cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jzgru3
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Annesorhiza articulata Magee"
 ]
}

 The dataset includes 12 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005253-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.afxb83
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "Country is Ecuador",
 "TaxonKey is Plica plica (Linnaeus, 1758)"
 ]
}

 The dataset includes 52 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0123325-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4aphlq
title: Occurrence Download

description: A dataset containing 352 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhipicephalus annulatus Say, 1821"
 ]
}

 The dataset includes 352 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012582-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bekwqj
title: Occurrence Download

description: A dataset containing 579073 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, United Kingdom of Great Britain and Northern Ireland, Svalbard and Jan Mayen, Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Portugal, Spain, France, Germany, Belgium, Netherlands, Switzerland, Austria, Italy, Greece, Cyprus, Türkiye, Russian Federation, Belarus, Ukraine, Poland, Czechia, Hungary, Slovakia, Romania, Bulgaria, North Macedonia, Albania, Montenegro, Serbia, Kosovo, Bosnia and Herzegovina, Slovenia, Croatia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cirsium palustre (L.) Scop."
 ]
}

 The dataset includes 579073 records from 1850 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041606-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5pt86m
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Belize",
 "DatasetKey is one of (Edinburgh (E) Herbarium Specimens, The New York Botanical Garden Herbarium (NY), Rapid Assessment Program (RAP) Biodiversity Survey Database, Tropicos MO Specimen Data, Royal Botanic Gardens, Kew - Herbarium Specimens, Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (Plantas vasculares), RSA - California Botanic Garden Herbarium)",
 "OccurrenceStatus is Present",
 "TaxonKey is Protium glabrum (Rose) Engl."
 ]
}

 The dataset includes 16 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002347-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2czexf
title: Occurrence Download

description: A dataset containing 64286 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera japonica Thunb.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 64286 records from 732 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002348-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/xxyzhpnc5w.1
title: Use of the Connor-Davidson Resilience Scale in a Complex Medical Dermatology Clinic: Cross-sectional study evaluating associations between resilience, Health-related Quality of Life and Mental Health

description: Supplemental material for article "Use of the Connor-Davidson Resilience Scale in a Complex Medical Dermatology Clinic: Cross-sectional study evaluating associations between resilience, Health-related Quality of Life and Mental Health" 

Contents: 
-Additional methods 
-Supplementary Table 1, description of patient characteristics in the cohort 
-Supplementary Table 2, description of CD-Risc 25 items
DOI: 10.17632/tzymttgfj4.1
title: Economic and social valuation of the hydrological services in the Pixquiac basin

description: Original and referenced research data for the economic and social valuation of the hydrological services in the Pixquiac basin.
DOI: 10.17632/tzymttgfj4.2
title: Integrated water management in watersheds: a social and economic valuation of the hydrological services in the Pixquiac territory, Veracruz State, Mexico

description: Original research data for the economic and social valuation of the hydrological services in the Pixquiac watershed.
DOI: 10.17632/2vt82dmpn7.1
title: HORVATH-DERZSI-HD-DATA-3451319

description: This dataset describes the histopathological database of 32 biopsy specimens of patientes with HD.
DOI: 10.6084/m9.figshare.28447681
title: Spatial Heterogeneous Additive Partial Linear Model: A Joint Approach of Bivariate Spline and Forest Lasso

description: Identifying spatial heterogeneous patterns has attracted a surge of research interest in recent years, due to its important applications in various scientific and engineering fields. In practice the spatially heterogeneous components are often mixed with components which are spatially smooth, making the task of identifying the heterogeneous regions more challenging. In this paper, we develop an efficient clustering approach to identify the model heterogeneity of the spatial additive partial linear model. Specifically, we aim to detect the spatially contiguous clusters based on the regression coefficients while introducing a spatially varying intercept to deal with the smooth spatial effect. On the one hand, to approximate the spatial varying intercept, we use the method of bivariate spline over triangulation, which can effectively handle the data from a complex domain. On the other hand, a novel fusion penalty termed the forest lasso is proposed to reveal the spatial clustering pattern. Our proposed fusion penalty has advantages in both the estimation and computation efficiencies when dealing with large spatial data. Theoretically properties of our estimator are established, and simulation results show that our approach can achieve more accurate estimation with a limited computation cost compared with the existing approaches. To illustrate its practical use, we apply our approach to analyze the spatial pattern of the relationship between land surface temperature measured by satellites and air temperature measured by ground stations in the United States. Supplemental materials for the article are available online.
DOI: 10.6084/m9.figshare.28447681.v1
title: Spatial Heterogeneous Additive Partial Linear Model: A Joint Approach of Bivariate Spline and Forest Lasso

description: Identifying spatial heterogeneous patterns has attracted a surge of research interest in recent years, due to its important applications in various scientific and engineering fields. In practice the spatially heterogeneous components are often mixed with components which are spatially smooth, making the task of identifying the heterogeneous regions more challenging. In this paper, we develop an efficient clustering approach to identify the model heterogeneity of the spatial additive partial linear model. Specifically, we aim to detect the spatially contiguous clusters based on the regression coefficients while introducing a spatially varying intercept to deal with the smooth spatial effect. On the one hand, to approximate the spatial varying intercept, we use the method of bivariate spline over triangulation, which can effectively handle the data from a complex domain. On the other hand, a novel fusion penalty termed the forest lasso is proposed to reveal the spatial clustering pattern. Our proposed fusion penalty has advantages in both the estimation and computation efficiencies when dealing with large spatial data. Theoretically properties of our estimator are established, and simulation results show that our approach can achieve more accurate estimation with a limited computation cost compared with the existing approaches. To illustrate its practical use, we apply our approach to analyze the spatial pattern of the relationship between land surface temperature measured by satellites and air temperature measured by ground stations in the United States. Supplemental materials for the article are available online.
DOI: 10.15468/dl.uajw2d
title: Occurrence Download

description: A dataset containing 6122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Macrothelypteris torresiana (Gaudich.) Ching",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 6122 records from 267 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002349-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/54rsxfytsn.1
title: A FRAMEWORK FOR DAMAGE DETECTION IN TRUSS STRUCTURES USING THE FINITE ELEMENT METHOD AND ARTIFICIAL INTELLIGENCE

description: Trabalho apresentado ao III Simpósio Gaúcho de Engenharia Aeroespacial e Mecânica, Santa Maria, RS, Brasil.
DOI: 10.15468/dl.t6t6zj
title: Occurrence Download

description: A dataset containing 4925 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Lepus townsendii Bachman, 1839, Lepus townsendii townsendii, Lepus townsendii campanius Hollister, 1915)"
 ]
}

 The dataset includes 4925 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002350-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7qfup5
title: Occurrence Download

description: A dataset containing 2129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magnolia champaca (L.) Baill. ex Pierre",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 2129 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002351-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26183/mkk5-0j38
title: IMMP2L Mouse Model Phenotyping

description: Comprehensive behavioural characterisation of a germline knockout model for IMMP2L, which has relevance for research into autism spectrum disorder. To enquire about accessing this dataset, please contact Tim Karl T.Karl@westernsydney.edu.au ORCID 0000-0001-9904-8482
DOI: 10.15468/dl.nm4p4e
title: Occurrence Download

description: A dataset containing 2992 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-116.08795 32.52631,-116.09555 32.53362,-116.10246 32.53672,-116.10134 32.57634,-116.10244 32.58783,-116.10809 32.60658,-116.1111 32.60891,-116.11507 32.6088,-116.11824 32.60499,-116.12057 32.54991,-116.11995 32.52834,-116.11336 32.52435,-116.11036 32.52058,-116.1055 32.52012,-116.09221 32.50704,-116.08854 32.50638,-116.08373 32.50819,-116.07631 32.50205,-116.0716 32.50162,-116.06508 32.49491,-116.05705 32.49491,-116.02886 32.47222,-116.0298 32.47133,-116.02799 32.46981,-116.02998 32.45735,-116.04346 32.44706,-116.05158 32.45505,-116.05412 32.45511,-116.05917 32.4597,-116.06147 32.46455,-116.06499 32.46286,-116.06296 32.44997,-116.04314 32.43162,-116.02612 32.3472,-116.00563 32.3481,-115.98554 32.35102,-115.99939 32.39336,-115.97832 32.4059,-115.93639 32.40866,-115.92207 32.35587,-115.90914 32.35893,-115.91037 32.38304,-115.91722 32.41071,-115.92626 32.4234,-115.95578 32.42424,-115.96199 32.43084,-115.94387 32.46236,-115.94923 32.47865,-115.98544 32.47828,-115.99012 32.49539,-116.00165 32.48846,-116.00043 32.43738,-116.00843 32.44066,-116.01196 32.45952,-116.00986 32.46619,-116.01013 32.47549,-116.01305 32.48134,-116.04823 32.50856,-116.05678 32.51042,-116.06203 32.51548,-116.07556 32.52381,-116.08605 32.5243,-116.08795 32.52631))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2992 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002353-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fzupbg
title: Occurrence Download

description: A dataset containing 62262 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mangifera indica L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 62262 records from 557 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002352-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0207945.10fu
title: SH0207945.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207946.10fu
title: SH0207946.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207947.10fu
title: SH0207947.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207948.10fu
title: SH0207948.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207949.10fu
title: SH0207949.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207950.10fu
title: SH0207950.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207951.10fu
title: SH0207951.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0207952.10fu
title: SH0207952.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.