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

description: A dataset containing 4858 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, 0.0) eeacellcode,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.basisofrecord IN ('OBSERVATION', 'MACHINE_OBSERVATION', 'HUMAN_OBSERVATION', 'MATERIAL_SAMPLE', 'MATERIAL_CITATION', 'OCCURRENCE', 'PRESERVED_SPECIMEN')
 AND occurrence.countrycode = 'BE'
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 5218786 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 5218786 OR CAST(occurrence.kingdomkey AS INTEGER) = 5218786 OR CAST(occurrence.phylumkey AS INTEGER) = 5218786 OR CAST(occurrence.classkey AS INTEGER) = 5218786 OR CAST(occurrence.orderkey AS INTEGER) = 5218786 OR CAST(occurrence.familykey AS INTEGER) = 5218786 OR CAST(occurrence.genuskey AS INTEGER) = 5218786 OR CAST(occurrence.specieskey AS INTEGER) = 5218786)
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2025)
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence.hascoordinate = TRUE)
 AND (LOWER(occurrence.identificationverificationstatus) NOT IN ('unverified', 'not validated', 'under validation') OR occurrence.identificationverificationstatus IS NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year",
 GBIF_EEARGCODE(1000, occurrence.decimallatitude, occurrence.decimallongitude, 0.0)

 The dataset's 4858 records were derived from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082270-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.49qkzw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.809854 24.367529, 71.732767 24.361952, 71.65759799999999 24.34536, 71.586215 24.318165, 71.520391 24.281044, 71.461754 24.234918, 71.41175200000001 24.180932, 71.371612 24.120425, 71.342314 24.054894, 71.324564 23.985958, 71.318782 23.91532, 71.325091 23.844719, 71.343316 23.775893, 71.372992 23.710532, 71.413375 23.65024, 71.46346 23.596492, 71.522014 23.550605, 71.587596 23.513698, 71.658601 23.486674, 71.733294 23.470191, 71.809854 23.464652, 71.886414 23.470191, 71.961107 23.486674, 72.032112 23.513698, 72.097694 23.550605, 72.15624800000001 23.596492, 72.206333 23.65024, 72.24671600000001 23.710532, 72.276392 23.775893, 72.294617 23.844719, 72.300926 23.91532, 72.29514399999999 23.985958, 72.277394 24.054894, 72.248096 24.120425, 72.207956 24.180932, 72.157954 24.234918, 72.099317 24.281044, 72.03349300000001 24.318165, 71.96211 24.34536, 71.88694099999999 24.361952, 71.809854 24.367529))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082278-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.18189980
title: ParaLiv: Livonian Paradigms in Phonemic Notation

description: ParaLiv is a collection of Livonian verbal and nominal paradigms in phonemic and orthographic notation, based on the Livonian Institute's collection. They are suited for both computational and manual analysis. The dataset conforms to the Paralex standard.
DOI: 10.5281/zenodo.11391420
title: ParaLiv: Livonian Paradigms in Phonemic Notation

description: ParaLiv is a collection of Livonian verbal and nominal paradigms in phonemic and orthographic notation, based on the Livonian Institute's collection. They are suited for both computational and manual analysis. The dataset conforms to the Paralex standard.
DOI: 10.15468/dl.c9nfbp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.809854 24.367529, 71.732767 24.361952, 71.65759799999999 24.34536, 71.586215 24.318165, 71.520391 24.281044, 71.461754 24.234918, 71.41175200000001 24.180932, 71.371612 24.120425, 71.342314 24.054894, 71.324564 23.985958, 71.318782 23.91532, 71.325091 23.844719, 71.343316 23.775893, 71.372992 23.710532, 71.413375 23.65024, 71.46346 23.596492, 71.522014 23.550605, 71.587596 23.513698, 71.658601 23.486674, 71.733294 23.470191, 71.809854 23.464652, 71.886414 23.470191, 71.961107 23.486674, 72.032112 23.513698, 72.097694 23.550605, 72.15624800000001 23.596492, 72.206333 23.65024, 72.24671600000001 23.710532, 72.276392 23.775893, 72.294617 23.844719, 72.300926 23.91532, 72.29514399999999 23.985958, 72.277394 24.054894, 72.248096 24.120425, 72.207956 24.180932, 72.157954 24.234918, 72.099317 24.281044, 72.03349300000001 24.318165, 71.96211 24.34536, 71.88694099999999 24.361952, 71.809854 24.367529))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082279-251120083545085/datasets/export for details.
DOI: 10.5439/3012143
title: E3SMv3 ‘Nephele’ Perturbed Parameter Ensemble Value-Added Product for ENA, SGP, NSA, and Global Means

description: Processed output from the E3SMv3 Nephele perturbed parameter ensemble (PPE). This product contains global means of present-day (using 2010 aerosol forcings) and preindustrial (using 1850 aerosol forcings) monthly and daily global-mean quantities, as well as hourly mean quantities at the E3SMv3 model grid cell closest to the ENA, SGP, and NSA ARM sites. In this PPE, the E3SMv3 model was run for 27 months in an atmosphere-only configuration with free tropospheric winds nudged to reanalysis. Each ensemble member represents a unique combination of 25 model parameter values (in the microphysics, convective microphysics, and aerosol schemes) that were perturbed randomly and simultaneously. The E3SMv3 default configuration is included as member 0. See https://doi.org/10.22541/essoar.174907165.57104591/v1 (in review) for more information on the Nephele PPE and the perturbed parameters.
DOI: 10.6084/m9.figshare.30780338
title: A Benchmark Resource for Dicrotic Notch Detection in Photoplethysmography

description: This dataset contains benchmark dicrotic notch (DN) annotations and comparison metadata derived from the Pulse Transit Time PPG Dataset and pyPPG toolbox analysis described in the manuscript “A Benchmark Resource for Dicrotic Notch Detection in Photoplethysmography: Methods, Annotations, and Performance Analysis.”​<br><br>Please see "Files description.docx" for a complete description of all contained files for this database.<br><br>**Paper as yet unpublished
DOI: 10.6084/m9.figshare.30780338.v2
title: A Benchmark Resource for Dicrotic Notch Detection in Photoplethysmography

description: This dataset contains benchmark dicrotic notch (DN) annotations and comparison metadata derived from the Pulse Transit Time PPG Dataset and pyPPG toolbox analysis described in the manuscript “A Benchmark Resource for Dicrotic Notch Detection in Photoplethysmography: Methods, Annotations, and Performance Analysis.”​<br><br>Please see "Files description.docx" for a complete description of all contained files for this database.<br><br>**Paper as yet unpublished
DOI: 10.6084/m9.figshare.30780338.v1
title: A Benchmark Resource for Dicrotic Notch Detection in Photoplethysmography

description: This dataset contains benchmark dicrotic notch (DN) annotations and comparison metadata derived from the Pulse Transit Time PPG Dataset and pyPPG toolbox analysis described in the manuscript “A Benchmark Resource for Dicrotic Notch Detection in Photoplethysmography: Methods, Annotations, and Performance Analysis.”​<br><br>Please see "Files description.docx" for a complete description of all contained files for this database.<br><br>**Paper as yet unpublished
DOI: 10.13140/rg.2.2.28713.25440
title: Global Industrial Transformation Model (GITM) Supplementary Materials - Model Inputs Jan 8, 2025
DOI: 10.15468/dl.jb4cdv
title: Occurrence Download

description: A dataset containing 18072 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lepus americanus"
 ]
}

 The dataset includes 18072 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082280-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18189919
title: Repositório Digital Café & Horizonte

description: Repositório Digital: Implementação do projeto 'Café & Horizonte', utilizando o software DSpace, voltado para a preservação e organização da informação em ambientes digitais.Organização da Informação e Arquitetura da Informação: Elaboração de Taxonomias e modelos de classificação aplicados, estruturação de Metadados e definição de fluxos de Arquitetura da Informação para recuperação de dados.

Curadoria Digital e Gestão de Coleções: Execução de ciclos completos de curadoria, incluindo a análise de Direito Autoral na Internet e a implementação de estratégias de Preservação Digital.Disseminação e Impacto: Desenvolvimento de interface para a web e análise de disseminação das coleções digitais, com foco no impacto em redes sociais e potencial de aplicação em dispositivos mobile através de plataformas CMS (Tainacan/WordPress).
DOI: 10.5281/zenodo.18189920
title: Repositório Digital Café & Horizonte

description: Repositório Digital: Implementação do projeto 'Café & Horizonte', utilizando o software DSpace, voltado para a preservação e organização da informação em ambientes digitais.Organização da Informação e Arquitetura da Informação: Elaboração de Taxonomias e modelos de classificação aplicados, estruturação de Metadados e definição de fluxos de Arquitetura da Informação para recuperação de dados.

Curadoria Digital e Gestão de Coleções: Execução de ciclos completos de curadoria, incluindo a análise de Direito Autoral na Internet e a implementação de estratégias de Preservação Digital.Disseminação e Impacto: Desenvolvimento de interface para a web e análise de disseminação das coleções digitais, com foco no impacto em redes sociais e potencial de aplicação em dispositivos mobile através de plataformas CMS (Tainacan/WordPress).
DOI: 10.15468/dl.d3v5k7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.92858200000001 24.341933, 71.85151 24.336356, 71.77635600000001 24.319764, 71.704988 24.292569, 71.639177 24.255447, 71.580552 24.209322, 71.530559 24.155336, 71.490427 24.094828, 71.461134 24.029297, 71.443388 23.960361, 71.437607 23.889723, 71.44391400000001 23.819122, 71.462136 23.750296, 71.491806 23.684934, 71.53218 23.624642, 71.582256 23.570894, 71.64079700000001 23.525006, 71.706367 23.488099, 71.77735800000001 23.461075, 71.852037 23.444592, 71.92858200000001 23.439053, 72.005127 23.444592, 72.079806 23.461075, 72.150797 23.488099, 72.21636700000001 23.525006, 72.274908 23.570894, 72.324984 23.624642, 72.365358 23.684934, 72.395028 23.750296, 72.41325000000001 23.819122, 72.419557 23.889723, 72.413776 23.960361, 72.39603 24.029297, 72.366737 24.094828, 72.326605 24.155336, 72.276612 24.209322, 72.21798699999999 24.255447, 72.152176 24.292569, 72.080808 24.319764, 72.00565400000001 24.336356, 71.92858200000001 24.341933))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082281-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.18176731
title: Population synthesis results in "The fate of Gaia's wide binaries: Interplay of white-dwarf recoil and tidal capture"

description: This dataset contains the full results of the wide-binary population synthesis experiments described in Section 3.3 of O'Connor (2025, submitted), which examine the interplay of stellar mass loss and tidal capture in the Galactic population of wide binaries. The data are stored as compressed NumPy arrays (.npz format). A README file explains the file naming scheme, the column names for each file, and the meaning of certain data values.

This description will be updated with a citation of the final published version.
DOI: 10.5281/zenodo.18176732
title: Population synthesis results in "The fate of Gaia's wide binaries: Interplay of white-dwarf recoil and tidal capture"

description: This dataset contains the full results of the wide-binary population synthesis experiments described in Section 3.3 of O'Connor (2025, submitted), which examine the interplay of stellar mass loss and tidal capture in the Galactic population of wide binaries. The data are stored as compressed NumPy arrays (.npz format). A README file explains the file naming scheme, the column names for each file, and the meaning of certain data values.

This description will be updated with a citation of the final published version.
DOI: 10.5281/zenodo.18189932
title: Terrestrial habitat use of male northern and southern giant petrels breeding at Marion Island

description: Terrestrial habitat use of male northern and southern giant petrels breeding at Marion Island

W. Chris Oosthuizen, Shaun Hoekstra, Ryan R. Reisinger, Pierre A. Pistorius

AbstractCoexisting species reduce direct competition by partitioning resource use through mechanisms such as spatial and temporal segregation, as well as dietary specialization. Northern giant petrels (Macronectes halli) and southern giant petrels (M. giganteus) are closely related seabirds that coexist on some Southern Ocean islands. Breeding male giant petrels often scavenge seal and penguin carrion on land, but their predominantly terrestrial movement patterns and habitat use remain understudied. We used tracking data collected at Marion Island to investigate the terrestrial space use of 28 incubating male northern and southern giant petrels. Our results show that both species exhibit highly localized terrestrial movement patterns. Despite their shared predator-scavenger trophic niche, both species concentrated their terrestrial foraging at a single site, where large numbers of king penguin chicks likely provided abundant foraging resources for male giant petrels during the three study years. The prominence of terrestrial foraging among male giant petrels reduces competition with females and other procellariiforms, but our findings do not indicate strong spatial segregation of terrestrial foraging between males of the two giant petrel species.

Royal Society Open Science 2026
DOI: 10.5281/zenodo.18189933
title: Terrestrial habitat use of male northern and southern giant petrels breeding at Marion Island

description: Terrestrial habitat use of male northern and southern giant petrels breeding at Marion Island

W. Chris Oosthuizen, Shaun Hoekstra, Ryan R. Reisinger, Pierre A. Pistorius

AbstractCoexisting species reduce direct competition by partitioning resource use through mechanisms such as spatial and temporal segregation, as well as dietary specialization. Northern giant petrels (Macronectes halli) and southern giant petrels (M. giganteus) are closely related seabirds that coexist on some Southern Ocean islands. Breeding male giant petrels often scavenge seal and penguin carrion on land, but their predominantly terrestrial movement patterns and habitat use remain understudied. We used tracking data collected at Marion Island to investigate the terrestrial space use of 28 incubating male northern and southern giant petrels. Our results show that both species exhibit highly localized terrestrial movement patterns. Despite their shared predator-scavenger trophic niche, both species concentrated their terrestrial foraging at a single site, where large numbers of king penguin chicks likely provided abundant foraging resources for male giant petrels during the three study years. The prominence of terrestrial foraging among male giant petrels reduces competition with females and other procellariiforms, but our findings do not indicate strong spatial segregation of terrestrial foraging between males of the two giant petrel species.

Royal Society Open Science 2026
DOI: 10.15468/dl.pcgfhs
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.634035 22.124563, 71.558226 22.118987, 71.484301 22.102395, 71.414096 22.075202, 71.349352 22.038081, 71.29167099999999 21.991955, 71.242476 21.937967, 71.202975 21.877453, 71.174133 21.811912, 71.15664700000001 21.742963, 71.150932 21.672307, 71.157112 21.601684, 71.175017 21.532833, 71.20419200000001 21.467445, 71.24390699999999 21.407125, 71.293176 21.35335, 71.35078300000001 21.307437, 71.415313 21.270509, 71.485185 21.243468, 71.558691 21.226975, 71.634035 21.221432, 71.709379 21.226975, 71.78288499999999 21.243468, 71.852757 21.270509, 71.917287 21.307437, 71.97489400000001 21.35335, 72.024163 21.407125, 72.063878 21.467445, 72.093053 21.532833, 72.110958 21.601684, 72.117138 21.672307, 72.111423 21.742963, 72.093937 21.811912, 72.065095 21.877453, 72.025594 21.937967, 71.976399 21.991955, 71.918718 22.038081, 71.85397399999999 22.075202, 71.78376900000001 22.102395, 71.709844 22.118987, 71.634035 22.124563))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 25 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082282-251120083545085/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.29553914.v1
title: Early_Cardiovascular-肾脏-代谢综合征

description: Header Explanations:SNP: Single Nucleotide Polymorphism identifier (e.g., rsID).CHR: Chromosome number where the SNP is located.BP: Base pair position of the SNP on the chromosome.MAF: Minor Allele Frequency (frequency of the less common allele, 0–0.5).A1: Other allele (reference/non-effect allele).A2: Effect allele (allele for which the effect size is estimated).i: Index/iteration number (e.g., model or subgroup identifier).lhs: Left-hand side of the SEM equation (dependent variable, e.g., trait/factor).op: SEM operator (e.g., ~ = regression, =~ = latent measurement).rhs: Right-hand side of the SEM equation (independent variable, e.g., SNP).est: Regression coefficient estimate (effect size of SNP on lhs).se_c: Standard error of the coefficient estimate (est).Z_Estimate: Z-score for the estimate (est / se_c).Pval_Estimate: P-value for the estimate (significance of est).Q: Cochran’s Q statistic (measures heterogeneity of effects across subgroups/studies).Q_df: Degrees of freedom for the Q statistic.Q_pval: P-value for Cochran’s Q (significance of heterogeneity; &lt;0.05 indicates significant heterogeneity).N_hat: Estimated effective sample size (total or weighted sample size used in analysis).
DOI: 10.6084/m9.figshare.30011653
title: Chain-Linked Multiple Matrix Integration via Embedding Alignment

description: Motivated by the increasing demand for multi-source data integration in various scientific fields, in this article we study matrix completion in scenarios where the data exhibits certain block-wise missing structures—specifically, where only a few noisy submatrices representing (overlapping) parts of the full matrix are available. We propose the Chain-linked Multiple Matrix Integration (CMMI) procedure to efficiently combine the information that can be extracted from these individual noisy submatrices. CMMI begins by deriving entity embeddings for each observed submatrix, then aligns these embeddings using overlapping entities between pairs of submatrices, and finally aggregates them to reconstruct the entire matrix of interest. We establish, under mild regularity conditions, entrywise error bounds and normal approximations for the CMMI estimates. Simulation studies and real data applications show that CMMI is computationally efficient and effective in recovering the full matrix, even when overlaps between the observed submatrices are minimal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.30011653.v2
title: Chain-Linked Multiple Matrix Integration via Embedding Alignment

description: Motivated by the increasing demand for multi-source data integration in various scientific fields, in this article we study matrix completion in scenarios where the data exhibits certain block-wise missing structures—specifically, where only a few noisy submatrices representing (overlapping) parts of the full matrix are available. We propose the Chain-linked Multiple Matrix Integration (CMMI) procedure to efficiently combine the information that can be extracted from these individual noisy submatrices. CMMI begins by deriving entity embeddings for each observed submatrix, then aligns these embeddings using overlapping entities between pairs of submatrices, and finally aggregates them to reconstruct the entire matrix of interest. We establish, under mild regularity conditions, entrywise error bounds and normal approximations for the CMMI estimates. Simulation studies and real data applications show that CMMI is computationally efficient and effective in recovering the full matrix, even when overlaps between the observed submatrices are minimal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.