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

description: A dataset containing 218167 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Viet Nam",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 218167 records from 343 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028950-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rufe7d
title: Occurrence Download

description: A dataset containing 1851 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Odontospiza caniceps (Reichenow, 1879)"
 ]
}

 The dataset includes 1851 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010152-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sj5y6p
title: Occurrence Download

description: A dataset containing 8175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-120.2124 32.50976,-114.14795 32.50976,-114.14795 35.31737,-120.2124 35.31737,-120.2124 32.50976))",
 "HasGeospatialIssue is false",
 "TaxonKey is Encelia farinosa A.Gray ex Torr."
 ]
}

 The dataset includes 8175 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0236771-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6lmzat
title: Occurrence Download

description: A dataset containing 20436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "DatasetKey is A global database for the distributions of crop wild relatives"
 ]
}

 The dataset includes 20436 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028951-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mhp8u
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Parnassius imperator Oberthür, 1883",
 "Year 2004-2024"
 ]
}

 The dataset includes 28 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047001-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s7km5r
title: Occurrence Download

description: A dataset containing 6046 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aedes atlanticus Dyar & Knab, 1906"
 ]
}

 The dataset includes 6046 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0149898-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h0kfyt
title: Occurrence Download

description: A dataset containing 742 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Elephas maximus Linnaeus, 1758"
 ]
}

 The dataset includes 742 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028948-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.twnt5v
title: Occurrence Download

description: A dataset containing 949 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heterotheca villosa (Pursh) Shinners",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Continent is North America"
 ]
}

 The dataset includes 949 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0162678-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.972tgt
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gaultheria novaguineensis J.J.Sm."
 ]
}

 The dataset includes 35 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010175-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpjenv
title: Occurrence Download

description: A dataset containing 2629806 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-56.04319 -31.40073,-70.20252 -51.32899,-67.7752 -56.11021,-38.2429 -59.4506,-18.82441 -48.73106,-56.04319 -31.40073))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (May, June, July, August, September, April, March, February, January)",
 "OccurrenceStatus is Present",
 "Year 1993-2024"
 ]
}

 The dataset includes 2629806 records from 453 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046978-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pfd35q
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Opistophthalmus pygmaeus Lamoral, 1979)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013137-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48677/dc081a8a-4eef-4770-b971-beaf3671183b
title: Adresse Relationale Tabellen Stichtag 01.04.2025

description: Das Adressregister gibt österreichweit authentisch alle von den Gemeinden offiziell vergeben Adressen wieder. Damit ist es die Referenz der Adressen Österreichs bezüglich Adressierbarkeit, Schreibweise, Ordnungsnummernvergabe und räumlicher Zuordnung. Dieser zentrale Adressdatenbestand ist Teil des Grenzkatasters (§9a VermG) und wird von den Gemeinden und Städten mit den im Vermessungsgesetz angeführten Merkmalen über eine zentrale Meldeschiene geführt und aktualisiert. Das BEV - Bundesamt für Eich- und Vermessungswesen gibt die Adressen in verschiedenen Ausprägungen ab. Jede Adresse hat zusätzlich zu vielen weiteren Informationen einen eindeutigen Schlüssel (Adresscode) und eine exakte räumliche koordinative Zuordnung (Geocodierung).
 
Der Adresscode ist ein nicht sprechender siebenstelliger eindeutiger Schlüssel aller Adressen. Aus der Zahlenfolge kann kein Rückschluss auf eine Systematik der Vergabe gezogen werden. Befinden sich Gebäude auf einer Adresse wird für jedes ein 3-stelliger Subcode vergeben. Adresscode und Subcode ergeben zusammen die Adressnummer.

Stichtag = Erstellungsdatum:
 Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
 Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.5281/zenodo.15183046
title: Effects of Dutasteride with Bacillus Calmette-Guérin Therapy on Recurrence, Progression, Metastasis, and Survival in Non-Muscle Invasive Bladder Cancer: A Retrospective Single-Center Study
DOI: 10.5281/zenodo.15183047
title: Effects of Dutasteride with Bacillus Calmette-Guérin Therapy on Recurrence, Progression, Metastasis, and Survival in Non-Muscle Invasive Bladder Cancer: A Retrospective Single-Center Study
DOI: 10.15468/dl.d4zp2w
title: Occurrence Download

description: A dataset containing 1471 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is South Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera pardus (Linnaeus, 1758)",
 "Year 2015-2025"
 ]
}

 The dataset includes 1471 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013140-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4grszc
title: Occurrence Download

description: A dataset containing 1662 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rosa chinensis Jacq."
 ]
}

 The dataset includes 1662 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006351-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dvcowo
title: Occurrence Download

description: A dataset containing 625 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Human Observation"
 ]
 },
 "Country is Mexico",
 "Locality is oaxaca (city)",
 "HasCoordinate is true",
 "TaxonKey is Animalia",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 625 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018604-190415153152247/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6anatz
title: Occurrence Download

description: A dataset listing the 6683 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 {
 "or" : [
 "Country is United States of America",
 "Country is Canada",
 "Country is Mexico",
 "Country is Cuba",
 "Country is Guatemala",
 "Country is Nicaragua",
 "Country is Costa Rica",
 "Country is Panama",
 "Country is Dominican Republic",
 "Country is Haiti"
 ]
 },
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 1960-2021"
 ]
}

 The dataset's 6683 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154159-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lqva8u
title: Occurrence Download

description: A dataset containing 54684 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Rhamnus cathartica L."
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 54684 records from 267 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018605-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xqwkap
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lapara coniferarum Smith, 1797",
 {
 "not" : {
 "DatasetKey" : [
 "is Global database of alien macrofungi"
 ]
 }
 },
 {
 "or" : [
 "Country is Poland",
 "Country is Poland"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0393395-210914110416597/datasets/export for details.
DOI: 10.15468/dl.t36zov
title: Occurrence Download

description: A dataset containing 81457 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "TaxonKey is Culicidae",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 81457 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018605-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.czviqg
title: Occurrence Download

description: A dataset containing 5534 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Rhea americana (Linnaeus, 1758)"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5534 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018606-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vjkzck
title: Occurrence Download

description: A dataset containing 6056197 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "Country is Norway",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 6056197 records from 287 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0178979-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.twneax
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Clessinia reticulata (Doering, 1877)"
 ]
}

 The dataset includes 13 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036800-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnxmxs
title: Occurrence Download

description: A dataset containing 126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is South America",
 "Country is Chile",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Crinodendron hookerianum Gay"
 ]
}

 The dataset includes 126 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036801-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14416832
title: Host-cleptoparasite biogeographical congruence through time: the case of cuckoo oil bees

description: Dataset for "Host-cleptoparasite biogeographical congruence through time: the case of cuckoo oil bees" in review at Journal of Biogeography
DOI: 10.5281/zenodo.14416833
title: Host-cleptoparasite biogeographical congruence through time: the case of cuckoo oil bees

description: Dataset for "Host-cleptoparasite biogeographical congruence through time: the case of cuckoo oil bees" in review at Journal of Biogeography
DOI: 10.5281/zenodo.14502043
title: Host-cleptoparasite biogeographical congruence through time: the case of cuckoo oil bees

description: Dataset for "Host-cleptoparasite biogeographical congruence through time: the case of cuckoo oil bees" in review at Journal of Biogeography
DOI: 10.7266/bzy62ek0
title: Videos of a laboratory study to simulate hydrocarbon leakages and their induced sound
DOI: 10.6084/m9.figshare.28034713
title: Integration of Neural Embeddings and Probabilistic Models in Topic Modeling

description: Topic modeling, a way to find topics in large volumes of text, has grown with the help of deep learning. This paper presents two novel approaches to topic modeling by integrating embeddings derived from Bert-Topic with the multi-grain clustering topic model (MGCTM). Recognizing the inherent hierarchical and multi-scale nature of topics in corpora, our methods utilize MGCTM to capture topic structures at multiple levels of granularity. We enhance the expressiveness of MGCTM by introducing the Generalized Dirichlet and Beta-Liouville distributions as priors, which provide greater flexibility in modeling topic proportions and capturing richer topic relationships. Comprehensive experiments on various datasets showcase the effectiveness of our proposed models in achieving superior topic coherence and granularity compared to state-of-the-art methods. Our findings underscore the potential of leveraging hybrid architectures, marrying neural embeddings with advanced probabilistic modeling, to push the boundaries of topic modeling.
DOI: 10.6084/m9.figshare.28034713.v1
title: Integration of Neural Embeddings and Probabilistic Models in Topic Modeling

description: Topic modeling, a way to find topics in large volumes of text, has grown with the help of deep learning. This paper presents two novel approaches to topic modeling by integrating embeddings derived from Bert-Topic with the multi-grain clustering topic model (MGCTM). Recognizing the inherent hierarchical and multi-scale nature of topics in corpora, our methods utilize MGCTM to capture topic structures at multiple levels of granularity. We enhance the expressiveness of MGCTM by introducing the Generalized Dirichlet and Beta-Liouville distributions as priors, which provide greater flexibility in modeling topic proportions and capturing richer topic relationships. Comprehensive experiments on various datasets showcase the effectiveness of our proposed models in achieving superior topic coherence and granularity compared to state-of-the-art methods. Our findings underscore the potential of leveraging hybrid architectures, marrying neural embeddings with advanced probabilistic modeling, to push the boundaries of topic modeling.
DOI: 10.6084/m9.figshare.25451248
title: Label Noise Robust Crowd Counting with Loss Filtering Factor

description: Crowd counting, a crucial computer vision task, aims at estimating the number of individuals in various environments. Each person in crowd counting datasets is typically annotated by a point at the center of the head. However, challenges like dense crowds, diverse scenarios, significant obscuration, and low resolution lead to inevitable label noise, adversely impacting model performance. Driven by the need to enhance model robustness in noisy environments and improve accuracy, we propose the Loss Filtering Factor (LFF) and the corresponding Label Noise Robust Crowd Counting (LNRCC) training scheme. LFF innovatively filters out losses caused by label noise during training, enabling models to focus on accurate data, thereby increasing reliability. Our extensive experiments demonstrate the effectiveness of LNRCC, which consistently improves performance across all models and datasets, with an average enhancement of 3.68% in Mean Absolute Error (MAE), 6.7% in Mean Squared Error (MSE) and 4.68% in Grid Average Mean Absolute Error (GAME). The universal applicability of this approach, coupled with its ease of integration into any neural network model architecture, marks a significant advancement in the field of computer vision, particularly in addressing the pivotal issue of accuracy in crowd counting under challenging conditions.
DOI: 10.6084/m9.figshare.25451248.v1
title: Label Noise Robust Crowd Counting with Loss Filtering Factor

description: Crowd counting, a crucial computer vision task, aims at estimating the number of individuals in various environments. Each person in crowd counting datasets is typically annotated by a point at the center of the head. However, challenges like dense crowds, diverse scenarios, significant obscuration, and low resolution lead to inevitable label noise, adversely impacting model performance. Driven by the need to enhance model robustness in noisy environments and improve accuracy, we propose the Loss Filtering Factor (LFF) and the corresponding Label Noise Robust Crowd Counting (LNRCC) training scheme. LFF innovatively filters out losses caused by label noise during training, enabling models to focus on accurate data, thereby increasing reliability. Our extensive experiments demonstrate the effectiveness of LNRCC, which consistently improves performance across all models and datasets, with an average enhancement of 3.68% in Mean Absolute Error (MAE), 6.7% in Mean Squared Error (MSE) and 4.68% in Grid Average Mean Absolute Error (GAME). The universal applicability of this approach, coupled with its ease of integration into any neural network model architecture, marks a significant advancement in the field of computer vision, particularly in addressing the pivotal issue of accuracy in crowd counting under challenging conditions.
DOI: 10.6084/m9.figshare.27613282
title: Maize Hybrids Performance Evaluation with Data Fusion by Matrix Factorization Algorithm

description: Crop breeders often face challenges due to limited data availability when making crucial decisions, such as selecting top-performing varieties/hybrids for further experiments, registration, and commercialization. Evaluating all varieties/hybrids across all fields is impractical due to high experimental and time costs, as well as the limited number of locations for planting. This article aims to evaluate the performance of various maize hybrids in untested locations using historical data. The problem is approached through a matrix framework, where hybrids and fields correspond to rows and columns, respectively, with entries representing the yield of a specific hybrid at a given location. As this matrix is typically sparse, the task is to fill in missing data. Agronomists are primarily interested in the performance of top hybrids at specific locations for smart seed selection. To address this, we introduce a novel application of the Data Fusion by Matrix Factorization (DFMF) algorithm for predicting crop yields using maize data from the 2019 Syngenta Crop Challenge. The DFMF results are compared with the Random Forest (RF) algorithm as a benchmark, focusing on model performance for smart seed selection. Our analysis highlights the advantages of the DFMF approach over the traditional RF method in this context.
DOI: 10.6084/m9.figshare.27613282.v1
title: Maize Hybrids Performance Evaluation with Data Fusion by Matrix Factorization Algorithm

description: Crop breeders often face challenges due to limited data availability when making crucial decisions, such as selecting top-performing varieties/hybrids for further experiments, registration, and commercialization. Evaluating all varieties/hybrids across all fields is impractical due to high experimental and time costs, as well as the limited number of locations for planting. This article aims to evaluate the performance of various maize hybrids in untested locations using historical data. The problem is approached through a matrix framework, where hybrids and fields correspond to rows and columns, respectively, with entries representing the yield of a specific hybrid at a given location. As this matrix is typically sparse, the task is to fill in missing data. Agronomists are primarily interested in the performance of top hybrids at specific locations for smart seed selection. To address this, we introduce a novel application of the Data Fusion by Matrix Factorization (DFMF) algorithm for predicting crop yields using maize data from the 2019 Syngenta Crop Challenge. The DFMF results are compared with the Random Forest (RF) algorithm as a benchmark, focusing on model performance for smart seed selection. Our analysis highlights the advantages of the DFMF approach over the traditional RF method in this context.