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

description: A dataset containing 26469 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia pycnantha Benth."
 ]
}

 The dataset includes 26469 records from 137 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012292-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u1vwkb
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Magnolia hernandezii (Lozano) Govaerts"
 ]
}

 The dataset includes 122 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004738-190307172214381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xq2maz
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sympagurus aurantium Lemaitre, 2004"
 ]
}

 The dataset includes 48 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0133199-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t5xhds
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prunus ×blireiana Andr"
 ]
}

 The dataset includes 46 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034734-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hgg3pk
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Senecio renjifoanus Phil."
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020501-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z3nbgt
title: Occurrence Download

description: A dataset listing the 0 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((87.74468 -44.2888,166.80885 -53.22831,233.08594 -52.48278,246.44531 -30.75128,221.83594 32.54681,175.42969 33.72434,158.55469 34.30714,144.49219 18.64625,140.29689 13.26536,128.4871 5.23095,123.14775 5.71214,114.63761 -7.24985,114.32999 -8.26222,114.59366 -8.77288,87.74468 -44.2888))",
 "HasCoordinate is true",
 {
 "or" : [
 "TaxonKey is Mammalia",
 "TaxonKey is Tracheophyta",
 "TaxonKey is Amphibia",
 "TaxonKey is Aves",
 "TaxonKey is Actinopterygii",
 "TaxonKey is Reptilia"
 ]
 },
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 0 records were derived from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031084-191105090559680/datasets/export for details.
DOI: 10.15468/dl.xa2yec
title: Occurrence Download

description: A dataset containing 169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Polylepis racemosa Ruiz & Pav."
 ]
}

 The dataset includes 169 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034745-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4hv9z8
title: Occurrence Download

description: A dataset containing 10594369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 10594369 records from 1998 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045492-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13144/lil-1544
title: Enquête sur les technologies de l'information et de la communication - 2021

description: L'enquête TIC 2021 s'inscrit dans le dispositif d'enquêtes ménages annuelles sur les TIC, inauguré par l'enquête TIC 2007 et visant à répondre au règlement européen 2019/1700. Il s'agit d'un règlement cadre qui prévoit notamment la réalisation d'une enquête annuelle destinée à collecter des données sur l'équipement et les comportements des ménages et des individus en matière de technologies de l'information et de la communication (TIC).L'enquête de 2021 comporte 6 thèmes : l'accès aux TIC et utilisation par les particuliers et/ou ménages, l'utilisation d'Internet et d'autres réseaux électroniques à différentes fins par les particuliers, l’utilisation des sites administratifs, le commerce en ligne, les compétences en informatique et la confidentialité et la protection de la vie privée sur Internet. L'enquête nationale est réalisée en métropole et dans les DOM (hors Mayotte), elle compte un total de 15 477 répondants.A noter qu'il n'y a pas de millésime 2020 pour cette enquête à cause de la crise de Covid-19.
DOI: 10.13144/lil-1554
title: Enquête Ménages Déplacements (EMC2), Grenoble / Grande région grenobloise - 2020

description: L'Enquête Ménages Déplacements "Grenoble / Grande région grenobloise 2020", réalisée selon la méthodologie nationale élaborée par le CERTU (aujourd'hui CEREMA), est une EMC² réalisée en période de pandémie COVID (enquête mobilité certifiée Cerema) portant sur la grande région grenobloise. Ce territoire, composé de 359 communes de l'Isère, du territoire de Beaurepaire à la Matheysine, et du Trièves au Pays Voironnais (pour une population de 826 300 habitants), a fait l'objet d'une enquête mixte (face à face ou téléphone).Cette enquête permet de connaître la mobilité, restreinte aux jours ouvrables, des individus (de 5 ans et plus) qui appartiennent à un ménage résidant dans les 359 communes de la zone d'étude. Les 7 339 ménages enquêtés sont caractérisés par leur lieu de résidence et les voitures, 2-3 roues motorisés et vélos à leur disposition. Les 12 453 individus interrogés l'ont principalement été sur leurs caractéristiques socio-démographiques et leur utilisation des modes de transport (individuels et collectifs). Des 44 600 déplacements, on connaît leurs heures et lieux de départ et d'arrivée, leur distance, leur durée, leur motif et le nombre de trajets (en mode mécanisé). Les 33 278 trajets sont décrits par leur mode de transport, les lieux de départ et d'arrivée, le temps de marche à pied au départ et à l'arrivée...A noter que cette enquête a été réalisée en période de pandémie COVID, et que le terrain s'est donc déroulé en 2 phases, avant et après le confinement.
DOI: 10.13144/lil-1558
title: Enquête Ménages Déplacements (EMC2), Pointe-à-Pitre / Guadeloupe - 2021

description: L'Enquête Ménages Déplacements "Pointe-à-Pitre / Guadeloupe - 2021", réalisée selon la méthodologie nationale élaborée par le CERTU (aujourd'hui CEREMA), est une EMC² (enquête mobilité certifiée Cerema). Le territoire étudié, composé des 32 communes du département de la Guadeloupe (pour une population de 390 000 habitants), a fait l'objet d'une enquête mixte (face à face ou téléphone).Cette enquête permet de connaître la mobilité, restreinte aux jours ouvrables, des individus (de 5 ans et plus) qui appartiennent à un ménage résidant dans les 32 communes de la zone d'étude. Les 2 614 ménages enquêtés sont caractérisés par leur lieu de résidence et les voitures, 2-3 roues motorisés et vélos à leur disposition. Les 5 271 individus interrogés l'ont principalement été sur leurs caractéristiques socio-démographiques et leur utilisation des modes de transport (individuels et collectifs). Des 14 040 déplacements, on connaît leurs heures et lieux de départ et d'arrivée, leur distance, leur durée, leur motif et le nombre de trajets (en mode mécanisé). Les 12 010 trajets sont décrits par leur mode de transport, les lieux de départ et d'arrivée, le temps de marche à pied au départ et à l'arrivée...
DOI: 10.13144/lil-1559
title: Enquête Ménages Déplacements (EMC2), Sables d'Olonne / Bassin de vie des Sables d'Olonne - 2021

description: L'Enquête Ménages Déplacements "Sables d'Olonne / Bassin de vie des Sables d'Olonne - 2021", réalisée selon la méthodologie nationale élaborée par le CERTU (aujourd'hui CEREMA), est une EMC² (enquête mobilité certifiée Cerema) centrée sur l'agglomération des Sables d'Olonne. Ce territoire, composé de 5 communes du département de la Vendée (pour une population de 52 200 habitants), a fait l'objet d'une enquête téléphonique.Cette enquête permet de connaître la mobilité, restreinte aux jours ouvrables, des individus (de 5 ans et plus) qui appartiennent à un ménage résidant dans les 5 communes de la zone d'étude. Les 1 380 ménages enquêtés sont caractérisés par leur lieu de résidence et les voitures, 2-3 roues motorisés et vélos à leur disposition. Les 1 560 individus interrogés l'ont principalement été sur leurs caractéristiques socio-démographiques et leur utilisation des modes de transport (individuels et collectifs). Des 5 342 déplacements, on connaît leurs heures et lieux de départ et d'arrivée, leur distance, leur durée, leur motif et le nombre de trajets (en mode mécanisé). Les 3 950 trajets sont décrits par leur mode de transport, les lieux de départ et d'arrivée, le temps de marche à pied au départ et à l'arrivée...
DOI: 10.15468/dl.y9qy4x
title: Occurrence Download

description: A dataset containing 774 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argentina sphyraena Linnaeus, 1758",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen, Material sample)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 774 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004269-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5rqrum
title: Occurrence Download

description: A dataset containing 180373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorostilbon lucidus",
 "HasCoordinate is true",
 "Country is one of (Argentina, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Ecuador, French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela (Bolivarian Republic of))"
 ]
}

 The dataset includes 180373 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011984-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9wju8k
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "TaxonKey is Senna guatemalensis"
 ]
}

 The dataset includes 20 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012058-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dvdnhu
title: Occurrence Download

description: A dataset containing 169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gentiana lutea",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 169 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012057-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zh3yq2
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gentiana lutea",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 36 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012059-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nbwgvs
title: Occurrence Download

description: A dataset containing 4549 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Wikstroemia indica",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 4549 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012060-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wm2rem
title: Occurrence Download

description: A dataset containing 230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Metallura baroni",
 "Year 2024-2025"
 ]
}

 The dataset includes 230 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012062-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8dyrf6
title: Occurrence Download

description: A dataset containing 107 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Stenocercus rhodomelas"
 ]
}

 The dataset includes 107 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012063-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6kpeje
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asperula laevigata",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012065-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.avbu28
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asperula laevigata",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012066-250920141307145/datasets/export for details.
DOI: 10.15468/dl.qxe3mt
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is An annoted list of the flora of the forest reserve of national importance «Berezovi kolky» (Kherson region, Ukraine)"
 ]
}

 The dataset includes 87 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012068-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rtg8hv
title: Occurrence Download

description: A dataset containing 44689 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Osmundastrum cinnamomeum"
 ]
}

 The dataset includes 44689 records from 441 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012067-250920141307145/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.30198093
title: A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models

description: Gaussian and discrete non-Gaussian spatial datasets are common across fields like public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixed models (SGLMMs) are a flexible class of models for analyzing such data, but they struggle to scale to large datasets. Many scalable Bayesian methods, built upon basis representations or sparse covariance matrices, still rely on posterior sampling via Markov chain Monte Carlo (MCMC). Variational Bayes (VB) methods have been applied to SGLMMs, but only for small areal datasets. We propose two computationally efficient VB approaches for analyzing moderately sized and massive (millions of locations) Gaussian and discrete non-Gaussian spatial data in the continuous spatial domain. Our methods leverage semi-parametric approximations of latent spatial processes and parallel computing to ensure computational efficiency. The proposed methods deliver inferential and predictive performance comparable to gold-standard MCMC methods while achieving computational speedups of up to 3600 times. In most cases, our VB approaches outperform state-of-the-art alternatives such as INLA and Hamiltonian Monte Carlo. We validate our methods through a comparative numerical study and applications to real-world datasets. These VB approaches can enable practitioners to model millions of discrete non-Gaussian spatial observations on standard laptops, significantly expanding access to advanced spatial modeling tools.
DOI: 10.6084/m9.figshare.30198093.v1
title: A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models

description: Gaussian and discrete non-Gaussian spatial datasets are common across fields like public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixed models (SGLMMs) are a flexible class of models for analyzing such data, but they struggle to scale to large datasets. Many scalable Bayesian methods, built upon basis representations or sparse covariance matrices, still rely on posterior sampling via Markov chain Monte Carlo (MCMC). Variational Bayes (VB) methods have been applied to SGLMMs, but only for small areal datasets. We propose two computationally efficient VB approaches for analyzing moderately sized and massive (millions of locations) Gaussian and discrete non-Gaussian spatial data in the continuous spatial domain. Our methods leverage semi-parametric approximations of latent spatial processes and parallel computing to ensure computational efficiency. The proposed methods deliver inferential and predictive performance comparable to gold-standard MCMC methods while achieving computational speedups of up to 3600 times. In most cases, our VB approaches outperform state-of-the-art alternatives such as INLA and Hamiltonian Monte Carlo. We validate our methods through a comparative numerical study and applications to real-world datasets. These VB approaches can enable practitioners to model millions of discrete non-Gaussian spatial observations on standard laptops, significantly expanding access to advanced spatial modeling tools.
DOI: 10.15468/dl.9spgft
title: Occurrence Download

description: A dataset containing 6981 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zanthoxylum americanum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6981 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012069-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17192998
title: Activities and impacts in Sardinian wetlands

description: Wetlands play a crucial role in climate regulation, water cycling, biodiversity conservation, and human well-being. Yet, they have faced centuries of degradation. This study aimed to analyze the relationship between human activities and their impacts across various wetland typologies, identify the most vulnerable systems, and investigate the main drivers of degradation. The focus was on Sardinian wetlands, located on the second-largest island in the Mediterranean. Our analysis revealed significative spatial co-occurrences between human activities and ecological impacts. The most frequent impacts included vegetation and habitat degradation, pollution, and hydrological alterations. Activities such as mining, urbanization, transportation, and tourism were particularly associated with these negative effects. While management practices yielded mixed outcomes—showing both positive and negative aspects—the extent of degradation was more closely linked to wetland size and ownership structure than to typology alone. In particular, larger wetlands and those with mixed ownership were more affected, suggesting that conventional inland vs. coastal classifications may no longer be sufficient to capture vulnerability patterns. Freshwater wetlands—often associated with small-scale agricultural use—showed lower levels of impact, highlighting the potential compatibility of traditional practices with conservation. Overall, our findings underscore the strong, often detrimental, connections between specific human activities and wetland degradation, while also pointing to the promising role of well-targeted management and conservation actions in supporting ecological resilience.

 

This is a supplemental material of a research paper, please use the following citation when using this dataset, or possible future updated versions:

Cuena-Lombraña A, Bacchetta G, Marignani M, Fois M (2025) Activities and impacts in wetlands: challenges for Sardinia. Regional Environmental Change, https://doi.org/10.1007/s10113-025-02462-1
description: WETLAND CODES, WETLAND NAMES: wetland codes and names are referred to those reported in the MedIsWet database: https://italiaiswet.it/general/search.php?lang=en

 

IMPACTS, ACTIVITIES: they were recorded using the numeric and alphabetic codes of Appendix P and Q respectively of the Natura Impact Statement (https://medwet.org/codde/APPENDICES.pdf).