cm (27 g)
DOI: 10.15468/dl.328fx8
title: Occurrence Download

description: A dataset containing 6644 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Juncus canadensis J.Gay"
 ]
}

 The dataset includes 6644 records from 172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0257419-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dsumav
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078606-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b0kuxm
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001561-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mglcty
title: Occurrence Download

description: A dataset containing 200118426 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 200118426 records from 3633 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008321-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/506-8jh-j43
title: Fratercula arctica grabae

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.3535/lfn-2w8-l0p
title: Fratercula arctica arctica

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.3535/px6-fj1-pk0
title: Fratercula arctica grabae

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.6084/m9.figshare.31046137
title: <b>Physics-Informed Neural Network Modeling of Saturation Dependent Soil Thermal Conductivity Curve</b>

description: This dataset contains pre-trained neural network models and training data for predicting soil thermal conductivity (λ) as a function of soil properties and saturation degree. The dataset supports both traditional Multi-Layer Perceptron (MLP) and Physics-Informed Neural Network (PINN) approaches for modeling the saturation-dependent thermal conductivity of soils.It is organized into three main directories that support model training, inference, and visualization. The MLP directory contains all files related to the MLP approach, including the compiled dataset (Complied dataset.xlsx) with 1,846 measurements from 211 unique soil types, the trained PyTorch model weights (final_model.pth), feature scaling parameters (scaler.pkl), train-test split information (train_test_datasets.xlsx), model predictions (final_mlp_predictions.xlsx), training loss history (final_loss_history.csv), hyperparameter optimization results (hyperparameter_tuning_results.csv), and the complete training script (MLP-sample.py). The PINN directory mirrors this structure but contains files specific to the Physics-Informed Neural Network approach, with identical file types that incorporate physical constraints into the neural network architecture. Both models utilize a 75/25 train-test split by soil type to ensure generalization to unseen soils. The Load model directory provides ready-to-use inference tools, including prediction scripts for both PINN (PINN-prediction.py) and MLP (MLP-predict.py) models, along with an input data template (input_data.xlsx) that demonstrates the required format for making predictions on new soil samples. Additionally, a Chart directory contains python scripts used to generate 10 figures for the associated research publication (manuscript number: 2026WR043461), including plots of thermal conductivity curves, dataset distributions, model architecture diagrams, training results, derivative analyses, and validation comparisons. All model files are saved in PyTorch format (.pth), data files are provided as Excel spreadsheets (.xlsx) for accessibility, and the feature scalers are serialized using Python's joblib library (.pkl) to ensure consistent preprocessing during inference.
DOI: 10.6084/m9.figshare.31046137.v2
title: <b>Physics-Informed Neural Network Modeling of Saturation Dependent Soil Thermal Conductivity Curve</b>

description: This dataset contains pre-trained neural network models and training data for predicting soil thermal conductivity (λ) as a function of soil properties and saturation degree. The dataset supports both traditional Multi-Layer Perceptron (MLP) and Physics-Informed Neural Network (PINN) approaches for modeling the saturation-dependent thermal conductivity of soils.It is organized into three main directories that support model training, inference, and visualization. The MLP directory contains all files related to the MLP approach, including the compiled dataset (Complied dataset.xlsx) with 1,846 measurements from 211 unique soil types, the trained PyTorch model weights (final_model.pth), feature scaling parameters (scaler.pkl), train-test split information (train_test_datasets.xlsx), model predictions (final_mlp_predictions.xlsx), training loss history (final_loss_history.csv), hyperparameter optimization results (hyperparameter_tuning_results.csv), and the complete training script (MLP-sample.py). The PINN directory mirrors this structure but contains files specific to the Physics-Informed Neural Network approach, with identical file types that incorporate physical constraints into the neural network architecture. Both models utilize a 75/25 train-test split by soil type to ensure generalization to unseen soils. The Load model directory provides ready-to-use inference tools, including prediction scripts for both PINN (PINN-prediction.py) and MLP (MLP-predict.py) models, along with an input data template (input_data.xlsx) that demonstrates the required format for making predictions on new soil samples. Additionally, a Chart directory contains python scripts used to generate 10 figures for the associated research publication (manuscript number: 2026WR043461), including plots of thermal conductivity curves, dataset distributions, model architecture diagrams, training results, derivative analyses, and validation comparisons. All model files are saved in PyTorch format (.pth), data files are provided as Excel spreadsheets (.xlsx) for accessibility, and the feature scalers are serialized using Python's joblib library (.pkl) to ensure consistent preprocessing during inference.
DOI: 10.6084/m9.figshare.31046137.v1
title: <b>Physics-Informed Neural Network Modeling of Saturation Dependent Soil Thermal Conductivity Curve</b>

description: This dataset contains pre-trained neural network models and training data for predicting soil thermal conductivity (λ) as a function of soil properties and saturation degree. The dataset supports both traditional Multi-Layer Perceptron (MLP) and Physics-Informed Neural Network (PINN) approaches for modeling the saturation-dependent thermal conductivity of soils.It is organized into three main directories that support model training, inference, and visualization. The MLP directory contains all files related to the MLP approach, including the compiled dataset (Complied dataset.xlsx) with 1,846 measurements from 211 unique soil types, the trained PyTorch model weights (final_model.pth), feature scaling parameters (scaler.pkl), train-test split information (train_test_datasets.xlsx), model predictions (final_mlp_predictions.xlsx), training loss history (final_loss_history.csv), hyperparameter optimization results (hyperparameter_tuning_results.csv), and the complete training script (MLP-sample.py). The PINN directory mirrors this structure but contains files specific to the Physics-Informed Neural Network approach, with identical file types that incorporate physical constraints into the neural network architecture. Both models utilize a 75/25 train-test split by soil type to ensure generalization to unseen soils. The Load model directory provides ready-to-use inference tools, including prediction scripts for both PINN (PINN-prediction.py) and MLP (MLP-predict.py) models, along with an input data template (input_data.xlsx) that demonstrates the required format for making predictions on new soil samples. Additionally, a Chart directory contains python scripts used to generate 10 figures for the associated research publication (manuscript number: 2026WR043461), including plots of thermal conductivity curves, dataset distributions, model architecture diagrams, training results, derivative analyses, and validation comparisons. All model files are saved in PyTorch format (.pth), data files are provided as Excel spreadsheets (.xlsx) for accessibility, and the feature scalers are serialized using Python's joblib library (.pkl) to ensure consistent preprocessing during inference.
DOI: 10.3535/v5e-dsx-6wq
title: Ceyx erithaca

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.3535/kt0-9pv-3c3
title: Fratercula arctica grabae

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.3535/pg5-nx4-ph5
title: Uria aalge aalge

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.3535/rck-8ws-tpm
title: Uria aalge albionis

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.3535/vc6-vtr-z4n
title: Uria aalge aalge

description: Digital Specimen for the physical specimen hosted at Natural History Museum of Denmark.
DOI: 10.15156/bio/sh1198909.10fu
title: SH1198909.10FU

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/sh1198910.10fu
title: SH1198910.10FU

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/sh1198911.10fu
title: SH1198911.10FU

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/sh1198912.10fu
title: SH1198912.10FU

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/sh1198913.10fu
title: SH1198913.10FU

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/sh1198915.10fu
title: SH1198915.10FU

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/sh1198916.10fu
title: SH1198916.10FU

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/sh1198917.10fu
title: SH1198917.10FU

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.