cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dp4cgc
title: Occurrence Download

description: A dataset containing 1288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "TaxonKey is Valeriana edulis Torr. & A.Gray"
 ]
}

 The dataset includes 1288 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016184-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.iscvup
title: Occurrence Download

description: A dataset containing 2246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromeliaceae",
 "Country is Brazil",
 "HasCoordinate is true",
 "StateProvince is Rio de Janeiro"
 ]
}

 The dataset includes 2246 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020729-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nspumq
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Camellia fascicularis Hung T.Chang"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077462-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.52sw5f
title: Occurrence Download

description: A dataset containing 3581 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "TaxonKey is Pycnanthus angolensis (Welw.) Exell"
 ]
}

 The dataset includes 3581 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065290-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.064rfl
title: Occurrence Download

description: A dataset containing 32148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "Country is Mexico",
 "Geometry POLYGON((-104.216308 21.105000,-107.248535 24.906367,-108.544921 27.078691,-111.445312 31.503629,-110.961914 31.353636,-109.885253 31.316101,-108.039550 31.372399,-106.699218 30.372875,-105.732421 28.381735,-104.084472 26.588527,-104.018554 25.005972,-103.469238 23.624394,-103.227539 22.492257,-103.293457 21.227941,-103.469238 20.961439,-104.216308 21.105000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 32148 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020730-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8zn9ec
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calamagrostis decora Hook.f."
 ]
}

 The dataset includes 22 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077452-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hnq9k3
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (CHN.15_1, CHN.31_1, CHN.1_1, CHN.24_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Impatiens jiulongshanica Y.L.Xu & Y.L.Chen"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018029-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.338pce
title: Occurrence Download

description: A dataset containing 45 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 Iranolacerta brandtii (De Filippi, 1863)"
 ]
}

 The dataset includes 45 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077453-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3tr0gt
title: Occurrence Download

description: A dataset containing 756 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cuscuta gronovii Willd. ex Schult.",
 "Country is United States of America"
 ]
}

 The dataset includes 756 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020733-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5p2hxc
title: Occurrence Download

description: A dataset containing 35198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Mimosa pigra L."
 ]
}

 The dataset includes 35198 records from 356 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018025-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7f9kut
title: Occurrence Download

description: A dataset containing 20170 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is SEWBReC Arachnids (South East Wales)"
 ]
}

 The dataset includes 20170 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002994-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13144/lil-1420
title: Recensement de la population 2017 : fichier détail logements

description: Le fichier détail logements du recensement de la population de 2017 porte sur la localisation, les caractéristiques d'un logement ordinaire (catégorie, type de construction, confort, surface, nombre de pièces, etc…), et les caractéristiques sociodémographiques du ménage qui y réside. Les informations relatives au ménage sont fournies uniquement lorsque le logement est occupé au titre de la résidence principale. Les variables y sont disponibles avec des modalités détaillées, y compris les variables à diffusion restreinte (nationalité, pays de naissance, ancienneté d'arrivée en France). A partir de ce fichier, il est donc possible de faire des analyses exploratoires de données, de modéliser des comportements, ou simplement de tabuler sur une sous-population particulière définie selon certains critères : appartenance à une zone géographique et/ou unité statistique présentant certaines caractéristiques.
DOI: 10.13144/lil-1421
title: Recensement de la population 2017 : fichier détail individus localisés au niveau du canton-ou-ville

description: Le fichier détail "individus localisés au niveau du canton-ou-ville" du recensement de la population de 2017 décrit les individus selon leurs caractéristiques sociodémographiques, celles du ménage auquel ils appartiennent, ainsi que les principales caractéristiques de leur résidence principale. Les variables y sont disponibles avec des modalités détaillées, y compris les variables à diffusion restreinte (nationalité, pays de naissance, ancienneté d'arrivée en France). A partir de ce fichier, il est donc possible de faire des analyses exploratoires de données, de modéliser des comportements, ou simplement de tabuler sur une sous-population particulière définie selon certains critères : appartenance à une zone géographique et/ou unité statistique présentant certaines caractéristiques.
DOI: 10.13144/lil-1423
title: État civil - Fichier détail Décès - 2019

description: Le fichier annuel des décès est le résultat de l'exploitation des bulletins d'état civil envoyés par les mairies ayant enregistré des décès en France métropolitaine, dans les DOM (Guadeloupe, Martinique, Guyane, La Réunion), et depuis 2014 à Mayotte. A partir de 2015, les fichiers détail n'intègrent plus les données enregistrées à Saint-Martin, Saint-Barthélemy et Saint-Pierre-et-Miquelon, afin d'être cohérentes avec l'ensemble des données diffusées sur l'état civil. Le fichier de 2019 comprend 613 243 observations. L'enregistrement du décès est effectué dans la commune où a eu lieu le décès. Les renseignements sont demandés au déclarant et inscrits sur le bulletin de décès. Les causes de décès sont traitées par l'Inserm et ne figurent pas dans ce fichier. A partir de 2010, le fichier annuel des décès contient en plus des bulletins habituels de décès, les bulletins de transcription relatifs à un jugement déclaratif de décès (JDD). Cette source administrative permet de comptabiliser et d'étudier les décès selon divers critères sociodémographiques (sexe, âge, état matrimonial...) ainsi que la mortalité infantile.
DOI: 10.15468/dl.469cjh
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-95.45698 -6.27817,-56.54302 -6.27817,-56.54302 9.27817,-95.45698 9.27817,-95.45698 -6.27817))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cochranella resplendens (Lynch & Duellman, 1973)"
 ]
}

 The dataset includes 15 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024437-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r4ells
title: Occurrence Download

description: A dataset containing 1147 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bombyx mori Linnaeus, 1758"
 ]
}

 The dataset includes 1147 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047472-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j3n8kr
title: Occurrence Download

description: A dataset containing 988 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Viola ambigua Waldst. & Kit."
 ]
}

 The dataset includes 988 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0226354-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qsgxcw
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alangium denudatum (Bloemb.) W.J.de Wilde & Duyfjes"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024421-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ah5ukn
title: Occurrence Download

description: A dataset containing 5934 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Agroforestry species in Parkia biglobosa's parklands in Northern Benin"
 ]
}

 The dataset includes 5934 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047478-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.augbkv
title: Occurrence Download

description: A dataset containing 21572 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Estudios bióticos (Plantas, Fauna Edáfica, Anfibios y Aves) en los Complejos de páramos Guantiva - La Rusia"
 ]
}

 The dataset includes 21572 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0424279-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rspcrt
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bacteria from Penguin Guano, Antarctica"
 ]
}

 The dataset includes 70 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047480-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yt8m64
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calea aldamoides G.H.L.da Silva, Bringel & A.M.Teles"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0149944-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fm8ad5
title: Occurrence Download

description: A dataset containing 11746 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sempervivum tectorum L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 50",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 11746 records from 213 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004210-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5xspmm
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sebastes pinniger (Gill, 1864)",
 "HasGeospatialIssue is true",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014573-210819072339941/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.25360319
title: Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data

description: Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer from the inability of producing interpretable results or clinically-meaningful risk factors. To improve EHR-based modeling and use the natural hierarchical structure of disease classification, we propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features, in which dimension reduction is achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of “or”. We convert the combinatorial problem into a convex linearly-constrained regularized estimation, which enables scalable computation with theoretical guarantees. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases. By balancing the rarity and specificity of the EHR diagnosis records, our strategy improves both prediction and interpretation. We identify important higher-level categories and subcategories of mental health conditions and simultaneously determine the level of specificity needed for each of them in associating with suicide risk. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.h8obds
title: Occurrence Download

description: A dataset containing 4108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is CABI Bioscience Fungus Collection",
 "TaxonKey is Botryosphaeriaceae"
 ]
}

 The dataset includes 4108 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047482-180508205500799/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.25360319.v1
title: Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data

description: Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer from the inability of producing interpretable results or clinically-meaningful risk factors. To improve EHR-based modeling and utilize the natural hierarchical structure of disease classification, we propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features, in which dimension reduction is achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of “or”. We convert the combinatorial problem into a convex linearly-constrained regularized estimation, which enables scalable computation with theoretical guarantees. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases. By balancing the rarity and specificity of the EHR diagnosis records, our strategy improves both prediction and interpretation. We identify important higher-level categories and subcategories of mental health conditions and simultaneously determine the level of specificity needed for each of them in associating with suicide risk.
DOI: 10.6084/m9.figshare.25360319.v2
title: Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data

description: Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer from the inability of producing interpretable results or clinically-meaningful risk factors. To improve EHR-based modeling and use the natural hierarchical structure of disease classification, we propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features, in which dimension reduction is achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of “or”. We convert the combinatorial problem into a convex linearly-constrained regularized estimation, which enables scalable computation with theoretical guarantees. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases. By balancing the rarity and specificity of the EHR diagnosis records, our strategy improves both prediction and interpretation. We identify important higher-level categories and subcategories of mental health conditions and simultaneously determine the level of specificity needed for each of them in associating with suicide risk. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.h6ujad
title: Occurrence Download

description: A dataset containing 367 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((25.82227 34.98926,44.45508 34.98926,44.45508 42.86426,25.82227 42.86426,25.82227 34.98926))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alburnus Rafinesque, 1820"
 ]
}

 The dataset includes 367 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015844-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kp3996
title: Occurrence Download

description: A dataset containing 900 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acanthurus sohal (Forsskål, 1775)"
 ]
}

 The dataset includes 900 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004243-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.db4von
title: Occurrence Download

description: A dataset containing 253310 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Flora-On: occurrence data of the flora of mainland Portugal"
 ]
}

 The dataset includes 253310 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047482-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ufqurd
title: Occurrence Download

description: A dataset containing 364 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, United Kingdom of Great Britain and Northern Ireland, Portugal, Spain, France, Germany, Belgium, Netherlands, Italy, Greece, Switzerland, Austria, Türkiye, Russian Federation, Belarus, Ukraine, Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Romania, Bulgaria, North Macedonia, Albania, Slovenia, Croatia, Bosnia and Herzegovina, Hungary, Slovakia, Czechia, Poland)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Acer obtusatum Waldst. & Kit. ex Willd."
 ]
}

 The dataset includes 364 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0149945-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.