cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/yfj-j4j-l06
title: Bacidia

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/h2x-qbr-yfc
title: Graphis fissurinoides van den Boom & Sipman

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/2d9-ng0-w2j
title: Flavoplaca arcis (Poelt & Vězda) Arup, Frödén & Søchting

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/xa1-h8f-wt7
title: Enterographa

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/g7p-9j3-9vn
title: Allographa mirabilis (Lücking, Sipman, L. Umaña & Chaves) Lücking & Kalb

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/jp4-pef-fwt
title: Cryptothecia methylmicrophyllinica Aptroot & Spier

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.15468/dl.pbec46
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gambusia holbrooki Girard, 1859, Gambusia holbrookii Girard, 1859, Gambusia holbrocki Girard, 1859, Gambusia holbrooki Girard, 1859, Gambusia holbrookii Girard, 1859, Gambusia holbrocki Girard, 1859, Gambusia holbrooki Girard, 1859, Gambusia holbrookii Girard, 1859, Gambusia holbrocki Girard, 1859)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "Country is Bermuda",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0273195-200613084148143/datasets/export for details.
DOI: 10.15468/dl.haiq9k
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.308 4.277,-74.029 4.277,-74.029 4.549,-74.308 4.549,-74.308 4.277))",
 "HasCoordinate is true",
 "TaxonKey is Zenaida auriculata (Des Murs, 1847)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 77 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010200-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.svemah
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is greater than or equal to 1970",
 "TaxonKey is Lymantria dispar japonica Motschulsky, 1860",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 102 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0258374-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dkvq6v
title: Occurrence Download

description: A dataset containing 33559 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amaranthus hybridus L."
 ]
}

 The dataset includes 33559 records from 482 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0292420-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26bksq
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Literature Occurrence"
 ]
 },
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aporrectodea Orley, 1885"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0251772-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.1pddnt
title: Occurrence Download

description: A dataset containing 729 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Zygophyllum fabago L.",
 "TaxonKey is Zygophyllum fabago L.",
 "TaxonKey is Zygophyllum fabago L.",
 "TaxonKey is Zygophyllum fabago L.",
 "TaxonKey is Zygophyllum fabago L."
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 729 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010200-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c6jgbw
title: Occurrence Download

description: A dataset listing the 971473 species recorded in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 971473 records were derived from 6795 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0258383-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzdyaq
title: Occurrence Download

description: A dataset containing 3188 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lophura nycthemera"
 ]
}

 The dataset includes 3188 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080758-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7srwdj
title: Occurrence Download

description: A dataset containing 21792 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bryophyte collection"
 ]
}

 The dataset includes 21792 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015239-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18357625
title: Supplementary data for Influences of Surface Waves on Sea Spray Heat Transfer and Evaporation

description: Data for the reproduction of figures found in the paper *Influences of Surface Waves on Sea Spray Heat Transfer and Evaporation* written by Difu Sun et al.
DOI: 10.5281/zenodo.18357624
title: Supplementary data for Influences of Surface Waves on Sea Spray Heat Transfer and Evaporation

description: Data for the reproduction of figures found in the paper *Influences of Surface Waves on Sea Spray Heat Transfer and Evaporation* written by Difu Sun et al.
DOI: 10.15468/dl.9sewxp
title: Occurrence Download

description: A dataset containing 11399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Geranium pusillum, Geranium pusillum, Geranium pusillum, Geranium pusillum)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 11399 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015240-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18500706
title: O'QUVCHILARNI QO'SHIMCHA KURSLARGA KELIB-KETISHI VA IMTIHON NATIJALARINI SMS ORQALI OGOHLANTIRISH TIZIMINI ISHLAB CHIQISH
DOI: 10.5281/zenodo.18500707
title: O'QUVCHILARNI QO'SHIMCHA KURSLARGA KELIB-KETISHI VA IMTIHON NATIJALARINI SMS ORQALI OGOHLANTIRISH TIZIMINI ISHLAB CHIQISH
DOI: 10.15468/dl.qa84cp
title: Occurrence Download

description: A dataset containing 7621 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bryophyte collection of Porto Herbarium (PO)"
 ]
}

 The dataset includes 7621 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015242-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.569r3r
title: Occurrence Download

description: A dataset containing 899 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bryophytes from Praia da Vitória (Terceira, Azores, Portugal)"
 ]
}

 The dataset includes 899 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015243-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d6fh5p
title: Occurrence Download

description: A dataset containing 69151 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Birds Distribution from the Birds Directive 2008-2012 RN2000"
 ]
}

 The dataset includes 69151 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015232-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2mjhve
title: Occurrence Download

description: A dataset containing 10341 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Geranium pyrenaicum, Geranium pyrenaicum)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 10341 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015245-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18500716
title: LINGUOAXIOLOGY IN FILM DISCOURSE
DOI: 10.5281/zenodo.18500717
title: LINGUOAXIOLOGY IN FILM DISCOURSE
DOI: 10.21227/2srz-vh81
title: "Dataset for Managerial Override of Artificial Intelligence in Operations: An Accountability and Decision Escalation Model "

description: "Artificial intelligence is increasingly embedded in operational planning and execution, yet many organizations fail to scale value because managers frequently override AI recommendations at the point of decision. Prior work on trust in automation shows that reliance depends on vulnerability and uncertainty, not accuracy alone [1]. Behavioral research also indicates that observing algorithmic error can trigger algorithm aversion, even when algorithms outperform humans [2]. At the same time, evidence of algorithm appreciation suggests that managers may prefer algorithmic advice under certain task conditions [3]. This study develops and tests a decision escalation model that explains AI override as a governance and behavioral control outcome shaped by task criticality, outcome uncertainty, and accountability pressure. Building on escalation of commitment [4] and accountability theory [5], we hypothesize that override increases with criticality and uncertainty, and that accountability strengthens these effects even when perceived AI accuracy is high. We propose a two-study design combining operational system logs with a multi-respondent survey and estimate multilevel logistic models with robustness and endogeneity checks. The study contributes to engineering management by reframing AI governance as decision rights and escalation design rather than a technical tool issue."
DOI: 10.15468/dl.f5qy6x
title: Occurrence Download

description: A dataset listing the 2847262 species recorded in GBIF matching the query:
{
 "and" : [
 "TaxonomicStatus is Accepted"
 ]
}

 The dataset's 2847262 records were derived from 86979 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015229-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.huf7sg
title: Occurrence Download

description: A dataset containing 531 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Geranium rotundifolium, Geranium rotundifolium, Geranium rotundifolium)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 531 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015246-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18500724
title: ALLERGIC DISEASES AND THEIR PREVENTION.
DOI: 10.5281/zenodo.18500725
title: ALLERGIC DISEASES AND THEIR PREVENTION.
DOI: 10.15468/dl.tvmvm4
title: Occurrence Download

description: A dataset containing 9243 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Geranium sanguineum)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 9243 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015247-260129131611470/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.30575660
title: Makino et al., Supplementary Data for "Anomaly-Detection-Driven Screening of Synthesizability from Composition Descriptors Alone"

description: Supplementary data for the paper, Makino et al., "Anomaly-Detection-Driven Screening of Synthesizability from Composition Descriptors Alone"A zip file contains following data and codes.01_training_datasetThe training dataset employed to train the autoencoder in this study.(CSV formatted)<br>02_best_hyparams_log.csvThis file is a CSV table summarizing the results of hyperparameter search performed by 01_PyTorch_optuna.py and 02_PyTorch_bestmodel.py, listing the objective value of the best trial, the corresponding mini-batch size, learning rate, and number of epochs, as well as the coefficients of determination (<i>R</i><sup><em>2</em></sup>) for the training and test data under these conditions.<br>03_model_best.pthA representative trained autoencoder model learned under the hyperparameters obtained from optimization. (PyTorch .pth format)<br>04_requirements.txtThis file is a requirements file summarizing the Python environment (main libraries and their versions) used for training the autoencoder in this study (.txt format).<br>01_PyTorch_optuna.pyThis file is a Python script for reading a compositional descriptor dataset (01_training_dataset.csv) and performing hyperparameter search of the training conditions (batch size and learning rate) for an autoencoder using Optuna. The output is in CSV format. To use this Python script with a different dataset or model architecture, you need to modify the "Load data" and "Autoencoder model" sections within the script.<br>02_Pytorch_bestmodel.pyThis file is a Python script for reading the CSV file output by 01_PyTorch_optuna.py, loading the optimal mini-batch size and learning rate, and retraining an autoencoder model on a compositional descriptor dataset (01_training_dataset.csv). Using 5-fold cross-validation, it records the training and validation losses, selects the “best epoch” as the one with a small validation loss and a minimal gap between training and validation losses, and then retrains the model with this number of epochs. The final model is saved in the PyTorch .pth file format, and prediction plots for the training and test data are also generated. To use this Python script with a different dataset or model architecture, you need to modify the "Load data" and "Autoencoder model" sections within the script.<br>03_calculate_RMSE.pyThis file is a Python script for loading the .pth file output by 02_PyTorch_bestmodel.py and computing the sample-wise reconstruction error (RMSE) for an arbitrary compositional descriptor dataset (datasets.csv). The calculated results are output in CSV format. To use this Python script with a different dataset, you need to modify the "Load data" section within the script.
DOI: 10.6084/m9.figshare.30575660.v1
title: Makino et al., Supplementary Data for "Anomaly-Detection-Driven Screening of Synthesizability from Composition Descriptors Alone"

description: Supplementary data for the paper, Makino et al., "Anomaly-Detection-Driven Screening of Synthesizability from Composition Descriptors Alone"A zip file contains following data and codes.01_training_datasetThe training dataset employed to train the autoencoder in this study.(CSV formatted)<br>02_best_hyparams_log.csvThis file is a CSV table summarizing the results of hyperparameter search performed by 01_PyTorch_optuna.py and 02_PyTorch_bestmodel.py, listing the objective value of the best trial, the corresponding mini-batch size, learning rate, and number of epochs, as well as the coefficients of determination (<i>R</i><sup><em>2</em></sup>) for the training and test data under these conditions.<br>03_model_best.pthA representative trained autoencoder model learned under the hyperparameters obtained from optimization. (PyTorch .pth format)<br>04_requirements.txtThis file is a requirements file summarizing the Python environment (main libraries and their versions) used for training the autoencoder in this study (.txt format).<br>01_PyTorch_optuna.pyThis file is a Python script for reading a compositional descriptor dataset (01_training_dataset.csv) and performing hyperparameter search of the training conditions (batch size and learning rate) for an autoencoder using Optuna. The output is in CSV format. To use this Python script with a different dataset or model architecture, you need to modify the "Load data" and "Autoencoder model" sections within the script.<br>02_Pytorch_bestmodel.pyThis file is a Python script for reading the CSV file output by 01_PyTorch_optuna.py, loading the optimal mini-batch size and learning rate, and retraining an autoencoder model on a compositional descriptor dataset (01_training_dataset.csv). Using 5-fold cross-validation, it records the training and validation losses, selects the “best epoch” as the one with a small validation loss and a minimal gap between training and validation losses, and then retrains the model with this number of epochs. The final model is saved in the PyTorch .pth file format, and prediction plots for the training and test data are also generated. To use this Python script with a different dataset or model architecture, you need to modify the "Load data" and "Autoencoder model" sections within the script.<br>03_calculate_RMSE.pyThis file is a Python script for loading the .pth file output by 02_PyTorch_bestmodel.py and computing the sample-wise reconstruction error (RMSE) for an arbitrary compositional descriptor dataset (datasets.csv). The calculated results are output in CSV format. To use this Python script with a different dataset, you need to modify the "Load data" section within the script.
DOI: 10.5281/zenodo.18500451
title: Right-to-food-bibliometric-dataset
title: Direito à alimentação - base de dados bibliométrica

description: This repository describes a reproducible bibliometric dataset on Brazilian academic production concerning the right to food between 2014 and 2023. Records were retrieved from the OpenAlex API and processed through a three-layer pipeline (bronze, silver, and gold), producing normalized tabular data and analytical aggregations suitable for bibliometric and network analyses. The published dataset contains a curated thematic corpus of 991 documents, derived from an initial retrieval of 3713 records prior to thematic filtering. The dataset includes metadata on authorship, institutional affiliations, citations, and OpenAlex concept assignments. All data and code are publicly available under open licenses, enabling transparent reuse for bibliometric studies, science policy analysis, and research evaluation.
DOI: 10.5281/zenodo.18500452
title: Right-to-food-bibliometric-dataset
title: Direito à alimentação - base de dados bibliométrica

description: This repository describes a reproducible bibliometric dataset on Brazilian academic production concerning the right to food between 2014 and 2023. Records were retrieved from the OpenAlex API and processed through a three-layer pipeline (bronze, silver, and gold), producing normalized tabular data and analytical aggregations suitable for bibliometric and network analyses. The published dataset contains a curated thematic corpus of 991 documents, derived from an initial retrieval of 3713 records prior to thematic filtering. The dataset includes metadata on authorship, institutional affiliations, citations, and OpenAlex concept assignments. All data and code are publicly available under open licenses, enabling transparent reuse for bibliometric studies, science policy analysis, and research evaluation.
DOI: 10.15468/dl.rqmbaa
title: Occurrence Download

description: A dataset containing 1254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Platanthera leucophaea"
 ]
}

 The dataset includes 1254 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015248-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8pg8md
title: Occurrence Download

description: A dataset containing 676 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Camellia species, hybrids, and their cultivars from Terra Nostra Garden (Azores, Portugal)"
 ]
}

 The dataset includes 676 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015249-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8bxct5
title: Occurrence Download

description: A dataset containing 7270 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is COLETA - IMAR/DOP-UAz Reference Collection from 1977 to 2012."
 ]
}

 The dataset includes 7270 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015250-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.