cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21233/9bgz-eh27
title: Jinbei testate amoebae surface sample dataset

description: Raw data for the Jinbei testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.p9eqry
title: Occurrence Download

description: A dataset containing 773 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Uperodon taprobanicus (Parker, 1934)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 773 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026177-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/99g1-tb06
title: Bean Pond water chemistry dataset

description: Raw data for the Bean Pond water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/gx37-pz84
title: Highland Lake diatom dataset

description: Raw data for the Highland Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2s96-3244
title: LDDsite3622 ostracode surface sample dataset

description: Raw data for the LDDsite3622 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/47qx-tm95
title: Caunahue A insect dataset

description: Raw data for the Caunahue A insect dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.9c26b3
title: Occurrence Download

description: A dataset containing 32520 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Triplaris cumingiana Fisch. & C.A.Mey. ex C.A.Mey., Bactris gasipaes Kunth, Ochroma pyramidale (Cav.) Urb., Oreocallis grandiflora (Lam.) R.Br., Cinchona officinalis L.)",
 "Year 1980-2025"
 ]
}

 The dataset includes 32520 records from 270 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026181-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/3bq2-3c27
title: ANIK pollen surface sample dataset

description: Raw data for the ANIK pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/jf53-zj85
title: Moosomin Lake water chemistry dataset

description: Raw data for the Moosomin Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/68ad-kx05
title: LDDsite2931 water chemistry dataset

description: Raw data for the LDDsite2931 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.69fj95
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xenophrys major (Boulenger, 1908)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 36 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026182-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/c1c5-5q23
title: LDDsite627 ostracode surface sample dataset

description: Raw data for the LDDsite627 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/3res-ah35
title: Nevada State Prison, Carson City vertebrate fauna dataset

description: Raw data for the Nevada State Prison, Carson City vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.57703/ce-wuuq5
title: C31_SurfaceTopographyChallenge_IWM

description: Surface Topography Challenge 2022, Fraunhofer Institute for Mechanics of Materials IWM, Woehlerstr. 11 | 79108 Freiburg, Germany, Dr. Tobias Amann, tobias.amann@iwm.fraunhofer.de, +49 761 5142-208
DOI: 10.21233/an4h-rv95
title: Lebel-G testate amoebae surface sample dataset

description: Raw data for the Lebel-G testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15366676
title: Dataset of Au atomic structures for training Machine Learning Interatomic Potentials

description: This dataset contains atomic structures of gold (Au) generated using density functional theory (DFT) calculations performed with the VASP package [1, 2]. The calculations were carried out using the projector-augmented wave (PAW) [3, 4] method and Perdew–Burke–Ernzerhof (PBE) pseudopotentials for gold, within the generalised gradient approximation (GGA) [5] for the exchange-correlation functional.

Molecular dynamics simulations were performed for at least 500 steps per structure. For bulk systems, the temperature range spans from 100 K to 1500 K, while for nanoparticles and slab structures, it extends from 100 K to 1000 K. For training our machine learning model (GAP [6]), the first 200 steps of each molecular dynamics trajectory were discarded to allow the thermostat to equilibrate the system to the target temperature. Using this dataset, we trained a GAP model for Au nanoparticles, whose applicability extends beyond the nanoparticle sizes included in the training set.

The dataset includes these starting configurations:





Small, low-energy Au nanoparticles (3 to 55 atoms)




Bulk Au in fcc, bcc, hcp, and simple cubic (sc) crystal structures




Slab models of fcc surfaces



The initial low-energy nanoparticle structures were adopted from a dataset reported in the literature [7].For each structure, we provide atomic coordinates along with corresponding total energies and per-atom forces. This dataset is suitable for training and validating machine learning interatomic potentials for gold.

References

[1] Kresse and Furthmüller. "Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set." Physical Review B 54.16 (1996): 11169.[2] https://www.vasp.at/[3] Blöchl. "Projector augmented-wave method." Physical Review B 50.24 (1994): 17953.[4] Kresse and Joubert. "From ultrasoft pseudopotentials to the projector augmented-wave method." Physical Review B 59.3 (1999): 1758.[5] Perdew, John P., Kieron Burke, and Matthias Ernzerhof. "Generalized gradient approximation made simple." Physical Review Letters 77.18 (1996): 3865[6] Bartók, Payne, et al. "Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons." Physical Review Letters 104.13 (2010): 136403.[7] Manna, Sukriti, et al. "A database of low-energy atomically precise nanoclusters." Scientific Data 10.1 (2023): 308.
DOI: 10.5281/zenodo.15366677
title: Dataset of Au atomic structures for training Machine Learning Interatomic Potentials

description: This dataset contains atomic structures of gold (Au) generated using density functional theory (DFT) calculations performed with the VASP package [1, 2]. The calculations were carried out using the projector-augmented wave (PAW) [3, 4] method and Perdew–Burke–Ernzerhof (PBE) pseudopotentials for gold, within the generalised gradient approximation (GGA) [5] for the exchange-correlation functional.

Molecular dynamics simulations were performed for at least 500 steps per structure. For bulk systems, the temperature range spans from 100 K to 1500 K, while for nanoparticles and slab structures, it extends from 100 K to 1000 K. For training our machine learning model (GAP [6]), the first 200 steps of each molecular dynamics trajectory were discarded to allow the thermostat to equilibrate the system to the target temperature. Using this dataset, we trained a GAP model for Au nanoparticles, whose applicability extends beyond the nanoparticle sizes included in the training set.

The dataset includes these starting configurations:





Small, low-energy Au nanoparticles (3 to 55 atoms)




Bulk Au in fcc, bcc, hcp, and simple cubic (sc) crystal structures




Slab models of fcc surfaces



The initial low-energy nanoparticle structures were adopted from a dataset reported in the literature [7].For each structure, we provide atomic coordinates along with corresponding total energies and per-atom forces. This dataset is suitable for training and validating machine learning interatomic potentials for gold.

References

[1] Kresse and Furthmüller. "Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set." Physical Review B 54.16 (1996): 11169.[2] https://www.vasp.at/[3] Blöchl. "Projector augmented-wave method." Physical Review B 50.24 (1994): 17953.[4] Kresse and Joubert. "From ultrasoft pseudopotentials to the projector augmented-wave method." Physical Review B 59.3 (1999): 1758.[5] Perdew, John P., Kieron Burke, and Matthias Ernzerhof. "Generalized gradient approximation made simple." Physical Review Letters 77.18 (1996): 3865[6] Bartók, Payne, et al. "Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons." Physical Review Letters 104.13 (2010): 136403.[7] Manna, Sukriti, et al. "A database of low-energy atomically precise nanoclusters." Scientific Data 10.1 (2023): 308.
DOI: 10.15468/dl.z6cz8u
title: Occurrence Download

description: A dataset containing 33823 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Milvus milvus (Linnaeus, 1758)",
 "Year 2017-2019"
 ]
}

 The dataset includes 33823 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026184-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/zd9y-bg04
title: LDDsite3114 ostracode surface sample dataset

description: Raw data for the LDDsite3114 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/at89-ft56
title: South Loon Lake diatom bottom sample dataset

description: Raw data for the South Loon Lake diatom bottom sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.34740/kaggle/dsv/11781438
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.21233/p7xr-8506
title: Zhuanglin testate amoebae surface sample dataset

description: Raw data for the Zhuanglin testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15387421
title: Social Work research from 2016-2024 and its relationship with the SDGs and world regions

description: This dataset includes all Social Work-related research from 2016 to 2024, as retrieved from Scopus using the following query:

TITLE ("Social Work") AND KEY ("Social Work") AND ABS (social AND work) AND PUBYEAR > 2015 AND (LIMIT-TO (DOCTYPE, "ar")) AND (LIMIT-TO (PUBSTAGE, "final")) AND (LIMIT-TO (SRCTYPE, "j")) AND (LIMIT-TO (LANGUAGE, "English"))

Additionaly, the dataset includes each entry's relationship with the SDGs, as obtained from the Aurora-SDG classifier machine learning model.

The last column represents each entry's relationship with world regions, as obtained using the SpaCy Named Entity Recognition (NER) library with the en_core_web_lg model.
DOI: 10.5281/zenodo.15387422
title: Social Work research from 2016-2024 and its relationship with the SDGs and world regions

description: This dataset includes all Social Work-related research from 2016 to 2024, as retrieved from Scopus using the following query:

TITLE ("Social Work") AND KEY ("Social Work") AND ABS (social AND work) AND PUBYEAR > 2015 AND (LIMIT-TO (DOCTYPE, "ar")) AND (LIMIT-TO (PUBSTAGE, "final")) AND (LIMIT-TO (SRCTYPE, "j")) AND (LIMIT-TO (LANGUAGE, "English"))

Additionaly, the dataset includes each entry's relationship with the SDGs, as obtained from the Aurora-SDG classifier machine learning model.

The last column represents each entry's relationship with world regions, as obtained using the SpaCy Named Entity Recognition (NER) library with the en_core_web_lg model.
DOI: 10.15468/dl.kqcuhk
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Panthera onca (Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-90 14, -80 14, -80 22, -90 22, -90 14))",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2010"
 ]
}

 The dataset includes 99 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026185-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57703/ce-j9kd4
title: P13_SurfaceTopographyChallenge_IWM

description: Surface Topography Challenge 2022, Fraunhofer Institute for Mechanics of Materials IWM, Woehlerstr. 11 | 79108 Freiburg, Germany, Dr. Tobias Amann, tobias.amann@iwm.fraunhofer.de, +49 761 5142-208
DOI: 10.15468/dl.sggkv9
title: Occurrence Download

description: A dataset containing 1952 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lithocarpus glaber (Thunb.) Nakai)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1952 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026186-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/1m1e-k944
title: BosquedeMaricao1 pollen surface sample dataset

description: Raw data for the BosquedeMaricao1 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.14799040
title: Time-series datasets for investigating association between short-term exposure to ambient temperature and influenza incidence in Kawasaki City, Japan

description: The R script reproduces analysis for the association between short-term exposure to mean temperature and influenza incidence in Kawasaki City, Japan.
DOI: 10.5281/zenodo.14799041
title: Open access time-series datasets for investigating association between short-term exposure to ambient temperature and influenza incidence in Kawasaki City, Japan

description: The R script reproduces analysis for the association between short-term exposure to mean temperature and influenza incidence in Kawasaki City, Japan.
DOI: 10.5281/zenodo.15387463
title: Time-series datasets for investigating association between short-term exposure to ambient temperature and influenza incidence in Kawasaki City, Japan

description: The R script reproduces analysis for the association between short-term exposure to mean temperature and influenza incidence in Kawasaki City, Japan.
DOI: 10.15468/dl.h6tdve
title: Occurrence Download

description: A dataset containing 122126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "OccurrenceStatus is Present",
 "TaxonKey is Erinaceus europaeus Linnaeus, 1758"
 ]
}

 The dataset includes 122126 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026174-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/dkbg-4c35
title: Palughetto microcharcoal dataset

description: Raw data for the Palughetto microcharcoal dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.17028/rd.lboro.29038187
title: Supplementary information files for "A meta-heuristic approach to estimate and explain classifier uncertainty"

description: Supplementary files for article "A meta-heuristic approach to estimate and explain classifier uncertainty"Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model’s recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex mathematical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model’s decision-making process. This work proposes a set of class-independent meta-heuristics that can characterise the complexity of an instance in terms of factors that are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities and entropy-based methods of identifying instances at risk of being misclassified. Furthermore, the proposed approach resulted in uncertainty estimates that proves more independent of model accuracy and calibration than existing approaches. The proposed measures and framework demonstrate promise for improving model development for more complex instances and provides a new means of model abstention and explanation.© The Author(s), CC BY 4.0
DOI: 10.17028/rd.lboro.29038187.v1
title: Supplementary information files for "A meta-heuristic approach to estimate and explain classifier uncertainty"

description: Supplementary files for article "A meta-heuristic approach to estimate and explain classifier uncertainty"Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model’s recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex mathematical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model’s decision-making process. This work proposes a set of class-independent meta-heuristics that can characterise the complexity of an instance in terms of factors that are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities and entropy-based methods of identifying instances at risk of being misclassified. Furthermore, the proposed approach resulted in uncertainty estimates that proves more independent of model accuracy and calibration than existing approaches. The proposed measures and framework demonstrate promise for improving model development for more complex instances and provides a new means of model abstention and explanation.© The Author(s), CC BY 4.0
DOI: 10.21233/0q3a-1458
title: Lake Tibenatus pollen dataset

description: Raw data for the Lake Tibenatus pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/ef1j-dn07
title: Ruby Mountain Divide Seep ostracode surface sample dataset

description: Raw data for the Ruby Mountain Divide Seep ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.